Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Lee, Sangseung"'
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
Autor:
Yang, Jiasheng, Stroh, Alexander, Lee, Sangseung, Bagheri, Shervin, Frohnapfel, Bettina, Forooghi, Pourya
The purpose of the present work is to examine two possibilities; firstly, predicting equivalent sand-grain roughness size $k_s$ based on the roughness height probability density function and power spectrum leveraging machine learning as a regression
Externí odkaz:
http://arxiv.org/abs/2304.08958
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:
Journal of Fluid Mechanics (2022)
Recent developments in neural networks have shown the potential of estimating drag on irregular rough surfaces. Nevertheless, the difficulty of obtaining a large high-fidelity dataset to train neural networks is deterring their use in practical appli
Externí odkaz:
http://arxiv.org/abs/2106.05995
Autor:
Lee, Sangseung, You, Donghyun
Publikováno v:
Revised version published in Physics of Fluids, 2021
Convolutional neural networks (CNNs) have recently been applied to predict or model fluid dynamics. However, mechanisms of CNNs for learning fluid dynamics are still not well understood, while such understanding is highly necessary to optimize the ne
Externí odkaz:
http://arxiv.org/abs/1909.06042
Tracks of typhoons are predicted using a generative adversarial network (GAN) with observational data in form of satellite images and meteorological data from a reanalysis database. Time series of images of typhoons which occurred in the Korean Penin
Externí odkaz:
http://arxiv.org/abs/1812.01943
Achievement of solutions in Navier-Stokes equation is one of challenging quests, especially for its closure problem. For achievement of particular solutions, there are variety of numerical simulations including Direct Numerical Simulation (DNS) or La
Externí odkaz:
http://arxiv.org/abs/1809.07021
Tracks of typhoons are predicted using satellite images as input for a Generative Adversarial Network (GAN). The satellite images have time gaps of 6 hours and are marked with a red square at the location of the typhoon center. The GAN uses images fr
Externí odkaz:
http://arxiv.org/abs/1808.05382
Autor:
Lee, Sangseung, You, Donghyun
Publikováno v:
Journal of Fluid Mechanics, 2019
Unsteady flow fields over a circular cylinder are trained and predicted using four different deep learning networks: convolutional neural networks with and without consideration of conservation laws, generative adversarial networks with and without c
Externí odkaz:
http://arxiv.org/abs/1804.06076