MetaOpenFOAM 2.0: Large Language Model Driven Chain of Thought for Automating CFD Simulation and Post-Processing (2502.00498v1)

Abstract: Computational Fluid Dynamics (CFD) is widely used in aerospace, energy, and biology to model fluid flow, heat transfer, and chemical reactions. While LLMs have transformed various domains, their application in CFD remains limited, particularly for complex tasks like post-processing. To bridge this gap, we introduce MetaOpenFOAM 2.0, which leverages Chain of Thought (COT) decomposition and iterative verification to enhance accessibility for non-expert users through natural language inputs. Tested on a new benchmark covering simulation (fluid flow, heat transfer, combustion) and post-processing (extraction, visualization), MetaOpenFOAM 2.0 achieved an Executability score of 6.3/7 and a pass rate of 86.9%, significantly outperforming MetaOpenFOAM 1.0 (2.1/7, 0%). Additionally, it proved cost-efficient, averaging $0.15 per case. An ablation study confirmed that COT-driven decomposition and iterative refinement substantially improved task performance. Furthermore, scaling laws showed that increasing COT steps enhanced accuracy while raising token usage, aligning with LLM post-training scaling trends. These results highlight the transformative potential of LLMs in automating CFD workflows for industrial and research applications. Code is available at https://github.com/Terry-cyx/MetaOpenFOAM

• The paper presents MetaOpenFOAM 2.0, which leverages a Chain of Thought framework to boost CFD simulation executability from 2.1/7 to 6.3/7 with an 86.9% pass rate and minimal cost.
• The paper employs a multi-agent system—including Architect, InputWriter, Runner, and Reviewer—to decompose complex CFD tasks into manageable subtasks using natural language inputs.
• The paper’s ablation analysis confirms that integrating both QDCOT and ICOT steps is vital for aligning with LLM scaling laws and enhancing overall CFD performance.

An Overview of MetaOpenFOAM 2.0: LLM-Driven Automation for Enhanced CFD Simulation and Post-Processing

The paper "Meta OpenFOAM 2.0: LLM Driven Chain of Thought for Automating CFD Simulation and Post-Processing" analyzes the integration of LLMs for automating Computational Fluid Dynamics (CFD) simulations. This paper defines the role and operational efficacy of MetaOpenFOAM 2.0, an advanced framework leveraging Chain of Thought (COT) decomposition and iterative refinement to address the limitations traditionally observed in CFD simulation software. Comprehensive testing has been executed on a newly developed benchmark incorporating CFD simulations and post-processing tasks, revealing significant progress over its predecessor, MetaOpenFOAM 1.0.

Key Methodological Advances

MetaOpenFOAM 2.0 improves non-expert access to CFD simulations by using a COT framework that sequentially decomposes complex tasks into manageable subtasks. This approach facilitates automatic completion through user-friendly natural language inputs. Employing OpenFOAM 10 for simulations, MetaOpenFOAM 2.0 applies a multi-agent system (including Architect, InputWriter, Runner, and Reviewer agents) to handle both primary tasks—CFD simulation and post-processing—via COT and iterative checks, with proven efficacy demonstrated through robust test results.

The revised framework introduces significant enhancements:

• Enhanced Executability and Cost-Efficiency: MetaOpenFOAM 2.0 achieved an Executability score of 6.3/7 and an 86.9% pass rate, reflecting substantial improvements over MetaOpenFOAM 1.0's 2.1/7 score and 0% pass rate. The system processes each case for just $0.15 on average, signifying substantial cost efficiency.
• Ablation Analysis and Scaling Laws: An in-depth ablation paper revealed that the incorporation of both QDCOT and ICOT steps was critical to the observed performance enhancements. As COT decomposition and iterative refinement steps increased, so did accuracy and token usage, presenting a correlation that aligns with recognized scaling laws for LLMs.

Experimental Protocols and Results

Utilizing the latest MetaGPT and LangChain technologies, experiments illustrated the model's capacity to handle diverse CFD tasks. The benchmark consisted of CFD tasks across different domains, such as elements of fluid flow, heat transfer, and combustion—each tested via quantitative metrics like Executability, cost, and Pass@k. MetaOpenFOAM 2.0 exhibited impressive metrics across the board, with Cavity simulations hitting the peak Executability scores (7.0) while others like HIT simulations underscored potential improvements in database matching.

Implications and Future Directions

This research positions MetaOpenFOAM 2.0 as a promising tool for streamlining CFD processes through automated, LLM-driven approaches. By effectively lowering the complexity barrier, it opens up participation to non-specialists while enhancing computational efficiency for seasoned professionals. Moreover, the scalability insights and cost analysis underpin its industrial and academic relevance, suggesting wider applications if integrated with mesh generation, pre-processing, and parameter calibration tasks.

Overall, this paper articulates a compelling narrative of innovation in CFD simulations, delivering detailed insights into a versatile and accessible LLM-driven framework. Future research can explore additional refinements to COT methodologies and broader applications within CFD workflows, potentially unlocking new efficiencies and capabilities in complex scientific simulations. This paper sets the stage for advancing the utility and accessibility of CFD workflows in the industrial and research sectors.