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Efficient Surrogate-Based NVH Optimization of a Full Vehicle Using FRF Based Substructuring
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The computer simulation with the Finite Element (FE) code for the structural dynamics becomes more attractive in the industry. However, it normally takes a prohibitive amount of computation time when design optimization is performed with running a large-scale FE simulation many times. Exploiting Dynamic Structuring (DS) leads to alleviating the computational complexity since DS necessities iterative reanalysis of only the substructure(s) to be optimally designed. In this research, Frequency Response Function (FRF) based substructuring is implemented to realize the benefits of DS for fast single- and multi-objective evolutionary design optimization. Also, Differential Evolution (DE) is first combined with two sorting approaches of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Infeasibility Driven Evolutionary Algorithm (IDEA) for effective constrained single- and multi-objective evolutionary optimization. The effectiveness of the proposed algorithm (NSGA-II/DE-IDEA) is verified using several test functions for constrained single- and multi-objective optimization. To circumvent the need for frequent time-consuming simulation runs, Kriging surrogate models are established by interpolating the responses simulated at the sample points, which are generated by executing an Optimal LHS algorithm. Besides, the Morris method is implemented to leave out unimportant design variables. A constrained single-objective and a constrained multi-objective NVH design optimization of a truck are carried out to demonstrate the surrogate-based design optimization process involving FRF based substructuring and the proposed algorithm.
CitationPark, I. and Papadimitriou, D., "Efficient Surrogate-Based NVH Optimization of a Full Vehicle Using FRF Based Substructuring," SAE Technical Paper 2020-01-0629, 2020, https://doi.org/10.4271/2020-01-0629.
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- de Klerk, D., Rixen, D.J., and Voormeeren, S.N. , “General Framework for Dynamic Substructuring: History, Review, and Classification of Techniques,” AIAA journal 46(5):1169-1181, 2008.
- Hou, G., Maroju, V., and Yang, R.J. , “Component Mode Synthesis-Based Design Optimization Method for Local Structural Modification,” Structural Optimization 10(2):128-136, 1995.
- Wind, J.W., Akçay Perdahcıoğlu, D., and de Boer, A. , “Distributed Multilevel Optimization for Complex Structures,” Structural and Multidisciplinary Optimization 36(1):71-81, 2008.
- Akçay Perdahcıoğlu, D., Ellenbroek, M.H.M., van der Hoogt, P.J.M., and de Boer, A. , “An Optimization Method for Dynamics of Structures with Repetitive Component Patterns,” Structural and Multidisciplinary Optimization 39(6):557-567, 2009.
- Lee, D.H., Hwang, W.S., and Kim, C.M. , “Design Sensitivity Analysis and Optimization of an Engine Mount System Using an FRF-Based Substructuring Method,” Journal of Sound and Vibration 255(2):383-397, 2002.
- Garambois, P., Besset, S., and Jézéquel, L. , “Multi-Objective Shape Optimization of Plate Structure under Stress Criteria Based on Sub-Structured Mixed FEM and Genetic Algorithms,” in 11th International Conference on Damage Assessment of Structures (DAMAS2015), Journal of Physics: Conference Series 628, Ghent, Belgium, Aug. 2015.
- Coello, C.A.C., Lamont, G.B., and Van Veldhuizen, D.A. , Evolutionary Algorithms for Solving Multi-Objective Problems (New York: Kluwer Academic Publishers, 2002).
- Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. , “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation 6(2):182-197, 2002.
- Storn, R. and Price, K. , “Differential Evolution: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces,” Journal of Global Optimization 11(4):341-359, 1997.
- Ray, T., Singh, H.K., Isaacs, A., Smith, W. , “Infeasibility Driven Evolutionary Algorithm for Constrained Optimization,” in Constraint-Handling in Evolutionary Optimization, Vol. 198, Springer-Verlag, Studies in Computational Intelligence Series, 2009, 145-165.
- Jetmundsen, B., Bielawa, R., and Flannelly, W.G. , “Generalized Frequency Domain Substructure Synthesis,” Journal of the American Helicopter Society 33(1):55-64, 1988.
- Morris, M.D. , “Factorial Sampling Plans for Preliminary Computational Experiments,” Technometrics 33(2):161-174, 1991.
- Campolongo, F., Cariboni, J., and Saltelli, A. , “An Effective Screening Design for Sensitivity Analysis of Large Models,” Environmental Modelling & Software 22(10):1509-1518, 2007.
- Morris, M.D. and Mitchell, T.J. , “Exploratory Designs for Computational Experiments,” Journal of Statistical Planning and Inference 43(3):381-402, 1995.
- Jin, R., Chen, W., and Sudjianto, A. , “An Efficient Algorithm for Constructing Optimal Design of Computer Experiments,” Journal of Statistical Planning and Inferences 134(1):268-287, 2005.
- Sacks, J., Welch, W.J., Mitchell, T.J., and Wynn, H.P. , “Design and Analysis of Computer Experiments,” Statistical Science 4(4):409-435, 1989.
- Simpson, T.W., Mauery, T.M., Korte, J.J., and Mistree, F. , “Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization,” AIAA Journal 39(12):2233-2241, 2001.
- Price, K., Storn, R., and Lampinen, J. , Differential Evolution - A Practical Approach to Global Optimization (Heidelberg: Springer, 2005).
- Hedar, A. and Fukushima, M. , “Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization,” Journal of Global Optimization 35(4):521-549, 2006.
- Mezura-Montes, E. and Coello Coello, C.A. , “A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems,” IEEE Transactions on Evolutionary Computation 9(1):1-17, 2005.
- Deb, K., Pratap, A., and Meyarivan, T. , “Constrained Test Problems for Multi-Objective Evolutionary Optimization,” in Proceedings of Evolutionary Multi-Criterion Optimization: First International Conference, EMO 2001, Zurich, Switzerland, March 2001, pp. 284-298.
- Zielinski, K., Peters, D., and Laur, R. , “Constrained Multi-Objective Optimization Using Differential Evolution,” in Proceedings of Third International Conference on Computational Intelligence, Robotics and Autonomous Systems (CIRAS 2005), Singapore, December 2005.
- BETA CAE Systems, S.A. , ANSA, EPILYSIS & META-Post, Thessaloniki, Greece, 2017.
- “Scipy: Scientific Tools for Python,” http://www.scipy.org, Release: 1.2.0, Dec. 2018.
- Liu, Y., Shi, Y., Zhou, Q., and Xiu, R. , “A Sequential Sampling Strategy to Improve the Global Fidelity of Metamodels in Multi-level System Design,” Structural and Multidisciplinary Optimization 53(6):1295-1313.