CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics

Nithin Somasekharan, Ling Yue, Yadi Cao, Weichao LI, Patrick Emami, Pochinapeddi Sai Bhargav, Anurag Acharya, Xingyu Xie, Shaowu Pan

Abstract: Large Language Models (LLMs) have demonstrated strong performance across general NLP tasks, but their utility in automating numerical experiments of complex physical system-a critical and labor-intensive component-remains underexplored. As the major workhorse of computational science over the past decades, Computational Fluid Dynamics (CFD) offers a uniquely challenging testbed for evaluating the scientific capabilities of LLMs. We introduce CFDLLMBench, a benchmark suite comprising three complementary components: CFDQuery, CFDCodeBench, and FoamBench, designed to holistically evaluate LLM performance across three key competencies: graduate-level CFD knowledge, numerical and physical reasoning of CFD, and context-dependent implementation of CFD workflows. Grounded in real-world CFD practices, our benchmark combines a detailed task taxonomy with a rigorous evaluation framework to deliver reproducible results and quantify LLM performance across code executability, solution accuracy, and numerical convergence behavior. CFDLLMBench establishes a solid foundation for the development and evaluation of LLM-driven automation of numerical experiments for complex physical systems. Code and data are available at https://github.com/NLR-Theseus/cfdllmbench/.

Video: https://drive.google.com/file/d/11lp_rNvrh56e8X-QgIW8rlJYJgjeLlSy/view?usp=sharing

Code: https://github.com/NLR-Theseus/cfdllmbench/

Submission Number: 5