Deep Learning Surrogates for Gas Dynamics: A Physics-Informed Pedagogical Approach

TL;DR

This paper presents a physics-informed deep learning framework to create high-fidelity surrogate models for canonical gas dynamics problems, improving visualization and understanding in education.

Contribution

It introduces neural network architectures and feature engineering strategies tailored for gas dynamics problems, integrating physics into deep learning models for educational purposes.

Findings

Models achieve high accuracy in surrogate predictions

Enables real-time visualization of complex flow phenomena

Enhances conceptual understanding in gas dynamics education

Abstract

Compressible flow problems are characterized by highly nonlinear, implicit, and often transcendental governing equations. In undergraduate gas dynamics education, solving these equations traditionally relies on iterative numerical methods or extensive look-up tables, which can obscure the physical intuition of the solution space. This paper introduces a comprehensive framework using Deep Learning to generate high-fidelity surrogate models for five canonical problems: Rayleigh flow, Fanno flow, oblique shocks, convergent-divergent nozzles, and unsteady shock tubes. We detail the specific neural network architectures and physics-informed feature engineering strategies required for each problem, such as using logarithmic inputs for Fanno friction parameters or geometric anchors for oblique shocks. The resulting models achieve high accuracy and enable instantaneous visualization of complex design spaces, such as thermodynamic T s diagrams and unsteady x t wave interactions. This approach demonstrates how modern data-driven techniques can be integrated into the physics curriculum to enhance conceptual understanding.