Physics-Infused Reduced Order Modeling of Aerothermal Loads for Hypersonic Aerothermoelastic Analysis

07/07/2022
by   Carlos Vargas Venegas, et al.
0

This paper presents a novel physics-infused reduced-order modeling (PIROM) methodology for efficient and accurate modeling of non-linear 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-three orders of magnitude while maintaining an accuracy comparable to high-fidelity solutions based on computational fluid dynamics (CFD). Moreover, the PIROM-based solver is benchmarked against the conventional POD-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 snap-through behavior with respect to spring constraints of boundary conditions, but also demonstrates the potential of PIROM to facilitate the rapid and accurate design and optimization of multi-disciplinary systems such as hypersonic structures.

READ FULL TEXT
research
05/05/2021

Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks

Physics-Informed Neural Networks (PINNs) have recently shown great promi...
research
01/31/2023

Physics-informed Reduced-Order Learning from the First Principles for Simulation of Quantum Nanostructures

Multi-dimensional direct numerical simulation (DNS) of the Schrödinger e...
research
11/30/2020

A Deep Learning-based Collocation Method for Modeling Unknown PDEs from Sparse Observation

Deep learning-based modeling of dynamical systems driven by partial diff...
research
12/27/2019

Estimating Dispersion Curves from Frequency Response Functions via Vector-Fitting

Driven by the need for describing and understanding wave propagation in ...
research
12/16/2022

Reduced order modelling using parameterized non-uniform boundary conditions in room acoustic simulations

Quick simulations for iterative evaluations of multi-design variables an...
research
05/13/2020

Learning Composable Energy Surrogates for PDE Order Reduction

Meta-materials are an important emerging class of engineered materials i...
research
05/26/2023

ModelFLOWs-app: data-driven post-processing and reduced order modelling tools

This article presents an innovative open-source software named ModelFLOW...

Please sign up or login with your details

Forgot password? Click here to reset