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pro vyhledávání: '"Shin, Heesoo"'
Autor:
Shi, Zhaoyu, Khorasani, Seyed Morteza Habibi, Shin, Heesoo, Yang, Jiasheng, Lee, Sangseung, Bagheri, Shervin
Efficient tools for predicting the drag of rough walls in turbulent flows would have a tremendous impact. However, methods for drag prediction rely on experiments or numerical simulations which are costly and time-consuming. Data-driven regression me
Externí odkaz:
http://arxiv.org/abs/2405.09256
Autor:
Shin, Heesoo, Khorasani, Seyed Morteza Habibi, Shi, Zhaoyu, Yang, Jiasheng, Lee, Sangseung, Bagheri, Shervin
Understanding the influence of surface roughness on drag forces remains a significant challenge in fluid dynamics. This paper presents a convolutional neural network (CNN) that predicts drag solely by the topography of rough surfaces and is capable o
Externí odkaz:
http://arxiv.org/abs/2405.09071
Publikováno v:
Energy, 128068 (2023)
This paper investigates the influence of incorporating spatiotemporal wind data on the performance of wind forecasting neural networks. While previous studies have shown that including spatial data enhances the accuracy of such models, limited resear
Externí odkaz:
http://arxiv.org/abs/2304.01545
Publikováno v:
JMST Advances; 20240101, Issue: Preprints p1-6, 6p
Publikováno v:
Fluids; Dec2022, Vol. 7 Issue 12, p367, 16p
Akademický článek
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