标题： CFDagent：一种语言引导的、零样本多智能体系统，用于复杂流动模拟 显示英文标题

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

摘要： 我们引入了CFDagent，这是一个零样本、多智能体系统，能够从自然语言提示中实现完全自主的计算流体力学（CFD）模拟。 CFDagent集成了三个专业LLM驱动的智能体：(i) 预处理智能体，利用混合文本到3D扩散模型（Point-E）从文本或视觉输入生成3D几何结构，并自动对几何结构进行网格划分；(ii) 求解器智能体，配置并执行浸入边界流动求解器；(iii) 后处理智能体，分析和可视化结果，包括多模态渲染。 这些智能体通过GPT-4o的对话提示进行交互引导，实现了直观且用户友好的交互。 我们通过使用三种不同的输入方式再现雷诺数为100和300的经典球体流动来验证CFDagent：一个简单的文本提示（即“sphere”）、基于图像的输入以及标准球体模型。 每种输入方法生成的网格计算出的阻力和升力系数与现有数据非常接近。 所提出的系统能够为复杂几何结构生成流动模拟和逼真的可视化效果。 通过在经典和现实场景上的大量测试，我们展示了CFDagent的鲁棒性、多样性和实际应用价值。 通过将生成式AI与高保真模拟相结合，CFDagent显著降低了专家级CFD的门槛，为教育、科学研究和实际工程应用开辟了广泛的机会。 显示英文摘要

摘要： 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.