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

机器学习辅助拍板搅拌器设计方法以增强热工水力性能

基本信息

批准号：
2033790
负责人：
Chung-Lung Chen
金额：
$ 33.5万
依托单位：
University of Missouri-Columbia
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-15 至 2023-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033790&HistoricalAwards=false
关键词：
Machine Learning Assisted Flapping Agitator

项目摘要

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.

风冷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，风冷式热交换器的性能通常受到热空气和冷空气的不充分混合的限制，这是减少所需风扇功率所必需的。在过去的几十年里，人们投入了大量精力来研究通过引入额外的空气湍流来增强传热。其中，柔性薄膜搅拌器的流致振动由于不需要外加动力，且其较大的拍动振幅增强了湍流的强度而备受关注。该项目将进行全面的实验研究，以充分建立搅拌器的扑动动力学和由此产生的传热特性。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动力学研究和教育中，通过夏令营向高中生灌输工程学的兴奋，并吸引来自代表性不足的群体的未来学生进入大学STEM领域。该项目的目标是开发一种机器学习辅助方法，在各种通道流动条件下优化柔性搅拌器设计，以实现最大的综合热工水力性能。研究方法包括：（1）通过系统的自搅拌器强化传热动力学特性实验获得训练数据;（2）利用先进的立体时间分辨粒子图像测速系统表征通道流中自搅拌器的涡动力学特性;（三）通过使用实验结果作为训练和指导数据，建立机器学习辅助设计方法，在给定的流动和结构条件与结果之间建立桥梁自搅拌器的振动模态、振幅、振动频率、旋涡脱落频率以及对最终热工水力性能的影响。这项工作将作为一个全面的一步，实现了流体-结构相互作用的影响下的涡流动力学的基本理解。这样的知识库将为设计新型空气侧热交换器铺平道路，这种热交换器可以在不过度增加泵功率的情况下实现高效传热。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。