A nonintrusive reduced order model for nonlinear transient thermal problems with nonparametrized variability

Abstract

In this work, we consider a transient thermal problem, with a nonlinear term coming from the radiation boundary condition and a nonparametrized variability in the form complex scenarios for the initial condition and the convection coefficients and external temperatures. We use a posteriori reduced order modeling by snapshot Proper Orthogonal Decomposition. To treat the nonlinearity, hyperreduction is required in our case, since precomputing the polynomial nonlinearities becomes too expensive for the radiation term. We applied the Empirical Cubature Method, originally proposed for nonlinear structural mechanics, to our particular problem. We apply the method to the design of high-pressure compressors for civilian aircraft engines, where a fast evaluation of the solution temperature is required when testing new configurations. We also illustrate that when using in the reduced solver the same model as the one from the high-fidelity code, the approximation is very accurate. However, when using a commercial code to generate the high-fidelity data, where the implementation of the model and solver is unknown, the reduced model is less accurate but still within engineering tolerances in our tests. Hence, the regularizing property of reduced order models, together with a nonintrusive approach, enables the use of commercial software to generate the data, even under some degree of uncertainty in the proprietary model or solver of the commercial software.