The numerical analysis of two-dimensional transient flow around the obstacle with rotated square cross sections was carried out. The obtained velocity distributions for each time step and each rotation angle were imaged to provide data for CNN(convolutional neural network). Both classification and regression neural networks were used for prediction of rotation angle. As results The classification method incorrectly predicted the rotation angle in only 2 of the 470 images. The regression method predicted the rotation angle errors within except 2 out of 470 images. From these facts, it could be concluded that both methods can be sufficiently applicable to the flow analysis.
목차
ABSTRACT 1. 서론 2. 해석 2.1 유동 해석 2.2 딥러닝(Deep learning) 3. 결과 및 검토 4. 결론 후기 References
키워드
과도유동컨볼루션 신경망딥러닝회전각속도분포Transient flowConvolutional neural networkDeep learningRotation angleVelocity distribution
저자
이태환 [ Tae-Hwan Lee | Mechatronics Eng., Gyeongnam Nat'I Univ. of Science and Technology ]
박진현 [ Jin-Hyun Park | Member, Mechanics Eng., Gyeongnam Nat'I Univ. of Science and Technology ]
Corresponding Author