Collaborative Research: CDS&E: Learning Convective Heat Transfer from Mass Transfer Visualization

合作研究：CDS

• 批准号: 2053370
• 负责人: Peter Kang
• 金额: $ 20.26万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053370&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& Learning; Convective

Understanding convective heat transfer is crucial in designing efficient heat exchangers, thermodynamic systems, biochemical fluid transport systems, and geothermal reservoirs. However, directly measuring temperature fields in unsteady and geometrically complex convection systems is often challenging. This project will develop machine learning based heat and mass transfer analogy functions that can approximate the temperature fields in convection systems from observed concentration fields. From simple to complicated flow passages and various flow regimes, this research will investigate the impact of dataset size and quality on learned functions. The educational goal of this project is to cultivate student interest in studying machine learning and advanced flow systems. This project will develop unique hands-on activities for K-12 students and provide research opportunities for undergraduate and graduate students on how to use machine learning for intriguing applications and ways to visualize convection.Machine learning-based heat and mass analogy functions will enable spatiotemporally-resolved thermal characterization of thermofluidic devices where direct temperature measurements are extremely challenging. Until recently, heat and mass analogy functions are expressed in mathematical forms mostly derived from simple convection systems. This research aims to overcome the limitations of traditional analogy functions by leveraging the inference ability of machine learning models. Particularly, this project will study how the machine learning models can be used to infer the temperature field from a mass concentration field for laminar mixed convections. Heat and mass transfer analogy has been observed in systems with laminar mixed convection, but the derivation of analogy functions has been challenging due to the unstable and spatially inhomogeneous nature of the flows. Furthermore, this research will elucidate the requirements of datasets, e.g., size and resolution of thermofluidic field information, as well as the capabilities and limitations of the machine learning approach when studying complex thermofluidic phenomena.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.

理解对流传热对于设计高效的热交换器、热力学系统、生化流体输送系统和地热储层至关重要。然而，在非定常和几何复杂的对流系统中，直接测量温度场往往具有挑战性。该项目将开发基于机器学习的传热和传质类比函数，该函数可以根据观测到的浓度场近似对流系统中的温度场。从简单到复杂的流动通道和各种流动状态，本研究将探讨数据集大小和质量对学习函数的影响。这个项目的教育目标是培养学生对学习机器学习和高级流系统的兴趣。该项目将为K-12学生开发独特的实践活动，并为本科生和研究生提供研究机会，了解如何使用机器学习进行有趣的应用和可视化对流的方法。基于机器学习的热量和质量类比功能将使直接温度测量极具挑战性的热流体装置的时空分辨热表征成为可能。直到最近，热和质量类比函数都是用数学形式来表示的，这些形式大多来源于简单的对流系统。本研究旨在利用机器学习模型的推理能力，克服传统类比函数的局限性。特别是，该项目将研究如何使用机器学习模型从层流混合对流的质量浓度场推断温度场。在层流混合对流系统中已经观察到传热传质类比，但由于流动的不稳定性和空间非均匀性，类比函数的推导一直具有挑战性。此外，本研究将阐明数据集的要求，如热流体场信息的大小和分辨率，以及机器学习方法在研究复杂热流体现象时的能力和局限性。该奖项反映了美国国家科学基金会的法定使命，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。