딥러닝을 이용한 평판에서의 과도 전도 열전달에 대한 연구

Abstract

The temperature distributions were numerically calculated for the two-dimensional transient conduction heat transfer problem of a square plate. The obtained temperature distributions were converted into colors to create images, and they were provided as learning and test data of CNN. Classification and regression networks were constructed to predict representative wall temperatures through CNN analysis. As results, the classification networks predicted the representative wall temperatures with an accuracy of 99.91% by erroneously predicting only 1 out of 1100 images. The regression networks predicted the representative wall temperatures within errors of C. From this fact, it was confirmed that the deep learning techniques are applicable to the transient conduction heat transfer problems.