CFDagent: A Language-Guided, Zero-Shot Multi-Agent System for Complex Flow Simulation

TL;DR

CFDagent is an innovative AI-driven multi-agent system that performs autonomous, natural language-guided CFD simulations, including geometry creation, meshing, solving, and visualization, making complex fluid dynamics accessible and user-friendly.

Contribution

This work introduces CFDagent, the first zero-shot multi-agent system integrating LLMs for end-to-end CFD simulation from natural language prompts.

Findings

Accurately reproduces sphere flow simulations at Reynolds numbers 100 and 300.

Produces geometries and meshes from text, images, and standard models with high fidelity.

Demonstrates robustness and versatility across canonical and realistic scenarios.

Abstract

We introduce CFDagent, a zero-shot, multi-agent system that enables fully autonomous computational fluid dynamics (CFD) simulations from natural language prompts. CFDagent integrates three specialized LLM-driven agents: (i) the Preprocessing Agent that generates 3D geometries from textual or visual inputs using a hybrid text-to-3D diffusion model (Point-E) and automatically meshes the geometries; (ii) the Solver Agent that configures and executes an immersed boundary flow solver; and (iii) the Postprocessing Agent that analyzes and visualizes the results, including multimodal renderings. These agents are interactively guided by GPT-4o via conversational prompts, enabling intuitive and user-friendly interaction. We validate CFDagent by reproducing canonical sphere flows at Reynolds numbers of 100 and 300 using three distinct inputs: a simple text prompt (i.e., "sphere"), an image-based input, and a standard sphere model. The computed drag and lift coefficients from meshes produced by each input approach closely match available data. The proposed system enables synthesization of flow simulations and photorealistic visualizations for complex geometries. Through extensive tests on canonical and realistic scenarios, we demonstrate the robustness, versatility, and practical applicability of CFDagent. By bridging generative AI with high-fidelity simulations, CFDagent significantly lowers barriers to expert-level CFD, unlocking broad opportunities in education, scientific research, and practical engineering applications.