AI驱动的流体实验机器人，自动完成从假设到论文的全流程研究。
Towards an AI Fluid Scientist: LLM-Powered Scientific Discovery in Experimental Fluid Mechanics
- 用程序控制水洞实现自动化实验，可调流速、位置与振动参数。
- 复现经典文献结果误差低于4%，并发现更多新振动现象。
- 结合人类协作与多智能体系统，端到端完成科研全流程。
将人工智能引入实验流体力学有望加速科学发现，但现有AI应用多局限于数值模拟。本文提出一个自主执行完整实验流程的AI流体科学家框架：包括假设生成、实验设计、机器人执行、数据分析和论文撰写。通过研究串列圆柱的涡激振动（VIV）和尾流诱导振动（WIV），验证了该框架的可行性。主要贡献有四方面：(1) 构建可程序化控制流速、圆柱位置及激励参数（振动频率与幅度）的计算机控制循环水洞，集成位移、力和扭矩数据采集；(2) 自动化实验复现经典文献结果（Khalak & Williamson [1999]，Assi et al. [2013, 2010]），频率锁定误差在4%以内，临界间距趋势一致；(3) 采用人机协同机制，发现更多WIV幅值响应现象，并利用神经网络从数据中拟合物理规律，性能比多项式拟合高31%；(4) 基于多智能体与虚实交互系统，实现数百次实验的端到端自动化，完整覆盖从假设生成、实验设计、执行、分析到论文撰写全过程，显著解放研究人员，提升研究效率，为实验流体力学研究提供新范式。
原文摘要 · Abstract (English)
The integration of artificial intelligence into experimental fluid mechanics promises to accelerate discovery, yet most AI applications remain narrowly focused on numerical studies. This work proposes an AI Fluid Scientist framework that autonomously executes the complete experimental workflow: hypothesis generation, experimental design, robotic execution, data analysis, and manuscript preparation. We validate this through investigation of vortex-induced vibration (VIV) and wake-induced vibration (WIV) in tandem cylinders. Our work has four key contributions: (1) A computer-controlled circulating water tunnel (CWT) with programmatic control of flow velocity, cylinder position, and forcing parameters (vibration frequency and amplitude) with data acquisition (displacement, force, and torque). (2) Automated experiments reproduce literature benchmarks (Khalak and Williamson [1999] and Assi et al. [2013, 2010]) with frequency lock-in within 4% and matching critical spacing trends. (3) The framework with Human-in-the-Loop (HIL) discovers more WIV amplitude response phenomena, and uses a neural network to fit physical laws from data, which is 31% higher than that of polynomial fitting. (4) The framework with multi-agent with virtual-real interaction system executes hundreds of experiments end-to-end, which automatically completes the entire process of scientific research from hypothesis generation, experimental design, experimental execution, data analysis, and manuscript preparation. It greatly liberates human researchers and improves study efficiency, providing new paradigm for the development and research of experimental fluid mechanics.
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