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.
목차
ABSTRACT 1. 서론 2. 해석 2.1 열전달 해석 2.2 딥러닝 해석 3. 결과 및 검토 4. 결론 후기 References
키워드
딥러닝컨볼루션 신경망전도 열전달온도 분포Deep learningConvolutional neural networkConduction heat transferTemperature distribution
저자
이태환 [ Tae-Hwan Lee | Mechatronics Eng., Gyeongnam Nat'l Univ. of Science and Technology ]
박진현 [ Jin-Hyun Park | Member, Mechatronics Eng., Gyeongnam Nat'l Univ. of Science and Technology ]
Corresponding Author