CompFlowLab: A Python code to develop and prototype new data-driven models for challenging compressible flow problems with shocks and chemical reactions

2026-09-26

AI Summary

This study addresses the failure of conventional data-driven methods in modeling compressible flows with shock waves and chemical reactions, as well as the lack of efficient prototyping platforms. To this end, we present an open-source Python computational framework that integrates a one-dimensional compressible Navier-Stokes solver with deeply coupled multi-species transport and chemical kinetics. The framework supports high-fidelity data generation, modular algorithm testing, and standalone CFD simulations. By significantly lowering the entry barrier for interdisciplinary research, this work provides an efficient validation environment for the rapid iterative development of machine learning and reduced-order models in complex physical scenarios involving shocks and flames.

Abstract

CompFlowLab is an open-source Python environment that is capable of modeling different classes of compressible flow problems (including shocks, flames, and detonation waves) using a one-dimensional compressible Navier-Stokes solver with multi-species transport and chemical-reaction models. It is designed specifically for the data-driven modeling community as a lightweight, accessible prototyping platform to test, develop, and evaluate new modeling methods on numerically and physically challenging compressible flow problems, especially those featuring shocks and chemical reactions. Specifically, CompFlowLab aims at (1) providing computationally efficient calculations on advection-dominanted problems that are well-recognized to be difficult for conventional data-driven modeling techniques, such as shocks, flames, and detonation waves, and more importantly (2) enabling rapid testing and prototyping of new data-driven models. The code serves three primary purposes: (1) generating high-fidelity full order model data for training of data-driven modeling, (2) providing a modular platform for implementing and testing novel data-driven algorithms (e.g., machine learning methods and reduced-order modeling techniques) on challenging physics, and (3) offering a standalone Computational Fluid Dynamics (CFD) solver with validated test cases that can also support numerical method development in the broader CFD community. By unifying these capabilities in a clean, extensible Python codebase, CompFlowLab lowers barriers to innovation at the intersection of model reduction and complex fluid dynamics.

Problem

• compressible flow
• data-driven modeling
• shocks
• chemical reactions
• prototyping platform

Innovation

• data-driven modeling
• compressible flow
• shocks and chemical reactions
• reduced-order modeling
• Python prototyping platform