Machine-Learning Assisted Flapping Agitator Design Methodology Towards Enhanced Thermal-Hydraulic Performance

风冷式换热器中的热传输对于发电厂、数据中心和电子设备的有效冷却至关重要。然而，风冷换热器的性能往往受到冷热空气混合不足的限制，这是降低所需风扇功率所必需的。在过去的几十年里，人们致力于通过引入额外的空气湍流来研究强化换热。在众多的研究方法中，柔性薄膜搅拌器的流致振动因其不需要外加动力而备受关注，而且搅拌器较大的拍动幅度会增强湍流的强度。该项目将进行全面的实验研究，以全面确定搅拌器的扑动动力学和由此产生的热传递特性。它还将开发一种机器学习辅助设计方法，以提高性能。该项目将把机器学习整合到流体力学研究和教学中，通过夏令营向高中生灌输工程方面的兴奋，并吸引来自代表性不足群体的潜在学生进入大学STEM领域。该项目的目标是开发一种机器学习辅助方法，用于快速优化各种流道流动条件下的柔性搅拌器设计，以实现最大的综合热工水力性能。该研究方法包括：i)通过系统的实验表征自搅拌器动力学对强化换热的作用，获得训练数据；ii)使用最先进的立体时间分辨粒子图像测速系统来表征通道流动中添加自搅拌器后的涡动力学；iii)建立机器学习辅助设计方法，将实验结果作为训练和指导数据，在给定的流动和结构条件与最终热工水力性能的自搅拌器振动模式、振幅、振动频率、涡脱频率和演化之间建立桥梁。这项工作将是全面了解流体-结构相互作用作用下的涡流流动动力学的重要一步。这样的知识库将为设计新型空气侧热交换器铺平道路，这些换热器可以在不过度增加泵送功率的情况下实现高效热传递。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Thermal transport in air-cooled heat exchangers is critical for effective cooling of power plants, data centers, and electronic devices. However, the performance of an air-cooled heat exchanger is often restricted by insufficient mixing of hot and cold air, which is needed to reduce the required fan power. Over the past few decades, much effort has been devoted to investigating enhanced heat transfer by introducing additional air turbulence. Among all the methods, flow-induced vibration of a flexible thin-film agitator has drawn much attention since it requires no external power, and the large flapping amplitude of the agitator strengthens the intensity of the turbulence. This project will conduct comprehensive experimental investigations to fully establish the flapping dynamics of agitators and resulting heat-transfer characteristics. It will also develop a machine-learning assisted design methodology to enhance performance. The project will integrate machine learning into fluid dynamics research and education, instill the excitement of engineering in high-school students through summer camps, and attract prospective students from under-represented groups towards STEM fields in college.The objective of this project is to develop a machine-learning assisted methodology for fast-optimization of the flexible agitator design in various channel flow conditions towards the maximum synthetic thermal-hydraulic performance. The research approach includes i) Acquire the training data through systematic experimental characterization of self-agitator dynamics on the heat transfer enhancement; ii) Characterize the vortex dynamics due to the added self-agitator in the channel flow with a state-of-the-art stereo time-resolved particle image velocimetry system; iii) Establish a machine‐learning assisted design methodology by using experimental results as training and guiding data to bridge between the given flow and structure conditions and the resultant self-agitator vibration mode, vibration amplitude, vibration frequency, vortex shedding frequency and evolution on the final thermal-hydraulic performances. This work will serve as a comprehensive step towards achieving a fundamental understanding of vorticial flow dynamics under the effect of fluid-structure interaction. Such a knowledge base will pave the way for designing novel air-side heat exchangers that can achieve high-efficiency heat transfer without unduly increasing pumping power.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.