Applied sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

Content

Bulletin of the Polish Academy of Sciences: Technical Sciences | 2022 | 70 | No. 1 (i.a. Special Section on Vibrations, mechanical waves, and propagation of heat in physical systems) |

Download PDF Download RIS Download Bibtex

Bibliography

  1.  S. Garus et al., “Mechanical vibrations: recent trends and engineering applications”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140351, 2022, doi: 10.24425/bpasts.2022.140351.
  2.  X. Li et al., “Investigation to the influence of additional magnets positions on four magnet bi-stable piezoelectric energy harvester”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140151, 2022, doi: 10.24425/bpasts.2022.140151.
  3.  A. Anand, S. Pal, and S. Kundu, “Bandwidth and power enhancement in the MEMS based piezoelectric energy harvester using magnetic tip mass”, vol. 70, no. 1, p. 140149, 2022, doi: 10.24425/BPASTS.2021.140149.
  4.  P. Kwiatoń, D. Cekus, M. Sofer, and P. Sofer, “Application of heuristic methods to identification of the parameters of discretecontinuous models”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140150, 2022, doi: 10.24425/bpasts.2022.140150.
  5.  S. Garus, W. Sochacki, M. Kubanek, and M. Nabiałek, “Minimizing the number of layers of the quasi one-dimensional phononic structures”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e139394, 2022, doi: 10.24425/bpasts.2021.139394.
  6.  A. Mackojć and B. Chiliński, “Preliminary modelling methodology of a coupled payload-vessel system for offshore lifts of light and heavyweight objects”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e139003, 2022, doi: 10.24425/bpasts.2021. 139003.
  7.  P. Bartkowski, H. Bukowiecki, F. Gawiński, and R. Zalewski, “Adaptive crash energy absorber based on a granular jamming mechanizm”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e139002, 2022, doi: 10.24425/bpasts.2021.139002.
  8.  D. Rodak, M. Żurawski, M. Gmitrzuk, and L. Starczewski, “Possibilities of Vacuum Packed Particles application in blast mitigation seat in military armored vehicles”, vol. 70, no. 1, p. e138238, 2022, doi: 10.24425/BPASTS.2021.138238.
  9.  K. Sokół and M. Pierzgalski, “Investigations on an influence of the material properties on vibrations of active Rocker-Boogie suspension”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e138239, 2022, doi: 10.24425/BPASTS.2021.138239.
Go to article

Authors and Affiliations

Tomasz Kapitaniak
1
ORCID: ORCID
Michal Šofer
2
ORCID: ORCID
Bartłomiej Błachowski
3
ORCID: ORCID
Wojciech Sochacki
4
ORCID: ORCID
Sebastian Garus
4
ORCID: ORCID

  1. Division of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90-924 Łódź, Poland
  2. Department of Applied Mechanics, Faculty of Mechanical Engineering, VŠB – Technical University of Ostrava,17. Listopadu 15/2127, 708 33 Ostrava-Poruba, Czech Republic
  3. Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawinskiego 5b, 02-106 Warsaw, Poland
  4. Department of Mechanics and Fundamentals of Machinery Design, Faculty of Mechanical Engineering and Computer Science, Częstochowa University of Technology, al. Armii Krajowej 21, 42-201 Częstochowa, Poland
Download PDF Download RIS Download Bibtex

Abstract

Although the study of oscillatory motion has a long history, going back four centuries, it is still an active subject of scientificr esearch. In this review paper prospective research directions in the field of mechanical vibrations were pointed out. Four groups of important issues in which advanced research is conducted were discussed. The first are energy harvester devices, thanks to which we can obtain or save significant amounts of energy, and thus reduce the amount of greenhouse gases. The next discussed issue helps in the design of structures using vibrations and describes the algorithms that allow to identify and search for optimal parameters for the devices being developed. The next section describes vibration in multi-body systems and modal analysis, which are key to understanding the phenomena in vibrating machines. The last part describes the properties of granulated materials from which modern, intelligent vacuum-packed particles are made. They are used, for example, as intelligent vibration damping devices.
Go to article

Bibliography

  1.  K. Di et al., “Dielectric elastomer generator for electromechanical energy conversion: A mini review,” Sustainability, vol. 13, p. 9881, 2021, doi: 10.3390/su13179881.
  2.  D. Wang, J. Mo, X. Wang, H. Ouyang, and Z. Zhou, “Experimental and numerical investigations of the piezoelectric energy harvesting via friction-induced vibration,” Energy Convers. Manage., vol. 171, pp. 1134–1149, 2018, doi: 10.1016/ j.enconman.2018.06.052.
  3.  A. Anand, S. Naval, P.K. Sinha, N.K. Das, and S. Kundu, “Effects of coupling in piezoelectric multi-beam structure,” Microsyst. Technol., vol. 26, no. 4, pp. 1235–1252, 2019, doi: 10.1007/s00542-019-04653-3.
  4.  A. Anand and S. Kundu, “Design of a spiral-shaped piezoelectric energy harvester for powering pacemakers,” Nanomater. Energy, vol. 8, no. 2, pp. 139–150, 2019, doi: 10.1680/jnaen.19.00016.
  5.  S.B. Ayed, A. Abdelkefi, F. Najar, and M.R. Hajj, “Design and performance of variable-shaped piezoelectric energy harvesters,” J. Intell. Mater. Syst. Struct., vol. 25, no. 2, pp. 174– 186, 2013, doi: 10.1177/1045389x13489365.
  6.  S. Kundu and H.B. Nemade, “Piezoelectric vibration energy harvester with tapered substrate thickness for uniform stress,” Microsyst. Technol., vol. 27, no. 1, pp. 105–113, 2020, doi: 10.1007/s00542-020-04922-6.
  7.  S. Paquin and Y. St-Amant, “Improving the performance of a piezoelectric energy harvester using a variable thickness beam,” Smart Mater. Struct., vol. 19, no. 10, p. 105020, 2010, doi: 10.1088/0964-1726/19/10/105020.
  8.  J. Zhang, X. Xie, G. Song, G. Du, and D. Liu, “A study on a near-shore cantilevered sea wave energy harvester with a variable cross section,” Energy Sci. Eng., vol. 7, no. 6, pp. 3174– 3185, 2019, doi: 10.1002/ese3.489.
  9.  R. Hosseini and M. Nouri, “Shape design optimization of unimorph piezoelectric cantilever energy harvester,” J. Comput. Appl. Mech., vol. 47, no. 2, 2016, doi: 10.22059/jcamech.2017. 224975.126.
  10.  A. Anand, S. Pal, and S. Kundu, “Bandwidth and power enhancement in the MEMS based piezoelectric energy harvester using magnetic tip mass,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e137509, 2022, doi: 10.24425/bpasts.2021.137509.
  11.  X. Li et al., “Investigation to the influence of additional magnets positions on four magnet bi-stable piezoelectric energy harvester,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140151, 2022, doi: 10.24425/bpasts.2022.140151.
  12.  P. Yingyong, P. Thainiramit, S. Jayasvasti, N. ThanachIssarasak, and D. Isarakorn, “Evaluation of harvesting energy from pedestrians using piezoelectric floor tile energy harvester,” Sens. Actuators A, vol. 331, p. 113035, 2021, doi: 10.1016/j.sna. 2021.113035.
  13.  P. Firoozy, S.E. Khadem, and S.M. Pourkiaee, “Broadband energy harvesting using nonlinear vibrations of a magnetopiezoelastic cantilever beam,” Int. J. Eng. Sci., vol. 111, pp. 113–133, 2017, doi: 10.1016/j.ijengsci.2016.11.006.
  14.  Y. Wu, J. Qiu, S. Zhou, H. Ji, Y. Chen, and S. Li, “A piezoelectric spring pendulum oscillator used for multi-directional and ultra-low frequency vibration energy harvesting,” Appl. Energy, vol. 231, pp. 600–614, 2018, doi: 10.1016/j.apenergy.2018. 09.082.
  15.  J. He et al., “Triboelectric piezoelectric electromagnetic hybrid nanogenerator for high efficient vibration energy harvesting and self powered wireless monitoring system,” Nano Energy, vol. 43, pp. 326–339, 2018, doi: 10.1016/j.nanoen.2017.11.039.
  16.  D. Zhu, S. Roberts, M.J. Tudor, and S.P. Beeby, “Design and experimental characterization of a tunable vibration-based electromagnetic micro- generator,” Sens. Actuators A, vol. 158, no. 2, pp. 284–293, Mar. 2010, doi: 10.1016/j.sna.2010.01.002.
  17.  W.-J. Su, J. Zu, and Y. Zhu, “Design and development of a broadband magnet-induced dual-cantilever piezoelectric energy harvester,” J. Intell. Mater. Syst. Struct., vol. 25, no. 4, pp. 430–442, Aug. 2013, doi: 10.1177/1045389x13498315.
  18.  D. Guo, X.F. Zhang, H. Y. Li, and H. Li, “Piezoelectric energy harvester array with magnetic tip mass,” in Volume 4B: Dynamics, Vibration, and Control. American Society of Mechanical Engineers, Nov. 2015, doi: 10.1115/imece2015-51044.
  19.  M. Ostrowski, B. Błachowski, M. Bocheński, D. Piernikarski, P. Filipek, and W. Janicki, “Design of nonlinear electromagnetic energy harvester equipped with mechanical amplifier and spring bumpers,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 6, pp. 1373–1383, 2020, doi: 10.24425/BPASTS.2020.135384.
  20.  S.-C. Kim, J.-G. Kim, Y.-C. Kim, S.-J. Yang, and H. Lee, “A study of electromagnetic vibration energy harvesters: Design optimization and experimental validation,” Int. J. Precis. Eng. Manuf. Green Technol., vol. 6, no. 4, pp. 779–788, Jul. 2019, doi: 10.1007/s40684-019- 00130-4.
  21.  X. Wang et al., “Similarity and duality of electromagnetic and piezoelectric vibration energy harvesters,” Mech. Syst. Sig. Process., vol. 52-53, pp. 672–684, Feb. 2015, doi: 10.1016/j.ymssp.2014.07.007.
  22.  K. Kecik, A. Mitura, S. Lenci, and J. Warminski, “Energy harvesting from a magnetic levitation system,” Int. J. Non Linear Mech., vol. 94, pp. 200–206, Sep. 2017, doi: 10.1016/j.ijnon linmec.2017.03.021.
  23.  A. Preumont, Mechatronics – Dynamics of Electromechanical and Piezoelectric Systems. Springer Netherlands, 2006, doi: 10.1007/1-4020- 4696-0.
  24.  I. Shahosseini and K. Najafi, “Mechanical amplifier for translational kinetic energy harvesters,” J. Phys. Conf. Ser., vol. 557, p. 012135, Nov. 2014, doi: 10.1088/1742-6596/557/1/012135.
  25.  H. Fu, S. Theodossiades, B. Gunn, I. Abdallah, and E. Chatzi, “Ultra-low frequency energy harvesting using bi-stability and rotary-translational motion in a magnet-tethered oscillator,” Nonlinear Dyn., vol. 101, no. 4, pp. 2131–2143, Sep. 2020, doi: 10.1007/s11071-020-05889-9.
  26.  H. Zhang, L. R. Corr, and T. Ma, “Issues in vibration energy harvesting,” J. Sound Vib., vol. 421, pp. 79–90, May 2018, doi: 10.1016/j. jsv.2018.01.057.
  27.  M. Mösch, G. Fischerauer, and D. Hoffmann, “A self-adaptive and self-sufficient energy harvesting system,” Sensors, vol. 20, no. 9, p. 2519, Apr. 2020, doi: 10.3390/s20092519.
  28.  M. Ostrowski, B. Blachowski, B. Poplawski, D. Pisarski, G. Mikulowski, and L. Jankowski, “Semi-active modal control of structures with lockable joints: general methodology and applications,” Struct. Control Health Monit., vol. 28, no. 5, p. e2710, Feb. 2021, doi: 10.1002/ stc.2710.
  29.  Y. Zhao, M. Alashmori, F. Bi, and X. Wang, “Parameter identification and robust vibration control of a truck driver’s seat system using multi- objective optimization and genetic algorithm,” Applied Acoustics, vol. 173, p. 107697, 2021, doi: 10.1016/j.apacoust.2020.107697.
  30.  S.S. Kessler, S. Spearing, M.J. Atalla, C.E. Cesnik, and C. Soutis, “Damage detection in composite materials using frequency response methods,” Composites Part B, vol. 33, no. 1, pp. 87–95, 2002, doi: 10.1016/S1359-8368(01)00050-6.
  31.  R. Hou and Y. Xia, “Review on the new development of vibration-based damage identification for civil eng. struct.: 2010– 2019,” J. Sound Vib., vol. 491, p. 115741, 2021, doi: 10.1016/ j.jsv.2020.115741.
  32.  K. Dziedziech, P. Czop, W.J. Staszewski, and T. Uhl, “Combined non-parametric and parametric approach for identification of time-variant systems,” Mech. Syst. Sig. Process., vol. 103, pp. 295–311, 2018, doi: 10.1016/j.ymssp.2017.10.020.
  33.  A. Abusoua and M. F. Daqaq, “On using a strong high-frequency excitation for parametric identification of nonlinear systems,” J. Vib. Acoust., vol. 139, no. 5, p. 051012, 2017, doi: 10.1115/ 1.4036504.
  34.  B. Zhu, Y. Dong, and Y. Li, “Nonlinear dynamics of a viscoelastic sandwich beam with parametric excitations and internal resonance,” Nonlinear Dyn., vol. 94, no. 4, pp. 2575–2612, 2018, doi: 10.1007/s11071-018-4511-8.
  35.  F. Beltran-Carbajal and G. Silva-Navarro, “Generalized nonlinear stiffness identification on controlled mechanical vibrating systems,” Asian J. Control, vol. 21, no. 3, pp. 1281–1292, 2018, doi: 10.1002/asjc.1807.
  36.  B.S. Razavi, M.R. Mahmoudkelayeh, and S.S. Razavi, “Damage identification under ambient vibration and unpredictable signal nature,” J. Civ. Struct. Health Monit., vol. 11, no. 5, pp. 1253–1273, 2021, doi: 10.1007/s13349-021-00503-x.
  37.  A.C. Altunıs¸ık, F.Y. Okur, and V. Kahya, “Modal parameter identification and vibration based damage detection of a multiple cracked cantilever beam,” Eng. Fail. Anal., vol. 79, pp. 154–170, 2017, doi: 10.1016/j.engfailanal.2017.04.026.
  38.  K. Ciecieląg, A. Skoczylas, J. Matuszak, K. Zaleski, and K. Kęcik, “Defect detection and localization in polymer composites based on drilling force signal by recurrence analysis,” Measurement, vol. 186, p. 110126, 2021, doi: 10.1016/j.measurement.2021.110126.
  39.  M. Bowkett and K. Thanapalan, “Comparative analysis of failure detection methods of composites materials’ systems,” Syst. Sci. Control Eng., vol. 5, no. 1, pp. 168–177, 2017, doi: 10.1080/ 21642583.2017.1311240.
  40.  D. Cekus, P. Kwiatoń, M. Šofer, and P. Šofer, “Application of heuristic methods to identification of the parameters of discretecontinuous models,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e140150, 2022, doi: 10.24425/bpasts.2022.140150.
  41.  S. Garus, W. Sochacki, M. Kubanek, and M. Nabiałek, “Minimizing the number of layers of the quasi one-dimensional phononic structures,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 1, p. e139394, 2022, doi: 10.24425/bpasts.2021.139394.
  42.  A. Cancelli, S. Laflamme, A. Alipour, S. Sritharan, and F. Ubertini, “Vibration-based damage localization and quantification in a pretensioned concrete girder using stochastic subspace identification and particle swarm model updating,” Struct. Health Monit., vol. 19, no. 2, pp. 587–605, 2019, doi: 10.1177/1475 921718820015.
  43.  S. Barman, M. Mishra, D. Maiti, and D. Maity, “Vibration-based damage detection of structures employing bayesian data fusion coupled with TLBO optimization algorithm,” Struct. Multidiscip. Optim., vol. 64, pp. 2243–2266, 2021, doi: 10.1007/s00158021-02980-6.
  44.  S. Das, S. Mondal, and S. Guchhait, “Particle swarm optimization-based characterization technique of nonproportional viscous damping parameter of a cantilever beam,” J. Vib. Control, p. 107754632110105, 2021, doi: 10.1177/1077546321101 0526.
  45.  R. Zenzen, I. Belaidi, S. Khatir, and M. A. Wahab, “A damage identification technique for beam-like and truss structures based on frf and bat algorithm,” Comptes Rendus Mécanique, vol. 346, pp. 1253–1266, 2018, doi: 10.1016/j.crme.2018.09.003.
  46.  M.-S. Huang, M. Gül, and H.-P. Zhu, “Vibration-based structural damage identification under varying temperature effects,” J. Aerosp. Eng., vol. 31, no. 3, p. 04018014, 2018, doi: 10.1061/(asce)as.1943-5525.0000829.
  47.  Y. Zhang, Y. Miyamori, S. Mikami, and T. Saito, “Vibrationbased structural state identification by a 1-dimensional convolutional neural network,” Comput.-Aided Civ. Infrastruct. Eng., vol. 34, no. 9, pp. 822–839, 2019, doi: 10.1111/mice.12447.
  48.  H. Nick and A. Aziminejad, “Vibration-based damage identification in steel girder bridges using artificial neural network under noisy conditions,” J. Nondestr. Eval., vol. 40, no. 1, p. 15, 2021, doi: 10.1007/s10921-020-00744-8.
  49.  Y. Yang, C. Dorn, C. Farrar, and D. Mascareñas, “Blind, simultaneous identification of full-field vibration modes and large rigid-body motion of output-only structures from digital video measurements,” Eng. Struct., vol. 207, p. 110183, 2020, doi: 10.1016/j.engstruct.2020.110183.
  50.  Z. Fu and J. He, Modal analysis, ser. 1st edition. Delhi, Oxford: Butterworth-Heinemann, 2001.
  51.  R. Craig and A. Kurdila, Fundamentals of Struct. Dyn., ser. 2nd edition. Hoboken, New Jersey: Wiley, 2006.
  52.  D. de Klerk, D.J. Rixen, and S.N. Voormeeren, “General framework for dynamic substructuring: History, review and classification of techniques,” AIAA Journal, vol. 46, no. 5, pp. 1169–1181, 2008, doi: 10.2514/1.33274.
  53.  J. Roy Craig, Coupling of substructures for dynamic analyses – An overview, 2000, doi: 10.2514/6.2000-1573.
  54.  A. Shabana, Dynamics of Multibody Systems, ser. 4th edition. Cambridge, Chicago: Cambridge University Press, 2013.
  55.  B. Rong, X. Rui, L. Tao, and G. Wang, “Theoretical modeling and numerical solution methods for flexible multibody system dynamics,” Nonlinear Dyn., vol. 98, p. 1519–1553, 2019, doi: 10.1007/s11071-019-05191-3.
  56.  V. Sonneville, M. Scapolan, M. Shan, and O. Bauchau, “Modal reduction procedures for flexible multibody dynamics,” Multibody Sys.Dyn., vol. 51, pp. 377–418, 2021, doi: 10.1007/s11044020-09770-w.
  57.  J. Kim, J. Han, H. Lee, and S. Kim, “Flexible multibody dynamics using coordinate reduction improved by dynamic correction,” Multibody Sys.Dyn., vol. 42, pp. 411–429, 2018, doi: 10.1007/s11044-017-9607-2.
  58.  A. Cammarata, “Global modes for the reduction of flexible multibody systems,” Multibody Sys.Dyn., vol. 53, pp. 59–83, 2021, doi: 10.1007/ s11044-021-09790-0.
  59.  Y. Tang, H. Hu, and Q. Tian, “Model order reduction based on successively local linearizations for flexible multibody dynamics,” Int. J. Numer. Methods Eng., vol. 118, no. 3, pp. 159–180, 2019, doi: 10.1002/nme.6011.
  60.  I. Palomba and R. Vidoni, “Flexible-link multibody system eigenvalue analysis parameterized with respect to rigid-body motion,” Applied Sciences, vol. 9, no. 23, p. 5156, 2019, doi: 10.3390/app9235156.
  61.  K. Worden and P. Green, “A machine learning approach to nonlinear modal analysis,” Mech. Syst. Sig. Process., vol. 84, pp. 34–53, 2017, doi: 10.1016/j.ymssp.2016.04.029.
  62.  G. Kerschen, Modal Analysis of Nonlinear Mechanical Systems, ser. CISM International Centre for Mechanical Sciences. Vienna, Udine: Springer, 2014.
  63.  G. Kerschen, M. Peeters, J. C. Golinval, and C. Stéphan, “Nonlinear modal analysis of a full-scale aircraft,” Journal of Aircraft, vol. 50, no. 5, pp. 1409–1419, 2013, doi: 10.2514/1.C031918.
  64.  A. Albu-Schäffer and C. Della Santina, “A review on nonlinear modes in conservative mechanical systems,” Annu. Rev. Control, vol. 50, pp. 49–71, 2020, doi: 10.1016/j.arcontrol.2020.10.002.
  65.  W. Heylen, S. Lammens, and P. Sas, Modal Analysis Theory and Testing, ser. 1st edition. Heverlee, Belgium: Katholieke Universiteit Leuven, 2007.
  66.  E. Orlowitz and A. Brandt, “Comparison of experimental and operational modal analysis on a laboratory test plate,” Measurement, vol. 102, pp. 121–130, 2017, doi: 10.1016/j.measurement. 2017.02.001.
  67.  F. Zahid, Z. Ong, and S. Khoo, “A review of operational modal analysis techniques for in-service modal identification,” J. Braz. Soc. Mech. Sci. Eng., vol. 42, p. 398, 2020, doi: 10.1007/s40430020-02470-8.
  68.  D. Montanari, A. Agostini, M. Bonini, G. Corti, and C. Ventisette, “The use of empirical methods for testing granular materials in analogue modelling,” Materials, vol. 10, no. 6, p. 635, Jun. 2017, doi: 10.3390/ma10060635.
  69.  B. Kou et al., “Granular materials flow like complex fluids,” Nature, vol. 551, no. 7680, pp. 360–363, Nov. 2017, doi: 10.1038/ nature24062.
  70.  C. Sandeep and K. Senetakis, “Effect of young’s modulus and surface roughness on the inter-particle friction of granu lar materials,” Materials, vol. 11, no. 2, p. 217, Jan. 2018, doi: 10.3390/ma11020217.
  71.  A. Wautier et al., “Multiscale modelling of granular materials in boundary value problems accounting for mesoscale mechanisms,” Comput. Geotech., vol. 134, p. 104143, 2021, doi: 10.1016/j.compgeo.2021.104143.
  72.  G. Recchia, H. Cheng, V. Magnanimo, and L. La Ragione, “Failure in granular materials based on acoustic tensor: a numerical analysis,” EPJ Web Conf. Powders and Grains, vol. 249, p. 10005, 2021.
  73.  J. Irazábal, F. Salazar, and E. Oñate, “Numerical modelling of granular materials with spherical discrete particles and the bounded rolling friction model. Application to railway ballast,” Comput. Geotech., vol. 85, pp. 220–229, 2017, doi: 10.1016/ j.compgeo.2016.12.034.
  74.  S. Zhao, T.M. Evans, and X. Zhou, “Shear-induced anisotropy of granular materials with rolling resistance and particle shape effects,” Int. J. Solids Struct., vol. 150, pp. 268–281, 2018, doi: 10.1016/j.ijsolstr.2018.06.024.
  75.  Z. Nie, C. Fang, J. Gong, and Z. Liang, “Dem study on the effect of roundness on the shear behaviour of granular materials,” Comput. Geotech., vol. 121, p. 103457, 2020, doi: 10.1016/ j.compgeo.2020.103457.
  76.  J. Huang, S. Hu, S. Xu, and S. Luo, “Fractal crushing of granular materials under confined compression at different strain rates,” Int. J. Impact Eng., vol. 106, pp. 259–265, 2017, doi: 10.1016/ j.ijimpeng.2017.04.021.
  77.  S. Larsson, J.M.R. Prieto, G. Gustafsson, H.-Å. Häggblad, and P. Jonsén, “The particle finite element method for transient granular material flow: modelling and validation,” Comput. Part. Mech., vol. 8, no. 1, pp. 135–155, Feb. 2020, doi: 10.1007/ s40571-020-00317-6.
  78.  C. Zhai, E. Herbold, S. Hall, and R. Hnourley, “Particle rotations and energy dissipation during mechanical compression of granular materials,” J. Mech. Phys. Solids, vol. 129, pp. 19–38, 2019, doi: 10.1016/j.jmps.2019.04.018.
  79.  S. Liu, Z. Nie, W. Hu, J. Gong, and P. Lei, “Effect of parti cle type on the shear behaviour of granular materials,” Particuology, vol. 56, pp. 124–131, 2021, doi: 10.1016/j.partic.2020. 11.001.
  80.  W. Fei, G.A. Narsilio, J.H. van der Linden, and M.M. Disfani, “Quantifying the impact of rigid interparticle structures on heat transfer in granular materials using networks,” Int. J. Heat Mass Transfer, vol. 143, p. 118514, 2019, doi: 10.1016/j.ijheatmasstransfer.2019.118514.
  81.  A.M. Druckrey, K.A. Alshibli, and R.I. Al-Raoush, “Discrete particle translation gradient concept to expose strain localisation in sheared granular materials using 3d experimental kinematic measurements,” Géotechnique, vol. 68, no. 2, pp. 162–170, Feb. 2018, doi: 10.1680/ jgeot.16.p.148.
  82.  R. Gupta, S. Salager, K. Wang, and W. Sun, “Open-source support toward validating and falsifying discrete mechanics models using synthetic granular materials – part i: Experimental tests with particles manufactured by a 3d printer,” Acta Geotech., vol. 14, no. 4, pp. 923–937, Jul. 2018, doi: 10.1007/s11440-0180703-0.
  83.  Y. Sun, S. Nimbalkar, and C. Chen, “Particle breakage of granular materials during sample preparation,” J. Rock Mech. Geotech. Eng., vol. 11, no. 2, pp. 417–422, 2019, doi: 10.1016/j.jrmge.2018.12.001.
  84.  T. Sweijen, B. Chareyre, S. Hassanizadeh, and N. Karadimitriou, “Grain-scale modelling of swelling granular materials; application to super absorbent polymers,” Powder Technol., vol. 318, pp. 411–422, 2017, doi: 10.1016/j.powtec.2017.06.015.
  85.  H. M. Beakawi Al-Hashemi and O.S. Baghabra Al-Amoudi, “A review on the angle of repose of granular materials,” Powder Technol., vol. 330, pp. 397–417, 2018, doi: 10.1016/j.powtec.2018.02.003.
  86.  P. Bartkowski, H. Bukowiecki, F. Gawiński, and R. Zalewski, “Adaptive crash energy absorber based on a granular jamming mechanism,” Bull. Pol. Acad. Sci. Tech. Sci., p. e139002, 2021.
  87.  P. Bartkowski, R. Zalewski, and P. Chodkiewicz, “Parameter identification of bouc-wen model for vacuum packed particles based on genetic algorithm,” Arch. Civ. Mech. Eng., vol. 19, no. 2, pp. 322–333, 2019, doi: 10.1016/j.acme.2018. 11.002.
  88.  P. Bartkowski, G. Suwała, and R. Zalewski, “Temperature and strain rate effects of jammed granular systems: experiments and modelling,” Granular Matter, vol. 23, no. 4, p. 79, Aug. 2021, doi: 10.1007/s10035-021-01138-x.
Go to article

Authors and Affiliations

Sebastian Garus
1
ORCID: ORCID
Bartłomiej Błachowski
2
ORCID: ORCID
Wojciech Sochacki
1
ORCID: ORCID
Anna Jaskot
3
ORCID: ORCID
Paweł Kwiatoń
1
ORCID: ORCID
Mariusz Ostrowski
2
ORCID: ORCID
Michal Šofer
4
ORCID: ORCID
Tomasz Kapitaniak
5
ORCID: ORCID

  1. Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, Poland
  2. Institute of Fundamental Technological Research, Polish Academy of Sciences, Poland
  3. Faculty of Civil Engineering, Czestochowa University of Technology, Poland
  4. Faculty of Mechanical Engineering, VŠB – Technical University of Ostrava, Czech Republic
  5. Division of Dynamics, Lodz University of Technology, Poland
Download PDF Download RIS Download Bibtex

Abstract

Although the study of oscillatory motion has a long history, going back four centuries, it is still an active subject of scientific research. In this review paper prospective research directions in the field of mechanical vibrations were pointed out. Four groups of important issues in which advanced research is conducted were discussed. The first are energy harvester devices, thanks to which we can obtain or save significant amounts of energy, and thus reduce the amount of greenhouse gases. The next discussed issue helps in the design of structures using vibrations and describes the algorithms that allow to identify and search for optimal parameters for the devices being developed. The next section describes vibration in multi-body systems and modal analysis, which are key to understanding the phenomena in vibrating machines. The last part describes the properties of granulated materials from which modern, intelligent vacuum-packed particles are made. They are used, for example, as intelligent vibration damping devices.
Go to article

Bibliography

  1. F.K. Shaikh and S. Zeadally, “Energy harvesting in wireless sensor networks: A comprehensive review”, Renew. Sustain. Energy Rev., vol. 55, pp. 1041–1054, 2016, doi: 10.1016/j.rser.2015.11.010.
  2.  M.T. Todaro et al., “Piezoelectric MEMS vibrational energy harvesters: Advances and outlook”, Microelectron. Eng., vol. 183– 184, pp. 23–36, 2017, doi: 10.1016/j.mee.2017.10.005.
  3.  F. Ali, W. Raza, X. Li, H. Gul, and K.H. Kim, “Piezoelectric energy harvesters for biomedical applications”, Nano Energy, vol. 57, pp. 879–902, 2019, doi: 10.1016/j.nanoen.2019. 01.012.
  4.  M.R. Sarker, S. Julai, M.F.M. Sabri, S.M. Said, M.M. Islam, and M. Tahir, “Review of piezoelectric energy harvesting system and application of optimization techniques to enhance the performance of the harvesting system”, Sensors Actuators, A Phys., vol. 300, p. 111634, 2019, doi: 10.1016/j.sna.2019.111634.
  5.  N. Tran, M. H. Ghayesh, and M. Arjomandi, “Ambient vibration energy harvesters: A review on nonlinear techniques for performance enhancement”, Int. J. Eng. Sci., vol. 127, pp. 162–185, 2018, doi: 10.1016/j.ijengsci.2018.02.003.
  6.  C. Wei and X. Jing, “A comprehensive review on vibration energy harvesting: Modelling and realization”, Renew. Sustain. Energy Rev., vol. 74, pp. 1–18, 2017, doi: 10.1016/j.rser.2017. 01.073.
  7.  T. Yildirim, M.H. Ghayesh, W. Li, and G. Alici, “A review on performance enhancement techniques for ambient vibration energy harvesters”, Renew. Sustain. Energy Rev., vol. 71, pp. 435– 449, 2017, doi: 10.1016/j.rser.2016.12.073.
  8.  H. Liu, J. Zhong, C. Lee, S.W. Lee, and L. Lin, “A comprehensive review on piezoelectric energy harvesting technology: Materials, mechanisms, and applications”, Appl. Phys. Rev., vol. 5, no. 4, 2018, doi: 10.1063/1.5074184.
  9.  A. Erturk and D.J. Inman, “A distributed parameter electromechanical model for cantilevered piezoelectric energy harvesters”,  J. Vib. Acoust. Trans. ASME, vol. 130, no. 4, pp. 1–15, 2008, doi: 10.1115/1.2890402.
  10.  Y. Yang and L. Tang, “Equivalent circuit modeling of piezoelectric energy harvesters”, J. Intell. Mater. Syst. Struct., vol. 20, no. 18, pp. 2223–2235, 2009, doi: 10.1177/1045389X09351757.
  11.  L. Yu, L. Tang, and T. Yang, “Piezoelectric passive self-tuning energy harvester based on a beam-slider structure”, J. Sound Vib., vol. 489, p. 115689, 2020, doi: 10.1016/j.jsv.2020.115689.
  12.  M. Sayed, A.A. Mousa, and I. Mustafa, “Stability and bifurcation analysis of a buckled beam via active control”, Appl. Math. Model., vol. 82, pp. 649–665, 2020, doi: 10.1016/j.apm.2020.01.074.
  13.  S. Zhou, J. Cao, and J. Lin, “Theoretical analysis and experimental verification for improving energy harvesting performance of nonlinear monostable energy harvesters”, Nonlinear Dyn., vol. 86, no. 3, pp. 1599–1611, 2016, doi: 10.1007/s11071-0162979-7.
  14.  H. T. Nguyen, D. Genov, and H. Bardaweel, “Mono-stable and bi-stable magnetic spring based vibration energy harvesting systems subject to harmonic excitation: Dynamic modeling and experimental verification”, Mech. Syst. Signal Process., vol. 134, p. 106361, 2019, doi: 10.1016/j.ymssp.2019.106361.
  15.  T. Huguet, A. Badel, O. Druet, and M. Lallart, “Drastic bandwidth enhancement of bistable energy harvesters: Study of subharmonic behaviors and their stability robustness”, Appl. Energy, vol. 226, pp. 607–617, 2018, doi: 10.1016/j.apenergy.2018. 06.011.
  16.  H. Wang and L. Tang, “Modeling and experiment of bistable two-degree-of-freedom energy harvester with magnetic coupling”, Mech. Syst. Signal Process., vol. 86, pp. 29–39, 2017, doi: 10.1016/j.ymssp.2016.10.001.
  17.  Y. Zhang, Y. Leng, S. Fan, “The Accurate Analysis of Magnetic Force of Bi-stable Piezoelectric Cantilever Energy Harvester”, presented at the ASME International Design Engineering Technical Conferences/Computers and Information in Engineering Conference, Cleveland, Ohio, USA, 2017, doi: 10.1115/ DETC2017-67168.
  18.  T. Tan, Z. Yan, K. Ma, F. Liu, L. Zhao, and W. Zhang, “Nonlinear characterization and performance optimization for broadband bistable energy harvester”, Acta Mech. Sin. Xuebao, vol. 36, no. 3, pp. 578–591, 2020, doi: 10.1007/s10409-020-00946-3.
  19.  K. Wang, X. Dai, X. Xiang, G. Ding, and X. Zhao, “Optimal potential well for maximizing performance of bi-stable energy harvester”, Appl. Phys. Lett., vol. 115, no. 14, 2019, doi: 10.1063/1.5095693.
  20.  V. Shah, R. Kumar, M. Talha, and J. Twiefel, “Numerical and experimental study of bistable piezoelectric energy harvester”, Integr. Ferroelectr., vol. 192, no. 1, pp. 38–56, 2018, doi: 10.1080/ 10584587.2018.1521669.
  21.  T. Yang and Q. Cao, “Dynamics and high-efficiency of a novel multi-stable energy harvesting system”, Chaos Solitons Fractals, vol. 131, p. 109516, 2020, doi: 10.1016/j.chaos.2019. 109516
  22.  Z. Zhou, W. Qin, and P. Zhu, “Improve efficiency of harvesting random energy by snap-through in a quad-stable harvester”, Sens. Actuators, A, vol. 243, pp. 151–158, 2016, doi: 10.1016/ j.sna.2016.03.024.
  23.  M. Panyam and M.F. Daqaq, “Characterizing the effective bandwidth of tri-stable energy harvesters”, J. Sound Vib., vol. 386, pp. 336–358, 2017, doi: 10.1016/j.jsv.2016.09.022.
  24.  Y. Leng, D. Tan, J. Liu, Y. Zhang, and S. Fan, “Magnetic force analysis and performance of a tri-stable piezoelectric energy harvester under random excitation”, J. Sound Vib., vol. 406, pp. 146–160, 2017, doi: 10.1016/j.jsv.2017.06.020.
  25.  M. Lallart, S. Zhou, Z. Yang, L. Yan, K. Li, and Y. Chen, “Coupling mechanical and electrical nonlinearities: The effect of synchronized discharging on tristable energy harvesters”, Appl. Energy, vol. 266, no. January, p. 114516, 2020, doi: 10.1016/ j.apenergy.2020.114516.
  26.  J. Wang and Z. Wang, “A double bi-stable energy harvester for enhanced ability of bi-stable energy harvesting from random vibration”, J. Appl. Sci. Eng., vol. 20, no. 3, pp. 387–392, 2017, doi: 10.6180/jase.2017.20.3.13.
  27.  G. Wang, W. Liao, B. Yang, X. Wang, W. Xu, and X. Li, “Dynamic and energetic characteristics of a bistable piezoelectric vibration energy harvester with an elastic magnifier”, Mech. Syst. Signal Process., vol. 105, pp. 427–446, 2018, doi: 10.1016/ j.ymssp.2017.12.025.
  28.  Z. Zhou, W. Qin, W. Du, P. Zhu, and Q. Liu, “Improving energy harvesting from random excitation by nonlinear flexible bistable energy harvester with a variable potential energy function”, Mech. Syst. Signal Process., vol. 115, pp. 162–172, 2019, doi: 10.1016/j.ymssp.2018.06.003.
  29.  X. Li et al., “Broadband spring-connected bi-stable piezoelectric vibration energy harvester with variable potential barrier”, Results Phys., vol. 18, no. May, p. 103173, 2020, doi: 10.1016/ j.rinp.2020.103173.
  30.  S. Zhou, J. Cao, D.J. Inman, J. Lin, S. Liu, and Z. Wang, “Broadband tristable energy harvester: Modeling and experiment verification”, Appl. Energy, vol. 133, pp. 33–39, 2014, doi: 10.1016/j.apenergy.2014.07.077.
  31.  Z. Zhou, W. Qin, Y. Yang, and P. Zhu, “Improving efficiency of energy harvesting by a novel penta-stable configuration”, Sensors Actuators A., vol. 265, pp. 297–305, 2017, doi: 10.1016/ j.sna.2017.08.039.
  32.  D. Huang, S. Zhou, and G. Litak, “Theoretical analysis of multistable energy harvesters with high-order stiffness terms”, Commun. Nonlinear Sci. Numer. Simul., vol. 69, pp. 270–286, 2019, doi: 10.1016/j.cnsns.2018.09.025.
  33.  C. Lan and W. Qin, “Enhancing ability of harvesting energy from random vibration by decreasing the potential barrier of bistable harvester”, Mech. Syst. Signal Process., vol. 85, pp. 71–81, 2017, doi: 10.1016/j.ymssp.2016.07.047.
  34.  M. Ostrowski, B. Błachowski, M. Bochen´ski, D. Piernikarski, P. Filipek, and W. Janicki, “Design of nonlinear electromagnetic energy harvester equipped with mechanical amplifier and spring bumpers”, Bull. Polish Acad. Sci. Tech. Sci., vol. 68, no. 6, pp. 1373–1383, 2020, doi: 10.24425/bpasts.2020.135384.
  35.  D. Tan, Y.G. Leng, and Y.J. Gao, “Magnetic force of piezoelectric cantilever energy harvesters with external magnetic field”, Eur. Phys. J. Spec. Top., vol. 224, no. 14–15, pp. 2839–2853, 2015, doi: 10.1140/epjst/e2015-02592-6.
Go to article

Authors and Affiliations

Xinxin Li
1
Kexue Huang
1
Zhilin Li
1
Jiangshu Xiang
1
Zhenfeng Huang
1
Hanling Mao
1
Yadong Cao
1

  1. College of Mechanical Engineering, Guangxi University, Nanning, China
Download PDF Download RIS Download Bibtex

Abstract

In this paper, the performance and frequency bandwidth of the piezoelectric energy harvester (PZEH) is improved by introducing two permanent magnets attached to the proof mass of a dual beam structure. Both magnets are in the vicinity of each other and attached in such a way to proof mass of a dual beam so that they create a magnetic field around each other. The generated magnetic field develops a repulsive force between the magnets, which improves electrical output and enhances the bandwidth of the harvester. The simple rectangular cantilever structure with and without magnetic tip mass has a frequency bandwidth of 4 Hz and 4.5 Hz, respectively. The proposed structure generates a peak voltage of 20 V at a frequency of 114.51 Hz at an excitation acceleration of 1 g (g= 9.8 m/s2 ). The peak output power of a proposed structure is 25.5 µW. The operational frequency range of a proposed dual beam cantilever with a magnetic tip mass of 30 mT is from 102.51 Hz to 120.51 Hz, i.e., 18 Hz. The operational frequency range of a dual beam cantilever without magnetic tip mass is from 104.18 Hz to 118.18 Hz, i.e., 14 Hz. There is an improvement of 22.22% in the frequency bandwidth of the proposed dual beam cantilever with a magnetic tip mass of 30 mT than the dual beam without magnetic tip mass.

Go to article

Bibliography

  1.  P. Glynne-Jones, M.J. Tudor, S.P. Beeby, and N.M. White, “An electromagnetic, vibration-powered generator for intelligent sensor systems”, Sens. Actuators, A, vol. 110, no. 1–3, pp. 344– 349, 2004, doi: 10.1016/j.sna.2003.09.045.
  2.  P.D. Mitcheson, P. Miao, B.H. Stark, E.M. Yeatman, A.S. Holmes, and T.C. Green, “MEMS electrostatic micropower generator for low frequency operation”, Sens. Actuators, A,vol. 115, no. 2–3, pp. 523–529, 2004, doi: 10.1016/j.sna.2004.04.026.
  3.  P.D. Mitcheson, E.M. Yeatman, G.K. Rao, A.S. Holmes, and T.C. Green, “Energy harvesting from human and machine motion for wireless electronic devices”, Proc. IEEE, vol. 96, no. 9, pp. 1457–1486, 2008, doi: 10.1109/ JPROC.2008.927494.
  4.  M. Ostrowski, B. Błachowski, M. Bocheński, D. Piernikarski, P. Filipek, and W. Janicki, “Design of nonlinear electromagnetic energy harvester equipped with mechanical amplifier and spring bumpers”, Bull. Pol. Acad. Sci. Tech. Sci. vol. 68, no. 6, pp. 1373–1383, 2020, doi: 10.24425/bpasts.2020.135384.
  5.  A. Anand, S. Pal, and S. Kundu, “Multi-perforated EnergyEfficient Piezoelectric Energy Harvester Using Improved Stress Distribution”, IETE J. Res., pp. 1–16, 2021, doi: 10.1080/03772063.2021.1913071.
  6.  A. Anand, S. Naval, P.K. Sinha, N.K. Das, and S. Kundu, “Effects of coupling in piezoelectric multi-beam structure”, Microsyst. Technol., vol. 26, no. 4, pp. 1235–1252, 2020, doi: 10.1007/s00542-019-04653-3.
  7.  A. Anand, and S. Kundu, “Improvement of Output Power in Piezoelectric Energy Harvester under Magnetic Influence”, Proceedings of 3rd International Conference on 2019 Devices for Integrated Circuit (DevIC 2019 IEEE), 2019, pp. 382–385, doi: 10.1109/DEVIC.2019.8783607.
  8.  A. Anand and S. Kundu, “Design of a spiral-shaped piezoelectric energy harvester for powering pacemakers”, Nanomater. Energy, vol. 8, no. 2, pp. 139–150, 2019, doi: 10.1680/jnaen.19.00016.
  9.  A. Anand and S. Kundu, “Design of Mems Based Piezoelectric Energy Harvester for Pacemaker”, Proceedings of 3rd International Conference on Devices for Integrated Circuit (DevIC 2019), 2019, pp. 465–469, doi: 10.1109/DEVIC.2019.8783311.
  10.  S. Roundy, P.K. Wright, and J. Rabaey, “A study of low level vibrations as a power source for wireless sensor nodes”, Comput. Commun., vol. 26, no. 11, pp. 1131–1144, 2003, doi: 10.1016/S0140-3664(02)00248-7.
  11.  S. Naval, P.K. Sinha, N.K. Das, A. Anand, and S. Kundu, “Wideband piezoelectric energy harvester design using parallel connection of multiple beams”, Int. J. Nanopart., vol. 12, no. 3, pp. 206–223, 2020, doi: 10.1504/IJNP.2020.109545.
  12.  S. Naval, P.K. Sinha, N.K. Das, A. Anand, and S. Kundu, “Bandwidth Increment of Piezoelectric Energy Harvester using Multibeam Structure”, Proceedings of 3rd International Conference on 2019 Devices for Integrated Circuit (DevIC 2019), 2019, pp. 370–373, doi: 10.1109/ DEVIC.2019.8783724.
  13.  H. S. Kim, J. H. Kim, and J. Kim, “A review of piezoelectric energy harvesting based on vibration”, Int. J. Precis. Eng. Manuf., vol. 12, no. 6, pp. 1129–1141, 2011, doi: 10.1007/s12541-0110151-3.
  14.  K. Sokół,“Passive control of instability regions by means of piezoceramic elements”, Lat. Am. J. Solids Struct., vol. 18, no. 1, p. e320, 2021, doi: 10.1590/1679-78256015.
  15.  H. Irschik, “A review on static and dynamic shape control of structures by piezoelectric actuation”, Eng. Struct., vol. 24, no. 1, pp. 5–11, 2002, doi: 10.1016/S0141-0296(01)00081-5.
  16.  J. Peng, G. Zhang, M. Xiang, H. Sun, X. Wang, and X. Xie, “Vibration control for the nonlinear resonant response of a piezoelectric elastic beam via time-delayed feedback”, Smart Mater. Struct., vol. 28, no. 9, p. 095010, 2019, doi: 10.1088/1361-665X/ab2e3d.
  17.  H. Hu, Y. Han, A. Song, S. Chen, C. Wang, and Z. Wang, “A finger-shaped tactile sensor for fabric surfaces evaluation by 2-dimensional active sliding touch”, Sensors, vol. 14, no. 3, pp. 4899–4913, 2014, doi: 10.3390/s140304899.
  18.  M.F. Daqaq, R. Masana, A. Erturk, and D. Dane Quinn, “On the role of nonlinearities in vibratory energy harvesting: a critical review and discussion”, Appl. Mech. Rev., vol. 66, no. 4, p. 040801, 2014, doi: 10.1115/1.4026278.
  19.  V.R. Challa, M.G. Prasad, Y. Shi, and F.T. Fisher, “A vibration energy harvesting device with bidirectional resonance frequency tunability”, Smart Mater. Struct., vol. 17, no. 1, p. 015035, 2008, doi: 10.1088/0964-1726/17/01/015035.
  20.  D.A. Barton, S.G. Burrow, and L.R. Clare, “Energy harvesting from vibrations with a nonlinear oscillator”, J. Vib. Acoust., vol. 132, no. 2, 2010, doi: 10.1115/1.4000809.
  21.  S.C. Stanton, C.C. McGehee, and B.P. Mann, “Reversible hysteresis for broadband magnetopiezoelastic energy harvesting”, Appl. Phys. Lett., vol. 95, no. 17, p. 174103, 2009, doi: 10.1063/1.3253710.
  22.  A. Erturk and D.J. Inman, “Broadband piezoelectric power generation on high-energy orbits of the bistable Duffing oscillator with electromechanical coupling”, J. Sound. Vib., vol. 330, no. 10, pp. 2339–2353, 2011, doi: 10.1016/j.jsv.2010.11.018.
  23.  S. Zhou, J. Cao, A. Erturk, and J. Lin, “Enhanced broadband piezoelectric energy harvesting using rotatable magnets”, Appl. Phys. Lett., vol. 102, no. 17, p. 173901, 2013, doi: 10.1063/1.4803445.
  24.  S. Zhou, J. Cao, W. Wang, S. Liu, and J. Lin, “Modeling and experimental verification of doubly nonlinear magnet-coupled piezoelectric energy harvesting from ambient vibration”, Smart Mater. Struct., vol. 24, no. 5, p. 055008, 2015, doi: 10.1088/0964-1726/24/5/055008.
  25.  S. Zhou, J. Cao, D.J. Inman, J. Lin, S. Liu, and Z. Wang, “Broadband tristable energy harvester: modeling and experiment verification”, Appl. Energy; vol. 133, pp. 33–39, 2014, doi: 10.1016/j.apenergy.2014.07.077.
  26.  L. Haitao, Q. Weiyang, L. Chunbo, D. Wangzheng, and Z. Zhiyong, “Dynamics and coherence resonance of tristable energy harvesting system”, Smart Mater. Struct., vol. 25, no. 1, p. 015001, 2015, doi: 10.1088/0964-1726/ 25/1/015001.
  27.  J.Y. Cao, S.X. Zhou, W. Wang, and J. Lin, “Influence of potential well depth on nonlinear tristable energy harvesting”, Appl. Phys. Lett., vol. 106, no. 7, p. 173903, 2015, doi: 10.1063/1.4919532.
  28.  P. Kim and J. Seok, “A multi-stable energy harvester: dynamic modeling and bifurcation analysis”, J. Sound Vib., vol. 333, no. 21, pp. 5525–5547, 2014, doi: 10.1016/j.jsv. 2014.05.054.
  29.  Z. Zhou, W. Qin, Y. Yang, and P. Zhu, “Improving efficiency of energy harvesting by a novel penta-stable configuration”, Sens. Actuators, A,, vol. 265, pp. 297–305, 2017, doi: 10.1016/j.sna.2017.08.039.
  30.  D. Tan, Y.G. Leng, and Y.J. Gao, “Magnetic force of piezoelectric cantilever energy harvesters with external magnetic field”, Eur. Phys. J. Spec. Top., vol. 224, no. 14, pp. 2839–2853, 2015, doi: 10.1140/epjst/e2015-02592-6.
  31.  D. Zhu, S. Roberts, M.J. Tudor, and S.P. Beeby, “Design and experimental characterization of a tunable vibration-based electromagnetic micro- generator”, Sens. Actuators, A,, vol. 158, no. 2, pp. 284–293, 2010, doi: 10.1016/j.sna.2010.01.002.
  32.  W.J. Su, J. Zu, and Y. Zhu, “Design and development of a broadband magnet-induced dual-cantilever piezoelectric energy harvester”, J. Intell. Mater. Syst. Struct., vol. 25, no. 4, pp. 430–442, 2014, doi: 10.1177/1045389X 13498315.
  33.  D. Guo, X.F. Zhang, H.Y. Li, and H. Li, “Piezoelectric Energy Harvester Array with Magnetic Tip Mass”, in ASME International Mechanical Engineering Congress and Exposition, 2015, vol. 57403, p. V04BT04A045, doi: 10.1115/IMECE201551044.
  34.  S.S. Rao, Vibration of continuous systems, John Wiley and Sons, Ltd, 2019, doi: 10.1002/9781119424284.
Go to article

Authors and Affiliations

Ashutosh Anand
1 2
ORCID: ORCID
Srikanta Pal
2
Sudip Kundu
3
ORCID: ORCID

  1. Department of Electronics and Communication Engineering, Presidency University Bangalore, India
  2. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra Ranchi, India
  3. Department of Electronics and Communication Engineering and Center for Nanomaterials, National Institute of Technology Rourkela, India
Download PDF Download RIS Download Bibtex

Abstract

The article presents the process of identifying discrete-continuous models with the use of heuristic algorithms. A stepped cantilever beam was used as an example of a discrete-continuous model. The theoretical model was developed based on the formalism of Lagrange multipliers and the Timoshenko theory. Based on experimental research, the theoretical model was validated and the optimization problem was formulated. Optimizations were made for two algorithms: genetic (GA) and particle swarm (PSO). The minimization of the relative error of the obtained experimental and numerical results was used as the objective function. The performed process of identifying the theoretical model can be used to determine the eigenfrequencies of models without the need to conduct experimental tests. The presented methodology regarding the parameter identification of the beams with the variable cross-sectional area (according to the Timosheno theory) with additional discrete components allows us to solve similar problems without the need to exit complex patterns.
Go to article

Bibliography

  1.  D. Cekus, B. Posiadała, and P. Warys, “Integration of modeling in SolidWorks and Matlab/Simulink environments,” Arch. Mech. Eng., vol. 61, no. 1, pp. 57–74, 2014, doi: 10.2478/meceng-2014-0003.
  2.  K. Kuliński and J. Przybylski, “Stability and vibrations control of a stepped beam using piezoelectric actuation,” MATEC Web Conf., vol. 157, p. 08004, 2018, doi: 10.1051/matecconf/201815708004.
  3.  S.A. Moezi, E. Zakeri, A. Zare, and M. Nedaei, “On the application of modified cuckoo optimization algorithm to the crack detection problem of cantilever Euler–Bernoulli beam,” Comput. Struct., vol. 157, pp. 42–50, 2015, doi: 10.1016/j.compstruc.2015.05.008.
  4.  P.K. Jena and D.R. Parhi, “A modified particle swarm optimization technique for crack detection in cantilever beams,” Arabian J. Sci Eng., vol. 40, no. 11, pp. 3263–3272, 2015, doi: 10.1007/s13369-015-1661-6.
  5.  X.-L. Li, R. Serra, and O. Julien, “Effects of the Particle Swarm Optimization parameters for structural dynamic monitoring of cantilever beam,” in Surveillance, Vishno and AVE conferences. Lyon, France: INSA-Lyon, Université de Lyon, 2019. Available: https://hal.archives-ouvertes. fr/hal-02188562.
  6.  S. Das, S. Mondal, and S. Guchhait, “Particle swarm optimization-based characterization technique of nonproportional viscous damping parameter of a cantilever beam,” J. Vib. Control, p. 107754632110105, 2021, doi: 10.1177/10775463211010526.
  7.  J. Zolfaghari, “Optimization of dynamic response of cantilever beam by genetic algorithm,” in Nonlinear Approaches in Engineering Applications. Springer International Publishing, 2019, pp. 403–448, doi: 10.1007/978-3-030-18963-1_10.
  8.  M.A. Wahab, I. Belaidi, T. Khatir, A. Hamrani, Y.-L. Zhou, and M.A. Wahab, “Multiple damage detection in composite beams using particle swarm optimization and genetic algorithm,” Mechanics, vol. 23, no. 4, 2017, doi: 10.5755/j01.mech.23.4.15254.
  9.  Z. Xia, K. Mao, S. Wei, X. Wang, Y. Fang, and S. Yang, “Application of genetic algorithm-support vector regression model to predict damping of cantilever beam with particle damper,” J. Low Freq. Noise Vibr. Act. Control, vol. 36, no. 2, pp. 138–147, 2017, doi: 10.1177/0263092317711987.
  10.  M. Friswell and J. Mottershead, Finite Element Model Updating in Structural Dynamics, Springer, Dordrecht, 1995.
  11.  S. Garus and W. Sochacki, “Structure optimization of quasi one-dimensional acoustic filters with the use of a genetic algorithm,” Wave Motion, vol. 98, p. 102645, 2020, doi: 10.1016/j.wavemoti.2020.102645.
  12.  S. Mirjalili, “Genetic algorithm,” in Studies in Computational Intelligence. Springer, Cham, 2018, pp. 43–55, doi: 10.1007/978-3-319-93025-1_4.
  13.  W. Sochacki, J. Garus, J. Szmidla, M. Nabiałek, K. Błoch, P. Kwiatoń, B. Jeż, K. Jeż, and S. Garus, “Designing two-band mechanical wave filters using genetic algorithm,” Acta Phys. Pol. A, vol. 139, no. 5, pp. 479–482, May 2021, doi: 10.12693/aphyspola.139.479.
  14.  J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 – International Conference on Neural Networks. IEEE, 1995, doi: 10.1109/icnn.1995.488968.
  15.  D. Skrobek and D. Cekus, “Optimization of the operation of the anthropomorphic manipulator in a three-dimensional working space,” Eng. Optim., vol. 51, no. 11, pp. 1997–2010, 2019, doi: 10.1080/0305215x.2018.1564919.
  16.  P. Dziwiński, Ł. Bartczuk, and J. Paszkowski, “A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm,” J. Artif. Intell. Soft Comput. Res., vol. 10, no. 2, pp. 95– 111, Mar. 2020, doi: 10.2478/jaiscr-2020-0007.
  17.  S. Timoshenko, “On the transverse vibrations of bars of uniform cross section,” Philos. Mag., vol. 43, no. 253, pp. 125–131, Jan. 1922, doi: 10.1080/14786442208633855.
  18.  W. Sochacki, “The dynamic stability of a stepped cantilever beam with attachments,” J. Vibroeng., vol. 15, no. 1, pp. 280– 290, 2013.
  19.  M.H. Korayem and A. Nahavandi, “Analyzing the effect of the forces exerted on cantilever probe tip of atomic force microscope with tapering- shaped geometry and double piezoelectric extended layers in the air and liquid environments,” J. Sound Vib., vol. 386, pp. 251–264, 2017, doi: 10.1016/j.jsv.2016. 08.031.
  20.  B. Posiadała, “Use of lagrange multiplier formalism to analyze free vibrations of combined dynamical systems,” J. Sound Vib., vol. 176, no. 4, pp. 563–572, Sep. 1994, doi: 10.1006/jsvi.1994.1396.
  21.  R. Pytlarz, “Experimental modal analysis of the beam with the help of non-contact vibration measurement method,” Master’s thesis, Czestochowa University of Technology, Częstochowa, 2008.
  22.  Z. Abo-Hammour, O.A. Arqub, O. Alsmadi, S. Momani, and A. Alsaedi, “An optimization algorithm for solving systems of singular boundary value problems,” Appl. Math. Inf. Sci., vol. 8, no. 6, pp. 2809–2821, 2014, doi: 10.12785/amis/080617.
  23.  I. Rejer, “Classic genetic algorithm vs. genetic algorithm with aggressive mutation for feature selection for a brain-computer interface,” Przegląd Elektrotechniczny, vol. 1, no. 2, pp. 100–104, 2015, doi: 10.15199/48.2015.02.24.
  24.  M. Nikoo, M. Hadzima-Nyarko, E.K. Nyarko, and M. Nikoo, “Determining the natural frequency of cantilever beams using ANN and heuristic search,” Appl. Artif. Intell, vol. 32, no. 3, pp. 309–334, Mar. 2018, doi: 10.1080/08839514.2018.1448003.
  25.  A. Mayer, “A genetic algorithm with randomly shifted gray codes and local optimizations based on quadratic approximations of the fitness,” in Proceedings of the Genetic and Evolutionary Computation Conference Companion. ACM, 2017, doi: 10.1145/3067695.3075968.
  26.  B. Ufnalski and L. Grzesiak, “Particle swarm optimization of artificial-neural-network-based on-line trained speed controller for battery electric vehicle,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 60, no. 3, pp. 661–667, 2012, doi: 10.2478/v10175-0120059-9.
  27.  M. Szczepanik and T. Burczyński, “Swarm optimization of stiffeners locations in 2-d structures,” Bull. Pol. Acad. Sci. Tech. Sci.,, vol. 60, no. 2, pp. 241–246, 2012, doi: 10.2478/v10175012-0032-7.
  28.  J.C. Bansal, “Particle swarm optimization,” in Studies in Computational Intelligence. Springer, Cham, 2018, pp. 11–23, doi: 10.1007/978-3- 319-91341-4_2.
  29.  D. Cekus and P. Warys, “Identification of parameters of discretecontinuous models,” in AIP Conference Proceedings. AIP Publishing LLC, 2015, doi: 10.1063/1.4913110.
  30.  D. Cekus and D. Skrobek, “The influence of inertia weight on the particle swarm optimization algorithm,” J. Appl. Math. Comput. Mech., vol. 17, no. 4, pp. 5–11, Dec. 2018, doi: 10.17512/jamcm.2018.4.01.
  31.  A.R. Jordehi and J. Jasni, “Parameter selection in particle swarm optimisation: a survey,” J. Exp. Theor. Artif. Intell., vol. 25, no. 4, pp. 527–542, Dec. 2013, doi: 10.1080/0952813x.2013.782348.
Go to article

Authors and Affiliations

Dawid Cekus
1
ORCID: ORCID
Paweł Kwiatoń
1
ORCID: ORCID
Michal Šofer
2
ORCID: ORCID
Pavel Šofer
3
ORCID: ORCID

  1. Department of Mechanics and Machine Design Fundamentals, Faculty of Mechanical Engineering and Computer Science, Czestochowa University of Technology, 42-201 Częstochowa, Poland
  2. Department of Applied Mechanics, Faculty of Mechanical Engineering, VŠB-Technical University of Ostrava, 17. listopadu 15/2127, 708 33 Ostrava-Poruba, Czech Republic
  3. Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VŠB-Technical University of Ostrava, 17. listopadu 15/2127, 708 33 Ostrava-Poruba, Czech Republic
Download PDF Download RIS Download Bibtex

Abstract

In the work, multi-criteria optimization of phononic structures was performed to minimize the transmission in the frequency range of acoustic waves, eliminate high transmission peaks with a small half-width inside of the band gap, and what was the most important part of the work – to minimize the number of layers in the structure. Two types of the genetic algorithm were compared in the study. The first one was characterized by a constant number of layers (GACL) of the phononic structure of each individual in each population. Then, the algorithm was run for a different number of layers, as a result of which the structures with the best value of the objective function were determined. In the second version of the algorithm, individuals in populations had a variable number of layers (GAVL) which required a different type of target function and crossover procedure. The transmission for quasi-one-dimensional phononic structures was determined with the use of the transfer matrix method algorithm. Based on the research, it can be concluded that the developed GAVL algorithm with an appropriately selected objective function achieved optimal solutions in a much smaller number of iterations than the GACL algorithm, and the value of the k parameter below 1 leads to faster achievement of the optimal structure.
Go to article

Bibliography

  1.  Y. Pennec, B. Djafari-Rouhani, H. Larabi, J. Vasseur, and A.-C. Hladky-Hennion, “Phononic crystals and manipulation of sound”, Phys. Status Solidi C, vol. 6, no. 9, pp. 2080–2085, Sep. 2009, doi: 10.1002/pssc.200881760.
  2.  Y.F. Li, F. Meng, S. Li, B. Jia, S. Zhou, and X. Huang, “Designing broad phononic band gaps for in-plane modes”, Phys. Lett. A, vol. 382, no. 10, pp. 679–684, Mar. 2018, doi: 10.1016/j.physleta.2017.12.050.
  3.  W. Elmadih, W.P. Syam, I. Maskery, D. Chronopoulos, and Leach, “Multidimensional Phononic Bandgaps in ThreeDimensional Lattices for Additive Manufacturing”, Materials, vol. 12, no. 11, p. 1878, Jun. 2019, doi: 10.3390/ma12111878.
  4.  S. Garus and W. Sochacki, “High-performance quasi onedimensional mirrors of mechanical waves built of periodic and aperiodic structures”, J. Appl. Math. Comput. Mech., vol. 17, no. 4, pp. 19–24, Dec. 2018, doi: 10.17512/jamcm.2018.4.03.
  5.  Z. Zhang, X.K. Han, and G.M. Ji, “Mechanism for controlling the band gap and the flat band in three-component phononic crystals”, J. Phys. Chem. Solids, vol. 123, pp. 235–241, Dec. 2018, doi: 10.1016/j.jpcs.2018.08.012.
  6.  Y. Sun et al., “Band gap and experimental study in phononic crystals with super-cell structure”, Results Phys., vol. 13, p. 102200, Jun. 2019, doi: 10.1016/j.rinp.2019.102200.
  7.  A.H. Safavi-Naeini, J.T. Hill, S. Meenehan, J. Chan, S. Gröblacher, and O. Painter, “Two-Dimensional Phononic-Photonic Band Gap Optomechanical Crystal Cavity”, Phys. Rev. Lett., vol. 112, no. 15, p. 153603, Apr. 2014, doi: 10.1103/PhysRevLett.112.153603.
  8.  W. Sochacki, “Transmission Properties of Phononical Dodecagonal Filter”, Acta Phys. Pol. A, vol. 138, no. 2, pp. 328–331, Aug. 2020, doi: 10.12693/APhysPolA.138.328.
  9.  H. Fan, B. Xia, L. Tong, S. Zheng, and D. Yu, “Elastic Higher-Order Topological Insulator with Topologically Protected Corner States”, Phys. Rev. Lett., vol. 122, no. 20, p. 204301, May 2019, doi: 10.1103/PhysRevLett.122.204301.
  10.  M. P. Bendsøe and O. Sigmund, Topology Optimization. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004.
  11.  O. Sigmund and J. Søndergaard Jensen, “Systematic design of phononic band–gap materials and structures by topology optimization”, Philos. Trans. R. Soc. London, Ser. A, vol. 361, no. 1806, pp. 1001–1019, May 2003, doi: 10.1098/rsta.2003.1177.
  12.  L. Xie, B. Xia, J. Liu, G. Huang, and J. Lei, “An improved fast plane wave expansion method for topology optimization of phononic crystals”, Int. J. Mech. Sci., vol. 120, pp. 171–181, Jan. 2017, doi: 10.1016/j.ijmecsci.2016.11.023.
  13.  Zhong Hui-Lin, Wu Fu-Gen, and Yao Li-Ning, “Application of genetic algorithm in optimization of band gap of twodimensional phononic crystals”, Acta. Phys. Sin., vol. 55, no. 1, p. 275, 2006, doi: 10.7498/aps.55.275
  14.  Z. Liu, B. Wu, and C. He, “Band-gap optimization of twodimensional phononic crystals based on genetic algorithm and FPWE”, Waves Random Complex Media, vol. 24, no. 3, pp. 286–305, Jul. 2014, doi: 10.1080/17455030.2014.901582.
  15.  X. Huang and Y.M. Xie, “Bi-directional evolutionary topology optimization of continuum structures with one or multiple materials”, Comput. Mech., vol. 43, no. 3, pp. 393–401, Feb. 2009, doi: 10.1007/s00466-008-0312-0.
  16.  H.-W. Dong, X.-X. Su, Y.-S. Wang, and C. Zhang, “Topological optimization of two-dimensional phononic crystals based on the finite element method and genetic algorithm”, Struct. Multidisc. Optim., vol. 50, no. 4, pp. 593–604, Oct. 2014, doi: 10.1007/s00158-014-1070-6.
  17.  Y. Li, X. Huang, and S. Zhou, “Topological Design of Cellular Phononic Band Gap Crystals”, Materials, vol. 9, no. 3, p. 186, Mar. 2016, doi: 10.3390/ma9030186.
  18.  G.A. Gazonas, D.S. Weile, R. Wildman, and A. Mohan, “Genetic algorithm optimization of phononic bandgap structures”, Int. J. Solids Struct., vol. 43, no. 18–19, pp. 5851–5866, Sep. 2006, doi: 10.1016/j.ijsolstr.2005.12.002.
  19.  M.I. Hussein, K. Hamza, G.M. Hulbert, R.A. Scott, and K. Saitou, “Multiobjective evolutionary optimization of periodic layered materials for desired wave dispersion characteristics”, Struct. Multidisc. Optim., vol. 31, no. 1, pp. 60–75, Jan. 2006, doi: 10.1007/s00158-005-0555-8.
  20.  K.L. Manktelow, M.J. Leamy, and M. Ruzzene, “Topology design and optimization of nonlinear periodic materials”, J. Mech. Phys. Solids, vol. 61, no. 12, pp. 2433–2453, Dec. 2013, doi: 10.1016/j.jmps.2013.07.009.
  21.  S. Hedayatrasa, M. Kersemans, K. Abhary, M. Uddin, J.K. Guest, and W. Van Paepegem, “Maximizing bandgap width and in-plane stiffness of porous phononic plates for tailoring flexural guided waves: Topology optimization and experimental validation”, Mech. Mater., vol. 105, pp. 188–203, Feb. 2017, doi: 10.1016/j.mechmat.2016.12.003.
  22.  L. Chen, Y. Guo, and H. Yi, “Optimization study of bandgaps properties for two-dimensional chiral phononic crystals base on lightweight design”, Phys. Lett. A, vol. 388, p. 127054, Feb. 2021, doi: 10.1016/j.physleta.2020.127054.
  23.  X.K. Han and Z. Zhang, “Bandgap design of three-phase phononic crystal by topological optimization”, Wave Motion, vol. 93, p. 102496, Mar. 2020, doi: 10.1016/j.wavemoti.2019. 102496.
  24.  S. Garus and W. Sochacki, “Structure optimization of quasi onedimensional acoustic filters with the use of a genetic algorithm”, Wave Motion, vol. 98, p. 102645, Nov. 2020, doi: 10.1016/j.wavemoti.2020.102645.
  25.  Y. Chen, F. Meng, G. Sun, G. Li, and X. Huang, “Topological design of phononic crystals for unidirectional acoustic transmission”, J. Sound Vib., vol. 410, pp. 103–123, Dec. 2017, doi: 10.1016/j.jsv.2017.08.015.
  26.  X.K. Han and Z. Zhang, “Topological Optimization of Phononic Crystal Thin Plate by a Genetic Algorithm”, Sci. Rep., vol. 9, no. 1, p. 8331, Dec. 2019, doi: 10.1038/s41598-019-44850-8.
  27.  Ł. Chruszczyk, “Genetic minimisation of peak-to-peak level of a complex multi-tone signal”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 3, pp. 621–629, 2019, doi: 10.24425/BPASTS.2019.129660.
  28.  M. Beniyel, M. Sivapragash, S.C. Vettivel, P. Senthil Kumar, K.K. Ajith Kumar, and K. Niranjan, “Optimization of tribology parameters of AZ91D magnesium alloy in dry sliding condition using response surface methodology and genetic algorithm”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 1, p. e135835, 2021, doi: 10.24425/BPASTS.2021.135835.
  29.  O. Dazel, J.-P. Groby, B. Brouard, and C. Potel, “A stable method to model the acoustic response of multilayered structures”, J. Appl. Phys., vol. 113, no. 8, p. 083506, Feb. 2013, doi: 10.1063/1.4790629.
  30.  S. Garus, W. Sochacki, and M. Bold, “Comparison of phononic structures with piezoelectric 0.62Pb(Mg1/3Nb1/3)O30.38PbTiO3 defect layers”, in Proc. Engineering Mechanics 2018, Svratka, Czech Republic, May 2018, pp. 229–232, doi: 10.21495/91-8-229.
  31.  M.M. Sigalas and C.M. Soukoulis, “Elastic-wave propagation through disordered and/or absorptive layered systems”, Phys. Rev. B, vol. 51, no. 5, pp. 2780–2789, Feb. 1995, doi: 10.1103/PhysRevB.51.2780.
  32.  P.-G. Luan and Z. Ye, “Acoustic wave propagation in a onedimensional layered system”, Phys. Rev. E, vol. 63, no. 6, p. 066611, May 2001, doi: 10.1103/PhysRevE.63.066611.
  33.  M.-I. Pop and N. Cretu, “Intrinsic transfer matrix method and split quaternion formalism for multilayer media”, Wave Motion, vol. 65, pp. 105–111, Sep. 2016, doi: 10.1016/j.wavemoti.2016.04.011.
  34.  S. Yang, W.-D. Yu, and N. Pan, “Band structure in two-dimensional fiber–air photonic crystals”, Physica B, vol. 406, no. 4, pp. 963–966, Feb. 2011, doi: 10.1016/j.physb.2010.12.039.
  35.  M. Fukuhara, X. Wang, and A. Inoue, “Acoustic analysis of the amorphous phase of annealed Zr55Cu30Ni5Al10 glassy alloy, using diffracted SH ultrasonic waves”, J. Non-Cryst. Solids, vol. 356, no. 33–34, pp. 1707–1710, Jul. 2010, doi: 10.1016/j.jnoncrysol.2010.06.025.
Go to article

Authors and Affiliations

Sebastian Garus
1
ORCID: ORCID
Wojciech Sochacki
1
ORCID: ORCID
Mariusz Kubanek
2
ORCID: ORCID
Marcin Nabiałek
3
ORCID: ORCID

  1. Faculty of Mechanical Engineering and Computer Science, Department of Mechanics and Fundamentals of Machinery Design, Czestochowa University of Technology, Dąbrowskiego 73, 42-201 Czestochowa, Poland
  2. Faculty of Mechanical Engineering and Computer Science, Department of Computer Science, Czestochowa University of Technology, Dąbrowskiego 73, 42-201 Czestochowa, Poland
  3. Faculty of Production Engineering and Materials Technology, Department of Physics, Czestochowa University of Technology, Armii Krajowej 19, 42-201 Czestochowa, Poland
Download PDF Download RIS Download Bibtex

Abstract

This paper presents the concept of the modelling methodology of a payload-vessel system allowing for a comprehensive investigation of mutual interactions of the system dynamics for lifting in the air. The proposed model consists of six degrees of freedom (6-DoF) vessel and three degrees of freedom (3-DoF) lifting model that can replace the industrial practice based on a simplified approach adopted for light lifts. Utilising the response amplitude operators (RAOs) processing methodology provides the ability to incorporate the excitation functions at the vessel crane tip as a kinematic and analyse a wide spectrum of lifted object weights on a basis of regular wave excitation. The analytical model is presented in detail and its solution in a form of numerical simulation results are provided and discussed within the article. The proposed model exposes the disadvantages of the models encountered in engineering practice and literature and proposes a novel approach enabling efficient studies addressing a lack of access to reliable modelling tools in terms of coupled models for offshore lifting operations planning..
Go to article

Bibliography

  1.  W.G. Acero, L. Li, Z. Gao, and T. Moan, “Methodology for assessment of the operational limits and operability of marine operations,” Ocean Eng., vol. 125, pp. 308–327, 2016, doi: 10.1016/j.oceaneng.2016.08.015.
  2.  W. Meng, L.H. Sheng, M. Qing, and B.G. Rong, “Intelligent control algorithm for ship dynamic positioning,” Arch. Control Sci., vol. 24, 2014, doi: 10.2478/acsc-2014-0026.
  3.  L. Li, Z. Gao, T. Moan, and H. Ormberg, “Analysis of lifting operation of a monopile for an offshore wind turbine considering vessel shielding effects,” Marine Struct., vol. 39, pp. 287–314, 2014, doi: 10.1016/j.marstruc.2014.07.009.
  4.  H. Zhu, L. Li, and M. Ong, “Study of lifting operation of a tripod foundation for offshore wind turbine,” in IOP Conf. Ser.: Mater. Sci. Eng., vol. 276, no. 1, 2017, doi: 10.1088/1757-899X/276/1/012012.
  5.  H.-S. Kang, C.H.-H. Tang, L.K. Quen, A. Steven, and X. Yu, “Prediction on parametric resonance of offshore crane cable for lowering subsea structures,” in 2016 IEEE International Conference on Underwater System Technology: Theory and Applications (USYS). IEEE, 2016, pp. 165–170, doi: 10.1109/USYS.2016.7893905.
  6.  H.-S. Kang, C.H.-H. Tang, L.K. Quen, A. Steven, and X. Yu, “Parametric resonance avoidance of offshore crane cable in subsea lowering operation through a* heuristic planner,” Indian J. Geo-Marine Sci., 2017.
  7.  V. Čorić, I. Ćatipović, and V. Slapničar, “Floating crane response in sea waves,” Brodogradnja: Teorija i praksa brodogradnje i pomorske tehnike, vol. 65, no. 2, pp. 111–120, 2014.
  8.  N. Sun, Y. Wu, H. Chen, and Y. Fang, “An energy-optimal solution for transportation control of cranes with double pendulum dynamics: Design and experiments,” Mech. Syst. Signal Process., vol. 102, pp. 87–101, 2018, doi: 10.1016/j.ymssp.2017.09.027.
  9.  X. Peng, Z. Geng et al., “Anti-swing control for 2-d underactuated cranes with load hoisting/lowering: A coupling-based approach,” ISA Trans., vol. 95, pp. 372–378, 2019, doi: 10.1016/j.isatra.2019.04.033.
  10.  Y.-G. Sun, H.-Y. Qiang, J. Xu, and D.-S. Dong, “The nonlinear dyn., and anti-sway tracking control for offshore container crane on a mobile harbor,” J. Marine Sci. Technol., vol. 25, no. 6, p. 5, 2017, doi: 10.6119/JMST-017-1226-05.
  11.  Q.H. Ngo, N.P. Nguyen, C.N. Nguyen, T.H. Tran, and Q.P. Ha, “Fuzzy sliding mode control of an offshore container crane,” Ocean Eng., vol. 140, pp. 125–134, 2017, doi: 10.1016/j.oceaneng.2017.05.019.
  12.  X. Xu and M. Wiercigroch, “Approximate analytical solutions for oscillatory and rotational motion of a parametric pendulum,” Nonlinear Dyn., vol. 47, no. 1-3, pp. 311–320, 2007, doi: 10.1007/s11071-006-9074-4.
  13.  D. Yurchenko and P. Alevras, “Stability, control and reliability of a ship crane payload motion,” Probab. Eng. Mech., vol. 38, pp. 173–179, 2014, doi: 10.1016/j.probengmech.2014.10.003.
  14.  X. Zhao and J. Huang, “Distributed-mass payload dynamics and control of dual cranes undergoing planar motions,” Mech. Syst. Signal Process., vol. 126, pp. 636–648, 2019, doi: 10.1016/j.ymssp.2019.02.032.
  15.  Z. Ren, A.S. Verma, B. Ataei, K.H. Halse, and H.P. Hildre, “Model-free anti-swing control of complex-shaped payload with offshore floating cranes and a large number of lift wires,” Ocean Eng., vol. 228, 2021, doi: 10.1016/j.oceaneng.2021.108868.
  16.  N.-K. Ku, J.-H. Cha, M.-I. Roh, and K.-Y. Lee, “A tagline proportional–derivative control method for the anti-swing motion of a heavy load suspended by a floating crane in waves,” Proc. Inst. Mech. Eng., Part M: J. Eng. Marit. Environ., vol. 227, no. 4, pp. 357–366, 2013, doi: 10.1177/1475090212445546.
  17.  S. Robak and R. Raczkowski, “Substations for offshore wind farms: A review from the perspective of the needs of the polish wind energy sector,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no. 4, 2018, doi: 10.24425/124268.
  18.  “Recommended practice modelling and analysis of marine operations n103,” DET NORSKE VERITAS GL, pp. Sec. 9.2–9.3, 2017.
  19.  “Recommended practice c205 environmental conditions and environmental loads,” DET NORSKE VERITAS GL, p. Sec. 3.3.2, 2010.
  20. Fathom Group Ltd. Engineering Procedure, 2018.
  21.  P. Boccotti, Wave mechanics and wave loads on marine structures. Butterworth-Heinemann, 2014.
  22.  B. Chilinski, A. Mackojc, R. Zalewski, and K. Mackojc, “Proposal of the 3-dof model as an approach to modelling offshore lifting dynamics,” Ocean Eng., vol. 203, pp. 287–314, 2020, doi: 10.1016/j.oceaneng.2020.107235.
Go to article

Authors and Affiliations

Anna Mackojć
1
ORCID: ORCID
Bogumil Chiliński
1
ORCID: ORCID

  1. Institute of Machine Design Fundamentals, Warsaw University of Technology, Poland
Download PDF Download RIS Download Bibtex

Abstract

The following discussion concerns the use of innovative smart materials called vacuum-packed particles (VPPs) as active energy absorbers. VPP, also known as a granular jamming system, is a structure composed of granular media contained within an elastomer coating. By changing the vacuum pressure inside the coating, it is possible to control the mechanical properties of the structure. VPPs have many applications, e.g. in medicine, robotics, and vibration damping. No attempts have yet been made to use VPPs to absorb the energy of a collision, although, given their properties, this could very well be an interesting application. In the first part of the paper, the general concept of the absorber is presented. Then a prototype and the empirical tests conducted are precisely described. The middle part of the paper considers the basic properties of VPP and modeling methodology. A proposal for a constitutive equation is presented, and a numerical simulation using LS-Dyna was performed. In the final section, the concept of a smart parking post is presented..
Go to article

Bibliography

  1.  J. Holnicki-Szulc, P. Pawlowski, and M. Wiklo, “High-performance impact absorbing materialsthe concept, design tools and applications,” Smart Mater. Struct., vol. 12, no. 3, pp. 461–467, May 2003, doi: 10.1088/0964-1726/12/3/317.
  2.  T. Fras, C.C. Rot, and D. Mohr, “Application of two fracture models in impact simulations”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 2, pp. 317–325, 2020.
  3.  N. Schmidová, T. Zavřelová, M. Vašíček, F. Zavadil, M. Růžička, and M. Rund, “Development of Adaptable CFRP Energy Absorbers for Car Crashes”, Mater. Today:. Proc., vol. 5, no. 13, part 2, pp. 26784–26791, 2018, doi: 10.1016/j.matpr.2018.08.152.
  4.  M. Pyrz and M. Krzywoblocki, “Crashworthiness optimization of thin-walled tubes using Macro Element Method and Evolutionary Algorithm,” Thin-Walled Struct., vol. 112, pp. 12–19, Feb. 2017.
  5.  C. Graczykowski and R. Faraj, “Development of control systems for fluid-based adaptive impact absorbers”, Mech. Syst. Signal Process., vol. 122, pp. 622–641, 2019, doi: 10.1016/j.ymssp.2018.12.006.
  6.  P. Bartkowski and R. Zalewski, “Designing Process of the Drone’s Passive Safety System”, in: New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol 445, Á. Rocha, A. Correia, H. Adeli, L. Reis, M. Mendonça Teixeira, (Eds.), Springer, Cham., doi: 10.1007/978-3-319-31307-8_74.
  7.  A.M. Molan, M. Rezapour, and K. Ksaibati, “Modeling traffic barriers crash severity by considering the effect of traffic barrier dimensions”, J. Modern Transp., vol. 27, no. 2, pp. 141–151, 2019, doi: 10.1007/s40534-019-0186-1.
  8.  R. Zalewski, Modelowanie i badania wpływu podcis´nienia na włas´ciwos´ci mechaniczne specjalnych struktur granulowanych, Wydawnictwo Komunikacji i Łączności, 2013.
  9.  L. Blanc, B. François, A. Delchambre, and P. Lambert, “Characterization and modeling of granular jamming: models for mechanical design”, Granular Matter, vol. 23, Feb. 2021, doi: 10.1007/s10035-020-01071-5.
  10.  D. Brigido, S. Burrow, and B. Woods, “Switchable stiffness morphing aerostructures based on granular jamming”, J. Intell. Mater. Syst. Struct., vol. 30, p. 14, Feb. 2019, doi: 10.1177/1045389X19862372.
  11.  L. Li, Z. Liu, M. Zhou, X. Li, Y. Meng, and Y. Tian, “Flexible adhesion control by modulating backing stiffness based on jamming of granular materials”, Smart Mater. Struct., vol. 28, no. 11, p. 115023, Oct. 2019, doi: 10.1088/1361-665x/ab46f3.
  12.  P. Bartkowski, R. Zalewski, and P. Chodkiewicz, “Parameter identification of Bouc-Wen model for vacuum packed particles based on genetic algorithm”, Arch. Civ. Mech. Eng., vol. 19, no. 2, pp. 322–333, 2019, doi: 10.1016/j.acme.2018.11.002.
  13.  M.D. Luscombe and J.L. Williams, “Comparison of a long spinal board and vacuum mattress for spinal immobilisation”, Emergency Med. J., vol. 20, no. 5, pp. 476–478, 2003.
  14.  L. Blanc, B. François, A. Delchambre, and P. Lambert, “Granular jamming as controllable stiffness mechanism for endoscopic and catheter applications”, 2016.
  15.  N.G.S. Cheng, “Design and analysis of jammable granular systems”, Thesis (Ph. D.)-Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.
  16.  J. Gómez–Paccapelo, A. Santarossa, H. Bustos, and L. Pugnaloni, “Effect of the granular material on the maximum holding force of a granular gripper”, Granular Matter, vol. 23, p. 4, 2021.
  17.  E. Brown et al., “Universal Robotic Gripper Based on the Jamming of Granular Material”, PNAS, vol. 107, Feb. 2010, doi: 10.1073/ pnas.1003250107.
  18.  R. Zalewski, P. Chodkiewicz, and M. Shillor, “Vibrations of a mass-spring system using a granular-material damper”, Appl. Math. Modell., vol. 40, no. 17-18, pp. 8033–8047, 2016.
  19.  S.-Q. An, H.-L. Zou, Z.-C. Deng, and D.-Y. Guo, “Damping effect of particle-jamming structure for soft actuators with 3Dprinted particles”, Smart Mater. Struct., vol. 29, no. 9, p. 95012, Aug. 2020, doi: 10.1088/1361-665x/ab9f47.
  20.  R. Zalewski, “Constitutive model for special granular structures”, Int. J. Non Linear Mech., vol. 45, pp. 279–285, Feb. 2010, doi: 10.1016/j. ijnonlinmec.2009.11.011.
  21.  F. Putzu, J. Konstantinova, and K. Althoefer, “Soft Particles for Granular Jamming”, in: Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science, vol 11650, K. Althoefer, J. Konstantinova, K. Zhang (Eds.), Springer, Cham., doi: 10.1007/978-3- 030-25332-5_6, 2019, pp. 65–74.
  22.  A. Jiang et al., “Robotic Granular Jamming: Does the Membrane Matter?”, Soft Robotics (SoRo), vol. 1, pp. 192–201, Feb. 2014, doi: 10.1089/ soro.2014.0002.
  23.  M.T. Huber, “O podstawach teoryi wytrzymałości”, Prace Matematyczno-Fizyczne, pp. 47–59, 1904.
  24.  P. Cyklis and P. Młynarczyk, “The CFD Based Estimation of Pressure Pulsation Damping Parameters for the Manifold Element”, Procedia Eng., vol. 157, pp. 387–395, 2016, doi: 10.1016/j.proeng.2016.08.381.
  25.  N. Vasiraja and P. Nagaraj, “The effect of material gradient on the static and dynamic response of layered functionally graded material plate using finite element method”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 4, pp. 828–838, 2019, doi: 10.24425/bpasts.2019.130191.
Go to article

Authors and Affiliations

Piotr Bartkowski
1
Hubert Bukowiecki
1
Franciszek Gawiński
1
Robert Zalewski
1

  1. Warsaw University of Technology, Faculty of Automotive and Construction Machinery Engineering, Poland
Download PDF Download RIS Download Bibtex

Abstract

Blast mitigation continues to be a popular field of research when military vehicles are concerned. The main problem is coping with the vehicle global motion consequences following an explosion. The paper presents a potential application of the linear vacuum packed particle (VPP) damper as a supplementation for a viscous shock absorber in a traditional blast mitigation seat design. The paper also presents field test results for the underbelly blast explosion, comparing them to the laboratory tests carried out on the impact bench. To collect accelerations, the anthropomorphic test device, i.e. the Hybrid III dummy, was used. A set of numerical simulations of the modified blast mitigation seat with the additional VPP linear damper were revealed. The VPP damper was modeled according to the Johnson–Cook model of viscoplasticity. The Hertzian contact theory was adopted to model the contact between the vehicle and the ground. The reduction of the dynamic response index (DRI) in the case of the VPP damper application was also proved.
Go to article

Bibliography

  1.  F. Melanie and P.V.S. Lee, Military Injury Biomechanics The Cause and Prevention of Impact Injuries. CRC Press, 2017.
  2.  H. Kamel, O. Harraz, M. Yacoub, and A. Ali, “Developing a custom Anthropomorphic Test Device for measuring blast effects on occupants inside armored vehicles”, J. Eng. Sci. Mil. Technol., vol. 3, no. 2, pp. 70–76, 2019, doi: 10.21608/ejmtc.2019.15041.1127.
  3.  I. Overton, “A decade of global IED harm reviewed |AOAV”, Action on Armed Violence, 2020. [Online]. Available: https:// aoav.org.uk/2020/a- decade-of-global-ied-harm-reviewed/ (accessed Feb. 05, 2021).
  4.  M. Müller, U. Dierkes, and J. Hampel, “Blast protection in military land vehicle programmes: Approach, methodology and testing”, WIT Trans. Built Environ., vol. 87, pp. 247–257, Jun. 2006, doi: 10.2495/SU060251.
  5.  A. Iluk, “Estimation of spine injury risk as a function of bulletproof vest mass in case of Under Body Blast load”, 2014 IRCOBI Conf. Proc. – Int. Res. Counc. Biomech. Inj., 2014, pp. 809–820.
  6.  Research and Technology Organisation North Atlantic Treaty Organisation, Protection level of armoured vehicles volume 2, AEP-55, vol. 2, no. AUGUST. Allied Engineering Publication, 2011.
  7.  Research and Technology Organisation North Atlantic Treaty Organisation, “Test Methodology for Protection of Vehicle Occupants against Anti-Vehicular Landmine Effects,” 2007.
  8.  M. Cheng, D. Bueley, J.P. Dionne, and A. Makris, “Survivability evaluation of blast mitigation seats for armored vehicles”, 26th Int. Symp. Ballist., 2011.
  9.  P. Baranowski and J. Malachowski, “Numerical study of selected military vehicle chassis subjected to blast loading in terms of tire strength improving”, Bull. Polish Acad. Sci. Tech. Sci., vol. 63, no. 4, pp. 867–878, 2015, doi: 10.1515/bpasts-2015-0099.
  10.  V. Denefeld, N. Heider, A. Holzwarth, A. Sättler, and M. Salk, “Reduction of global effects on vehicles after IED detonations”, Def. Technol., vol. 10, no. 2, pp. 219–225, 2014, doi: 10.1016/j.dt.2014.05.005.
  11.  M. Żurawski and R. Zalewski, “Damping of Beam Vibrations Using Tuned Particles Impact Damper”, Appl. Sci., vol. 10, p. 6334, 2020, doi: 10.3390/app10186334.
  12.  J. Ramalingam and R. Thyagarajan, “Analysis of Design Range for a Stroking Seat on a Stroking Floor to Mitigate Blast Loading Effects”, NATO Sci. Technol. Organ. Publ., 2017.
  13.  G. Hiemenz, M. Murugan, W. Hu, N. Wereley, and J.H. Yoo, “Adaptive Seat Energy Absorbers for Enhanced Crash Safety: Technology Demonstration,” 2016.
  14.  S.A. Venkatesh Babu, R. Thyagarajan, “Retractor-Based Stroking Seat System and Energy-Absorbing Floor to Mitigate High Shock and Vertical Acceleration”, NATO/STO AVT-221 Spec. Meet. “Design Prot. Technol. L. Amphib. NATO Veh.”, 2014.
  15.  S.P. Desjardins, “The evolution of energy absorption systems for crashworthy helicopter seats”, J. Am. Helicopter Soc., vol. 51, no. 2, pp. 150–163, 2006, doi: 10.4050/JAHS.51.150.
  16.  M. Żurawski, B. Chiliński, and R. Zalewski, “A Novel Method for Changing the Dynamics of Slender Elements Using Sponge Particles Structures”, Materials (Basel)., vol. 13, no. 21, p. 4874, 2020, doi: 10.3390/ma13214874.
  17.  P. Bartkowski and R. Zalewski, “A concept of smart multiaxial impact damper made of vacuum packed particles”, MATEC Web Conf., vol. 157, p. 05001, 2018.
  18.  G. Bienioszek and S. Kciuk, “Determination of Boundary Conditions for the Optimization Process of Blast Mitigation”, in 23rd International Conference Engineering Mechanics 2017, 2017.
  19.  R. Zalewski, P. Chodkiewicz, and M. Shillor, “Vibrations of a mass-spring system using a granular-material damper”, Appl. Math. Model., vol. 40, no. 17–18, pp. 8033–8047, 2016, doi: 10.1016/j.apm.2016.03.053.
  20.  R. Zalewski and T. Szmidt, “Application of Special Granular Structures for semi-active damping of lateral beam vibrations”, Eng. Struct., vol. 65, pp. 13–20, 2014, doi: 10.1016/j.engstruct.2014.01.035.
  21.  R. Zalewski and M. Pyrz, “Mechanics of Materials Experimental study and modeling of polymer granular structures submitted to internal underpressure”, Int. J. Mech. Mater., vol. 57, pp. 75–85, 2013, doi: 10.1016/j.mechmat.2012.11.002.
  22.  E. Brown et al., “Universal robotic gripper based on the jamming of granular material”, Proc. National Academy of Sciences, vol. 107, no. 44 pp. 18809–18814, 2010, doi: 10.1073/pnas.1003250107.
  23.  M.D. Luscombe and J.L. Williams, “Comparison of a long spinal board and vacuum mattress for spinal immobilisation”, Emerg. Med. J., vol. 20, pp. 476–478, 2003.
  24.  P. Bartkowski, R. Zalewski, and P. Chodkiewicz, “Parameter identification of Bouc-Wen model for vacuum packed particles based on genetic algorithm”, Arch. Civ. Mech. Eng., vol. 19, pp. 322–333, 2019, doi: 10.1016/j.acme.2018.11.002.
  25.  D. Rodak and R. Zalewski, “Innovative Controllable Torsional Damper Based on Vacuum Packed Particles”, Materials (Basel)., vol. 13, p. 4356, 2020.
  26.  Y. Tsuji, T. Tanaka, and T. Ishida, “Lagrangian numerical simulation of plug flow of cohesionless particles in a horizontal pipe”, Powder Technol., vol. 71, pp. 239–250, 1992.
  27.  R. Chakrabarty and J. Song, “A modified Johnson–Cook material model with strain gradient plasticity consideration for numerical simulation of cold spray process”, Surf. Coat. Technol., vol. 397, p. 125981, 2020, doi: 10.1016/j.surfcoat.2020.125981.
  28.  I.P. Herman, Biological and Medical Physics, Biomedical Engineering. Springer, 2008. p.16–17.
Go to article

Authors and Affiliations

Dominik Rodak
1
ORCID: ORCID
Mateusz Żurawski
1
ORCID: ORCID
Michał Gmitrzuk
2
ORCID: ORCID
Lech Starczewski
2

  1. Faculty of Automotive and Construction Machinery Engineering, Warsaw University of Technology, Poland
  2. Military Institute of Armoured and Automotive Technology, Poland
Download PDF Download RIS Download Bibtex

Abstract

Unmanned vehicles are often used in everyday life, mostly by rescue teams or scientists exploring new terrains. In those constructions, the suspension has constant dimensions, which leads to many disadvantages and limits the application area. The solution to these problems can be creating a six-wheeled mobile platform that can dynamically change the wheelbase in relation to the area of action or terrain inclination angle. The active change in location of the center of gravity gives a possibility to access sloppy obstacles not available with classical suspensions. The main scope of this study is to investigate the influence of material properties on vibration frequency at different lengths of suspension members. The obtained results will allow finding the optimum material for producing a prototype unit.
Go to article

Bibliography

  1.  S. García, P. Pelliccione, C. Menghi, T. Berger, and T. Bures, ”High-level mission specification for multiple robots”, in Proceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering, 2019, pp. 127–140, doi: 10.1145/3357766.3359535.
  2.  P. Flocchini, G. Prencipe, N. Santoro, and P. Widmayer, “Hard tasks for weak robots: the role of common knowledge in pattern formation by autonomous mobile robots”, in Lecture Notes in Computer Science, vol. 1741, 1999, doi: 10.1007/3-540-466320_10.
  3.  L. Moskvin, R. Lavrenov, E. Magid, and M., Svinin, “Modelling a crawler robot using wheels as pseudo-tracks: model complexity vs performance” in IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), pp. 1–5, 2020.
  4.  A. Halme, I. Leppanen, S. Salmi, and S. Ylonen, “Hybrid locomotion of wheel-legged machine”, in Proc. CLAWAR 2000 Conf. Professional Engineering, 2000, vol. 1, pp. 167–173.
  5.  Ch. Grand, F. BenAmar, F. Plumet, and Ph. Bidaud, “Stability control of a wheel-legged mini-rover”, in Proc. CLAWAR 2002 Conf. Professional Engineering, 2002, vol. 1, pp. 323–330.
  6.  A. Gronowicz and J. Szrek, “Idea of a quadruped wheel-legged robot”, The Arch. Mech. Eng., vol. 54, pp. 263–278, 2009, doi: 10.24425/ ame.2009.132101.
  7.  J. Szrek and P. Wójtowicz, “Idea of wheel-legged robot and its control system design”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 58, no. 1, pp. 43–50, 2010, doi: 10.2478/v10175-010-0004-8.
  8.  V. Ţoţu and C. Alexandru, “Multi-criteria kinematic optimization of a front multi-link suspension mechanism using DOE screening and regression model”, Appl. Mech. Mater., vol. 332, 351–356, 2013.
  9.  P. Ptak, M. Pierzgalski, D. Cekus, and K. Sokół, “Modeling and stress analysis of a frame with a suspension of a mars rover”, Procedia Eng., vol. 177, pp. 175–181, 2017, doi: 10.1016/j.proeng.2017.02.215.
  10.  B. Babu, N. Dhayanidhi, and S. Dhamotharan, “Design and fabrication of rocker bogie mechanism geosurvey rover”, Int. J. Sci. Develop. Res., vol. 3, no. 8, pp. 154–159, 2018.
  11.  R. Murambikar, V. Omase, V. Nayak, K. Pati, and Y. Mahulkar, “Design and fabrication of rocker bogie mechanism using solar energy”, Int. Res. J. Eng. Technol., vol. 6, no. 4, pp. 143–147, 2019.
  12.  K. Sokół, D. Cekus, and M. Pierzgalski, “Design and stress analysis of a frame with suspension to multitask terrain rover”, in Engineering Mechanics 2019, 2019, pp. 283–286, doi: 10.21495/71-0-283.
  13.  K. Sokół and M. Pierzgalski, “An influence of the material properties on the endurance of the self-adjustable rocker-bogie suspension”, Arch. Metall. Mater., vol. 66, no. 2, pp. 543–548, 2021, doi: 10.24425/amm.2021.135891.
  14.  P. Pierzgalski, P. Ptak, D. Cekus, and K. Sokół, “Modeling and stress analysis of a manipulator mounted on a mars rover”, Procedia Eng., vol. 177, pp. 121–126, 2017, doi: 10.1016/j.proeng.2017.02.199.
  15.  M. Caffrey et al., “The processing electronics and detector of the Mars 2020 SHERLOC Instrument”, in IEEE Aerospace Conference, pp. 1–8, 2020. doi: 10.1109/AERO47225.2020.9172527.
  16.  L. Deflores, R. Beegle, and L. Bhartia, “SHERLOC: Scanning habitable environments with Raman & luminescence for organics & chemicals”, IEEE Aerospace Conference, 2015, doi: 10.1109/AERO.2015.7119105.
  17.  T.I. Uday et al., “Design and Implementation of the Next Generation Mars Rover”. in 21st International Conference of Computer and Information Technology (ICCIT), 2018, pp. 1–6, doi: 10.1109/ICCITECHN.2018.8631928.
  18.  The University Rover Challenge. [Online]. Available: http://urc.marssociety.org/home/urc-news [Accessed: 10.03.2021].
  19.  O. Zienkiewicz, Metoda elementów skończonych. Arkady, Warszawa, 1972.
  20.  I. Rokach, “Generation and modification of meshes, assessment of their quality, achieving the convergence of results manually and using the self-adaptation procedure”. [Online] Available: http://www.tu.kielce.pl/~rokach/instr/mes_siatki.htm [Accessed 08.02.2021].
Go to article

Authors and Affiliations

Krzysztof Sokół
1
ORCID: ORCID
Maciej Pierzgalski
1
ORCID: ORCID

  1. Institute of Mechanic and Machine Design Foundations, Czestochowa University of Technology, Czestochowa, Poland
Download PDF Download RIS Download Bibtex

Abstract

Industrial processes such as batch distillation columns, supply chain, level control etc. integrate dead times in the wake of the transportation times associated with energy, mass and information. The dead time, the cause for the rise in loop variability, also results from the process time and accumulation of time lags. These delays make the system control poor in its asymptotic stability, i.e. its lack of self-regulating savvy. The haste of the controller’s reaction to disturbances and congruence with the design specifications are largely influenced by the dead time; hence it exhorts a heed. This article is aimed at answering the following question: “How can a fractional order proportional integral derivative controller (FOPIDC) be tuned to become a perfect dead time compensator apposite to the dead time integrated industrial process?” The traditional feedback controllers and their tuning methods do not offer adequate resiliency for the controller to combat out the dead time. The whale optimization algorithm (WOA), which is a nascent (2016 developed) swarm-based meta-heuristic algorithm impersonating the hunting maneuver of a humpback whale, is employed in this paper for tuning the FOPIDC. A comprehensive study is performed and the design is corroborated in the MATLAB/Simulink platform using the FOMCON toolbox. The triumph of the WOA tuning is demonstrated through the critical result comparison of WOA tuning with Bat and particle swarm optimization (PSO) algorithm-based tuning methods. Bode plot based stability analysis and the time domain specification based transient analysis are the main study methodologies used.
Go to article

Bibliography

  1.  A. Tepljakov, “Fractional-order Modeling and Control of Dynamic Systems”, Ph.D. Thesis, Dept. Comput. Syst., Tallinn University of Technology, Tallinn, Estonia, 2017.
  2.  J.C. Shen, “New tuning method for PID controller”, ISA Trans., vol. 41, no. 4, pp. 473–484, 2002, doi: 10.1016/S0019-0578(07)60103-7.
  3.  G.M. Malwatkara, S.H. Sonawane, and L.M. Waghmare, “Tuning PID Controllers for higher order oscillatory systems with improved performance”, ISA Trans., vol. 48, pp. 347–353, 2009, doi: 10.1016/S0019-0578(07)60103-7.
  4.  R. Rajesh, “Optimal tuning of FOPID controller based on PSO algorithm with reference model for a single conical tank system”, SN Appl. Sci., vol. 1, p. 758, 2019, doi: 10.1007/s42452-019-0754-3.
  5.  A. Tepljakov, E. Petlenkov, J. Belikov, and E.A. Gonzalez, “Design of retuning fractional PID controllers for a closed loop magnetic levitation control system”, Proc. 13th Int. Conf. Control, Automation, Robotics and Vis., 2014, pp. 1345–1350, doi: 10.1109/ICARCV.2014.7064511.
  6.  M. Zhang and G. Wang, “Study on integrating process with dead time”, Proc. 29th Chinese Control Conf., 2010, pp. 207–209.
  7.  F. Peterle, M. Rampazzo, and A. Beghi, “Control of second order processes with dead time: the predictive PID solutions”, IFAC Papers Online, vol. 51, no. 4, pp. 793–798, 2018, doi: 10.1016/j.ifacol.2018.06.183.
  8.  I. Podlubny, “Fractional-order systems and PIλDµ-controllers”, IEEE Trans. Autom. Control, vol. 44, no. 1, pp. 208–214, Jan 1999, doi: 10.1109/9.739144.
  9.  I. Podlubny, L. Dorcák, and I. Kostial, “On fractional derivatives, fractional-order dynamic systems and PIλDµ-controllers”, Proc. 36th IEEE Conf. on Decision and Control, 1997, vol. 5, pp. 4985–4990.
  10.  Z. Bingul and O. Karahan, “Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay”, Optim. Control Appl. Methods, vol. 39, no. 5, pp. 1581–1596, 2018, doi: 10.1002/oca.2419.
  11.  M. Cech and M .Schlegel, “The fractional-order PID controller outperforms the classical one”, Conf. Process Control, pp. 1–6, 2006.
  12.  C.A. Monje, Y.Q. Chen, B.M. Vinagre, D. Xue, and V. Feliu, “Fractional-Order Systems and Controls: Fundamentals and Applications”, in Advances in Industrial Control, 2010, doi: 10.1007/978-1-84996-335-0.
  13.  D. Valerio and J. Costa, “A review of tuning methods for fractional PIDs”, in Preprint 4th IFAC Workshop on Fractional Differentiation and its Applications, 2010.
  14.  M. Buslowicz, “Stability conditions for linear continuous time fractional order state delayed systems”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 64, no. 1, pp. 3–7, 2016, doi: 10.1515/bpasts-2016-0001.
  15.  C. Ionesai and D. Copot, “Hands on MPC tuning for industrial application”, Bull. Pol. Acad. Sci. Tech. Sci., vol 67, no. 5, pp. 925–945, 2019, doi: 10.24425/bpasts.2019.130877.
  16.  D. Mozyrska, P. Ostalczyk and M. Wyrwas, “Stability conditions for fractional-order linear equations with delays”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, no. 4, pp. 449–454, 2018, doi: 10.24425/124261.
  17.  W. Jakowluk, “Optimal input signal design for fractional-order system identification”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 1, pp. 3744, 2019, doi: 10.24425/bpas.2019.127336.
  18.  J. Klamka, J. Wyrwal and R. Zawiski, “On controllability of second order dynamical system survey”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 65, no. 3, pp. 279–295, 2017, doi: 10.1515/bpasts-2017-0032.
  19.  S. Das, S. Saha, S. Das, and A. Gupta, “On the selection of tuning methodology of FOPID controllers for the control of higher order processes”, ISA Trans., vol. 50, no. 3, pp. 376–388, 2011, doi: 10.1016/j.isatra.2011.02.003.
  20.  H. Gozde and M.C. Taplamacioglu, “Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system”, J. Franklin Inst., vol. 348, no. 8, pp. 1927–1946, 2011, doi: 10.1016/j.jfranklin.2011.05.012.
  21.  D.L. Zhang, Y.G. Tang, and X.P. Guan, “Optimum design of fractional order PID controller for an AVR system using an improved artificial bee colony algorithm”, Acta Auto. Sin., vol. 40, no. 5, pp. 973–979, 2014, doi: 10.1016/S1874-1029(14)60010-0.
  22.  S. Das, I. Pan, S. Das, and A. Gupta, “A novel fractional order fuzzy PID controller and its optimal time domain tuning based on integral performance indices”, Eng. Appl. Artif. Intel., vol. 25, no. 2, pp. 430–442, Mar. 2012, doi: 10.1016/j.engappai.2011.10.004.
  23.  L. Liu, “Optimization design on fractional order PID controller based on adaptive particle swarm Optimization algorithm”, Nonlinear Dyn., vol. 84, pp. 379–386, 2016, doi: 10.1007/s11071015-2553-8.
  24.  M. Seyedali and L. Andrew, “The whale optimization algorithm”, Adv. Eng. Soft., vol. 95, pp. 51–67, 2016, doi: 10.1016/j.advengsoft.2016.01.008.
  25.  R.S. Preeti, H. Prakash Kumar, and P. Sidhartha, “Power system stability enhancement by fractional order multi input SSSC based controller employing whale optimization algorithm”, J. Electr. Syst. Inf. Technol., vol. 5, no. 3, pp. 326–2018, doi: 10.1016/j.jesit.2018.02.008.
Go to article

Authors and Affiliations

R. Anuja
1
T.S. Sivarani
1
M. Germin Nisha
2

  1. Arunachala College of Engineering For Women, India
  2. St. Xavier’s Catholic College of Engineering, India
Download PDF Download RIS Download Bibtex

Abstract

Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
Go to article

Bibliography

  1.  U.N. Ahmed and K.R. Rao, Orthogonal Transforms for Digital Signal Process. Secaucus, NJ, USA: Springer-Verlag, New York, Inc., 1974.
  2.  Y. Su and Z. Xu, “Parallel implementation of wavelet-based image denoising on programmable pc-grade graphics hardware,” Signal Process., vol. 90, pp. 2396–2411, 2010, doi: 10.1016/j.sigpro.2009.06.019.
  3.  P. Lipinski and D. Puchala, “Digital image watermarking using fast parametric transforms,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, pp. 463–477, 2019.
  4.  K.R. Rao and P. Yip, Discrete cosine transform: algorithms, advantages, applications. San Diego, CA, USA: Academic Press Professional, Inc., 1990.
  5.  D. Salomon, A Guide to Data Compression Methods. New York: Springer-Verlag
  6. D. Puchala and M. Yatsymirskyy, “Joint compression and encryption of visual data using orthogonal parametric transforms,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 64, pp. 373–382, 2016.
  7.  M. Akay, Time Frequency and Wavelets in Biomedical Signal Process., ser. IEEE Press Series in Biomed. Eng. Wiley-IEEE Press, 1998.
  8.  S. Babichev, J. Skvor, J. Fiser, and V. Lytvynenko, “Technology of gene expression profiles filtering based on wavelet analysis,” Int. J. Intell. Syst. Appl., vol. 10, pp. 1–7, 2018.
  9.  Z. Jakovljevic, R. Puzovic, and M. Pajic, “Recognition of planar segments in point cloud based on wavelet transform,” IEEE Trans. Ind. Inf., vol. 11, no. 2, pp. 342–352, 2015.
  10.  J. Cheng, M. Grossman, and T. McKercher, Professional CUDA C Programming. Indianapolis, IN 46256: John Wiley & Sons, Inc., 2014.
  11.  J. Sanders and E. Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional, 2010.
  12.  G. Barlas, Multicore and GPU Programming: An Integrated Approach. Morgan Kaufmann Publishers, 2015.
  13.  K. Stokfiszewski and K. Wieloch, “ Time effectiveness optimization of cross correlation methods for OCR systems with the use of graphics processing units,” J. Appl. Comput. Sci., vol. 23, no. 2, pp. 79–100, 2015.
  14.  A. Wojciechowski and T. Gałaj, “GPU supported dual quaternions based skinning,” in Computer Game Innovations. A. Wojciechowski, P. Napieralski (Eds.), Lodz University of Technology Press, 2016, pp. 5–23.
  15.  M. Wawrzonowski, D. Szajerman, M. Daszuta, and P. Napieralski, “Mobile devices’ GPUs in cloth dynamics simulation,” in Proceedings of the Federated Conference on Computer Science and Information Systems. M. Ganzha, L. Maciaszek, M. Paprzycki (Eds.), 2017, pp. 1283–1290.
  16.  D. Puchala, K. Stokfiszewski, B. Szczepaniak, and M. Yatsymirskyy, “Effectiveness of fast fourier transform implementations on GPU and CPU,” Przegla˛d Elektrotechniczny, vol. 92, no. 7, pp. 69–71, 2016.
  17.  K. Stokfiszewski, K. Wieloch, and M. Yatsymirskyy, “The fast Fourier transform partitioning scheme for GPU’s computation effectiveness improvement,” in Advances in Intelligent Systems and Computing II (CSIT), N. Shakhovska and V. Stepashko (Eds.), Springer, Cham, 2017, vol. 689, no. 1, pp. 511–522.
  18.  B.H.H. Juurlink and H.A.G. Wijshoff, “A quantitive comparison of parallel computation models,” ACM Trans. Comput. Syst., vol. 16, no. 3, pp. 271–318, 1988.
  19.  S.G. Akl, Parallel computation. Models and methods. Upple Saddle River, NJ: Prentice Hall, 1997.
  20.  A. Madougou, S. Varbanescu, C. Laat, and R. van Nieuwpoort, “The landscape of GPGPU performance modeling tools,” Parallel Comput., vol. 56, pp. 18–33, 2016.
  21.  H. Sunpyo and K. Hyesoon, “An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness,” ACM SIGARCH Comput. Architect. News, vol. 37, pp. 152–163, 2009.
  22.  C. Luo and R. Suda, “An execution time prediction analytical model for GPU with instruction-level and thread-level parallelism awareness,” IPSJ SIG Tech. Rep., vol. 2011-HPC-130, no. 19, pp. 1–9, 2011.
  23.  M. Amaris, D. Cordeiro, A. Goldman, and R.Y. de Camargo, “A simple BSP-based model to predict execution time in GPU applications,” in Proc. IEEE 22nd International Conference on High Performance Computing (HiPC), 2015, pp. 285–294.
  24.  L. Ma, R.D. Chamberlain, and K. Agrawal, “Performance modeling for highly-threaded many-core GPUs,” in Proc. IEEE 25th International Conference on Application-Specific Systems, Arch’s and Processors, 2014, pp. 84–91.
  25.  K. Kothapalli, R. Mukherjee, M.S. Rehman, S. Patidar, P.J. Narayanan, and K. Srinathan, “A performance prediction model for the CUDA GPGPU platform,” in Proc. International Conference on High Performance Computing (HiPC), 2009, pp. 463–472.
  26.  M. Amaris, R.Y. de Camargo, M. Dyab, A. Goldman, and D. Trystram, “A comparison of GPU execution time prediction using machine learning and analytical modeling,” in Proc. 15th IEEE International Symposium on Network Computing and Applications (NCA), 2016, pp. 326–333.
  27.  A. Karami, S.A. Mirsoleimani, and F. Khunjush, “A statistical performance prediction model for OpenCL kernels on NVIDIA GPUs,” in Proc. 17th CSI Int. Symposium on Computer Architecture & Digital Systems (CADS), 2013, pp. 15–22.
  28.  A. Kerr, E. Anger, G. Hendry, and S. Yalamanchili, “Eiger: A framework for the automated synthesis of statistical performance models,” in Proc. 19th Int. Conference on High Performance Computing, 2012, pp. 1–6.
  29.  Y. Zhang, Y. Hu, B. Li, and L. Peng, “Performance and power analysis of ATI GPU: A statistical approach,” in Proc. 6th IEEE International Conference on Networking, Architecture, and Storage, 2011, pp. 149–158.
  30.  G. Wu, J.L. Greathouse, A. Lyashevsky, N. Jayasena, and D. Chiou, “GPGPU performance and power estimation using machine learning,” in Proc. 21st IEEE Int. Symposium on High Performance Computer Architecture (HPCA), 2015, pp. 564– 576.
  31.  E. Ipek, B. Supinski, M. Schulz, and S. McKee, “An approach to performance prediction for parallel applications,” in Proc. 11th International Euro-Par Conference on Parallel Processing, 2005, pp. 196–205.
  32.  N. Ardalani, C. Lestourgeon, K. Sankaralingam, and X. Zhu, “Cross architecture performance prediction (XAPP) using CPU code to predict GPU performance,” in Proc. 48th Annual IEEE/ ACM International Symposium on Microarchitecture (MICRO), 2015, pp. 725–737.
  33.  “GPGPU-Sim project.” [Online]. Available: http://www.gpgpu-sim.org.
  34.  A. Bakhoda, W.L. Fung, H. Wong, and G.L. Yuan, “Analyzing CUDA workloads using a detailed GPU simulator,” in Proc. ISPASS International Symposium on Performance Analysis of Systems and Software, 2009, pp. 163–174.
  35.  “GPUSimPow – AES LPGPU Group Power Simulation Project.” [Online]. Available: https://www.aes.tu-berlin.de/menue/forschung/projekte/ gpusimpow_simulator/.
  36.  Z. Yu, L. Eeckhout, N. Goswami, T. Li, L.K. John, H. Jin, C. Xu, and J. Wu, “Accelerating GPGPU micro-architecture simulation,” IEEE Trans. Comput., vol. 64, no. 11, pp. 3153–3166, 2015.
  37.  R. Ubal, B. Jang, P. Mistry, D. Schaa, and D. Kaeli, “Multi2Sim: a simulation framework for CPU-GPU computing,” in Proc. 21st International Conf. on Parallel Architectures and Compilation Techniques (PACT), 2012, pp. 335–344.
  38.  G. Malhotra, S. Goel, and S. Sarangi, “GpuTejas: a parallel simulator for GPU architectures,” in Proc. 21st International Conference on High Performance Computing, 2014, pp. 1–10.
  39.  Y. Arafa, A.A. Badawy, G. Chennupati, N. Santhi, and S. Eidenbenz, “PPT-GPU: Scalable GPU performance modeling,” IEEE Comput. Archit. Lett., vol. 18, no. 1, pp. 55–58, 2019.
  40.  X. Wang, K. Huang, A. Knoll, and X. Qian, “A hybrid framework for fast and accurate GPU performance estimation through source-level analysis and trace-based simulation,” in Proc. IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 506–518.
  41.  K. Punniyamurthy, B. Boroujerdian, and A. Gerstlauer, “GATSim: Abstract timing simulation of GPUs,” in Proc. Design, Automation & Test, Europe Conf. & Exhibition (DATE), 2017, pp. 43–48.
  42.  M. Khairy, Z. Shen, T.M. Aamodt, and T.G. Rogers, “AccelSim: An extensible simulation framework for validated GPU modeling,” in Proc. 47th IEEE/ACM Int. Symposium on Computer Architecture (ISCA), 2020, pp. 473–486.
  43.  S. Collange, M. Daumas, D. Defour, and D. Parello, “Barra: A parallel functional simulator for GPGPU,” in Proc. IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2010, pp. 351–360.
  44.  “GPU Ocelot project: a dynamic compilation framework for GPU computing.” [Online]. Available: http://www.gpuocelot.gatech.edu/
  45.  J. Power, J. Hestness, M.S. Orr, M.D. Hill, and D.A. Wood, “gem5-gpu: A heterogeneous CPU-GPU simulator,” IEEE Comput. Archit. Lett., vol. 14, no. 1, pp. 34–36, 2015.
  46.  “FusionSim GPU simulator project.” [Online]. Available: https://sites.google.com/site/fusionsimulator/
  47.  A. Nakonechny and Z. Veres, “The wavelet based trained filter for image interpolation,” in Proc. IEEE 1st International Conference on Data Stream Mining & Processing, 2016, pp. 218–221.
  48.  G. Strang and T. Nguyen, Wavelets and Filter Banks. Welleslay, UK: Welleslay-Cambridge Press, 1996.
  49.  P. Lipiński and J. Stolarek, “Improving watermark resistance against removal attacks using orthogonal wavelet adaptation,” in Proc. 38th Conference on Current Trends in Theory and Practice of Computer Science, vol. 7147, 2012, pp. 588–599.
  50.  D. Bařina, M. Kula, and P. Zemčík, “Parallel wavelet schemes for images,” J. Real-Time Image Process., vol. 16, no. 5, pp. 1365–1381, 2019.
  51.  D. Bařina, M. Kula, M. Matýšek, and P. Zemčík, “Accelerating discrete wavelet transforms on GPUs,” in Proc. International Conference on Image Processing (ICIP), 2017, pp. 2707– 2710.
  52.  D. Bařina, M. Kula, M. Matýšek, and P. Zemčík, “Accelerating discrete wavelet transforms on parallel architectures,” J. WSCG, vol. 25, no. 2, pp. 77–85, 2017.
  53.  W. van der Laan, A. Jalba, and J. Roerdink, “Accelerating wavelet lifting on graphics hardware using CUDA,” IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 1, pp. 132–146, 2011.
  54.  M. Yatsymirskyy, “A novel matrix model of two channel biorthogonal filter banks,” Metody Informatyki Stosowanej, pp. 205–212, 2011.
  55.  M. Yatsymirskyy and K. Stokfiszewski, “Effectiveness of lattice factorization of two-channel orthogonal filter banks,” in Proc. Joint Conference NTAV/SPA, 2012, pp. 275–279.
  56.  M. Yatsymirskyy, “Lattice structures for synthesis and implementation of wavelet transforms,” J. Appl. Comput. Sci., vol. 17, no. 1, pp. 133–141, 2009.
  57.  J. Stolarek, “Adaptive synthesis of a wavelet transform using fast neural network,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 59, pp. 9– 13, 2011.
  58.  D. Puchala, K. Stokfiszewski, K. Wieloch, and M. Yatsymirskyy, “Comparative study of massively parallel GPU realizations of wavelet transform computation with lattice structure and matrixbased approach,” in Proc. IEEE International Conference on Data Stream Mining & Processing, 2018, pp. 88–93.
  59.  M. Harris, S. Sengupta, and J.D. Owens, “Parallel prefix sum (scan) with CUDA,” in GPU Gems 3, Part VI: GPU Computing, H. Nguyen, Ed. Addison Wesley, 2007, pp. 851–876.
  60.  S. Sengupta, A.E. Lefohn, and J.D. Owens, “A work-efficient step-efficient prefix sum algorithm,” in Proc. Workshop on Edge Computing Using New Commodity Architectures, 2006, pp. D–26–27.
  61.  J. Franco, G. Bernabe, J. Fernandez, and M.E. Acacio, “A parallel implementation of the 2d wavelet transform using CUDA,” in Proc. 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing, 2009, pp. 111–118.
  62.  H. Bantikyan, “CUDA based implementation of 2-D discrete Haar wavelet transformation,” in Proc. International Conference Parallel and Distributed Computing Systems, 2014, pp. 20–26.
  63.  M.J. Flynn and S.F. Oberman, Advanced Computer Arithmetic Design. New York, NY, USA: John Wiley & Sons, Inc., 2001.
  64.  Ł. Napierała, “Effectiveness measurements of linear transforms realized on graphics processing units with the use of GPGPUSim emulator” – MSc thesis, Institute of Information Technology, Łódz´ University of Technology, Poland, 2020.
Go to article

Authors and Affiliations

Dariusz Puchala
1
Kamil Stokfiszewski
1
ORCID: ORCID
Kamil Wieloch
1

  1. Institute of Information Technology, Łódź University of Technology, ul. Wólczańska 215, 90-924 Łódź, Poland
Download PDF Download RIS Download Bibtex

Abstract

Tuning rules for PID and PI-PI servo controllers are developed using a pole placement approach with a multiple pole, i.e. a triple one in the case of PID and a quadruple for PI-PI. The controllers involve complex roots in the numerators of the transfer functions. This is not possible in the classical P-PI structure which admits real roots only. The settling time of the servos determined by the multiple time constant is the only design parameter. Nomograms to read out discrete controller settings in terms of the time constant and control cycle are given. As compared to the classical structures, the upper limit on the control cycle is now twice longer in the case of PID, and four times in the case of PI-PI. This implies that the settling times can be shortened by the same ratios. Responses of a PLC-controlled servo confirm the validity of the design.
Go to article

Bibliography

  1.  B. Siciliano and O. Khatib, Eds., Springer Handbook of Robotics. Berlin Heidelberg: Springer, 2008.
  2.  G. Ellis, Ed., Control System Design Guide, 4th ed. ButterworthHeinemann, 2012.
  3.  G.W. Younkin, Industrial Servo Control Systems, 2nd ed. New York: Marcel Dekker, 2002.
  4.  S.-M. Yang and K.-W. Lin, “Automatic Control Loop Tuning for Permanent-Magnet AC Servo Motor Drives,” IEEE Trans. Ind. Electron., vol. 63, no. 3, pp. 1499–1506, 2016.
  5.  G.F. Franklin, J.D. Powell, and A.F. Emami-Naeini, Feedback Control of Dynamic Systems, 7th ed. Reading: Addison-Wesley, 2019.
  6.  L. Sciavicco and B. Siciliano, Modelling and Control of Robot Manipulators. London: Springer, 2000.
  7.  T. Tarczewski, M. Skiwski, L.J. Niewiara, and L.M. Grzesiak, “High-performance PMSM servo-drive with constrained state feedback position controller,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, pp. 49–58, 2018.
  8.  V. Rao and D. Bernstein, “Naive control of the double integrator,” IEEE Control Syst. Mag., vol. 21, pp. 86–97, 2001.
  9.  P.B. Schmidt and R.D. Lorenz, “Design principles and implementation of acceleration feedback to improve performance of DC drives,” IEEE Trans. Ind. Appl., vol. 28, no. 3, pp. 594–599, 1992.
  10.  T. Żabiński and L. Trybus, “Tuning P-PI and PI-PI controllers for electrical servos,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 58, pp. 51–58, 2010.
  11.  D.E. Seborg, T.F. Edgar, D.A. Mellichamp, and F.J. Doyle, Process Dynamics and Control, 4th ed. New York: Wiley, 2016.
  12.  C. Grimholt and S. Skogestad, “Optimal PI and PID control of first-order plus delay processes and evaluation of the original and improved SIMC rules,” J. Process Control, vol. 70, pp. 36–46, 2018.
  13.  K.J. Åström and T. Hägglund, Advanced PID Control, Research Triangle Park, 2005.
  14.  “Maxima CAS homepage.” [Online]. Available: https://maxima.sourceforge.io/.
  15.  “ESTUN Industrial Technology Europe.” [Online]. Available: https://www.estuneurope.eu/.
  16.  “BECKHOFF New Automation Technology.” [Online]. Available: https://www.beckhoff.com/.
  17. EN 61131-3, Programmable controllers – Part 3: Programming languages (IEC 61131-3:2013), International Standard, CENELEC Std., 2013.
Go to article

Authors and Affiliations

Andrzej Bożek
1
ORCID: ORCID
Leszek Trybus
1
ORCID: ORCID

  1. Department of Computer and Control Engineering, Rzeszów University of Technology, W. Pola 2, 35-959 Rzeszów, Poland
Download PDF Download RIS Download Bibtex

Abstract

The positivity and cyclicity of descriptor linear electrical circuits with chain structure is considered. Two classes of descriptor linear electrical circuits are analyzed. Some new properties of these classes of electrical circuits are established. The results are extended to fractional descriptor linear electrical circuits.
Go to article

Bibliography

  1.  A. Berman and R.J. Plemmons, Nonnegative Matrices in the Mathematical Sciences. Philadelphia: SIAM, 1994.
  2.  L. Farina and S. Rinaldi, Positive Linear Systems; Theory and Applications. New York: J. Wiley, 2000.
  3.  T. Kaczorek, Positive 1D and 2D Systems. London: Springer-Verlag, 2002.
  4.  T. Kaczorek, “Positive linear systems with different fractional orders,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 58, no. 3, pp. 453– 458, 2010.
  5.  T. Kaczorek, Selected Problems of Fractional Systems Theory. Berlin: Springer, 2011.
  6.  T. Kaczorek, “Normal fractional positive linear systems and electrical circuits,” in Proc. Conf. Automation 2019, Warsaw, 2020, pp. 13–26.
  7.  T. Kaczorek and K. Rogowski, Fractional Linear Systems and Electrical Circuits. Cham: Springer, 2015.
  8.  W. Mitkowski, “Dynamical properties of Metzler systems,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 54, no. 4, pp. 309–312, 2008.
  9.  W. Mitkowski, Outline of Control Theory. Kraków: Publishing House AGH, 2019.
  10.  P. Ostalczyk, Discrete Fractional Calculus. River Edge, NJ: World Scientific, 2016.
  11.  I. Podlubny, Fractional Differential Equations. San Diego: Academic Press, 1999.
  12.  T. Kaczorek, “Reachability and observability of positive discrete-time linear systems with integer positive and negative powers of the state frobenius matrices,” Arch. Control Sci., vol. 28, no. 1, pp. 5–20, 2018.
  13.  M.D. Ortigueira and J.A. Tenreiro Machado, “New discrete-time fractional derivatives based on the bilinear transformation: definitions and properties,” J. Adv.Res., vol. 25, pp. 1–10, 2020.
  14.  A. Ruszewski, “Stability of discrete-time fractional linear systems with delays,” Arch. Control Sci., vol. 29, no. 3, pp. 549– 567, 2019.
  15.  L. Sajewski, “Stabilization of positive descriptor fractional discrete-time linear systems with two different fractional orders by decentralized controller,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 65, no. 5, pp. 709–714, 2017.
  16.  R. Stanisławski, K. Latawiec, and M. Łukaniszyn, “A Comparative Analysis of Laguerre-Based Approximatiors to the Grunwald-Letnikov Fractional-Order Difference,” Math. Probl. Eng., vol. 2015, p. 512104, 2015.
  17.  L. Dai, Singular Control Systems, ser. Lecture Notes in Control and Information Sciences. Berlin: Springer, 1989, vol. 118.
  18.  D. Guang-Ren, Analysis and Design of Descriptor Linear Systems. New York: Springer, 2010.
  19.  T. Kaczorek, Linear Control Systems. vol. 1. New York, USA: J. Wiley, 1992.
  20.  T. Kaczorek and K. Borawski, Descriptor Systems of Integer and Fractional Orders, ser. Studies in Systems, Decision and Control. Cham: Springer, 2021, vol. 367.
  21.  K. Borawski, “Superstabilization of Descriptor ContinuousTime Linear Systems via State-Feedback Using Drazin Inverse Matrix Method,” Symmetry, vol. 12, no. 6, p. 940, 2020.
  22.  M. Rami and D. Napp, “Characterization and Stability of Autonomous Positive Descriptor Systems,” IEEE Trans. Autom. Contr., vol. 57, no. 10, pp. 2668–2673, 2012.
  23.  E. Virnik, “Stability analysis of positive descriptor systems,” Linear Algebra Appl., vol. 429, no. 10, pp. 2640–2659, 2008.
  24.  F.G. Gantmacher, The Theory of Matrices. London: Chelsea Pub. Comp., 1959.
  25.  T. Kaczorek, “Positive electrical circuits with the chain structure and cyclic Metzler state matrices,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 4, pp. 1–5, 2021.
Go to article

Authors and Affiliations

Tadeusz Kaczorek
1
ORCID: ORCID
Kamil Borawski
1

  1. Bialystok University of Technology, Faculty of Electrical Engineering, ul. Wiejska 45D, 15-351 Białystok, Poland
Download PDF Download RIS Download Bibtex

Abstract

In order to ensure that all the connected Equipment in the distribution network operates smoothly, the voltage stability of photovoltaic (PV) integrated distribution systems is very important. Sustaining the voltage profile when integrating PV is a particularly difficult issue. The primary goal of this article is to provide a consistent voltage profile to a sensitive load. A three-phase PV integrated distribution system has been chosen for investigation. An innovative feature of this system is that UPQC DVR and STATCOM systems are powered by Z-source inverters instead of traditional inverters. The ability to actively decouple power is the primary benefit of utilizing a Z-source inverter. The objective of the study effort is to use this new UPQC to synchronize a solar PV system with the distribution system. For the UPQC with battery energy storage system (BESS), the research study examines and develops the most appropriate control approach. A UPQC is a device that is used to integrate solar panels and improve the voltage stability of the distribution system. The prototype model is being developed, and the experimental findings confirm the main objective.
Go to article

Authors and Affiliations

A. Raja
1
M. Vijayakumar
2
C. Karthikeyan
3

  1. Electrical and Electronics Engineering Department, SSM College of Engineering, Kumarapalayam, Namakkal – 638 183, Tamilnadu, India
  2. Electrical and Electronics Engineering Department, K.S.R. College of Engineering, Tiruchengode, Namakkal-637 215, Tamilnadu, India
  3. Electrical Department, Tamil Nadu Generation and Distribution Corporation Ltd., Erode – 638009, Tamilnadu, India
Download PDF Download RIS Download Bibtex

Abstract

Feeder reconfiguration (FR), capacitor placement and sizing (CPS) are the two renowned methods widely applied by the researchers for loss minimization with node voltage enrichment in the electrical distribution network (EDN), which has an immense impact on economic savings. In recent years, optimization of FR and CPS together can proficiently yield better power loss minimization and save costs compared to the individual optimization of FR and CPS. This work proposes an application of an improved salp swarm optimization technique based on weight factor (ISSOT-WF) to solve the cost-based objective function using CPS with and without FR for five different cases and three load levels, subject to satisfying operating constraints. In addition, to ascertain the impact of real power injection on additional power loss reduction, this work considers the integration of dispersed generation units at three optimal locations in capacitive compensated optimal EDN. The effectiveness of ISSOT-WF has been demonstrated on the standard PG&E-69 bus system and the outcomes of the 69-bus test case have been validated by comparing with other competing algorithms. Using FR and CPS at three optimal nodes and due to power loss reduction, cost-saving reached up to a maximum of 71%, and a maximum APLR of 26% was achieved after the installation of DGs at three optimal locations with the significant improvement in the bus voltage profile.
Go to article

Authors and Affiliations

G. Srinivasan
ORCID: ORCID
K. Amaresh
1
Kumar Reddy Cheepathi
1

  1. Department of Electrical & Electronics Engineering, KSRM College of Engineering, Yerramasupalli, Kadappa – 516003, Andhra Pradesh, India
Download PDF Download RIS Download Bibtex

Abstract

The birth of electricity witnessed “the battle of currents” between AC and DC as a medium of power transfer. AC won the battle in the first place because of its ability to transform voltage levels. However, with the development of power electronic converters (PECs), DC is striking back. Most of the electronic loads in our conventional AC-based homes are DC by nature. Moreover, the modern concept of energy-efficient variable speed drive (VSD) based loads, i.e. DC-inverter based air-conditioners and refrigerators, require a DC link for their operation. The driving component of all such loads is the PEC. The operational efficiency of PECs depends on the loading which varies throughout the day. This paper presents a mathematical model based on a bottom-up approach to the comparative efficiency analysis of AC and DC distribution systems considering daily load variation. Two topologies are presented where AC and DC distribution systems are compared in terms of efficiency. The first topology (T1) defines a separate/independent converter for each load, whereas in the second topology (T2) loads of a particular class are lumped and driven by a single converter. The results present DC distribution better than AC distribution with an efficiency advantage of 2.28% and 1.57% for T1 and T2, respectively.
Go to article

Authors and Affiliations

Hasan Erteza Gelani
1
ORCID: ORCID
Sidra Khan
2
Faizan Dastgeer
1
ORCID: ORCID
Zeba Idrees
1 3
Muhammad Waqas Afzal
1 2
Mashood Nasir
4
ORCID: ORCID

  1. Electrical Engineering Department, University of Engineering and Technology Lahore, Pakistan
  2. Electrical Engineering Department, COMSATS Lahore, Pakistan
  3. School of Information Science and Engineering, Fudan University, Shanghai, China
  4. Energy Technology Department, Aalborg University, Denmark
Download PDF Download RIS Download Bibtex

Abstract

Modern induction motor (IM) drives with a higher degree of safety should be equipped with fault-tolerant control (FTC) solutions. Current sensor (CS) failures constitute a serious problem in systems using vector control strategies for IMs because these methods require state variable reconstruction, which is usually based on the IM mathematical model and stator current measurement. This article presents an analysis of the operation of the direct torque control (DTC) for IM drive with stator current reconstruction after CSs damage. These reconstructed currents are used for the stator flux and electromagnetic torque estimation in the DTC with space-vector-modulation (SVM) drive. In this research complete damage to both stator CSs is assumed, and the stator current vector components in the postfault mode are reconstructed based on the DC link voltage of the voltage source inverter (VSI) and angular rotor speed measurements using the so-called virtual current sensor (VCS), based on the IM mathematical model. Numerous simulation and experimental tests results illustrate the behavior of the drive system in different operating conditions. The correctness of the stator current reconstruction is also analyzed taking into account motor parameter uncertainties, especially stator and rotor resistances, which usually are the main parameters that determine the proper operation of the stator flux and torque estimation in the DTC control structure.
Go to article

Authors and Affiliations

Michal Adamczyk
1
ORCID: ORCID
Teresa Orlowska-Kowalska
1
ORCID: ORCID

  1. Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, ul. Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

The electrical grid integration takes great attention because of the increasing population in the nonlinear load connected to the power distribution system. This manuscript deals with the power quality issues and mitigations associated with the electrical grid. The proposed single comprehensive artificial neural network (SCANN) controller with unified power quality conditioner (UPQC) is modelled in MATLAB Simulink environment. It provides series and shunt compensation that helps mitigate voltage and current distortion at the end of the distribution system. Initially, four proportional integral (PI) controllers are used to control the UPQC. Later the trained SCANN controller replaces four PI Controllers for better control action. PI and SCANN controllers’ simulation results are compared to find the optimal solutions. A prototype model of SCANN controller is constructed and tested. The test results show that the SCANN based UPQC maintains grid voltage and current magnitude within permissible limits under fluctuating conditions.
Go to article

Authors and Affiliations

Varadharajan Balaji
1
Subramanian Chitra
2

  1. Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore, Tamilnadu – 641049, India and Research Scholar (Electrical), Anna University, Chennai, Tamilnadu, India
  2. Department of Electrical and Electronics Engineering, Government College of Technology, Coimbatore, Tamilnadu – 641049, India
Download PDF Download RIS Download Bibtex

Abstract

In microgrid distribution generation (DG) sources are integrated parallelly for the economic and efficient operation of a power system. This integration of DG sources may cause many challenges in a microgrid. The islanding condition is termed a condition in which the DG sources in the microgrid continue to power the load even when the grid is cut off. This islanding situation must be identified as soon as possible to avoid the collapse of the microgrid. This work presents the hybrid islanding detection technique. This technique consists of both active and parametric estimation methods such as slip mode shift frequency (SMS) and exact signal parametric rotational invariance technique (ESPRIT), respectively. This technique will easily distinguish between islanding and non-islanding events even under very low power perturbations. The proposed method also has no power quality impact. The proposed method is tested with UL741 standard test conditions.
Go to article

Authors and Affiliations

S. Jayanthi
1
S. Arockia Edwin Xavier
2
ORCID: ORCID
P.S. Manoharan
2
ORCID: ORCID

  1. Sapthagiri College of Engineering, Periyanahali, Dharmapuri, India
  2. Thiagarajar College of Engineering, Madurai, India
Download PDF Download RIS Download Bibtex

Abstract

To better extract feature maps from low-resolution (LR) images and recover high-frequency information in the high-resolution (HR) images in image super-resolution (SR), we propose in this paper a new SR algorithm based on a deep convolutional neural network (CNN). The network structure is composed of the feature extraction part and the reconstruction part. The extraction network extracts the feature maps of LR images and uses the sub-pixel convolutional neural network as the up-sampling operator. Skip connection, densely connected neural networks and feature map fusion are used to extract information from hierarchical feature maps at the end of the network, which can effectively reduce the dimension of the feature maps. In the reconstruction network, we add a 3×3 convolution layer based on the original sub-pixel convolution layer, which can allow the reconstruction network to have better nonlinear mapping ability. The experiments show that the algorithm results in a significant improvement in PSNR, SSIM, and human visual effects as compared with some state-of-the-art algorithms based on deep learning.
Go to article

Authors and Affiliations

Xin Yang
1
Yifan Zhang
1
Dake Zhou
1

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, 210016 Nanjing, Jiangsu, China
Download PDF Download RIS Download Bibtex

Abstract

The presented results are for the numerical verification of a method devised to identify an unknown spatio-temporal distribution of heat flux that occurs at the surface of a thin aluminum plate, as a result of pulsed laser beam excitation. The presented identification of boundary heat flux function is a part of the newly proposed laser beam profiling method and utilizes artificial neural networks trained on temperature distributions generated with the ANSYS Fluent solver. The paper focuses on the selection of the most effective neural network hyperparameters and compares the results of neural network identification with the Levenberg–Marquardt method used earlier and discussed in previous articles. For the levels of noise measured in physical experiments (0.25–0.5 K), the accuracy of the current parameter estimation method is between 5 and 10%. Design changes that may increase its accuracy are thoroughly discussed.
Go to article

Authors and Affiliations

Karol Pietrak
1
ORCID: ORCID
Radosław Muszyński
1
Adam Marek
1
Piotr Łapka
1
ORCID: ORCID

  1. Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, ul. Nowowiejska 24, 00-665 Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

Multi-focus image fusion is a method of increasing the image quality and preventing image redundancy. It is utilized in many fields such as medical diagnostic, surveillance, and remote sensing. There are various algorithms available nowadays. However, a common problem is still there, i.e. the method is not sufficient to handle the ghost effect and unpredicted noises. Computational intelligence has developed quickly over recent decades, followed by the rapid development of multi-focus image fusion. The proposed method is multi-focus image fusion based on an automatic encoder-decoder algorithm. It uses deeplabV3+ architecture. During the training process, it uses a multi-focus dataset and ground truth. Then, the model of the network is constructed through the training process. This model was adopted in the testing process of sets to predict the focus map. The testing process is semantic focus processing. Lastly, the fusion process involves a focus map and multi-focus images to configure the fused image. The results show that the fused images do not contain any ghost effects or any unpredicted tiny objects. The assessment metric of the proposed method uses two aspects. The first is the accuracy of predicting a focus map, the second is an objective assessment of the fused image such as mutual information, SSIM, and PSNR indexes. They show a high score of precision and recall. In addition, the indexes of SSIM, PSNR, and mutual information are high. The proposed method also has more stable performance compared with other methods. Finally, the Resnet50 model algorithm in multi-focus image fusion can handle the ghost effect problem well.
Go to article

Authors and Affiliations

K. Hawari
1
Ismail Ismail
1 2

  1. Universiti Malaysia Pahang, Faculty of Electrical and Electronics Engineering, 26300 Kuantan, Malaysia
  2. Politeknik Negeri Padang, Electrical Engineering Department, 25162, Padang, Indonesia
Download PDF Download RIS Download Bibtex

Abstract

The influence of friction stir welding (FSW) in automotive applications is significantly high in recent days as it can boast beneficial factors such as less distortion, minimized residual stresses and enhanced mechanical properties. Since there is no emission of harmful gases, it is regarded as a green technology, which has an energy efficient clean environmental solid-state welding process. In this research work, the FSW technique is employed to weld the AA8011–AZ31B alloy. In addition, the L16 orthogonal array is employed to conduct the experiments. The influences of parameters on the factors such as microstructure, hardness and tensile strength are determined. Microstructure images have shown tunnel formation at low rotational speed and vortex occurrence at high rotational speed. To attain high quality welding, the process parameters are optimized by using a hybrid method called an artificial neural network based genetic algorithm (ANN-GA). The confirmation tests are carried out under optimal welding conditions. The results obtained are highly reliable, which exhibits the optimal features of the hybrid method.
Go to article

Authors and Affiliations

S. Dharmalingam
1
K. Lenin
2
D. Srinivasan
2

  1. Department of Mechanical Engineering, OASYS Institute of Technology, Trichy, Tamilnadu, India
  2. Department of Mechanical Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamilnadu, India
Download PDF Download RIS Download Bibtex

Abstract

Computational intelligence (CI) can adopt/optimize important principles in the workflow of 3D printing. This article aims to examine to what extent the current possibilities for using CI in the development of 3D printing and reverse engineering are being used, and where there are still reserves in this area. Methodology: A literature review is followed by own research on CI-based solutions. Results: Two ANNs solving the most common problems are presented. Conclusions: CI can effectively support 3D printing and reverse engineering especially during the transition to Industry 4.0. Wider implementation of CI solutions can accelerate and integrate the development of innovative technologies based on 3D scanning, 3D printing, and reverse engineering. Analyzing data, gathering experience, and transforming it into knowledge can be done faster and more efficiently, but requires a conscious application and proper targeting.
Go to article

Authors and Affiliations

Izabela Rojek
1
ORCID: ORCID
Dariusz Mikołajewski
1
ORCID: ORCID
Joanna Nowak
2
ORCID: ORCID
Zbigniew Szczepański
2
ORCID: ORCID
Marek Macko
2
ORCID: ORCID

  1. Institute of Computer Science, Kazimierz Wielki University, Bydgoszcz, Poland
  2. Faculty of Mechatronics, Kazimierz Wielki University, Bydgoszcz, Poland
Download PDF Download RIS Download Bibtex

Abstract

This paper outlines the principle of the DNP-NMR technique. The gyrotron, as a very promising microwave source for NMR spectroscopy, is evaluated. Four factors: power stability, power tuning, frequency stability, and frequency tuning determine the usability of the gyrotron device. The causes of instabilities, as well as the methods of overcoming limitations and extending usability are explained with reference to the theory, the numerical and experimental results reported by gyrotron groups.
Go to article

Authors and Affiliations

Kacper Nowak
1
ORCID: ORCID

  1. Wroclaw University of Science and Technology, ul. Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

In this work, we present findings on the syntheses and study of properties of InSe<PTHQ> nanohybrid. The introduction of guest component in GaSe matrix leads to an increase in inhomogeneities, which is clearly confirmed by the strengthening of the low-frequency horizontal branch of Nyquist diagrams. A constant magnetic field counteracts this effect and changes the behavior of the impedance hodograph at low frequencies to the opposite. Illumination leads to a colossal increase in quantum capacitance, which is clearly demonstrated in the Nyquist diagram. For the synthesized InSe<PTHQ> nanohybrid the interesting behavior of the current-voltage characteristic is reported. As a result of studies of the synthesized InSe<PTHQ> nanohybrid the effect of “negative capacity” is observed, the magnitude of which can be controlled by the electric field. Based on the constructed impedance model and proposed N-barrier model, the physical mechanisms of the investigated processes are suggested.
Go to article

Authors and Affiliations

Fedir Ivashchyshyn
1
ORCID: ORCID
Vitaliy Maksymych
2
ORCID: ORCID
Dariusz Calus
1
ORCID: ORCID
Myroslava Klapchuk
2
ORCID: ORCID
Glib Baryshnikov
3
ORCID: ORCID
Rostislav Galagan
3
ORCID: ORCID
Valentina Litvin
3
ORCID: ORCID
Piotr Chabecki
1
ORCID: ORCID
Ihor Bordun
1 2
ORCID: ORCID

  1. Czestochowa University of Technology, Al. Armii Krajowej 17, Czestochowa, 42-200, Poland
  2. Lviv Polytechnic National University, Bandera Str. 12, Lviv, 79013, Ukraine
  3. Bohdan Khmelnytsky National University, blvd. Shevchnko 81, 18031, Cherkasy, Ukraine
Download PDF Download RIS Download Bibtex

Abstract

The aim of the study was to determine the influence of selected nanoparticles, namely diesel exhaust particles, Arizona test dust, silver and gold on the rheology of human blood. The rheological properties of human blood were determined with the use of a modular rheometer, at two various temperatures, namely 36.6◦C and 40◦C. Experimental results were used to calculate the constants in blood constitutive equations. The considered models were power-law, Casson and Cross ones. The obtained results demonstrate that the presence of different nanoparticles in the blood may have different effect on its apparent viscosity depending on the type of particles and shear rate.
Go to article

Authors and Affiliations

Urszula Michalczuk
1
Rafał Przekop
1
Arkadiusz Moskal
1

  1. Warsaw University of Technology, Faculty of Chemical and Process Engineering, ul. Waryńskiego 1, 00-645 Warsaw, Poland

This page uses 'cookies'. Learn more