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International Journal for Multiscale Computational Engineering

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MULTI-LEVEL K-d TREE-BASED DATA-DRIVEN COMPUTATIONAL METHOD FOR THE DYNAMIC ANALYSIS OF MULTI-MATERIAL STRUCTURES

Volume 18, Issue 4, 2020, pp. 421-438
DOI: 10.1615/IntJMultCompEng.2020035167
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Zhangcheng Zheng
International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, People's Republic of China
Hongfei Ye
International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, People's Republic of China
Hongwu Zhang
International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, People's Republic of China
Yonggang Zheng
International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, People's Republic of China
Zhen Chen
International Research Center for Computational Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Department of Engineering Mechanics, Faculty of Vehicle Engineering and Mechanics, Dalian University of Technology, Dalian 116024, China; Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO 65211, USA

ABSTRACT

The model-free distance-minimizing data-driven computational method has recently become a novel paradigm for solving various mechanics problems. However, the paradigm may suffer from low efficiency since tremendous iterative searches of key data points in the material dataset are needed during the solution process. A fast data-driven solver is therefore proposed here for the accurate and efficient analysis of multi-material structural responses to dynamic loading. In the proposed approach, a multi-material database (MMD) with different kinds of constituents is constructed, and a multi-level K-d tree (MKT) is developed for effective data addition and fast data search in the MMD. An efficient data-driven dynamics solver (DDDS) is then designed based on the MMD/MKT, which can deal with the complicated dynamic analysis of different structures containing multiple material datasets. Representative types of dynamic problems are considered to verify and demonstrate the capability of the proposed approach. Numerical results demonstrate that the MMD/MKT and the corresponding DDDS possess high accuracy and efficiency, which might be further developed for the dynamic analysis of composite structures containing constituents at different scales.

KEY WORDS: data-driven solver, multi-material database, data addition method, multi-level K-d tree, dynamic response
REFERENCES

  1. Albert, S.T. and Shadmehr, R., The Neural Feedback Response to Error as a Teaching Signal for the Motor Learning System, J. Neurosci, vol. 36, no. 17, pp. 4832-4845,2016. DOI: 10.1523/JNEUR0SCI.0159-16.2016.

  2. Bach, F., Breaking the Curse of Dimensionality with Convex Neural Networks, J. Machine Learning Res., vol. 18, no. 1, pp. 629-681,2017.

  3. Bentley, J.L., Multidimensional Binary Search Trees Used for Associative Searching, Commun. ACM, vol. 18, no. 9, pp. 509-517, 1975. DOI: 10.1145/361002.361007.

  4. Chaboche, J.L., A Review of Some Plasticity and Viscoplasticity Constitutive Theories, Int. J. Plasticity, vol. 24, no. 10, pp. 1642-1693,2008. DOI: 10.1016/j.ijplas.2008.03.009.

  5. Ching, F.D.K., Onouye, B.S., and Zuberbuhler, D., Building Structures Illustrated: Patterns, Systems, and Design, Hoboken, NJ: John Wiley & Sons, p. 352,2013.

  6. Courant, R. and Hilbert, D., Methods of Mathematical Physics: Partial Differential Equations, Hoboken, NJ: John Wiley & Sons, p. 855, 2008.

  7. Eggersmann, R., Kirchdoerfer, T., Reese, S., Stainier, L., and Ortiz, M., Model-Free Data-Driven Inelasticity, Comput. Methods Appl. Mech. Eng., vol. 350, pp. 81-99,2019. DOI: 10.1016/j.cma.2019.02.016.

  8. Feyel, F., A Multilevel Finite Element Method (FE2) to Describe the Response of Highly Non-Linear Structures Using Generalized Continua, Comput. Methods Appl. Mech. Eng., vol. 192, no. 28, pp. 3233-3244,2003. DOI: 10.1016/S0045-7825(03)00348-7.

  9. Feyel, F., Multiscale FE2 Elastoviscoplastic Analysis of Composite Structures, Comput. Mater. Sci., vol. 16, no. 1, pp. 344-354, 1999. DOI: 10.1016/S0927-0256(99)00077-4.

  10. Garza, J. and Millwater, H., Multicomplex Newmark-Beta Time Integration Method for Sensitivity Analysis in Structural Dynamics, AIAAJ, vol. 53, no. 5, pp. 1188-1198,2015. DOI: 10.2514/1.J053282?.

  11. Gonzalez Valenzuela, R.E., Schwartz, W.R., and Pedrini, H., Dimensionality Reduction through PCA over SIFT and SURF Descriptors, 2012 IEEE 11th Intl. Conf. on Cybernetic Intelligent Systems (CIS), Limerick, Ireland, pp. 58-63, Aug. 23-24,2012.

  12. Gu, Y., He, X., Chen, W., and Zhang, C., Analysis of Three-Dimensional Anisotropic Heat Conduction Problems on Thin Domains Using an Advanced Boundary Element Method, Comput. Math. Appl, vol. 75, no. 1, pp. 33-44, 2018. DOI: 10.1016/j.camwa.2017.08.030.

  13. Gutierrez, P.A., Hervas, C., Carbonero, M., and Fernandez, J.C., Combined Projection and Kernel Basis Functions for Classification in Evolutionary Neural Networks, Neurocomputing, vol. 72, no. 13, pp. 2731-2742, 2009. DOI: 10.1016/j.neucom.2008.09.020.

  14. Hughes, T. J.R., The Finite Element Method: Linear Static and Dynamic Finite Element Analysis, North Chelmsford, MA: Courier Corporation, p. 706, 2012.

  15. Kim, B., Yu, D., and Won, J.-H., Comparative Study of Computational Algorithms for the Lasso with High-Dimensional, Highly Correlated Data, Appl. Intell., vol. 48, no. 8, pp. 1933-1952,2018. DOI: 10.1007/s10489-016-0850-7.

  16. Kirchdoerfer, T. and Ortiz, M., Data-Driven Computational Mechanics, Comput. Methods Appl. Mech. Eng., vol. 304, pp. 81-101, 2016. DOI: 10.1016/j.cma.2016.02.001.

  17. Kirchdoerfer, T. and Ortiz, M., Data-Driven Computing in Dynamics, Int. J. Numer. Methods Eng., vol. 113, no. 11, pp. 1697-1710, 2018. DOI: 10.1002/nme.5716.

  18. Kozdon, J.E. and Wilcox, L.C., Stable Coupling of Nonconforming, High-Order Finite Difference Methods, SIAMJ. Sci. Comput., vol. 38, no. 2, pp. A923-A952,2016. DOI: 10.1137/15M1022823.

  19. Kronberger, G., Kommenda, M., Lughofer, E., Saminger-Platz, S., Promberger, A., Nickel, F., Winkler, S., and Affenzeller, M., Using Robust Generalized Fuzzy Modeling and Enhanced Symbolic Regression to Model Tribological Systems, Appl. Soft Comput, vol. 69, pp. 610-624,2018. DOI: 10.1016/j.asoc.2018.04.048.

  20. Larkoski, A.J., Moult, I., and Nachman, B., Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning, Phys. Rep, vol. 841, pp. 1-63,2020. DOI: 10.1016/j.physrep.2019.11.001.

  21. Lemaitre, J. and Chaboche, J.-L., Mechanics of Solid Materials, Cambridge: Cambridge University Press, p. 588,1994.

  22. Leygue, A., Coret, M., Rethore, J., Stainier, L., and Verron, E., Data-Based Derivation of Material Response, Comp. Methods Appl. Mech. Eng., vol. 331, pp. 184-196,2018. DOI: 10.1016/j.cma.2017.11.013.

  23. Lopez, E., Gonzalez, D., Aguado, J.V., Abisset-Chavanne, E., Cueto, E., Binetruy, C., and Chinesta, F., A Manifold Learning Approach for Integrated Computational Materials Engineering, Arch. Comput. Methods Eng., vol. 25, no. 1, pp. 59-68, 2018. DOI: 10.1007/s11831-016-9172-5.

  24. Moayedi, H. and Rezaei, A., An Artificial Neural Network Approach for Under-Reamed Piles Subjected to Uplift Forces in Dry Sand, Neural Comput. Appl, vol. 31, no. 2, pp. 327-336,2019. DOI: 10.1007/s00521-017-2990-z.

  25. Nakahira, K., Tago, H., Sasaki, T., Suzuki, K., and Miura, H., Measurement of the Local Residual Stress between Fine Metallic Bumps in 3D Flip Chip Structures, Int. J. Mater. Struct. Integrity, vol. 8, nos. 1-3, pp. 21-31, 2014. DOI: 10.1504/IJMSI.2014.064770.

  26. Nguyen, L.T.K. and Keip, M.-A., A Data-Driven Approach to Nonlinear Elasticity, Comput. Struct., vol. 194, pp. 97-115, 2018. DOI: 10.1016/j.compstruc.2017.07.031.

  27. Oishi, A. and Yagawa, G., Computational Mechanics Enhanced by Deep Learning, Comput. Methods Appl. Mech. Eng., vol. 327, pp. 327-351,2017. DOI: 10.1016/j.cma.2017.08.040.

  28. Orchard, M.T., A Fast Nearest-Neighbor Search Algorithm, Proc. ICASSP 91: 1991 Intl. Conf. on Acoustics, Speech, and Signal Processing, Los Alamitos, CA, USA, vol. 4, pp. 2297-2300, Apr. 14-17,1991.

  29. Oskay, C. and Fish, J., Eigendeformation-Based Reduced Order Homogenization for Failure Analysis of Heterogeneous Materials, Comput. Methods Appl. Mech. Eng., vol. 196,no.7,pp. 1216-1243,2007. DOI: 10.1016/j.cma.2006.08.015.

  30. Picard, R., Trostorff, S., and Waurick, M., On Evolutionary Equations with Material Laws Containing Fractional Integrals, Math. Methods Appl. Sci., vol. 38, no. 15, pp. 3141-3154,2015. DOI: 10.1002/mma.3286.

  31. Potluri, S. and Diedrich, C., Accelerated Deep Neural Networks for Enhanced Intrusion Detection System, 2016 IEEE 21st Intl. Conf. on Emerging Technologies and Factory Automation (ETFA), Berlin, Germany, pp. 1-8, Sep. 6-9,2016.

  32. Ringner, M., What Is Principal Component Analysis?, Nat. Biotechnol, vol. 26, no. 3, pp. 303-304,2008. DOI: 10.1038/nbt0308-303.

  33. Soares, D. and GroBeholz, G., Nonlinear Structural Dynamic Analysis by a Stabilized Central Difference Method, Eng. Struct., vol. 173, pp. 383-392,2018. DOI: 10.1016/j.engstruct.2018.06.115.

  34. Stainier, L., Leygue, A., and Ortiz, M., Model-Free Data-Driven Methods in Mechanics: Material Data Identification and Solvers, Comput. Mech., vol. 64, no. 2, pp. 381-393,2019. DOI: 10.1007/s00466-019-01731-1.

  35. Su, H., Li, G., Yu, D., and Seide, F., Error Back Propagation for Sequence Training of Context-Dependent Deep Networks for Conversational Speech Transcription, 2013 IEEE Intl. Conf. on Acoustics, Speech and Signal Processing, (ICASSP), Vancouver, BC, Canada, pp. 6664-6668,2013.

  36. Xu, W., Jiao, Y., and Fish, J., An Atomistically-Informed Multiplicative Hyper-Elasto-Plasticity-Damage Model for High-Pressure Induced Densification of Silica Glass, Comput. Mech, vol. 66, pp. 155-187,2020. DOI: 10.1007/s00466-020-01846-w.

  37. Yang, J., Xu, R., Hu, H., Huang, Q., and Huang, W., Structural-Genome-Driven Computing for Composite Structures, Compos. Struct., vol. 215, pp. 446-453,2019. DOI: 10.1016/j.compstruct.2019.02.064.

  38. Yuan, X., Tan, Q., Lei, X., Yuan, Y., and Wu, X., Wind Power Prediction Using Hybrid Autoregressive Fractionally Integrated Moving Average and Least Square Support Vector Machine, Energy, vol. 129, pp. 122-137, 2017. DOI: 10.1016/j.energy.2017.04.094.

  39. Zhang, X., Chen, Y., and Hu, J., Recent Advances in the Development of Aerospace Materials, Prog;. Aerospace Sci., vol. 97, pp. 22-34,2018. DOI: 10.1016/j.paerosci.2018.01.001.

CITED BY

  1. Amores Víctor J., Montáns Francisco J., Cueto Elías, Chinesta Francisco, Crossing Scales: Data-Driven Determination of the Micro-scale Behavior of Polymers From Non-homogeneous Tests at the Continuum-Scale, Frontiers in Materials, 9, 2022. Crossref

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