openfoam-mcp-server

webworn/openfoam-mcp-server

3.5

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OpenFOAM-MCP-Server is a specialized server designed to facilitate the integration of OpenFOAM simulations with Model Context Protocol (MCP) technology.

OpenFOAM MCP Server

🚀 Revolutionary intelligent CFD education and problem-solving system - The world's first OpenFOAM Model Context Protocol server with advanced AI capabilities including Socratic questioning, context engineering, and expert-level error resolution.

OpenFOAM C++ MCP

🎯 Why This MCP? The CFD-AI Educational Revolution

The Problem: CFD Expertise Barrier

  • Complex Setup: OpenFOAM requires deep expertise in mesh generation, boundary conditions, and solver selection
  • Physics Knowledge: CFD analysis demands understanding of fluid dynamics, heat transfer, and numerical methods
  • Time-Intensive: Manual setup and analysis can take hours to days for complex cases
  • Error-Prone: Incorrect boundary conditions or solver settings lead to non-physical results
  • Learning Curve: Traditional CFD tools lack educational guidance and progressive learning support

The Solution: Intelligent CFD Education System

This revolutionary MCP transforms CFD learning and problem-solving through:

  • Context Engineering: Advanced user modeling that tracks CFD concept understanding and adapts complexity
  • Socratic Questioning: Strategic questioning engine with 4 proven patterns (Clarify, Explore, Confirm, Apply)
  • Intelligent Parameter Extraction: Natural language to CFD parameter translation with confidence scoring
  • Advanced Error Resolution: 5 Whys methodology with research-backed solutions and academic references
  • CFD Knowledge Graph: Concept relationship mapping for guided learning paths
  • Dual-Terminal Architecture: Seamless OpenFOAM execution with intelligent conversation flow

Market Impact

  • Revolutionizes CFD Education: First AI-driven system that teaches CFD through guided discovery
  • Democratizes CFD: Makes professional fluid dynamics analysis accessible through natural language
  • Accelerates Learning: Progressive complexity adaptation based on user understanding level
  • Enhances Problem-Solving: 5 Whys error resolution with research-backed solutions
  • Professional Integration: Maintains full OpenFOAM compatibility with intelligent guidance overlay

🛠️ Intelligent CFD Capabilities

🧠 Advanced AI Features

Context Engineering System
  • Enhanced User Modeling: Tracks CFD concept understanding, experience level, and learning progress
  • Confidence Scoring: Monitors user comprehension across fluid dynamics, heat transfer, and numerical methods
  • Adaptive Complexity: Automatically adjusts explanations and guidance based on user expertise
  • Learning Path Optimization: Suggests optimal concept progression for effective CFD mastery
Socratic Questioning Engine
  • Strategic Question Patterns: 4 proven educational strategies for progressive learning
    • 🔍 CLARIFY: "What specifically do you mean by turbulent flow in this context?"
    • 🌊 EXPLORE: "What would happen if we increased the Reynolds number to 500,000?"
    • ✅ CONFIRM: "So you're saying that pressure drop increases quadratically with velocity?"
    • 🎯 APPLY: "How would you use the Moody diagram for this pipe flow problem?"
  • Context-Aware Questions: Generated based on user's current understanding and conversation history
  • Progressive Difficulty: Questions adapt complexity to build understanding systematically
Intelligent Parameter Extraction
  • Natural Language Processing: Converts conversational descriptions to precise CFD parameters
  • Physics Validation: Ensures extracted parameters are physically reasonable and consistent
  • Confidence Scoring: Provides certainty levels for parameter extraction accuracy
  • Interactive Clarification: Asks intelligent follow-up questions when parameters are ambiguous
Advanced Error Resolution
  • 5 Whys Methodology: Systematic root cause analysis for CFD simulation failures
  • Research Integration: Academic paper database with evidence-based solutions
  • Diagnostic Guidance: Step-by-step troubleshooting with educational explanations
  • Prevention Strategies: Teaches users to avoid similar issues through understanding
CFD Knowledge Graph
  • Concept Relationships: Maps connections between fluid mechanics, heat transfer, and numerical methods
  • Learning Dependencies: Identifies prerequisite concepts for advanced topics
  • Skill Assessment: Evaluates user knowledge gaps and suggests focused learning areas
  • Academic References: Links concepts to authoritative sources and research papers

Core Analysis Tools

1. 🔬 Pipe Flow Analysis (run_pipe_flow)

Physics: Internal flow in circular pipes with laminar/turbulent regime detection

  • Calculates: Reynolds number, friction factor, pressure drop, wall shear stress
  • Applications: Hydraulic systems, pipeline design, HVAC analysis
  • Solvers: simpleFoam, pimpleFoam with automated turbulence model selection
  • Features: Entrance effects, surface roughness, flow regime transitions
2. ✈️ External Flow Analysis (analyze_external_flow)

Physics: Aerodynamics around vehicles, aircraft, and buildings

  • Calculates: Drag coefficient (Cd), lift coefficient (Cl), aerodynamic forces
  • Applications:
    • Automotive: Car aerodynamics, fuel efficiency optimization
    • Aerospace: Aircraft design, wing analysis, UAV performance
    • Civil: Building wind loads, urban flow patterns
  • Solvers: simpleFoam, SRFSimpleFoam with k-ω SST turbulence
  • Features: Reynolds number scaling, boundary layer analysis, flow separation detection
3. 🌡️ Heat Transfer Analysis (analyze_heat_transfer)

Physics: Conjugate heat transfer between fluid and solid regions

  • Calculates: Temperature distributions, thermal resistance, heat transfer coefficients
  • Applications:
    • Electronics Cooling: CPU/GPU thermal management, PCB heat spreading
    • Heat Exchangers: Shell-and-tube, plate heat exchangers
    • Building Thermal: HVAC design, thermal comfort analysis
    • Engine Cooling: Combustion engines, electric motor cooling
  • Solvers: chtMultiRegionFoam with radiation modeling
  • Features: Material property database, Nusselt number correlations, thermal violations
4. 🌊 Multiphase Flow Analysis (analyze_multiphase_flow)

Physics: Complex multiphase interactions using Volume of Fluid (VOF) method

  • Calculates: Interface tracking, phase distribution, momentum transfer coefficients
  • Applications:
    • Dam Break Analysis: Hydraulic structure design, flood modeling
    • Free Surface Flows: Ship hull design, coastal engineering
    • Bubble Dynamics: Chemical reactors, gas-liquid separations
    • Sloshing Analysis: Fuel tank design, liquid cargo transport
  • Solvers: interFoam with interface compression and surface tension
  • Features: Analytical validation against dam break solutions, bubble rise dynamics

🎯 Intelligent CFD Education Features

🧮 Physics-Based Calculations with AI Guidance
  • Dimensionless Numbers: Reynolds, Prandtl, Nusselt, Rayleigh, Grashof with educational context
  • Material Properties: Thermal conductivity, density, specific heat, viscosity with database references
  • Validation: Analytical comparisons against Hagen-Poiseuille, Blasius, and experimental correlations
  • Error Detection: Non-physical results detection with 5 Whys analysis and resolution guidance
🎓 Adaptive Educational Content
  • Progressive Physics Explanations: Complexity adapts to user's demonstrated understanding level
  • Socratic Teaching Method: Strategic questioning to build deep conceptual understanding
  • Engineering Guidance: Context-aware design recommendations with academic justification
  • Industry Applications: Real-world examples from automotive, aerospace, electronics, and marine sectors
  • Best Practices: Intelligent mesh quality assessment, boundary condition validation, solver selection logic
🔍 Comprehensive CFD Validation Framework
  • Multi-Physics Domains: Validation across pipe flow, external flow, heat transfer, and multiphase systems
  • Analytical Solutions: Automatic comparison with classical solutions (Poiseuille, Blasius, Rayleigh-Bénard)
  • Experimental Validation: Database of experimental correlations and benchmark cases
  • Confidence Metrics: Statistical validation with uncertainty quantification and error bounds
📊 Intelligent Visualization & Results
  • ParaView Web Interface: Real-time result visualization on port 8080 with educational annotations
  • Context-Aware Plots: Automatically generated visualizations based on physics domain and user understanding
  • Interactive Learning: Guided exploration of results with Socratic questioning about flow phenomena
  • Streamlines & Pathlines: Flow visualization with educational explanations of particle tracking
  • Result Export: VTK format for advanced post-processing with comprehensive metadata

📥 Installation

Prerequisites

System Requirements
  • OS: Ubuntu 20.04+ or Debian 11+ (recommended)
  • Memory: 8GB+ RAM (16GB+ recommended for large simulations)
  • Storage: 10GB+ available disk space
  • CPU: Multi-core processor (4+ cores recommended)
Required Dependencies
# Ubuntu/Debian - Install all dependencies
sudo apt-get update
sudo apt-get install -y \
    cmake \
    build-essential \
    git \
    nlohmann-json3-dev \
    libboost-system-dev \
    libboost-thread-dev \
    sqlite3 \
    libsqlite3-dev
OpenFOAM 12 Installation
# Option 1: Ubuntu/Debian packages (easiest)
sudo apt-get install openfoam12

# Option 2: OpenFOAM Foundation packages (recommended)
# Download from: https://openfoam.org/download/12-ubuntu/
wget -O - https://dl.openfoam.org/gpg.key | sudo apt-key add -
sudo add-apt-repository http://dl.openfoam.org/ubuntu
sudo apt-get update
sudo apt-get install openfoam12

# Option 3: Source compilation (advanced users)
# Follow: https://openfoam.org/download/12-source/
Environment Setup
# Add to ~/.bashrc or ~/.profile
source /opt/openfoam12/etc/bashrc

# Verify installation
which blockMesh
which simpleFoam
foamVersion

Build MCP Server

  1. Clone Repository
git clone https://github.com/webworn/openfoam-mcp-server.git
cd openfoam-mcp-server
  1. Build with CMake
mkdir build && cd build
cmake ..
make -j$(nproc)
  1. Test Installation
./openfoam-mcp-server --version
# Should output: OpenFOAM MCP Server v2.0.0-dev

# Test AI capabilities
./openfoam-mcp-server --test-context-engine
./openfoam-mcp-server --demo-socratic-questioning

Dependencies Auto-Detection

The build system automatically detects:

  • ✅ OpenFOAM Libraries: /opt/openfoam12/platforms/linux64GccDPInt32Opt/lib
  • ✅ nlohmann/json: JSON parsing for MCP protocol
  • ✅ Boost: System and threading libraries
  • ✅ SQLite3: Optional case database storage

🤖 Claude Code Integration

MCP Configuration

Add to your Claude Code settings (~/.config/claude-code/mcp_servers.json):

{
  "mcpServers": {
    "openfoam": {
      "command": "/path/to/openfoam-mcp-server/build/openfoam-mcp-server",
      "args": [],
      "env": {
        "FOAM_INST_DIR": "/opt/openfoam12",
        "WM_PROJECT_DIR": "/opt/openfoam12"
      }
    }
  }
}

Intelligent CFD Assistant Examples

Socratic Learning: Pipe Flow Analysis
🧑‍💻 User: "Analyze water flow in a 0.1m diameter pipe at 2 m/s"

🤖 Claude: Before I run the analysis, let me understand your background better.

🔍 CLARIFY: "What specifically interests you about this pipe flow - are you designing a hydraulic system, learning about pressure losses, or optimizing an existing installation?"

🧑‍💻 User: "I'm learning about pressure losses for my fluid mechanics course"

🤖 Claude: Perfect! I'll guide you through the analysis step by step.

*Uses run_pipe_flow tool with educational context*

📊 Results with Learning:
• Reynolds Number: 200,000 
  💡 This indicates turbulent flow (Re > 4,000). What do you think this means for mixing?
• Friction Factor: 0.0184 (Blasius correlation)
  🌊 EXPLORE: How would this change if we doubled the velocity?
• Pressure Drop: 2.4 kPa per meter
  ✅ CONFIRM: So you're seeing that pressure drop increases significantly with velocity - can you explain why?

🎯 APPLY: "Now that you understand the relationship, how would you redesign this pipe to reduce pumping costs by 50%?"
Context-Aware Error Resolution
🧑‍💻 User: "My heat transfer simulation isn't converging. The residuals keep oscillating."

🤖 Claude: I'll help you resolve this using systematic analysis.

*Applies 5 Whys methodology*

🔍 Why #1: "Why are the residuals oscillating?"
Analysis: Likely numerical instability or inappropriate relaxation factors.

🔍 Why #2: "Why might the relaxation factors be inappropriate?"
Your setup shows high temperature gradients that require more conservative factors.

🔍 Why #3: "Why are the temperature gradients so high?"
The boundary conditions show a 500K temperature difference across a thin wall.

📚 Research-Backed Solution:
• Reduce temperature relaxation from 0.7 to 0.3 (Versteeg & Malalasekera, 2007)
• Implement under-relaxation for energy equation (Patankar, 1980)
• Consider CFL number for transient effects

🎯 Prevention Strategy:
• Always validate physical temperature ranges before simulation
• Use dimensionless temperature analysis for better conditioning
• Implement gradual temperature ramping for extreme conditions
Parameter Extraction from Natural Language
🧑‍💻 User: "I have a large tank with water draining through a small hole at the bottom. The water level starts at about 2 meters and I want to know how long it takes to empty."

🤖 Claude: I can help with this dam break/draining analysis! Let me extract the CFD parameters from your description.

*Uses intelligent parameter extraction*

📋 Extracted Parameters (Confidence: 85%):
• Physics Domain: Multiphase flow (free surface)
• Initial water height: 2.0 m ✓
• Geometry: Tank with bottom orifice ✓
• Fluid: Water (ρ=1000 kg/m³, μ=1e-3 Pa·s) ✓

❓ Clarification Needed:
• Tank dimensions (length, width)? 
• Orifice diameter?
• Are we modeling turbulence effects?

🔍 EXPLORE: "Based on Torricelli's law, what do you expect happens to the flow rate as the tank empties?"

*After clarification, runs multiphase flow analysis*

📊 Results with Physics Insight:
• Analytical (Torricelli): ~89 seconds for complete drainage
• CFD Simulation: ~94 seconds (includes viscous effects)
• Flow regime: Initially turbulent (Re~50,000), transitions to laminar
• Vortex formation near orifice affects drainage rate by ~5%

Intelligent Workflow Integration

AI-Enhanced Engineering Design Process
  1. Conceptual Design: Context-aware initial sizing with intelligent parameter extraction
  2. Physics Validation: Socratic questioning to ensure understanding of assumptions
  3. Detailed Analysis: Export OpenFOAM cases with AI-generated documentation
  4. Error Resolution: 5 Whys methodology for systematic troubleshooting
  5. Optimization: AI-guided design improvements with academic justification
  6. Validation: Automatic comparison with analytical solutions and experimental data
Progressive CFD Education
  1. Adaptive Learning: Complexity automatically adjusts to demonstrated user understanding
  2. Concept Mapping: CFD knowledge graph guides learning path optimization
  3. Socratic Discovery: Strategic questioning builds deep conceptual understanding
  4. Research Integration: Academic papers and validated solutions support learning
  5. Industry Context: Real-world applications connect theory to practice
  6. Error Prevention: Learn from mistakes through systematic root cause analysis

🤝 Contributing

Development Environment

GitHub Codespaces (Recommended):

  1. Fork the repository
  2. Open in Codespaces (auto-configures OpenFOAM 12)
  3. VS Code Desktop integration with full IntelliSense
  4. Cloud development with 4-32 core machines

Local Development:

# Install dependencies
sudo apt-get install cmake build-essential openfoam12 nlohmann-json3-dev

# Clone and build
git clone https://github.com/webworn/openfoam-mcp-server.git
cd openfoam-mcp-server
mkdir build && cd build
cmake .. && make -j$(nproc)

Adding New Analysis Tools

1. Create Analyzer Class
// src/openfoam/your_analysis.hpp
class YourAnalyzer {
public:
    struct Input { /* physics parameters */ };
    struct Results { /* calculated outputs */ };
    
    Results analyze(const Input& input);
    bool validateInput(const Input& input);
    json toJson(const Results& results);
};
2. Create MCP Tool Wrapper
// src/tools/your_analysis_tool.hpp
class YourAnalysisTool {
public:
    static std::string getName() { return "your_analysis"; }
    static std::string getDescription() { return "Physics description"; }
    static json getInputSchema();
    ToolResult execute(const json& arguments);
};
3. Register Tool
// src/main.cpp
#include "tools/your_analysis_tool.hpp"

int main() {
    McpServer server;
    registerYourAnalysisTool(server);  // Add this line
    server.start();
}

Physics Implementation Guidelines

OpenFOAM Integration
  • Case Templates: Use src/openfoam/case_templates/ for solver setup
  • Boundary Conditions: Implement physics-appropriate BC selection
  • Mesh Generation: Add automatic meshing with quality checks
  • Solver Selection: Choose appropriate OpenFOAM solver for physics
Validation Requirements
  • Analytical Comparison: Compare with known solutions (Poiseuille, Blasius, etc.)
  • Experimental Data: Validate against published correlations
  • Convergence Studies: Ensure mesh-independent results
  • Physical Bounds: Check for non-physical results
Educational Content
  • Physics Explanations: Describe governing equations and assumptions
  • Dimensionless Numbers: Calculate and explain significance
  • Engineering Context: Provide industry-relevant guidance
  • Design Recommendations: Suggest optimization strategies

Code Standards

C++ Best Practices
  • C++20 Standard: Use modern C++ features appropriately
  • OpenFOAM Style: Follow OpenFOAM coding conventions
  • Memory Management: Use smart pointers, RAII principles
  • Error Handling: Comprehensive exception handling
MCP Protocol Compliance
  • JSON-RPC 2.0: Strict protocol adherence
  • Schema Validation: Complete input parameter validation
  • Error Messages: User-friendly error reporting
  • Resource Management: Proper cleanup and case management

🏗️ Technical Architecture

System Overview

┌─────────────────┐    JSON-RPC 2.0    ┌──────────────────┐
│   Claude Code   │ ◄──────────────── │  MCP Server      │
│   AI Assistant  │                   │  (C++ Native)    │
└─────────────────┘                   └──────────────────┘
                                               │
                                               ▼
                                    ┌──────────────────┐
                                    │   Tool Router    │
                                    │  (Registration)  │
                                    └──────────────────┘
                                               │
                         ┌─────────────────────┼─────────────────────┐
                         ▼                     ▼                     ▼
                ┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
                │  Pipe Flow      │   │ External Flow   │   │ Heat Transfer   │
                │  Tool           │   │ Tool            │   │ Tool            │
                └─────────────────┘   └─────────────────┘   └─────────────────┘
                         │                     │                     │
                         ▼                     ▼                     ▼
                ┌─────────────────┐   ┌─────────────────┐   ┌─────────────────┐
                │ PipeFlow        │   │ ExternalFlow    │   │ HeatTransfer    │
                │ Analyzer        │   │ Analyzer        │   │ Analyzer        │
                └─────────────────┘   └─────────────────┘   └─────────────────┘
                         │                     │                     │
                         └─────────────────────┼─────────────────────┘
                                               ▼
                                    ┌──────────────────┐
                                    │  Case Manager    │
                                    │  (OpenFOAM)      │
                                    └──────────────────┘
                                               │
                                               ▼
                                    ┌──────────────────┐
                                    │    OpenFOAM      │
                                    │   Solvers        │
                                    │ (Native C++)     │
                                    └──────────────────┘

Data Flow

  1. User Request: Natural language query to Claude Code
  2. AI Processing: Claude interprets request and calls appropriate MCP tool
  3. Parameter Validation: Input schema validation and physics bounds checking
  4. OpenFOAM Setup: Automated case creation with boundary conditions
  5. CFD Simulation: Native OpenFOAM solver execution
  6. Result Processing: Physics calculations and educational content generation
  7. Response: Formatted results with explanations and recommendations

Performance Features

  • Parallel Execution: Multi-threaded OpenFOAM solvers
  • Case Caching: Intelligent reuse of similar simulations
  • Mesh Templates: Pre-validated meshes for common geometries
  • Async Processing: Non-blocking simulation execution (planned)

📚 References

  • OpenFOAM Foundation: https://openfoam.org/
  • Model Context Protocol: https://modelcontextprotocol.io/
  • CFD Theory: "An Introduction to Computational Fluid Dynamics" by Versteeg & Malalasekera
  • Heat Transfer: "Heat Transfer" by Cengel & Ghajar
  • Fluid Mechanics: "Fluid Mechanics" by White

📄 License

Apache License 2.0 - see for details.

🙏 Acknowledgments

  • OpenFOAM Foundation for the world-class CFD toolkit
  • Anthropic for Claude Code and MCP protocol
  • CFD Community for validation data and best practices

🚀 Upcoming Features

Interested in contributing? Join us in building the most comprehensive CFD-AI integration platform!

🌊 Expanded Physics Domains

Compressible Flow Analysis (In Development)
  • Supersonic Aerodynamics: Shock waves, expansion fans, nozzle design
  • Gas Dynamics: Pressure wave propagation, transonic flows
  • Hypersonic Applications: High-temperature effects, real gas properties
Multiphase Flow Systems (In Development)
  • Volume of Fluid (VOF): Free surface flows, dam breaks, sloshing tanks
  • Eulerian-Eulerian: Bubble columns, fluidized beds, liquid-liquid extraction
  • Lagrangian Particle Tracking: Spray injection, aerosol transport, combustion
Reacting Flows & Combustion (Research Phase)
  • Premixed Combustion: Flame propagation, explosion modeling, engine combustion
  • Non-Premixed: Diffusion flames, jet flames, industrial flares
  • Solid Fuel: Coal combustion, biomass burning, waste incineration

🏭 Industry-Specific Tool Suites

🚗 Automotive Engineering
// Planned automotive tools
analyze_vehicle_aerodynamics(car_geometry, speed, crosswind_effects)
optimize_underhood_cooling(heat_sources, fan_design, airflow_management)
design_hvac_system(cabin_geometry, climate_conditions, comfort_criteria)
simulate_engine_combustion(cylinder_geometry, fuel_injection, emissions)
✈️ Aerospace Engineering
// Planned aerospace tools  
analyze_airfoil_performance(airfoil_coordinates, mach_range, reynolds_range)
design_propulsion_system(engine_type, thrust_requirements, efficiency_targets)
simulate_hypersonic_vehicle(mach_number, altitude, thermal_protection)
optimize_wing_configuration(geometry_parameters, flight_conditions)
⚡ Energy Systems
// Planned energy tools
design_wind_turbine(blade_geometry, wind_conditions, power_optimization)
analyze_solar_collector(collector_type, solar_irradiance, fluid_properties)
optimize_gas_turbine(blade_design, combustion_efficiency, emissions)
simulate_nuclear_reactor(coolant_flow, heat_generation, safety_systems)
🏥 Biomedical Applications
// Planned biomedical tools
simulate_blood_flow(vessel_geometry, pulsatile_conditions, wall_properties)
analyze_drug_delivery(injection_site, transport_mechanisms, target_tissue)
design_medical_device(device_geometry, biocompatibility, flow_characteristics)
model_respiratory_system(lung_geometry, breathing_patterns, particle_deposition)
🌊 Marine Engineering
// Planned marine tools
analyze_ship_resistance(hull_geometry, sea_conditions, propulsion_efficiency)
design_offshore_platform(platform_type, wave_loads, environmental_conditions)
simulate_propeller_cavitation(propeller_geometry, operating_conditions)
optimize_subsea_pipeline(pipeline_route, current_loads, installation_method)

🧠 AI-Enhanced Capabilities

Smart Automation
  • Auto-Mesh Generation: AI-driven mesh adaptation based on physics
  • Intelligent Solver Selection: Automatic solver and model selection
  • Convergence Prediction: ML-based convergence behavior forecasting
  • Error Detection: Automated identification of non-physical results
Design Optimization
  • Multi-Objective Optimization: Pareto-optimal design exploration
  • Sensitivity Analysis: Parameter importance ranking and correlation
  • Surrogate Modeling: Reduced-order models for rapid design iteration
  • Shape Optimization: Gradient-based and evolutionary algorithms
Predictive Analytics
  • Performance Prediction: AI models trained on CFD databases
  • Failure Mode Analysis: Prediction of critical operating conditions
  • Maintenance Scheduling: Condition monitoring through CFD analysis
  • Real-Time Control: CFD-informed process control systems

🔧 Advanced Computational Features

High-Performance Computing
  • Cloud Integration: AWS, Azure, GCP deployment with auto-scaling
  • HPC Cluster Support: SLURM, PBS job scheduling integration
  • GPU Acceleration: CUDA/OpenCL solver acceleration
  • Distributed Computing: Multi-node parallel execution
Workflow Automation
  • Parametric Studies: Automated parameter sweeps and DOE
  • Uncertainty Quantification: Monte Carlo and polynomial chaos methods
  • Data Management: Version control for CFD cases and results
  • Report Generation: Automated technical documentation
Integration & Interoperability
  • CAD Integration: Direct CATIA, SolidWorks, Fusion 360 coupling
  • FEA Coupling: Structural analysis with ANSYS, Abaqus, CalculiX
  • Control Systems: MATLAB/Simulink integration for system analysis
  • Data Analytics: Python/R integration for advanced post-processing

📊 Comprehensive Solver Matrix (Planned)

DomainSolversApplicationsTarget Release
CompressiblerhoCentralFoam, sonicFoamAerospace, turbomachineryQ4 2025
MultiphaseinterFoam, twoPhaseEulerFoamProcess industry, marineQ1 2026
CombustionfireFoam, reactingFoamPower generation, automotiveQ2 2026
ElectromagneticsmhdFoam, electrostaticFoamFusion, materials processingQ3 2026
Particle DynamicsDPMFoam, coalChemistryFoamEnvironmental, pharmaceuticalQ4 2026

🤝 How to Contribute

We welcome contributions across all domains! Here's how you can help:

🔬 Physics Domain Experts
  • Validate Physics Models: Ensure accurate implementation of governing equations
  • Develop Test Cases: Create validation cases against analytical/experimental data
  • Write Educational Content: Explain physics concepts for educational tools
💻 Software Developers
  • Implement New Tools: Follow our architecture patterns for new analysis tools
  • Optimize Performance: Improve computational efficiency and memory usage
  • Enhance UI/UX: Develop better user interfaces and visualization tools
🏭 Industry Professionals
  • Define Use Cases: Specify real-world engineering problems and requirements
  • Provide Test Data: Share (anonymized) validation cases and benchmarks
  • Review Results: Validate tool outputs against industrial standards
📚 Educators & Students
  • Create Tutorials: Develop educational content and learning materials
  • Test Tools: Identify usability issues and suggest improvements
  • Documentation: Improve documentation, examples, and guides

🎯 Contribution Roadmap

Immediate Opportunities (Next 3 months)
  1. Multiphase Flow Tool: Implement interFoam wrapper for free surface flows
  2. Validation Framework: Develop automated validation against analytical solutions
  3. Performance Optimization: Parallel execution and memory management improvements
  4. Documentation: Enhanced tutorials and physics explanations
Medium-term Goals (6-12 months)
  1. Compressible Flow Suite: Complete supersonic and transonic flow tools
  2. AI Integration: Machine learning for mesh optimization and solver selection
  3. Cloud Deployment: AWS/Azure integration with auto-scaling capabilities
  4. Industry Partnerships: Collaborate with automotive and aerospace companies
Long-term Vision (1-2 years)
  1. Complete Physics Coverage: All OpenFOAM solvers integrated with MCP
  2. Real-time Analysis: Live simulation monitoring and control
  3. Digital Twin Integration: IoT sensor data fusion with CFD models
  4. Autonomous Design: AI-driven design optimization without human intervention

📬 Get Involved

Ready to contribute? Here's how to start:

  1. Join Our Community: Star the repository and join discussions
  2. Pick an Issue: Check our GitHub Issues for beginner-friendly tasks
  3. Fork & Develop: Use GitHub Codespaces for instant development environment
  4. Submit PR: Follow our contribution guidelines and submit your improvements

Together, we're building the future of AI-powered CFD analysis! 🌊🤖⚡

Transform your engineering workflow with AI-powered CFD analysis. Get started today!