TFRD: A Benchmark Dataset for Research on Temperature Field Reconstruction of Heat-Source Systems

Temperature field reconstruction of heat source systems (TFR-HSS) with limited monitoring sensors occurred in thermal management plays an important role in real time health detection system of electronic equipment in engineering. However, prior methods with common interpolations usually cannot provide accurate reconstruction performance as needed. In addition, there exists no public dataset for widely research of reconstruction methods to further boost the reconstruction performance and engineering applications. To overcome this problem, this work constructs a novel dataset, namely Temperature Field Reconstruction Dataset (TFRD), for TFR-HSS task with commonly used methods, including the interpolation methods and the machine learning based methods, as baselines to advance the research over temperature field reconstruction. First, the TFR-HSS task is mathematically modelled from real-world engineering problem and four types of numerically modellings have been constructed to transform the problem into discrete mapping forms. Besides, this work selects three typical reconstruction problem over heat-source systems with different heat-source information and boundary conditions, and generate the training and testing samples for further research. Finally, a comprehensive review of the prior methods for TFR-HSS task as well as recent widely used deep learning methods is given and a performance analysis of typical methods is provided on TFRD, which can be served as the baseline results on this benchmark.