AI-POWERED SIMULATION AUTOMATION
CFD-copilot: Leveraging Domain-Adapted LLMs and MCP for Enhanced Simulation Automation
By Zhehao Donga, Shanghai Duª, Zhen Luª,*, Yue Yanga,b,*
Configuring computational fluid dynamics (CFD) simulations requires significant expertise in physics modeling and numerical methods, posing a barrier to non-specialists. Although automating scientific tasks with large language models (LLMs) has attracted attention, applying them to the complete, end-to-end CFD workflow remains a challenge due to its stringent domain-specific requirements. We introduce CFD-copilot, a domain-specialized LLM framework designed to facilitate natural language-driven CFD simulation from setup to post-processing. The framework employs a fine-tuned LLM to directly translate user descriptions into executable CFD setups. A multi-agent system integrates the LLM with simulation execution, automatic error correction, and result analysis. For post-processing, the framework utilizes the model context protocol (MCP), an open standard that decouples LLM reasoning from external tool execution. This modular design allows the LLM to interact with numerous specialized post-processing functions through a unified and scalable interface, improving the automation of data extraction and analysis. The framework was evaluated on benchmarks including the NACA 0012 airfoil and the three-element 30P-30N airfoil. The results indicate that domain-specific adaptation and the incorporation of the MCP jointly enhance the reliability and efficiency of LLM-driven engineering workflows.
EXECUTIVE IMPACT
Unlock Unprecedented Efficiency in CFD Workflows
CFD-copilot revolutionizes computational fluid dynamics by automating complex simulation and analysis tasks, significantly reducing the expertise barrier and accelerating engineering design cycles. Our solution delivers robust performance, translating natural language into actionable insights with high accuracy.
0
Average Simulation Success Rate (NACA 0012)
0
Average Velocity Field Accuracy
0
Average Pressure Field Accuracy
0
Success Rate for Complex 30P-30N Airfoil
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
CFD-copilot: An Integrated Multi-Agent Framework
CFD-copilot employs a sophisticated multi-agent system to automate the entire CFD workflow, from simulation setup to post-processing. At its core, a fine-tuned Large Language Model (LLM) translates natural language descriptions into executable OpenFOAM configurations, making complex CFD accessible to non-specialists.
Enterprise Process Flow
User Input (Natural Language)
→
Pre-checker (Input/Mesh Validation)
→
Fine-tuned LLM (Config Generation)
→
Runner (Simulation Execution)
→
Corrector (Error Analysis & Adjustment)
→
Post-processor (Analysis & Visualization)
This robust, self-correcting loop ensures a high success rate for automated simulation setup, while the specialized post-processor handles data extraction and visualization based on natural language commands.
Modular & Scalable Post-processing with Model Context Protocol (MCP)
The Model Context Protocol (MCP) is central to CFD-copilot's post-processing capabilities, offering a standardized, model-agnostic approach to tool integration. This decouples the LLM's reasoning from specific tool execution, enhancing scalability and maintainability.
| Feature | Conventional LLM Function Calling | CFD-copilot with MCP |
|---|---|---|
| Tool Definition |
|
|
| Scalability & Maintainability |
|
|
| Flexibility |
|
|
This approach allows CFD-copilot to seamlessly integrate over 100 OpenFOAM post-processing tools, from force coefficient calculations to streamline visualizations, all controlled via natural language.
High Reliability for Canonical Aerodynamic Benchmarks
The framework was rigorously evaluated on the 2D NACA 0012 airfoil, a standard benchmark for CFD configuration validation. CFD-copilot demonstrated strong performance in autonomously setting up and running simulations across a range of angles of attack.
97.5%
Average Accuracy for Velocity and Pressure Fields (up to 10° AoA)
For angles of attack up to 10°, velocity and pressure field accuracies consistently remained above 95%. The system achieved an average simulation success rate of 52.86% across all tested AoAs, confirming its ability to configure simulations reliably. While lift and drag coefficient errors increased at higher AoA due to mesh limitations, the overall results align well with experimental data.
Scalability to Complex, Multi-Element High-Lift Configurations
The three-element 30P-30N airfoil, known for its complex flow features like boundary layer interactions and wake merging, presented a significant challenge. CFD-copilot demonstrated its capacity to manage this realistic engineering problem.
Comparative Advantage on 30P-30N Airfoil
Challenge: Autonomously generating valid solver configurations for the highly complex 30P-30N airfoil at an 8.12° Angle of Attack.
CFD-copilot Performance: Achieved an 80% simulation completion rate, resulting in a 10% final success rate after iterative corrections. For the successful runs, the lift coefficient error was 2.58%, and velocity/pressure accuracies were 93.14% and 88.44% respectively. This success highlights the critical role of domain-specific adaptation, as the fine-tuned model applied advanced variable-specific discretization schemes.
General-Purpose LLM Performance: In direct comparison, much larger general-purpose LLMs (Qwen3-Next-80B and Qwen3-235B) only achieved a 10% completion rate and failed to produce a single converged solution, resulting in a 0% success rate. They tended to produce overly simplistic setups inadequate for this complex case.
Impact: CFD-copilot's domain-adapted LLM, combined with the MCP workflow, effectively managed multi-surface data extraction, aggregation, and advanced visualizations, enabling sophisticated analysis on multi-body geometries without exposing the user to underlying operational complexity.
This case demonstrates that domain-specific adaptation is crucial for handling complex simulations, where general-purpose models fall short. CFD-copilot's framework successfully translates high-level prompts into multi-step CFD operations, proving its potential to simplify and accelerate engineering workflows.
ROI PROJECTION
Calculate Your Potential Savings with AI Automation
Estimate the time and cost savings your enterprise could achieve by automating CFD workflows and post-processing with our domain-adapted AI.
Annual Cost Savings
Annual Hours Reclaimed
IMPLEMENTATION
Your Roadmap to AI-Driven CFD Automation
Our proven framework guides your enterprise through a seamless transition to AI-powered CFD, ensuring maximum impact with minimal disruption.
Phase 1: Discovery & Strategy
We begin with a comprehensive analysis of your existing CFD workflows, identifying key pain points and high-impact automation opportunities. We define clear objectives, establish success metrics, and outline a tailored AI strategy for your engineering teams.
Phase 2: Data & Model Adaptation
Leveraging your proprietary simulation data and expertise, we fine-tune our LLMs to your specific domain, solver configurations, and post-processing requirements. This ensures the AI understands your unique engineering language and operational nuances.
Phase 3: Integration & Testing
CFD-copilot is integrated with your existing OpenFOAM environment or other CFD platforms. Rigorous testing is conducted on your specific benchmarks and real-world cases to validate performance, accuracy, and reliability, ensuring the system meets all engineering standards.
Phase 4: Deployment & Optimization
The AI-powered framework is deployed, and your teams are onboarded with comprehensive training. We provide ongoing support, continuous monitoring, and iterative optimization to ensure sustained performance gains and adaptation to evolving engineering needs.
NEXT STEPS
Ready to Transform Your CFD Engineering?
Book a personalized consultation with our AI specialists to discover how CFD-copilot can accelerate your design cycles, reduce costs, and empower your engineers.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat