发表机构
Iowa State University（爱荷华州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对湍流物理机制发现难题，提出PhysMiner框架，结合速度梯度张量三重分解与大语言模型推理，经多模块协作及库积累知识，实现自动分析并验证，为湍流物理发现奠定基础。
AI 中文摘要
揭示湍流流动的物理机制仍然是流体力学中的一项基本挑战。特别是，传统的速度梯度分析方法存在剪切污染问题，这阻碍了对主导物理机制的准确识别。本研究提出了PhysMiner，这是一个自动化框架，将速度梯度张量的三重分解方法与大语言模型驱动的推理相结合，用于湍流物理发现。
英文摘要
Uncovering the physical mechanisms of turbulent flows remains a fundamental challenge in fluid mechanics. In particular, conventional velocity-gradient analysis methods suffer from shear contamination, which hinders accurate identification of the dominant physical mechanisms. This study presents PhysMiner, an automated framework integrating the triple decomposition method of the velocity gradient tensor with large language model-driven reasoning for turbulence-physics discovery. The triple decomposition module automatically decomposes flow fields into rigid rotation, pure shearing, and normal straining components, enabling statistical analysis, contour visualization, vortex-line extraction, and threshold-insensitive vortex identification while eliminating shear contamination. These automated capabilities are validated across five benchmarks, ranging from canonical configurations to complex engineering flows. A discover-physics agent combines flow statistics, spatial structures, and literature-derived knowledge to perform pattern recognition and physical inference, while a review Agent iteratively validates physical consistency to ensure reliable conclusions. A continuously evolving Triple Decomposition Library accumulates statistical knowledge from successfully analyzed flows, enabling cross-case comparison and progressive enhancement of inductive capability. The complete PhysMiner pipeline is validated end-to-end on the periodic hill flow, where the framework autonomously generates turbulence modeling recommendations and derives an improved subgrid-scale model with superior Reynolds-stress predictions. PhysMiner is open to the public and establishes a foundation for long-term collaborative advancement in automated turbulence-physics discovery.
Comments43 pages, 26 figures
