딥러닝을 이용한 회전된 정사각형 단면을 가진 장애물 주위의 과도유동 해석
Analysis of Transient Flow Around the Obstacle with Rotated Square Cross Sections using Deep Learning
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
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.
Abstract
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.
참고문헌 (10)
- 이태환, "증기표의 과열, 포화 및 압축영역의 신경회로망 모델링", 한국기계기술학회지, 20(6), pp.872~878 (2018), 한국기계기술학회[DOI]
- Junhyuk Kim, "Prediction of turbulence heat transfer in channel flow using deep learning", 2018 Conference of The Korean Society of Mechanical Engineers, 18FETH01A03, pp.463~464 (2018)
- Jonghwan Park, "Prediction and control of near-wall turbulent flow with deep learning", 2018 Conference of The Korean Society of Mechanical Engineers, pp.518~520 (2018)
- Miyanawala, T. P., "An efficient deep learning technique for the Navier-Stokes equations: Application to unsteady wake flow dynamics", arXiv preprint arXiv:1710.09099, ~ (2017)
- 이태환, "딥러닝 기법을 사용한 이차원 과도유동 해석", 한국기계기술학회지, 22(1), pp.12~18 (2020), 한국기계기술학회[DOI]
- Y. LeCun, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11), pp.2278~2324 (1998)
- Y. Bengio, "Learning deep architectures for AI", Foundations and Trends in Machine Learning, 2(1), pp.1~127 (2009)
- G. E. Hinton, "A fast learning algorithm for deep belief nets", Neural Computation, 18(7), pp.1527~1554 (2006)
- Zhong, Zhuoyao, "High performance offline handwritten chinese character recognition using googlenet and directional feature maps", 2015 13th International Conference on Document Analysis and Recognition (ICDAR) (2015), IEEE
- Jimmy Ba, "Adam : A Method for Stochastic Optimization", arXiv preprint arXiv:1412.6980, ~ (2014)