Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder

By Xiaowei Jin, Peng Cheng, Wenli Chen, Hui Li. 2018

309 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/ca31f23b1bf3903995e07b837a5ff9b0cc95ae86

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Shengze Cai
2. G. Em Karniadakis
3. Xi-yun Lu
4. Dongbin Xiu
5. Nicholas Zabaras
6. Xiaoliang Wan
7. Yang Liu
8. Chengping Rao

In Computational Mechanics

1. Shengze Cai
2. G. Em Karniadakis
3. Hui Li
4. Xiaowei Jin
5. Haibo Huang
6. Xi-yun Lu
7. Maizar Raissi
8. Paris Perdikaris

In Environmental Engineering

1. Hui Li
2. G. Em Karniadakis
3. Haibo Huang
4. Xi-yun Lu
5. Dongbin Xiu
6. Nicholas Zabaras
7. Xiaoliang Wan
8. Anthony Nouy

Citations in the corpus

• Deep learning methods for super-resolution reconstruction of turbulent flows (2020, methodology): “Jin et al. 10 designed a fusion CNN to predict the velocity field around a circular cylinder solely based on pressure fluctuations on the cylinder, which are readily available in fluid mechanics experiments.”
• NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations (2020, methodology): “For example, convolutional neural networks (CNNs) were used to construct the prediction model of cylinder wake in [5], and a temporal CNN was used to establish a data-driven model for predicting the coefficients of proper orthogonal decomposition (POD) modes of cylinder wake in [6].”