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OpenFOAMGPT: A retrieval-augmented large language model (LLM) agent for OpenFOAM-based computational fluid dynamics DOI

Sandeep Pandey,

Ran Xu,

Wenkang Wang

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

This work presents a large language model (LLM)-based agent OpenFOAMGPT tailored for OpenFOAM-centric computational fluid dynamics (CFD) simulations, leveraging two foundation models from OpenAI: the GPT-4o (GPT means Generative Pre-trained Transformer) and chain-of-thought–enabled o1 preview model. Both agents demonstrate success across multiple tasks. While price of token with is six times as that GPT-4o, it consistently exhibits superior performance in handling complex tasks, zero-shot/few-shot case setup to boundary condition modifications, zero-shot turbulence adjustments, code translation. Through an iterative correction loop, efficiently addressed single-phase multiphase flow, heat transfer, Reynolds-averaged Navier–Stokes modeling, eddy simulation, other engineering scenarios, often converging limited number iterations at low costs. To embed domain-specific knowledge, we employed retrieval-augmented generation pipeline, demonstrating how preexisting simulation setups can further specialize subdomains such energy aerospace. Despite great agent, human oversight remains crucial ensuring accuracy adapting shifting contexts. Fluctuations over time suggest need monitoring mission-critical applications. Although our demonstrations focus on OpenFOAM, adaptable nature this framework opens door developing LLM-driven into wide range solvers codes. By streamlining CFD approach has potential accelerate both fundamental research industrial advancements.

Language: Английский

Citations

Explainable artificial intelligence for energy systems maintenance: A review on concepts, current techniques, challenges, and prospects DOI