Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Lim, Hee Chang"'
Autor:
Yousif, Mustafa Z., Zhou, Dan, Yu, Linqi, Zhang, Meng, Mohammadikarachi, Arash, Lee, Jung Sub, Lim, Hee-Chang
This study aims to reconstruct the complete flow field from spatially restricted domain data by utilizing an Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model. The difficulty in flow field reconstruction lies in accurately captu
Externí odkaz:
http://arxiv.org/abs/2408.01658
In this study, we proposed an efficient approach based on a deep learning (DL) denoising autoencoder (DAE) model for denoising noisy flow fields. The DAE operates on a self-learning principle and does not require clean data as training labels. Furthe
Externí odkaz:
http://arxiv.org/abs/2408.01659
Inspired by the unique textures of shark skin and bird flight feathers and tails, the convergent-divergent surface pattern holds promise in modulating boundary layer structures. This surface pattern exhibits protrusions precisely aligned obliquely (a
Externí odkaz:
http://arxiv.org/abs/2309.09138
The present study aims to investigate the effectiveness of plasma actuators in controlling the flow around a finite wall-mounted square cylinder (FWMSC) with a longitudinal aspect ratio of 4. The test is conducted in a small-scale closed return-type
Externí odkaz:
http://arxiv.org/abs/2304.10056
Autor:
Yu, Linqi, Yousif, Mustafa Z., Lee, Young-Woo, Zhu, Xiaojue, Zhang, Meng, Kolesova, Paraskovia, Lim, Hee-Chang
In this study, an efficient deep-learning model is developed to predict unavailable parameters, e.g., streamwise velocity, temperature, and pressure from available velocity components. This model, termed mapping generative adversarial network (M-GAN)
Externí odkaz:
http://arxiv.org/abs/2304.07762
Publikováno v:
In Results in Engineering December 2024 24
Publikováno v:
In International Journal of Thermal Sciences November 2024 205
This study presents a deep learning-based framework to reconstruct high-resolution turbulent velocity fields from extremely low-resolution data at various Reynolds numbers using the concept of generative adversarial networks (GANs). A multiscale enha
Externí odkaz:
http://arxiv.org/abs/2110.05047
Autor:
Yousif, Mustafa Z., Lim, Hee Chang
This study presents an artificial neural network and proper orthogonal decomposition (POD)-based reduced-order model (ROM) of turbulent flow around a finite wall-mounted square cylinder. The proposed model is suitable for turbulent wake control appli
Externí odkaz:
http://arxiv.org/abs/2109.09413
Publikováno v:
In Ocean Engineering 1 March 2024 295