TY - JOUR
T1 - CAMERA
T2 - A method for cost-aware, adaptive, multifidelity, efficient reliability analysis
AU - Ashwin Renganathan, S.
AU - Rao, Vishwas
AU - Navon, Ionel M.
N1 - Funding Information:
This work is partially supported by the Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory , provided by the Director, Office of Science , of the U.S. Department of Energy under contract DE-AC02-06CH11357 , and by the Faculty Startup Funds at the University of Utah . We thank Carlo Graziani from Argonne National Laboratory for his valuable feedback on the manuscript.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. We propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.
AB - Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. We propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.
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U2 - 10.1016/j.jcp.2022.111698
DO - 10.1016/j.jcp.2022.111698
M3 - Article
AN - SCOPUS:85140323292
SN - 0021-9991
VL - 472
JO - Journal of Computational Physics
JF - Journal of Computational Physics
M1 - 111698
ER -