AI Enhanced Workflows for Aerodynamic Simulation in Aerospace

Introduction

This content outlines the traditional and AI-enhanced workflows in aerodynamic simulation, detailing each step from geometry creation to reporting. It highlights how integrating AI tools can optimize processes, improve accuracy, and reduce time spent on repetitive tasks, ultimately fostering innovation in aerospace engineering.

Traditional Workflow

1. Geometry Creation
   • Engineers create or import 3D CAD models of aircraft components or complete vehicles.
   • Software used: CATIA, SolidWorks, NX
2. Mesh Generation
   • The geometry is discretized into a computational mesh.
   • Software used: ANSYS Meshing, Pointwise
3. Physics Setup
   • Engineers define boundary conditions, fluid properties, and simulation parameters.
   • Software used: ANSYS Fluent, OpenFOAM
4. Solver Execution
   • The CFD solver runs the simulation, often on high-performance computing clusters.
   • Software used: ANSYS Fluent, Star-CCM , OpenFOAM
5. Post-processing
   • Results are analyzed and visualized.
   • Software used: ParaView, Tecplot
6. Reporting
   • Engineers compile results and insights into reports.
   • Software used: Microsoft Office, LaTeX

AI-Enhanced Workflow

Integrating AI-Powered Code Generation can significantly improve this process:

1. Intelligent Geometry Creation
   • AI tool: Autodesk’s Generative Design
   • Benefit: Rapidly generates optimized geometry based on design constraints and aerodynamic goals.
2. Automated Mesh Generation
   • AI tool: Neural Concept Shape
   • Benefit: Utilizes machine learning to create high-quality meshes with minimal human intervention, thereby reducing setup time.
3. AI-Assisted Physics Setup
   • AI tool: NVIDIA SimNet
   • Benefit: Suggests optimal simulation parameters based on similar past cases, enhancing accuracy and minimizing setup errors.
4. Intelligent Solver Execution
   • AI tool: MIT’s AI-accelerated CFD solver
   • Benefit: Employs machine learning to expedite CFD calculations, potentially reducing simulation time significantly.
5. Advanced Post-processing
   • AI tool: Monash Motorsport’s CFD Workflow system
   • Benefit: Automatically generates plots, images, and videos to visualize results, saving time and providing deeper insights.
6. Automated Reporting
   • AI tool: GPT-3 based report generation
   • Benefit: Automatically compiles simulation results into coherent reports, allowing engineers to focus on higher-level analysis.
7. Continuous Optimization
   • AI tool: Lockheed Martin’s AI Factory
   • Benefit: Utilizes machine learning to continuously refine simulations based on real-world data, enhancing accuracy over time.

Process Integration

To fully leverage these AI tools, aerospace companies can create an integrated workflow:

1. Central AI Orchestrator
   • Coordinates the entire process, from geometry creation to final reporting.
   • Example: Siemens’ NX integrated with AI modules.
2. Data Pipeline
   • Ensures seamless data flow between different stages and tools.
   • Example: Altair’s HyperWorks Virtual Wind Tunnel.
3. Version Control and Collaboration
   • Manages different versions of simulations and facilitates team collaboration.
   • Example: GitHub’s version control integrated with AI-powered code review.
4. Continuous Learning
   • Incorporates real-world flight test data back into the simulation process to enhance accuracy.
   • Example: Lockheed Martin’s AI Factory concept.

By integrating these AI-powered tools, aerospace companies can significantly reduce the time and effort required for aerodynamic simulations while improving accuracy and fostering innovation. For instance, Lockheed Martin reported that their AI Factory was actively utilized by over 8,000 engineers by 2024, accelerating the development of AI applications across defense systems, aircraft maintenance, and space missions.

This AI-enhanced workflow allows engineers to concentrate on high-level design decisions and complex problem-solving, rather than repetitive tasks. It also facilitates rapid iteration and exploration of design spaces that would be impractical with traditional methods. As AI technology continues to advance, we can anticipate even greater integration and automation in aerospace simulation workflows, leading to faster development cycles and more innovative aircraft designs.