发表机构
University of Kansas（堪萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CompFlowLab是一个开源Python平台，基于一维可压缩Navier-Stokes求解器，为激波、火焰和爆震波等挑战性可压缩流问题提供数据驱动建模的原型开发与测试环境。
AI 中文摘要
CompFlowLab是一个开源Python环境，能够使用一维可压缩Navier-Stokes求解器，结合多组分输运和化学反应模型，对不同类别的可压缩流问题（包括激波、火焰和爆震波）进行建模。它专门为数据驱动建模社区设计，作为一个轻量级、易访问的原型平台，用于在数值和物理上具有挑战性的可压缩流问题（尤其是具有激波和化学反应的问题）上测试、开发和评估新的建模方法。具体而言，CompFlowLab旨在（1）在平流主导问题上提供计算高效的求解，这类问题被公认对传统数据驱动建模技术（如激波、火焰和爆震波）是困难的，更重要的是（2）实现新数据驱动模型的快速测试和原型化。该代码服务于三个主要目的：（1）生成高保真全阶模型数据，用于数据驱动建模的训练；（2）提供一个模块化平台，用于在具有挑战性的物理问题上实现和测试新颖的数据驱动算法（例如，机器学习方法和降阶建模技术）；（3）提供一个独立的计算流体动力学（CFD）求解器，带有经过验证的测试案例，也可支持更广泛CFD社区中的数值方法开发。通过将这些能力统一在一个干净、可扩展的Python代码库中，CompFlowLab降低了模型降阶与复杂流体动力学交叉领域创新的障碍。
英文摘要
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.
