Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

Many high-performance fluid dynamic models do not consider fluids in a rotating environment and often require a significant amount of computational time. The current study proposes a novel parameter-based field reconstruction convolutional neural network (PFR-CNN) approach to model the solute concen...

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Bibliographic Details
Main Authors: Xiaohui Yan, Abdolmajid Mohammadian, Huijuan Yu, Tianqi Zhang, Jianwei Liu, Sheng Chang, Hongyi Song
Format: Article
Language:English
Published: MDPI AG 2023-07-01
Series:Water
Subjects:
concentration field
OpenFOAM
convolutional neural network
TwoLiquidMixingCoriolisFoam
Online Access:https://www.mdpi.com/2073-4441/15/13/2451

Internet

https://www.mdpi.com/2073-4441/15/13/2451

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