Identification of Thermophysical Parameters Using an Artificial Immune System

  • Jolanta Dziatkiewicz Department of Computational Mechanics and Engineering, Silesian University of Technology, Gliwice, Poland
  • Arkadiusz Poteralski Department of Computational Mechanics and Engineering, Silesian University of Technology, Gliwice, Poland

Abstract

In this paper, the identification of thermophysical parameters using the hyperbolic twotemperature model is made. We investigate the influence of ultra-fast laser pulses on the heating of a thin metal film using this model. Two differential equations coupled with the electron-phonon coupling factor G are used. One of these equations concerns electron temperatures and the other addresses lattice temperatures. Appropriate initial and boundary conditions are imposed for this model. The finite difference method with a staggered grid is used to solve this direct problem. Temperatures for even nodes and heat fluxes for odd nodes are calculated. The results of the direct problem and results of the experiment are compared. In the optimization process, an artificial immune system is used.

Keywords

microscale heat transfer, two-temperature model, final difference method, artificial immune system,

References

1. M.A. Al-Nimr, Heat transfer mechanisms during short duration laser heating of thin metal films, International Journal of Thermophysics, 18(5): 1257–1268, 1997, doi: 10.1007/BF02575260.
2. R.R. de Faissol Attux, M.B. Loiola, R. Suyama, L.N. de Castro, F.J. Von Zuben, J.M.T. Romano, Blind search for optimal Wiener equalizers using an artificial immune network model, EURASIP Journal on Advances in Signal Processing, 2003: 460216, 2003, doi: 10.1155/S1110865703303014.
3. M. Bereta, T. Burczynski, Comparing binary and real-valued coding in hybrid immune algorithm for feature selection and classification of ECG signals, Engineering Applications of Artificial Intelligence, 20(5): 571–585, 2007, doi: 10.1016/j.engappai.2006.11.004.
4. M. Bereta, T. Burczynski, Immune K-means and negative selection algorithms for data analysis, Information Sciences, 179(10): 1407–1425, 2009, doi: 10.1016/j.ins.2008.10.034.
5. T. Burczynski et al., Intelligent computing in evolutionary optimal shaping of solids, [in:] Proceedings of the 3rd International Conference on Computing, Communications and Control Technologies, Vol. 3, pp. 294–298, 2005.
6. L.N. de Castro, J. Timmis, Artificial immune systems: a novel approach to pattern recognition, [in:] J.M. Corchado, L. Alonso, C. Fyfe [Eds], Artificial Neural Networks in Pattern Recognition, pp. 67–84, University of Paisley, UK, 2002, https://kar.kent.ac.uk/id/eprint/13832.
7. P.A.D. Castro, F.J. Von Zuben, Multi-objective feature selection using a Bayesian artificial immune system, International Journal of Intelligent Computing and Cybernetics, 3(2): 235–256, 2010, doi: 10.1108/17563781011049188.
8. P.A.D. Castro, F.O. de França, H.M. Ferreira, G.P. Coelho, F.J. Von Zuben, Query expansion using an immune-inspired biclustering algorithm, Natural Computing, 9: 579–602, 2010, doi: 10.1007/s11047-009-9127-y.
9. L.N. de Castro, F.J. Von Zuben, Immune and neural network models: theoretical and empirical comparisons, International Journal on Computational Intelligence and Applications, 1(3): 239–257, 2001, doi: 10.1142/S1469026801000238.
10. P.A.D. Castro, F.J. Von Zuben, BAIS: A Bayesian artificial immune system for the effective handling of building blocks, Information Sciences, 179(10): 1426–1444, 2009, doi: 10.1016/j.ins.2008.11.040.
11. L.N. de Castro, F.J. Von Zuben, The construction of a Boolean competitive neural network using ideas from immunology, Neurocomputing, 50: 51–85, 2003, doi: 10.1016/S0925-2312(01)00698-1.
12. P.A.D. Castro, F.J. Von Zuben, Multi-objective Bayesian artificial immune system: empirical evaluation and comparative analyses, Journal of Mathematical Modelling and Algorithms, 8: 151–173, 2009, doi: 10.1007/s10852-009-9108-2.
13. L.N. de Castro, F.J. Von Zuben, Learning and optimization using the clonal selection principle, IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6(3): 239–251, 2002, doi: 10.1109/TEVC.2002.1011539.
14. J.K. Chen, J.E. Beraun, Numerical study of ultrashort laser pulse interactions with metal films, Numerical Heat Transfer, Part A: Applications, 40(1): 1–20, 2001, doi: 10.1080/104077801300348842.
15. G.P. Coelho, A.E.A. da Silva, F.J. Von Zuben, An immune-inspired multi-objective approach to the reconstruction of phylogenetic trees, Neural Computing & Applications, 19: 1103–1132, 2010, doi: 10.1007/s00521-010-0389-1.
16. J. Dziatkiewicz, W. Kus, E. Majchrzak, T. Burczynski, Ł. Turchan, Bioinspired identification of parameters in microscale heat transfer, International Journal for Multiscale Computational Engineering, 12(1): 79–89, 2014, doi: 10.1615/IntJMultCompEng.2014007963.
17. J. Dziatkiewicz, E. Majchrzak, Numerical analysis of laser ablation using the axisymmetric two-temperature model, [in:] AIP Conference Proceedings, vol. 1922, pp. 060004-1–060004-8, AIP Publishing, Melville, 2018, doi: 10.1063/1.5019065.
18. M. Gong, C. Liu, L. Jiao, G. Cheng, Hybrid immune algorithm with Lamarckian local search for multi-objective optimization, Memetic Computing, 2(1): 47–67, 2010, doi: 10.1007/s12293-009-0028-5.
19. T. Huang, X. Song, M. Liu, A Kriging-based non-probability interval optimization of loading path in T-shape tube hydroforming, The International Journal of Advanced Manufacturing Technology, 85(5–8): 1615–1631, 2016, doi: 10.1007/s00170-015-8034-x.
20. L. Jiao, Y. Li, M. Gong, X. Zhang, Quantum-inspired immune clonal algorithm for global optimization, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(5): 1234–1253, 2008, doi: 10.1109/TSMCB.2008.927271.
21. W. Kus, J. Dziatkiewicz, Multicriteria identification of parameters in microscale heat transfer, International Journal of Numerical Methods for Heat & Fluid Flow, 27(3): 587–597, 2017, doi: 10.1109/TSMCB.2008.927271.
22. H.Y.K. Lau, V.W.K. Wong, An immunity approach to strategic behavioral control of intelligent transportation systems, Engineering Applications of Artificial Intelligence, 20(3): 289–306, 2007, doi: 10.1016/j.engappai.2006.06.002.
23. H.Y.K. Lau, W.W.P. Tsang, A parallel immune optimization algorithm for numeric function optimization, Evolutionary Intelligence, 1(3): 171–185, 2008, doi: 10.1007/s12065-008-0014-8.
24. Z. Lin, L.V. Zhigilei, V. Celli, Electron-phonon coupling and electron heat capacity of metals under conditions of strong electron-phonon nonequilibrium, Physical Review B, 77: 075133-1–075133-17, 2008, doi: 10.1103/PhysRevB.77.075133.
25. E. Majchrzak, J. Dziatkiewicz, Second-order two-temperature model of heat transfer processes in a thin metal film subjected to an ultrashort laser pulse, Archives of Mechanics, 71(4/5): 377–391, 2019, doi: 10.24423/aom.3131.
26. E. Majchrzak, J. Dziatkiewicz, Ł. Turchan, Analysis of thermal processes occurring in the microdomain subjected to the ultrashort laser pulse using the axisymmetric twotemperature model, International Journal for Multiscale Computational Engineering, 15(5): 395–411, 2017.
27. E. Majchrzak, J. Dziatkiewicz, Ł. Turchan, Sensitivity analysis and inverse problems in microscale heat transfer, [in:] Fluid Flow, Energy Transfer and Design II, Defect and Diffusion Forum, vol. 362, pp. 209–223, Trans Tech Publications Ltd., 2015, doi: 10.4028/www.scientific.net/ddf.362.209.
28. A.S. Perelson, Applications of optimal control theory to immunology, [in:] Recent Developments in Variable Structure Systems, Economics and Biology, R.R. Mohler, A. Ruberti [Eds], vol. 162, pp. 272–287, Springer, New York, 1978, doi: 10.1007/978-3-642-45509-4_20.
29. A. Poteralski, Hybrid artificial immune strategy in identification and optimization of mechanical systems, Journal of Computer Science, 23: 216–225, 2017, doi: 10.1016/j.jocs.2017.04.015.
30. A. Poteralski, M. Szczepanik, W. Beluch, T. Burczynski, Optimization of composite structures using bio-inspired methods, [in:] L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada [Eds], Artificial Intelligence and Soft Computing, ICAISC 2014, Lecture Notes in Computer Science, vol. 8468, pp. 385–395, 2014, doi: 10.1007/978-3-319-07176-3_34.
31. A. Poteralski, M. Szczepanik, R. Górski, T. Burczynski, Swarm and immune computing of dynamically loaded reinforced structures, [in:] L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. Zadeh, J. Zurada [Eds], International Conference on Artificial Intelligence and Soft Computing (ICAISC), Lecture Notes in Computer Science, vol. 9120, pp. 483–494, Springer, Cham, 2015, doi: 10.1007/978-3-319-19369-4_43.
32. A. Poteralski, M. Szczepanik, J. Ptaszny, W. Kus, T. Burczynski, Hybrid artificial immune system in identification of room acoustic properties, Inverse Problems in Science and Engineering, 21(6): 957-967, 2013, doi: 10.1080/17415977.2013.788174.
33. A. Poteralski, M. Szczepanik, G. Dziatkiewicz, W. Kus, T. Burczynski, Comparison between PSO and AIS on the basis of identification of material constants in piezoelectrics, [in:] L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada [Eds], Artificial Intelligence and Soft Computing, ICAISC 2013, Lecture Notes in Computer Science, vol. 7895, pp. 569–581, Springer, Berlin–Heidelberg, 2013, doi: 10.1007/978-3-642-38610-7_52.
34. A. Poteralski. G. Dziatkiewicz, Artificial immune system for effective properties optimization of magnetoelectric composites, AIP Conference Proceedings, 1922(1): 140007-1–140007-10, 2018, doi: 10.1063/1.5019149.
35. J. Ptaszny, A. Poteralski, Optimization of porous structure effective elastic properties by the fast multipole boundary element method and an artificial immune system, [in:] Proceedings of the 6th International Conference on Engineering Optimization, Springer, 2018, doi: 10.1007/978-3-319-97773-7_88.
36. M. Szczepanik, A. Poteralski, A. Długosz, W. Kus, T. Burczynski, Bio-inspired optimization of thermomechanical structures, [in:] L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L.A. Zadeh, J.M. Zurada [Eds], Artificial Intelligence and Soft Computing, ICAISC 2013, Lecture Notes in Computer Science, vol. 7895, pp. 79–90, Springer, Berlin–Heidelberg, 2013, doi: 10.1007/978-3-642-38610-7_8.
37. S.T. Wierzchon, Artificial Immune Systems. Theory and Applications, [in Polish: Sztuczne systemy immunologiczne. Teoria i zastosowania], Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2001.
38. Z.M. Zhang, Nano/Microscale Heat Transfer, McGraw-Hill, 2007.
Published
Dec 4, 2023
How to Cite
DZIATKIEWICZ, Jolanta; POTERALSKI, Arkadiusz. Identification of Thermophysical Parameters Using an Artificial Immune System. Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 4, p. 521–537, dec. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/1015>. Date accessed: 18 dec. 2024. doi: http://dx.doi.org/10.24423/cames.1015.
Section
CMM-SolMech 2022