Square cavity coupled heat transfer dataset

doi: 10.4121/66b5bd53-ef82-41da-bc45-104e8de0c4cf.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/66b5bd53-ef82-41da-bc45-104e8de0c4cf
Datacite citation style:
Zou, Qingfeng (2024): Square cavity coupled heat transfer dataset. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/66b5bd53-ef82-41da-bc45-104e8de0c4cf.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Physically Informed Neural Networks (PINNs) integrate physical knowledge into deep learning by mathematically encoding a system of partial differential equations, with successful applications in fluid mechanics and heat transfer. For instance, in the aerospace industry, PINNs are employed to predict the flow and temperature fields within a turbine blade. In this manuscript, a dual cavity model is employed as a simplified representation of the turbine blade, and the coupled heat transfer process within this model is numerically simulated to validate the efficacy of PINNs in this field. However, the strong coupling between solid thermal conductivity and fluid heat transfer renders it challenging for the generalized PINNs to effectively address the coupled heat transfer problem in this field. Consequently, in this manuscript, a PINNs based on a partitioned coupling strategy is employed to numerically simulate the coupled heat transfer problem. The strategy effectively couples the temperature fields in each subregion by dividing the complex coupled problem into multiple subregions and performing numerical simulations in each subregion using PINNs. The coupled heat transfer process is simulated in both cavities using COMSOL® software, and the predicted data set is exported for use in the model. The size of the dataset is 200×420×1000 (x×y×t), and the dataset is divided into a training set (0~80,000,000) and a validation set (80,000,000~84,000,000).

  • 2024-06-06 first online, published, posted
associated peer-reviewed publication
In submission
Southwest University of Science and Technology


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