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Abstract

The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.

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Bibliography

[1] G. Renner and A. Ekárt. Genetic algorithms in computer aided design. Computer-Aided Design, 35(8):709–726, 2003. doi: 10.1016/S0010-4485(03)00003-4.
[2] V. Schramm. Labyrinth Seals of Maximum Sealing: A Approach to Computer-Based Form Optimization, volume 46. Logos Verlag Berlin GmbH, 2011. (in German).
[3] W. Wróblewski, S. Dykas, K. Bochon, and S. Rulik. Optimization of tip seal with honeycomb land in LP counter rotating gas turbine engine. Task Quarterly, 14(3):189–207, 2010.
[4] G. Nowak and W. Wróblewski. Cooling system optimisation of turbine guide vane. Applied Thermal Engineering, 29(2-2):567–572, 2009. doi: 10.1016/j.applthermaleng.2008.03.015.
[5] G. Nowak, W. Wróblewski, and I. Nowak. Convective cooling optimization of a blade for a supercritical steam turbine. International Journal of Heat and Mass Transfer, 55(17-18):4511– 4520, 2012. doi: 10.1016/j.ijheatmasstransfer.2012.03.072.
[6] G. Nowak and A. Rusin. Shape and operation optimisation of a supercritical steam turbine rotor. Energy Conversion and Management, 74:417–425, 2013. doi: 10.1016/j.enconman.2013.06.037.
[7] A. Jahangirian and A. Shahrokhi. Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver. Computers & Fluids, 46(1):270–276, 2011. doi: 10.1016/j.compfluid.2011.02.010.
[8] J. Antony. Design of experiments for engineers and scientists. Elsevier, 2nd edition, 2014.
[9] L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikström, and S. Wold. Design of Experiments, Principles and Applications. Umetrics AB, Sweden, 2000.
[10] H.B. Demuth, M.H. Beale, O. De Jess, and M.T. Hagan. Neural Network Design. Martin Hagan, USA, 2nd edition, 2014.
[11] T. Back. Evolutionary algorithms in theory and practice. Oxford University Press, 1996.
[12] Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer, 1996.
[13] V. Schramm, K. Willenborg, S. Kim, and S. Wittig. Influence of a honeycomb facing on the flow through a stepped labyrinth seal. In ASME Turbo Expo 2000: Power for Land, Sea, and Air, pages V003T01A092–V003T01A092. ASME, 2000. doi: 10.1115/2000-GT-0291.
[14] M.D. Morris. Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2):161–174, 1991.
[15] B. Iooss and P. Lemaître. A review on global sensitivity analysis methods. In Dellino G. and Meloni C., editors, Uncertainty Management in Simulation-Optimization of Complex Systems, chapter 5, pages 101–122. Springer, 2015.
[16] F. Campolongo and J. Cariboni. Sensitivity analysis: How to detect important factors in large models. Technical report, 2007. http://publications.jrc.ec.europa.eu/repository/ handle/ JRC37120.
[17] F. Pianosi, F. Sarrazin, and T. Wagener. A Matlab toolbox for global sensitivity analysis. Environmental Modelling & Software, 70:80–85, 2015. doi: 10.1016/j.envsoft.2015.04.009.
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Authors and Affiliations

Sebastian Rulik
1
Włodzimierz Wróblewski
1
Daniel Frączek
1

  1. Silesian University of Technology, Institute of Power Engineering and Technology, Gliwice, Poland
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Abstract

A mathematical method for nonlinear surrogate synthesis of frame surface eddy current probes providing a uniform eddy current density distribution in the testing object area is proposed. A metamodel of a frame movable eddy-current probe with a planar excitation system structure, used in the algorithm for surrogate optimal synthesis was created. The examples of a nonlinear synthesis of excitation systems with the application of the modern metaheuristic stochastic algorithms for finding the global extremum are considered. The numerical findings of the problem analyses are presented. The efficiency of the synthesized excitation structures was demonstrated on the basis of the eddy current density distribution graphs on the surface of the control zone of the object in comparison with classical analogues.
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Bibliography

[1] Rosado, L. S., Gonzalez, J. C., Santos, T. G., Ramos, P. M., & Pieda, M. (2013). Geometric optimization of a differential planar eddy currents probe for non-destructive testing. Sensors and Actuators A: Physical., 197, 96–105. https://doi.org/10.1016/j.sna.2013.04.010
[2] Su, Z., Efremov, A., Safdarnejad, M., Tamburrino, A., Udpa, L., & Udpa, S. (2015). Optimization of coil design for near uniform interrogating field generation. AIP Conference Proceedings, 1650, 405–413. https://doi.org/10.1063/1.4914636
[3] Su, Z.,Ye, C., Tamburrino, A., Udpa, L.,&Udpa, S. (2016). Optimization of coil design for eddy current testing of multi-layer structures. International Journal of Applied Electromagnetics and Mechanics, 52(1–2), 315–322. https://doi.org/10.3233/JAE-162030
[4] Liu, Z., Yao, J., He, C., Li, Z., Liu, X., & Wu, B. (2018). Development of a bidirectional-excitation eddy-current sensor with magnetic shielding: Detection of subsurface defects in stainless steel. IEEE Sensors J., 18(15), 6203–6216. https://doi.org/10.1109/JSEN.2018.2844957
[5] Ye, C., Udpa, L., & Udpa, S. (2016). Optimization and Validation of Rotating Current Excitation with GMR Array Sensors for Riveted Structures Inspection. Sensors, 16(9), 1512. https://doi.org/10.3390/s16091512
[6] Rekanos, I. T., Antonopoulos, C. S., & Tsiboukis, T. D. (1999). Shape design of cylindrical probe coils for the induction of specified eddy current distributions. IEEE Transactions Magnetics, 35(3), 1797–1800. https://doi.org/10.1109/20.767380
[7] Li, Y., Ren, S., Yan, B., Zainal Abidin, I. M., & Wang, Y. (2017). Imaging of subsurface corrosion using gradient-field pulsed eddy current probes with uniform field excitation. Sensors, 17, 1747. https://doi.org/10.3390/s17081747
[8] Hashimoto, M., Kosaka, D., Ooshima, K., & Nagata, Y. (2002). Numerical analysis of eddy current testing for tubes using uniform eddy current distribution. International Journal of Applied Electromagnetics and Mechanics, 15(1–4), 27–32. https://doi.org/10.3233/JAE-2002-511
[9] Repelianto, A. S., Kasai, N., Sekino, K., & Matsunaga, M. (2019). A Uniform Eddy Current Probe with a Double-Excitation Coil for Flaw Detection on Aluminium Plates. Metals, 9(10), 1116. https://doi.org/10.3390/met9101116
[10] Halchenko, V. Ya., Trembovetskaya, R. V., & Tychkov, V. V. (2020). Surface eddy current probes: excitation systems of the optimal electromagnetic field (review). Devices and Methods of Measurements, 11(2), 91–104. https://doi.org/10.21122/2220-9506-2020-11-2-91-104
[11] Trembovetska, R. V., Halchenko, V. Ya., Tychkov, V. V., & Storchak, A. V. (2020). Linear Synthesis of Uniform Anaxial Eddy Current Probes with a Volumetric Structure of the Excitation System. International Journal “NDT Days”, 3(4), 184–190. https://www.bg-s-ndt.org/journal/ vol3/JNDTD-v3-n4-a01.pdf (in Russian)
[12] Halchenko, V. Ya., Yakimov, A. N., & Ostapuschenko, D. L. (2010). Global optimum search of functions with using of multiagent swarm optimization hybrid with evolutional composition formation of population. Information Technology, 10, 9–16. http://novtex.ru/IT/it2010/It1010.pdf (in Russian)
[13] Itaya, T., Ishida, K., Kubota, Y., Tanaka, A., & Takehira, N. (2016). Visualization of Eddy Current Distributions for Arbitrarily Shaped Coils Parallel to a Moving Conductor Slab. Progress In Electromagnetics Research M, 47, 1–12. https://doi.org/10.2528/PIERM16011204
[14] Itaya, T., Ishida, K., Tanaka, A., Takehira, N., & Miki, T. (2012). Eddy Current Distribution for a Rectangular Coil Arranged Parallel to a Moving Conductor Stab. IET Science, Measurement & Technology, 6(2), 43–51. https://doi.org/10.1049/iet-smt.2011.0015
[15] Kozieł, S., & Bekasiewicz, A. (2017). Multi-objective design of antennas using surrogate models, World Scientific Publishing Europe Ltd. [16] Forrester, A. I. J., Sóbester, A., & Keane, A. J. (2008). Engineering design via surrogate modelling: a practical guide. Chichester: Wiley.
[17] Burnaev, E. V., Erofeev, P., Zaitsev, A., Kononenko, D., & Kapushev E. (2015). Surrogate modeling and optimization of the airplane wing profile based on Gaussian processes. http://itas2012.iitp.ru/pdf/ 1569602325.pdf (in Russian)
[18] Koziel, S., Echeverría Ciaurri, D., & Leifsson L. (2011). Surrogate-based methods. In Koziel S., Yang XS. (Eds.), Computational Optimization, Methods and Algorithms. Studies in Computational Intelligence, 356, Springer-Verlag. https://doi.org/10.1007/978-3-642-20859-1_3
[19] Halchenko, V. Ya., Trembovetska, R. V., Tychkov, V. V., & Storchak, A. V. (2019). Nonlinear surrogate synthesis of the surface circular eddy current probes. Przegla˛d Elektrotechniczny, 9, 76–82. https://doi.org/10.15199/48.2019.09.15
[20] Halchenko,V. Ya., Trembovetska, R. V.,&Tychkov, V. V. (2019). Linear synthesis of non-axial surface eddy current probes. International Journal “NDT Days”, 2(3), 259–268. https://www.ndt.net/article/ NDTDays2019/papers/JNDTD-v2-n3-a03.pdf (in Russian)
[21] Trembovetska, R. V., Halchenko, V. Y., & Tychkov, V. V. (2019). Multiparameter hybrid neural network metamodel of eddy current probes with volumetric structure of excitation system. International Scientific Journal Mathematical Modeling, 4(3), 113–116. https://stumejournals.com/journals/ mm/2019/4/113
[22] Koshevoy, N. D., Gordienko, V. A., & Sukhobrus, Ye. A. (2014). Optimization for the design matrix realization value with the aim to investigate technological processes. Telecommunications and radio engineering, 73(15), 1383–1386. https://doi.org/10.1615/TelecomRadEng.V73.i15.60 (in Russian)
[23] Halchenko, V. Ya., Trembovetska, R. V., Tychkov, V. V., & Storchak, A. V. (2020). The Construction of Effective Multidimensional Computer Designs of Experiments Based on a Quasi-random Additive Recursive Rd–sequence. Applied Computer Systems, 25(1), 70–76. https://doi.org/10.2478/ acss-2020-0009
[24] Brink, H., Richards, J., & Fetherolf, M. (2017). Real-World Machine Learning. Manning Publications Co.
[25] Kuznetsov, B. I., Nikitina, T. B.,& Bovdui, I. V. (2020). Active shielding of magnetic field of overhead power line with phase conductors of triangle arrangement. Tekhnichna elektrodynamika, 4, 25–28. https://doi.org/10.15407/techned2020.04.025
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Authors and Affiliations

Volodymyr Ya. Halchenko
1
Ruslana Trembovetska
1
ORCID: ORCID
Volodymyr Tychkov
1
ORCID: ORCID

  1. Cherkasy State Technological University, Instrumentation, Mechatronics and Computer Technologies Department, Blvd. Shevchenka, 460, 18006, Cherkasy, Ukraine
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Abstract

New methods for identifying the material properties of planar objects as a result of measurements by the eddy current method are proposed. The methods are based on the latest surrogate strategies and advanced optimization techniques that improve efficiency and reduce resource consumption of problem solutions, and balance computational complexity with the accuracy of the results. High-performance metamodels for global surrogate optimization are based on deep truly meaningful fully connected neural networks, serving as an additional function of accumulating apriori information about objects. High accuracy of the approximation of the multidimensional response surface, which is determined by the “exact” electrodynamic model of the testing process, is ensured by performing calculations according to the computer design of a homogeneous experiment with a low weighted symmetric centered discrepancy. The results of numerical experiments performed for full and reduced dimensional search spaces, which can be obtained by linear transformations using the principal component method, are presented. The verification of the methods proved their sufficiently high accuracy and computational performance.
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Authors and Affiliations

Volodymyr Y. Halchenko
1
ORCID: ORCID
Ruslana Trembovetska
1
ORCID: ORCID
Volodymyr Tychkov
1
ORCID: ORCID
Nataliia Tychkova
1
ORCID: ORCID

  1. Instrumentation, Mechatronics and Computer Technologies Department Cherkasy State Technological University Blvd. Shevchenka, 460, 18006, Cherkasy, Ukraine

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