This paper presents a novel physics-infused reduced-order modeling (PIROM) methodology for efficient and accurate modeling of nonlinear dynamical systems. The PIROM consists of a physics-based analytical component that represents the known physical processes and a data-driven dynamical component that represents the unknown physical processes. The PIROM is applied to the aerothermal load modeling for hypersonic aerothermoelastic (ATE) analysis and is found to accelerate the ATE simulations by two to three orders of magnitude while maintaining an accuracy comparable to high-fidelity solutions based on computational fluid dynamics. Moreover, the PIROM-based solver is benchmarked against the conventional proper-orthogonal decomposition/kriging surrogate model and is found to significantly outperform the accuracy, generalizability, and sampling efficiency of the latter in a wide range of operating conditions and in the presence of complex structural boundary conditions. Finally, the PIROM-based ATE solver is demonstrated by a parametric study on the effects of boundary conditions and rib supports on the ATE response of a compliant and heat-conducting panel structure. The results not only reveal the dramatic snapthrough behavior with respect to spring constraints of boundary conditions but also demonstrate the potential of the PIROM to facilitate the rapid and accurate design and optimization of multidisciplinary systems such as hypersonic structures.
All Science Journal Classification (ASJC) codes
- Aerospace Engineering