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    上海交通大學楊力副教授講座通知
    發布時間:2021-07-07 20:00:09 點擊量:


    題 目: Heat Transfer Studies: from “Feature-based” to “Rule-based”

    報告人: 楊力 上海交通大學副教授

    時 間: 2021 年 7 月 11 日(周日) 上午 10:00~11:00

    地 點: 1號巨構5078

    邀請人: 馬挺 教授

     

    報告簡介:Geometry definition, heat transfer evaluation and design optimization are the main processes of heat transfer structure research. At present, the bottlenecks of heat transfer performance in many applications mainly come from these three aspects because of the constraints on design caused by "feature thinking". This report will introduce the limitations of feature-based traditional research methods including parametric modeling, data regression and genetic optimization, and the advantages of rule-based research methods including self-organizing geometry, operator neural networks and reinforcement learning. Taking the typical convective heat transfer problem as an example, it will be explained the significance of "rule thinking" for the study of heat transfer structure. The report shall include three parts. In the first part, in view of the limitations of parametric modeling, the presenters explored the mechanism of geometry and its programmable approach, and analyze the compatibility of geometry with flow field and scalar field through the geometric shape design method based on self-organization theory. In the second part, the presenters shall talk about the limitations of regression deep learning which is based on image processing for temperature field reconstruction. Besides, a rule-based operator deep learning method shall be proposed for multi-size, multi-working conditions, and variable topological geometry heat transfer information learning method. In the third part, the feasibility of using the intelligent body for spatial structure design is discussed in view of the limitations of parameter optimization. At the moment, the versatility of the artificial body for optimization problems is analyzed. Reinforcement learning methods are applied to significantly accelerate the optimization speed of the structure, and to a certain extent avoid the dimensional explosion.

     

    報告人簡介: 楊力,上海交通大學副教授。2015年于清華大學獲得博士學位,2018年完成美國匹茲堡大學的博士后研究。先后在清華大學、美國弗吉尼亞理工大學、美國匹茲堡大學和上海交通大學開展熱端部件冷卻技術的前沿研究。在沖擊冷卻、發散冷卻、金屬增材制造和機器學習等研究方向發表多篇國際期刊論文,并參與多項美國能源部的增材制造先進冷卻技術項目研究。2018年加入上海交通大學后,主要集中于航空發動機和燃氣輪機熱端部件冷卻相關的復雜結構傳熱優化、傳熱問題機器學習和增材制造。

     

     

     

     

     

     

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