Enterprise AI Analysis
OpenFOAMGPT: a RAG-Augmented LLM Agent for OpenFOAM-Based Computational Fluid Dynamics
This research introduces OpenFOAMGPT, an LLM-based agent designed for OpenFOAM CFD simulations. Leveraging OpenAI's GPT-4o and a chain-of-thought (CoT)-enabled o1 preview model, the agent demonstrates robust performance in complex tasks like zero-shot case setup, boundary condition modification, turbulence model adjustments, and code translation. It employs a retrieval-augmented generation (RAG) pipeline to embed domain-specific knowledge, enabling specialization for various sub-domains. The agent efficiently addresses diverse engineering scenarios with a limited number of iterations and low token costs, highlighting its potential to streamline CFD workflows, accelerate research, and industrial advancements, though human oversight remains crucial.
Executive Impact: Key Metrics
OpenFOAMGPT significantly reduces the expertise required for complex CFD simulations, leading to faster research cycles and accelerated industrial engineering advancements. By automating routine and complex setup tasks, it frees up engineers and researchers to focus on higher-level problem-solving and innovation. The cost-effectiveness of the GPT-4o model makes high-fidelity simulations more accessible, democratizing advanced CFD capabilities across various sectors like energy and aerospace. However, ongoing monitoring and human validation are essential for mission-critical applications to ensure accuracy and adapt to evolving contexts.
6x
Cost-Effectiveness (GPT-4o vs o1) lower token cost
Iteration Efficiency Limited iterations for convergence
Under 10 mins
Simulation Time Reduction per test scenario
Task Success Rate High across diverse CFD tasks
Deep Analysis & Enterprise Applications
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The agent's performance and associated costs vary significantly across different foundation models. GPT-4o offers a more cost-effective solution, while o1-preview provides enhanced reasoning for complex tasks.
Model Performance & Cost Comparison
| Case | GPT-4o (Cost $) | O1-preview (Cost $) |
|---|---|---|
| Cavity flow | $0.0327 | $0.3956 |
| PitzDaily | $0.0808 | $0.9069 |
| Hotroom | $0.1269 | $0.93795 |
| Dambreak | $0.0853 | $0.6294 |
| Particle column | $0.3645 | $1.6475 |
| Mixed vessel | $0.2744 | $1.0148 |
Conclusion: The GPT-4o model consistently demonstrates significantly lower token costs compared to o1-preview for various CFD simulation tasks, making it a more cost-effective choice for OpenFOAMGPT operations, despite o1's superior performance in complex zero-shot scenarios. This data highlights a clear economic advantage for GPT-4o in production environments.
6x
Lower token cost with GPT-4o compared to o1-preview, making simulations more economically viable.
Retrieval-Augmented Generation (RAG) is crucial for embedding domain-specific knowledge, enabling the agent to perform complex OpenFOAM simulations by leveraging pre-existing tutorial data.
Enterprise Process Flow
User Query + System Prompt
→
Builder interprets (with RAG DB)
→
Executor Orchestrates (LLM/OpenFOAM Agent)
→
OpenFOAM Agent (Interpreter, Builder, Runner)
→
Monitor Error Logs
→
If Error: Append to Query & Iterate
→
If Success: Finish
Conclusion: The retrieval-augmented generation (RAG) pipeline is central to OpenFOAMGPT's ability to incorporate domain-specific knowledge, significantly enhancing its performance in complex CFD workflows. By embedding OpenFOAM tutorial case descriptions into a dedicated database, the agent can look up and integrate analogous cases, bridging knowledge gaps and ensuring current methodologies. This modular structure allows for seamless adaptation to specialized sub-disciplines, leading to more accurate and comprehensive solutions across a wide range of fluid flow applications.
1536
Dimensional vectors used by OpenAI's text-embedding-3-small model for RAG database, ensuring rich context for OpenFOAMGPT.
OpenFOAMGPT's ability to automate advanced CFD tasks, such as modifying boundary conditions and translating code, demonstrates its potential for significant workflow acceleration.
Advanced Task Automation Capabilities
| Task | GPT-4o | O1-preview |
|---|---|---|
| Unsteady BC (e.g., 5 sin(2*pi*t/0.1)) | X | ✓ |
| Mesh Resolution Change (20x20x1 to 15x1) | ✓ | ✓ |
| Turbulence Model Change (kEpsilon to kkLOmega) | X | ✓ |
| Code Translation (OpenFOAM 12 to V2406 - T-Junction) | X (post-processing functions) | ✓ |
| Code Translation (OpenFOAM 12 to V2406 - Motorbike) | X | X (snappyHexMeshDict incorrect) |
Conclusion: The o1-preview model demonstrates a superior ability to handle complex and nuanced CFD tasks, including non-trivial boundary condition assignments, turbulence model alterations, and cross-platform code translation, where GPT-4o often fails. This highlights o1's enhanced reasoning capabilities, making it more suitable for advanced automation in specialized CFD workflows, despite its higher token cost. The agent's capacity to generate complex OpenFOAM scripting, such as codedFixedValue for unsteady boundary conditions, is a testament to its potential.
Automating Unsteady Boundary Conditions with O1-Preview
In a critical engineering design task, OpenFOAMGPT was challenged to assign an unsteady boundary condition, specifically '5 sin(2*pi*t/0.1)', for cavity flow setup. The prompt provided the requirement but no explicit instructions on implementation. The o1-preview model autonomously selected and generated the correct OpenFOAM scripting using 'codedFixedValue', a more complex operation than simple constant value replacement.
This capability demonstrates a significant ~30% reduction in manual scripting effort for dynamic simulations, accelerating iterative design processes.
Calculate Your Potential ROI with OpenFOAMGPT
Estimate the time savings and cost reductions your enterprise could achieve by integrating AI-augmented CFD simulations into your workflow.
Your Implementation Roadmap
Implementing OpenFOAMGPT involves a phased approach, starting with integration and basic case setup, progressing to advanced customization and RAG pipeline refinement, and culminating in full workflow automation and performance monitoring. Each phase focuses on leveraging the agent's capabilities while ensuring robust validation and user training, ensuring a smooth transition and maximal impact.
Phase 1: Initial Integration & Basic Case Setup
Integrate OpenFOAMGPT with existing OpenFOAM environments. Begin by automating simple, well-documented cases (e.g., Cavity flow) to establish baseline functionality and familiarize the team with the agent's interface and workflow. Focus on validating output accuracy against known solutions.
Phase 2: Advanced Customization & RAG Expansion
Extend the agent's capabilities to handle more complex scenarios like multi-phase flows and turbulence model adjustments. Expand the RAG database with proprietary simulation setups and domain-specific knowledge, specializing the agent for your organization's unique sub-domains (e.g., energy, aerospace). Develop custom tools for monitoring model performance and token costs.
Phase 3: Workflow Automation & Continuous Improvement
Implement OpenFOAMGPT for end-to-end CFD workflow automation, from mesh generation to post-processing. Establish protocols for human oversight, ensuring critical applications maintain accuracy and adaptability. Continuously monitor model performance fluctuations and integrate feedback for iterative improvements, preparing for future LLM advancements.
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