AI CFD Scientist: Toward Open-Ended Computational Fluid Dynamics Discovery with Physics-Aware AI Agents

Key points

• Problem: LLM agents have automated much of the discovery loop in machine-learning research, chemistry and biology, but with high-fidelity physics simulators a solver can finish cleanly and still give a physically wrong result. Failures such as a wrong geometry or a missing key flow feature leave clean solver logs and show up only in field-level images.
• Input and output: The system takes a natural-language research topic plus an optional base case for OpenFOAM (an open-source CFD solver) and optional reference data. It outputs a study JSON, executed OpenFOAM cases, compiled case-local C++ model libraries, diagnostic figures, cross-case analysis and a LaTeX manuscript. The paper is a system paper that builds this whole workflow and tests it on five tasks.
• Method: Three pathways run on OpenFOAM through Foam-Agent: parameter sweeps in a fixed solver, compiling case-local C++ libraries for new physical models, and open-ended hypothesis search scored against a reference comparator. Before any result is accepted, rerun or written into a manuscript, a VLM (vision-language model) inspects rendered flow fields for physical consistency. A mesh-independence gate compares the baseline mesh with one refined by about 10% near the wall and 5% in the bulk, and sends quantities that differ by more than 5% to stricter convergence checks.
• Implementation: The authors built a checkpointed LangGraph workflow and a skills-based version whose parts can be reused in other orchestrators, and ran every experiment with a single GPT-5.5 (Codex) backbone. The five experiments took 2,765 LLM calls and cost 43.75 US dollars. The official repository README describes three run modes (an interactive CLI built on deepagents, a LangGraph orchestrator and markdown SKILLs) and a stage-gate audit script that confirms a run is complete, and the repository top level holds src, scripts, prompts, skills and cfd-skills folders.
• Results: On the periodic hill flow at Reh=5600, a Spalart-Allmaras runtime correction found over 44 iterations cut the lower-wall skin-friction (Cf) RMSE against DNS (direct numerical simulation) from 0.004297 to 0.003958, a 7.89% reduction. In 16 cases with planted failures the VLM gate caught 14, and both misses were runs cut short before convergence. Comparing the four standard tasks (T1 to T4) under the same GPT-5.5, ARIS and DeepScientist reported turbulence-model rankings or Reynolds-number frequency correlations on T1 and T2 without mesh or reference-data evidence, while AI CFD Scientist kept such values marked unresolved.
• Limits: All numbers come from one backbone, GPT-5.5, and sweeps over other LLMs and more baselines were deferred for cost. With no automated rubric for CFD papers, the framework comparison relies on expert reading of artifacts, and the authors describe the system as supervised scientific assistance. The model found in the open-ended task (T5) is classified as a candidate because wall-shear extraction failed for its six validation cases, and transfer to other Reynolds numbers and geometries was not tested.

Paper links

External research summaries. These are not HDATF publications or measured product results.