Hanane Khatouri, Research Engineer, Platon Team
Title: Adaptive full-field multi-fidelity surrogate-based optimization dedicated to turbomachinery
Abstract: We are interested in the reduction of optimization costs during aircraft engine design where the quantities of interest are derived from a 2D or 3D solution physics-based surrogate models. The cost associated with the high-fidelity simulation makes direct optimization unaffordable for industrial applications. In response to these difficulties, it is proposed to address the problem of constrained derivative-free optimization in a multi-fidelity (or variable-complexity) [1, 2] framework using Bayesian optimization techniques. It is assumed that the objective and constraints involved in the optimization problem can be evaluated using either an accurate but long-running computer program or a low-fidelity but fast-running one. In this setting, the aim is to solve the optimization problem using as few calls to the long-running program as possible. To this end, it is proposed to use Gaussian process models with trend functions built from the projection of low-fidelity snapshots on a reduced-order function basis synthesized from scarce high-fidelity runs [4]. A study on the ability of such models to accurately represent the functions of the problem and a comparison of two improvement-based infill strategies are performed on a representative benchmark test case. [3].
REFERENCES
[1] A.I. Forrester, A. So ́bester and A.J. Keane. Multi-fidelity optimization via surrogate modelling. Proceedings of the Royal Society A : Mathematical, Physical and Engineering Sciences, 463(2088) :3251-3269, 2007.
[2] M. G. Fernandez-Godino, C. Park, N.-H. Kim, and R.T. Haftka. Review of multi-fidelity models, 41, 2017.
[3] T. Benamara. Full-field Multi-Fidelity Surrogate Models for Optimal Design of Turbomachines. PhD thesis, 2017.
[4] R. Chakir, Y. Maday, and P. Parnaudeau. A non-intrusive reduced basis approach for parametrized heat transfer problems. Journal of Computational Physics, 376:617633, Jan. 2019.
