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High-displacement electret-based energy harvesting system for powering leadless pacemakers from heartbeats

Bilel Maamer Nesrine Jaziri Mohamed Hadj Said Fares Tounsi
Archives of Electrical Engineering | 2023 | vol. 72 | No 1 | 229-238 | DOI: 10.24425/aee.2023.143699
Keywords electret-based system electrostatic harvester energy harvesting heartbeats vibration
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Abstract

In vivo biomedical devices are one of the most studied applications for vibrational energy harvesting. In this paper, we investigated a novel high-displacement device for harvesting heartbeats to power leadless implantable pacemakers. Due to the location peculiarities, certain constraints must be respected for the design of such devices. Indeed, the total dimension of the system must not exceed 5.9 mm to be usable within the leadless pacemakers and it must be able to generate accelerations lower than 0.25 m/s2 at frequencies of less than 50 Hz. The proposed design is an electrostatic system based on a square electret of dimension 4.5 mm. It is based on the Quasi-Concertina structure, which has a very low resonant frequency of 26.02 Hz and a low stiffness of 0.492 N/m, allowing it to be very useful in such an application. Using a Teflon electret charged at 1000 V, the device was able to generate an average power of 10.06 μW at a vibration rate of 0.25 m/s2 at the resonant frequency.
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Authors and Affiliations

Bilel Maamer
1
ORCID: ORCID
Nesrine Jaziri
1 2
ORCID: ORCID
Mohamed Hadj Said
3
ORCID: ORCID
Fares Tounsi
1
ORCID: ORCID
  1. Systems Integration and Emerging Energies (SI2E), École nationale d’ingénieurs de Sfax, Université de Sfax 3038 Sfax, Tunisia
  2. Electronics Technology Group, Institute of Micro and Nanotechnologies MacroNanoTechnische Universität Ilmenau, Gustav-Kirchhoff-Straße 1 Ilmenau 98693, Germany
  3. Center for Research in Microelectronics and Nanotechnology (CRMN) Sousse 4050, Tunisia

Numerical Assessment of a One-Mass Spring-Based ElectromagneticEnergy Harvester on a Vibrating Object

Min-Chie Chiu Ying-Chun Chang Long-Jyi Yeh Chiu-Hung Chung
Archives of Acoustics | 2016 | vol. 41 | No 1 | 119-131 | DOI: 10.1515/aoa-2016-0012
Keywords spring harvester generator permanent magnet simulated annealing optimization buckling fatigue
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Abstract

The paper is an exploration of the optimal design parameters of a space-constrained electromagnetic vibration-based generator. An electromagnetic energy harvester is composed of a coiled polyoxymethylen circular shell, a cylindrical NdFeB magnet, and a pair of helical springs. The magnet is vertically confined between the helical springs that serve as a vibrator. The electrical power connected to the coil is actuated when the energy harvester is vibrated by an external force causing the vibrator to periodically move through the coil. The primary factors of the electrical power generated from the energy harvester include a magnet, a spring, a coil, an excited frequency, an excited amplitude, and a design space. In order to obtain maximal electrical power during the excitation period, it is necessary to set the system’s natural frequency equal to the external forcing frequency. There are ten design factors of the energy harvester including the magnet diameter (Dm), the magnet height (Hm), the system damping ratio (ζsys), the spring diameter (Ds), the diameter of the spring wire (ds), the spring length (ℓs), the pitch of the spring (ps), the spring’s number of revolutions (Ns), the coil diameter (Dc), the diameter of the coil wire (dc), and the coil’s number of revolutions (Nc). Because of the mutual effects of the above factors, searching for the appropriate design parameters within a constrained space is complicated. Concerning their geometric allocation, the above ten design parameters are reduced to four (Dm, Hm, ζsys, and Nc). In order to search for optimal electrical power, the objective function of the electrical power is maximized by adjusting the four design parameters (Dm, Hm, ζsys, and Nc) via the simulated annealing method. Consequently, the optimal design parameters of Dm, Hm, ζsys, and Nc that produce maximum electrical power for an electromagnetic energy harvester are found.
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Authors and Affiliations

Min-Chie Chiu
Ying-Chun Chang
Long-Jyi Yeh
Chiu-Hung Chung

Design and Performance Analysis of Peltier & Piezoelectric Human Energy Harvesting Hybrid Model for WBAN Application

Hardeep Singh Dhillon Paras Chawla
International Journal of Electronics and Telecommunications | 2019 | vol. 65 | No 3 | 435-440 | DOI: 10.24425/ijet.2019.129796
Keywords Wireless energy harvesting Body node Body node coordinator (BNC) Piezo-electric harvester Peltier
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Abstract

Wireless body area network (WBAN) has evolved from Wireless personal area network (WPAN), a prominent area of research with vast applications in last decade. In WBAN, various wirelessly interconnected body node (BN) are implanted in or around the human body. Also due to advancement in technology a miniature low power device/BN is developed. The main challenge in WBAN body node is to maintain finite size of battery as well as to increase its capacity. Hence this issue can be resolved by using energy harvesting. Generally researchers have used piezoelectric, electromagnetic or solar harvester only. But, in this research energy harvesting using the hybrid optimization of Piezoelectric and Peltier sensors by controlling on-off timing of body nodes is introduced. A hybrid optimized algorithm is developed using MATLAB 2015b platform and extensive simulation is performed considering four different human gestures (relaxing, walking, running and fast running) which in turn improves overall Quality of Service (QoS) including average (packet loss, end to end delay, throughput) and overall detection efficiency.

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Authors and Affiliations

Hardeep Singh Dhillon
Paras Chawla

Converting sewage holding tanks to rainwater harvesting tanks in Poland

Sadżide Murat-Błażejewska Ryszard Błażejewski
Archives of Environmental Protection | 2020 | vol. 46 | No 4 | 121-131 | DOI: 10.24425/aep.2020.135770
Keywords rainwater harvesting sewage holding tank storage irrigation cost
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Abstract

The aim of this study was an assessment of feasibility of conversion of sewage holding (SH) tanks to rainwater harvesting (RWH) tanks in Poland. Such a conversion may partly solve the problem of water scarcity for irrigation of plants in individual small gardens and reduce tap water consumption. Seven methods of RWH tanks sizing were applied to an example of a small harvesting system of the roof area equal to the garden irrigation area of 100 m2 for three different irrigation doses. A new criterion was introduced to optimize the tank capacity. Economic optimization was provided for new RWH tanks and for the tanks adapted from abandoned SH tanks. Results obtained for a system sited in west-central Poland in an average year have shown that design capacity of RWH tanks varied markedly between sizing methods. The conversion of SH tanks to RWH tanks is profitable, especially for irrigation due to scarcity of water in relatively dry west-central regions. Conversion of individual SH tanks in a good technical state to RWH tanks is relatively simple and cheap. The potential increase in storage volume due to the conversion of individual SH tanks to RWH tanks could reach all over Poland 215–350 dam3 per year, and individually can save up to 18–25% of total annual water use.

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Authors and Affiliations

Sadżide Murat-Błażejewska
1
Ryszard Błażejewski
1
  1. Poznań University of Life Sciences, Poland

Evaluation of Pelargonium graveolens essential oil to prevent gray mold in rose flowers

María Inés Stegmayer Norma Hortensia Álvarez Néstor Gaspar Sager Marcela Alejandra Buyatti Marcos Gabriel Derita
Journal of Plant Protection Research | 2022 | vol. 62 | No 2 | 145-152 | DOI: 10.24425/jppr.2022.141353
Keywords Botrytis cinerea flower production natural products post-harvest terpenes
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Abstract

The main pathogen that deteriorates the quality of rose flowers during the postharvest stage belongs to the fungal genus Botrytis. The chemical products used to control the disease caused by this pathogen have been losing effectiveness due to the appearance of resistance. The present study describes the in vitro and in vivo fungicidal activity of Pelargonium graveolens essential oil and its chemical composition. The essential oil was obtained by hydrodistillation, and the in vitro fungicidal activity was determined by agar diffusion assays, showing 100% of fungal growth inhibition at 250 ppm. In vivo assays were performed on Rosa grandiflora flowers treated with 250 ppm of P. graveolens essential oil, using distillate water as a positive control and the commercial fungicide carbendazim as a negative one. No significant differences were obtained between the treatment with the essential oil and the treatment with the commercial fungicide. The chemical profile of the essential oil was determined by GC-MS. The main compounds detected were geraniol (24.89%), citronellol (19.50%), linalool (10.92%) and γ-eudesmol (8.93%). These results encourage the possible use of P. graveolens essential oil for the control of B. cinerea in rose flowers.
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Authors and Affiliations

María Inés Stegmayer
1
ORCID: ORCID
Norma Hortensia Álvarez
1 2
ORCID: ORCID
Néstor Gaspar Sager
2
ORCID: ORCID
Marcela Alejandra Buyatti
2
ORCID: ORCID
Marcos Gabriel Derita
1 3
ORCID: ORCID
  1. Producción y Protección Vegetal, ICiAgro Litoral, UNL, CONICET, FCA, Argentina
  2. Cultivos Intensivos, Facultad de Ciencias Agrarias, Universidad Nacional del Litoral, Santa Fe, Argentina
  3. Farmacognosia, Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario, Rosario, Argentina

A simple and affordable powering circuit for iot sensor nodes with energy harvesting

Adam Bouřa
Metrology and Measurement Systems | 2020 | vol. 27 | No 4 | 575-587 | DOI: 10.24425/mms.2020.134839
Keywords energy harvesting energy management Internet of Things wireless sensor networks
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Abstract

The paper presents a circuit structure that can be used for powering an IoT (Internet of Things) sensor node and that can use energy just from its surroundings. The main advantage of the presented solution is its very low cost that allows mass applicability e.g. in the IoT smart grids and ubiquitous sensors. It is intended for energy sources that can provide enough voltage but that can provide only low currents such as piezoelectric transducers or small photovoltaic panels (PV) under indoor light conditions. The circuit is able to accumulate energy in a capacitor until a certain level and then to pass it to the load. The presented circuit exhibits similar functionality to a commercially available EH300 energy harvester (EH). The paper compares electrical properties of the presented circuit and the EH300 device, their form factors and costs. The EH circuit’s performance is tested together with an LTC3531 buck-boost DC/DC converter which can provide constant voltage for the following electronics. The paper provides guidelines for selecting an optimal capacity of the storage capacitor. The functionality of the solution presented is demonstrated in a sensor node that periodically transmits measured data to the base station using just the power from the PV panel or the piezoelectric generator. The presented harvester and powering circuit are compact part of the sensor node’s electronics but they can be also realized as an external powering module to be added to existing solutions.

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Authors and Affiliations

Adam Bouřa

Bandwidth and power enhancement in the MEMS-based piezoelectric energy harvester using magnetic tip mass

Ashutosh Anand Srikanta Pal Sudip Kundu
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 1 | e137509 | DOI: 10.24425/bpasts.2021.137509
Keywords vibration piezoelectric energy harvester magnetic tip mass bandwidth stress
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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.

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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.
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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

Investigation to the influence of additional magnets positions on four-magnet bi-stable piezoelectric energy harvester

Xinxin Li Kexue Huang Zhilin Li Jiangshu Xiang Zhenfeng Huang Hanling Mao Yadong Cao
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 1 | e140151 | DOI: 10.24425/bpasts.2022.140151
Keywords bi-stable piezoelectric energy harvesting nonlinear dynamic potential energy
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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.
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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.
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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

Mechanical vibrations: recent trends and engineering applications

Sebastian Garus Bartłomiej Błachowski Wojciech Sochacki Anna Jaskot Paweł Kwiatoń Mariusz Ostrowski Michal Šofer Tomasz Kapitaniak
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 1 | e140351 | DOI: 10.24425/bpasts.2022.140351
Keywords mechanical vibrations energy harvesting modal analysis granular materials
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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.
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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.
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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

The Optimal Routing of Raspberry Pickers in an Analytical and Simulation Approach

Ireneusz KACZMAR Tamás BANYAI
Management and Production Engineering Review | 2024
Keywords raspberries harvest optimization shortest path simulation flexsim raspberry cultivation
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Abstract

The purpose of the work presented here is a comparative analysis of two methods of solving the problem of optimizing the working time and path length of operators for manual harvesting of raspberries over an area of one hectare. An analytical solution is a method of solving mathematical problems based on finding an exact formula that describes a phenomenon or process. A simulation solution is the opposite of a numerical solution, which is based on calculating an approximation using statistical methods. An analytical and simulation approach will be presented to show how to calculate the number of workers needed, the minimum working time and the length of the path taken by raspberry fruit pickers. The results obtained for the two methods are compared.
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Authors and Affiliations

Ireneusz KACZMAR
Tamás BANYAI

Evaluation of the operational effectiveness and feasibility of small-scale water supply systems in the hinterlands in Ethiopia: Case of Afar Regional State

Melese C. Shumie
Journal of Water and Land Development | 2022 | No 55 | 220-228 | DOI: 10.24425/jwld.2022.142325
Keywords hand-dug wells roof water harvesting rural communities water management water supply
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Abstract

Access to clean and sufficient drinking water is difficult in much of Ethiopia’s Afar Region. It is observed that many schemes in the region are non-functional. The study was conducted to overcome the observed problem in seven selected districts of the region. The study regarded hand-dug wells and roof water collection systems, which are the two most common features in the research areas. Eight hand-dug wells and sixteen roof water harvestings are purposively included in the study. All the water points are constructed by Kelem Ethiopia which is a non-governmental organisation and the foremost local organisation for the communities. As per the research survey, the average functional status of the hand-dug well schemes is 65.75% and the roof water harvesting schemes is 22.94%. The research was based on the qualitative data collected on site. The hand-dug well sites were evaluated using 10 parameters, and the roof water harvesting schemes were analysed using 12 parameters. The main non-functional aspects of the scheme are lack of community ownership, drying up of water sources, lack of maintenance and rehabilitation, poor coordination of beneficiaries and school roofs blowing off. Most schemes still require minor to major maintenance and rehabilitation. According to the research, the solutions for water supply are identified in relation to the desired objective.
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Authors and Affiliations

Melese C. Shumie
1
  1. Debre Berhan University, Department of Civil Engineering, Debre Berhan, PO Box 445, Ethiopia

Analysis of the Magnetoelectric Sensor's Usability for the Energy Harvesting

Karol Kuczynski Adrian Bilski Piotr Bilski Jerzy Szymanski
International Journal of Electronics and Telecommunications | 2020 | vol. 66 | No 4 | 787-792 | DOI: 10.24425/ijet.2020.135193
Keywords magnetoelectric sensor current sensor magnetostriction amorphous metal ribbon piezoelectric materials energy harvesting
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Abstract

The paper presents the analysis of the magnetic sensor’s applicability to the energy harvesting operations. The general scheme and technical advancement of the energy extraction from the electric vehicle (such as a tram or a train) is presented. The proposed methodology of applying the magnetic sensor to the energy harvesting is provided. The experimental scheme for the sensor characteristics and measurement results is discussed. Conclusions and future prospects regarding the practical implementation of the energy harvesting system are provided.

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Authors and Affiliations

Karol Kuczynski
ORCID: ORCID
Adrian Bilski
ORCID: ORCID
Piotr Bilski
ORCID: ORCID
Jerzy Szymanski
ORCID: ORCID

Estimation and Compensation of IQ Imbalance in SWIPT System

Ajin R. Nair S. Kirthiga M. Jayakumar
International Journal of Electronics and Telecommunications | 2021 | vol. 67 | No 4 | 679-684 | DOI: 10.24425/ijet.2021.137862
Keywords SWIPT Power Splitting IQ imbalance Energy harvesting Hardware impairments Blind Compensation
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Abstract

Although there are many articulations of SWIPT architecture implementations, the hardware impairment aspect involved in the SWIPT architecture system is not given much attention. This paper evaluates the performance of SWIPT PS Reciever architecture in the presence of IQ imbalance hardware impairment with 16-QAM transmitter and AWGN channel. The parameters SNR, BER is evaluated in the presence of amplitude, phase imbalance, and PS factor at the SWIPT receiver side. Further, the IQ imbalance is estimated and compensated using a blind compensation algorithm. The system achieved a maximum BER of 10−7 in the presence of amplitude and phase imbalance of 0.2 and 1.6 respectively.
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Bibliography

[1] L. R. Varshney, “Transporting information and energy simultaneously,” in 2008 IEEE international symposium on information theory. IEEE, 2008, pp. 1612–1616. [Online]. Available: https://doi.org/10.1109/ISIT.2008.4595260
[2] G. Vieeralingaam and R. Ramanathan, “Parametric study of rf energy harvesting in swipt enabled wireless networks under downlink scenario,” Procedia Computer Science, vol. 143, pp. 835–842, 2018. [Online]. Available: https://doi.org/10.1016/j.procs.2018.10.380
[3] R. Zhang, R. G. Maunder, and L. Hanzo, “Wireless information and power transfer: From scientific hypothesis to engineering practice,” IEEE Communications Magazine, vol. 53, no. 8, pp. 99–105, 2015. [Online]. Available: https://doi.org/10.1109/MCOM.2015.7180515
[4] X. Zhou, R. Zhang, and C. K. Ho, “Wireless information and power transfer: Architecture design and rate-energy tradeoff,” IEEE Transactions on communications, vol. 61, no. 11, pp. 4754–4767, 2013. [Online]. Available: https://doi.org/10.1109/TCOMM.2013.13.120855
[5] S. Kirthiga and M. Jayakumar, “Performance of dualbeam mimo for millimeter wave indoor communication systems,” Wireless personal communications, vol. 77, no. 1, pp. 289–307, 2014. [Online]. Available: https://doi.org/10.1007/s11277-013-1506-0
[6] G. Dhanesh, A. Rydberg, E. Ojefors et al., “Design of millimeterwave micro-machined patch antennas for wlan applications using a computationally efficient method,” in 2001 31st European Microwave Conference. IEEE, 2001, pp. 1–4. [Online]. Available: https: //doi.org/10.1109/EUMA.2001.338902
[7] S. A. Rao, N. Kumar et al., “Characterization of mmwave link for outdoor communications in 5g networks,” in 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2015, pp. 44–49. [Online]. Available: https://doi.org/10.1109/ICACCI.2015.7275582
[8] J. Kim, H.-S. Jo, K.-J. Lee, D.-H. Lee, D.-H. Choi, and S. Kim, “A low-complexity i/q imbalance calibration method for quadrature modulator,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 4, pp. 974–977, 2018. [Online]. Available: https://doi.org/10.1109/TVLSI.2018.2883758
[9] T. N. Nguyen, M. Tran, P. T. Tran, P. T. Tin, T.-L. Nguyen, D.-H. Ha, and M. Voznak, “On the performance of power splitting energy harvested wireless full-duplex relaying network with imperfect csi over dissimilar channels,” Security and Communication Networks, vol. 2018, 2018. [Online]. Available: https://doi.org/10.1155/2018/6036087
[10] Y. Chen, Energy Harvesting Communications: Principles and Theories. John Wiley & Sons, 2019.
[11] D. N. K. Jayakody, J. Thompson, S. Chatzinotas, and S. Durrani, Wireless information and power transfer: A new paradigm for green communications. Springer, 2017. [Online]. Available: https://doi.org/10.1007/978-3-319-56669-6
[12] T. Wang, G. Lu, Y. Ye, and Y. Ren, “Dynamic power splitting strategy for swipt based two-way multiplicative af relay networks with nonlinear energy harvesting model,” Wireless Communications and Mobile Computing, vol. 2018, 2018. [Online]. Available: https://doi.org/10.1155/2018/1802063
[13] M. Sundaram and R. Ramanathan, “Performance optimization of rf energy harvesting wireless sensor networks,” Procedia computer science, vol. 115, pp. 831–837, 2017. [Online]. Available: https://doi.org/10.1016/j.procs.2017.09.165
[14] F. Jameel, A. Ali, and R. Khan, “Optimal time switching and power splitting in swipt,” in 2016 19th International Multi-Topic Conference (INMIC). IEEE, 2016, pp. 1–5. [Online]. Available: https://doi.org/10.1109/INMIC.2016.7840157
[15] D. K. Nguyen, D. N. K. Jayakody, S. Chatzinotas, J. S. Thompson, and J. Li, “Wireless energy harvesting assisted two-way cognitive relay networks: Protocol design and performance analysis,” IEEE Access, vol. 5, pp. 21 447–21 460, 2017. [Online]. Available: https: //doi.org/10.1109/ACCESS.2016.2644758
[16] S. Q. Nguyen, H. Y. Kong et al., “Performance analysis of energyharvesting relay selection systems with multiple antennas in presence of transmit hardware impairments,” in 2016 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2016, pp. 126–130. [Online]. Available: https://doi.org/10.1109/ATC.2016.7764758
[17] T. Schenk, RF imperfections in high-rate wireless systems: impact and digital compensation. Springer Science & Business Media, 2008. [Online]. Available: https://doi.org/10.1007/978-1-4020-6903-1
[18] Y. Li, In-Phase and Quadrature Imbalance: Modeling, Estimation, and Compensation. Springer Science & Business Media, 2013. [Online]. Available: https://doi.org/10.1007/978-1-4614-8618-3
[19] L. Anttila, M. Valkama, and M. Renfors, “Blind compensation of frequency-selective i/q imbalances in quadrature radio receivers: Circularity-based approach,” in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, vol. 3. IEEE, 2007, pp. III–245. [Online]. Available: https://doi.org/10.1109/ICASSP.2007.366518
[20] T. D. P. Perera and D. N. K. Jayakody, “Analysis of timeswitching and power-splitting protocols in wireless-powered cooperative communication system,” Physical Communication, vol. 31, pp. 141–151, 2018. [Online]. Available: https://doi.org/10.1016/j.phycom.2018.09.007
[21] W. Chien, C.-C. Chiu, Y.-T. Cheng, W.-L. Fang, and E. H. Lim, “Multi-objective function for swipt system by sadde,” Applied Sciences, vol. 10, no. 9, p. 3124, 2020. [Online]. Available: https://doi.org/10.3390/app10093124
[22] S. Arzykulov, G. Nauryzbayev, T. Tsiftsis, and M. Abdallah, “Error performance of wireless powered cognitive relay network with interference alignment,” in IEEE PIMRC, pp. 1–5. [Online]. Available: https://doi.org/10.1109/PIMRC.2017.8292459
[23] Y. Zhao, J. Hu, A. Xie, K. Yang, and K.-K. Wong, “Receive spatial modulation aided simultaneous wireless information and power transfer with finite alphabet,” IEEE Transactions on Wireless Communications, vol. 19, no. 12, pp. 8039–8053, 2020. [Online]. Available: https://doi.org/10.1109/TWC.2020.3019011
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Authors and Affiliations

Ajin R. Nair
1
S. Kirthiga
1
M. Jayakumar
1
  1. Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

Time-slot based architecture for power beam-assisted relay techniques in CR-WSNs with transceiver hardware inadequacies

Mushtaq Muhammad Umer Hong Jiang Qiuyun Zhang Liu ManLu Muhammad Owais
Bulletin of the Polish Academy of Sciences Technical Sciences | 2023 | 71 | 5 | e146620 | DOI: 10.24425/bpasts.2023.146620
Keywords cognitive radio WSNs energy harvesting DF relaying relay selection schemes in WSNs hardware inadequacy in WSNs
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Abstract

Over the past two decades, numerous research projects have concentrated on cognitive radio wireless sensor networks (CR-WSNs) and their benefits. To tackle the problem of energy and spectrum shortfall in CR-WSNs, this research proposes an underpinning decode-&-forward (DF) relaying technique. Using the suggested time-slot architecture (TSA), this technique harvests energy from a multi-antenna power beam (PB) and delivers source information to the target utilizing energy-constrained secondary source and relay nodes. The study considers three proposed relay selection schemes: enhanced hybrid partial relay selection (E-HPRS), conventional opportunistic relay selection (C-ORS), and leading opportunistic relay selection (L-ORS). We present evidence for the sustainability of the suggested methods by examining the outage probability (OP) and throughput (TPT) under multiple primary users (PUs). These systems leverage time switching (TS) receiver design to increase end-to-end performance while taking into account the maximum interference constraint and transceiver hardware inadequacies. In order to assess the efficacy of the proposed methods, we derive the exact and asymptotic closed-form equations for OP and TPT & develop an understanding to learn how they affect the overall performance all across the Rayleigh fading channel. The results show that OP of the L-ORS protocol is 16% better than C-ORS and 75% better than E-HPRS in terms of transmitting SNR. The OP of L-ORS is 30% better than C-ORS and 55% better than E-HPRS in terms of hardware inadequacies at the destination. The L-ORS technique outperforms C-ORS and E-HPRS in terms of TPT by 4% and 11%, respectively.
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Authors and Affiliations

Mushtaq Muhammad Umer
1 2
ORCID: ORCID
Hong Jiang
1
Qiuyun Zhang
1
ORCID: ORCID
Liu ManLu
1
ORCID: ORCID
Muhammad Owais
1
ORCID: ORCID
  1. School of Information Engineering, Southwest University of Science & Technology (SWUST) Mianyang, 621010, P.R. China
  2. Department of Software Engineering, Mirpur University of Science & Technology (MUST), Mirpur, Azad Jammu & Kashmir, Pakistan

An Autonomous Wearable Sensor Node for Long-Term Healthcare Monitoring Powered by a Photovoltaic Energy Harvesting System

Saeed Mohsen Abdelhalim Zekry Khaled Youssef Mohamed Abouelatta
International Journal of Electronics and Telecommunications | 2020 | vol. 66 | No 2 | 267-272 | DOI: 10.24425/ijet.2020.131873
Keywords energy harvesting healthcare monitoring wearable sensor nodes photovoltaic Bluetooth low energy (BLE)
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Abstract

In this paper, an autonomous wearable sensor node is developed for long-term continuous healthcare monitoring. This node is used to monitor the body temperature and heart rate of a human through a mobile application. Thus, it includes a temperature sensor, a heart pulse sensor, a low-power microcontroller, and a Bluetooth low energy (BLE) module. The power supply of the node is a lithium-ion rechargeable battery, but this battery has a limited lifetime. Therefore, a photovoltaic (PV) energy harvesting system is proposed to prolong the battery lifetime of the sensor node. The PV energy harvesting system consists of a flexible photovoltaic panel, and a charging controller. This PV energy harvesting system is practically tested outdoor under lighting intensity of 1000 W/m2. Experimentally, the overall power consumption of the node is 4.97 mW and its lifetime about 246 hours in active-sleep mode. Finally, the experimental results demonstrate long-term and sustainable operation for the wearable sensor node.

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Authors and Affiliations

Saeed Mohsen
Abdelhalim Zekry
Khaled Youssef
Mohamed Abouelatta

Design of nonlinear electromagnetic energy harvester equipped with mechanical amplifier and spring bumpers

M. Ostrowski B. Błachowski M. Bocheński D. Piernikarski P. Filipek W. Janicki
Bulletin of the Polish Academy of Sciences Technical Sciences | 2020 | 68 | No. 6 | 1373-1383 | DOI: 10.24425/bpasts.2020.135384
Keywords energy harvesting velocity amplification nonlinear electromagnetic circuit spring bumper quarter car model
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Abstract

The main drawback of vibration-based energy harvesting is its poor efficiency due to small amplitudes of vibration and low sensitivity at frequencies far from resonant frequency. The performance of electromagnetic energy harvester can be improved by using mechanical enhancements such as mechanical amplifiers or spring bumpers. The mechanical amplifiers increase range of movement and velocity, improving also significantly harvester efficiency for the same level of excitation. As a result of this amplitude of motion is much larger comparing to the size of the electromagnetic coil. This in turn imposes the need for modelling of electromagnetic circuit parameters as the function of the moving magnet displacement. Moreover, high velocities achieved by the moving magnet reveal nonlinear dynamics in the electromagnetic circuit of the energy harvester. Another source of nonlinearity is the collision effect between magnet and spring bumpers. It has been shown that this effect should be carefully considered during design process of the energy harvesting device. The present paper investigates the influence of the above-mentioned nonlinearities on power level generated by the energy harvester. A rigorous model of the electromagnetic circuit, derived with aid of the Hamilton’s principle of the least action, has been proposed. It includes inductance of the electromagnetic coil as the function of the moving magnet position. Additionally, nonlinear behaviour of the overall electromagnetic device has been tested numerically for the case of energy harvester attached to the quarter car model moving on random road profiles. Such a source of excitation provides wide band of excitation frequencies, which occur in variety of real-life applications.

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Authors and Affiliations

M. Ostrowski
B. Błachowski
M. Bocheński
D. Piernikarski
P. Filipek
W. Janicki

Modelling rainfall runoff for identification of suitable water harvesting sites in Dawe River watershed, Wabe Shebelle River basin, Ethiopia

Arus E. Harka Negash T. Roba Asfaw K. Kassa
Journal of Water and Land Development | 2020 | No 47 | 186-195 | DOI: 10.24425/jwld.2020.135313
Keywords Geographic Information System (GIS) rainfall runoff rainwater harvesting soil and water assessment tool (SWAT) the Dawe River watershed the Wabe Shebelle River basin
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Abstract

Scarcity of freshwater is one of the major issues which hinders nourishment in large portion of the countries like Ethio-pia. The communities in the Dawe River watershed are facing acute water shortage where water harvesting is vital means of survival. The purpose of this study was to identify optimal water harvesting areas by considering socioeconomic and biophysical factors. This was performed through the integration of soil and water assessment tool (SWAT) model, remote sensing (RS) and Geographic Information System (GIS) technique based on multi-criteria evaluation (MCE). The parame-ters used for the selection of optimal sites for rainwater harvesting were surface runoff, soil texture, land use land cover, slope gradient and stakeholders’ priority. Rainfall data was acquired from the neighbouring weather stations while infor-mation about the soil was attained from laboratory analysis using pipette method. Runoff depth was estimated using SWAT model. The statistical performance of the model in estimating the runoff was revealed with coefficient of determination (R2) of 0.81 and Nash–Sutcliffe Efficiency (NSE) of 0.76 for monthly calibration and R2 of 0.79 and NSE of 0.72 for monthly validation periods. The result implied that there's adequate runoff water to be conserved. Combination of hydrological model with GIS and RS was found to be a vital tool in estimating rainfall runoff and mapping suitable water harvest home sites.

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Authors and Affiliations

Arus E. Harka
Negash T. Roba
Asfaw K. Kassa

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