Two-scale identification of composites’ material constants by means of computational intelligence methods

• Autorzy: Beluch, W. , Burczyński, T.
• Języki publikacji: EN

Abstrakty

EN

The paper deals with the two-scale approach to the identification of material constants in composite materials. Structures made of unidirectionally fibre-reinforced composites are examined. Composite constituents’ elastic constants in a micro scale are identified on the basis of measurements performed in a macro scale. Numerical homogenization methods using a representative volume element are employed. Static (displacements in sensor points) and dynamic (eigenfrequencies) data are considered as measurements. Ideal and disturbed measurements are taken into account. Computational intelligence methods in the form of evolutionary algorithms and artificial immune systems are used to perform the identification procedure. Finite element method is used to solve the boundary-value problem for composites in both scales. Numerical examples presenting the effectiveness of the proposed approach are attached. Statistical data are considered to compare the efficiency of the identification procedure for both algorithms and different measurement data.

Słowa kluczowe

PL

homogenizacja numeryczna; algorytm ewolucyjny; dane pomiarowe

EN

composite; numerical homogenization; identification; evolutionary algorithm; artificial immune system

• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2014
• Tom: Vol. 14, no. 4
• Strony: 636--646
• Opis fizyczny: Bibliogr. 31 poz., rys., tab., wykr.

Twórcy

autor

Beluch, W.

• Institute of Computational Mechanics and Engineering, Silesian University of Technology, ul. Konarskiego 18A, 44-100 Gliwice, Poland,

autor

Burczyński, T.

• Institute of Fundamental Technological Research, Polish Academy of Sciences, ul. Pawinskiego 5B, 02-106 Warsaw, Poland

Bibliografia

• [1] J. Arabas, Lectures on Evolutionary Algorithms, WNT, Warsaw, 2001 (in Polish).
• [2] T. Bäck, D.B. Fogel, Z. Michalewicz (Eds.), Evolutionary Computation. 1: Basic Algorithms and Operators, Taylor & Francis, Bristol and Philadelphia, 2000.
• [3] T. Bäck, D.B. Fogel, Z. Michalewicz (Eds.), Evolutionary Computation. 2: Advanced Algorithms and Operators, Taylor & Francis, Bristol and Philadelphia, 2000.
• [4] W. Beluch, T. Burczyński, W. Kuś, Parallel Artificial Immune System in Optimization and Identification of Composite Structures, in: Parallel Problem Solving from Nature – PPSN XI, Proceedings, Part 2. LCNS 6239, Springer-Verlag, Berlin/ Heidelberg, 2010, pp. 171–180.
• [5] W. Beluch, T. Burczyński, W. Kuś, Parallel and distributed computations and evolutionary and immune optimization of laminates, short papers, in: 19th International Conference on Computer Methods in Mechanics CMM-2011, Warsaw, (2011), pp. 129–130.
• [6] W. Beluch, Evolutionary identification and optimization of composite structures, Mechanics of Advanced Materials and Structures 14 (8) (2007) 677–686. http://dx.doi.org/10.1080/ 15376490701673250.
• [7] H.D. Bui, Inverse Problems in the Mechanics of Materials: An Introduction, CRC Press, Bosa Roca, 1994.
• [8] T. Burczyński, W. Kuś, A. Poteralski, M. Szczepanik, Global optimization using artificial immune systems and comparison with evolutionary algorithms, in: Proc. 17-th International Conference on Computer Methods in Mechanics CMM-2007, Łódź-Spała, 2007 (CD-ROM).
• [9] T. Burczyński, W. Kuś, A. Długosz, P. Orantek, Optimization and defect identification using distributed evolutionary algorithms, Engineering Applications of Artificial Intelligence 17 (4) (2004) 337–344.
• [10] D. Dasgupta, S. Yu, F. Nino, Recent advances in artificial immune systems: models and applications review article, Applied Soft Computing 11 (2) (2011) 1574–1587.
• [11] L.N. De Castro, J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Springer, London, 2002.
• [12] 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) (2002) 239–251.
• [13] K.A. De Jong, Analysis of the behavior of a class of genetic adaptive systems, (Doctoral dissertation), University of Michigan, Dissertation Abstracts International, 36 (10), 5149B, 1975.
• [14] K.A. De Jong, Evolutionary Computation: A Unified Approach, MIT Press, 2006.
• [15] S. Forrest, A.S. Perelson, L. Allen, R. Cherukuri, Self-nonself discrimination in a computer (PDF), in: Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA, (1994), pp. 202–212.
• [16] Z.-F. Fu, J. He, Modal Analysis, Butterworth-Heinemann, Oxford, 2001.
• [17] U. Galvanetto, M.H. Aliabadi (Eds.), Multiscale Modeling In Solid Mechanics. Computational Approaches, Imperial College Press, London, 2010.
• [18] M.G.D. Geers, V. Kouznetsova, T. Massart, I. Özdemir, E.W.C. Coenen, W.A.M. Brekelmans, R.H.J. Peerlings, Computational homogenization of structures and materials, in: Proceedings of the 9th Neuvième Colloque National en Calcul des Structures, Giens, France, (2009), pp. 17–28.
• [19] R.F. Gibson, Principles of Composite Material Mechanics, CRC Press, Boca Raton, 2012.
• [20] Z. Gürdal, R.T. Haftka, P. Hajela, Design and Optimization of Laminated Composite Materials, Wiley, New York, 1999.
• [21] Z. Hashin, Theory of mechanical behavior of heterogeneous media, Applied Mechanics Reviews 17 (1964) 1–9.
• [22] J.H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Michigan, 1975.
• [23] S. Ilic, K. Hackl, Application of the multiscale FEM to the modeling of nonlinear multiphase materials, Journal of Theoretical and Applied Mechanics 47 (3) (2009) 537–551.
• [24] J.O. Kephart, A biologically inspired immune system for computers, in: Proc. Artificial Life IV: The Fourth International Workshop on the Synthesis and Simulation of Living Systems, MIT Press, 1994, pp. 130–139.
• [25] V. Kouznetsova, Computational homogenization for the multi-scale analysis of multi-phase materials, (Ph.D. thesis), Technische Universiteit Eindhoven, 2002.
• [26] V.G. Kouznetsova, M.G.D. Geers, W.A.M. Brekelmans, Multi-scale second-order computational homogenization of multi-phase materials: a nested finite element solution strategy, Computer Methods in Applied Mechanics and Engineering 193 (48–51) (2004) 5525–5550.
• [27] E. Kröner, Statistical Continuum Mechanics, Springer, Berlin, 1972.
• [28] J.P. Leiva, D.K. Ghosh, N. Rastogi, A new approach in stacking sequence optimization of composite laminates using genesis structural analysis and optimization software, in: Proc 9th Symposium on Multidisciplinary Analysis and Optimization, Atlanta, 2002.
• [29] Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin, Heidelberg, New York, 1996 .
• [30] MSC Patran/Nastran Marc/Mentat Documentation, MSC Software.
• [31] A. Muc, W. Gurba, Genetic algorithms and finite element analysis in optimisation of composite structures, Composite Structures 55 (2001) 275–281.

Identyfikatory

DOI

10.1016/j.acme.2013.12.007