Multi-fidelity Metamodels Nourished by Reduced Order Models
- 41 Downloads
Engineering simulation provides better designed products by allowing many options to be quickly explored and tested. In that context, the computational time is a strong issue because using high-fidelity direct resolution solvers is not always suitable. Metamodels are commonly considered to explore design options without computing every possible combination of parameters, but if the behavior is nonlinear, a large amount of data is required to build this metamodel. A possibility is to use further data sources to generate a multi-fidelity surrogate model by using model reduction. Model reduction techniques constitute one of the tools to bypass the limited calculation budget by seeking a solution to a problem on a reduced-order basis (ROB). The purpose of this study is an online method for generating a multi-fidelity metamodel nourished by calculating the quantity of interest from the basis generated on-the-fly with the LATIN-PGD framework for elasto-viscoplastic problems. Low-fidelity (LF) fields are obtained by stopping the solver before convergence, and high-fidelity (HF) information is obtained with converged solutions. In addition, the solver ability to reuse information from previously calculated PGD basis is exploited.
- 1.Bhattacharyya, M., Fau, A., Nackenhorst, U., Néron, D., & Ladevèze, P. (2017). A LATIN-based model reduction approach for the simulation of cycling damage. Computational Mechanics, 1–19. https://doi.org/10.1007/s00466-017-1523-z.
- 2.Chinesta, F., Keunings, R., & Leygue, A. (2014). The proper generalized decomposition for advanced numerical simulations. In SpringerBriefs in applied sciences and technology. Cham: Springer International Publishing.Google Scholar
- 4.Forrester, A. I., Bressloff, N. W., & Keane, A. J. (2006). Optimization using surrogate models and partially converged computational fluid dynamics simulations. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 462(2071), 2177–2204. https://doi.org/10.1098/rspa.2006.1679.CrossRefzbMATHGoogle Scholar
- 8.Kramer, B., Marques, A.N., Peherstorfer, B., Villa, U., & Willcox, K. (2019). Multifidelity probability estimation via fusion of estimators. Journal of Computational Physics, 392, 385–402 . http://www.sciencedirect.com/science/article/pii/S0021999119303249.
- 9.Ladevèze, P. (1999). Nonlinear computational structural mechanics: New approaches and non-incremental methods of calculation. In Mechanical engineering series. Springer.Google Scholar
- 10.Lemaitre, J., & Chaboche, J. L. (1994). Mechanics of solid materials. Cambridge University Press.Google Scholar
- 15.Vitse, M. (2016) Model-order reduction for the parametric analysis of damage in reinforced concrete structures (Ph.D. thesis). Université Paris-Saclay.Google Scholar