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.

ABSTRACT
1. 서 론
2. 해 석
    2.1 열전달 해석
    2.2 딥러닝 해석
3. 결과 및 검토
4. 곁 론
References

• 이태환(경남과학기술대학교) | LEE TAE HWAN
• 박진현(경남과학기술대학교) | Park Jin-Hyun 교신