Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7052732 | International Communications in Heat and Mass Transfer | 2018 | 7 Pages |
Abstract
A deep learning approach combining with the traditional solid isotropic material with penalization (SIMP) method is presented in this paper to accelerate the topology optimization of the conductive heat transfer. This deep learning predictor is structured based on the deep fully convolutional neural network. The validity and accuracy of this deep learning approach is investigated based on the typical 'Volume-Point' heat conduction problems. The time consumption of the optimization process will be reduced significantly by introducing the deep learning approach.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Fluid Flow and Transfer Processes
Authors
Qiyin Lin, Jun Hong, Zheng Liu, Baotong Li, Jihong Wang,