A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
In this work, we propose a deep learning-based model for mapping between the data of the flow field of the propeller generated by the Reynolds-averaged Navier–Stokes (RANS) and those generated by Large Eddy Simulation (LES). The goal of establishing the mapping model is to generate LES data, which n...
Saved in:
Main Authors: | Jianhai Jin, Yuhuang Ye, Xiaohe Li, Liang Li, Min Shan, Jun Sun |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/15/1/460 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
by: Guolin Ma, et al.
Published: (2024-12-01) -
Dynamic subgrid-scale model constant-value estimation refined by vector-level identity in an atmospheric flow field
by: Hiroki SUZUKI, et al.
Published: (2024-12-01) -
Characteristics of Flow Field around Double Rectangular Piersin Tandem Based on RNG k-ε Turbulence Model
by: DING Anna
Published: (2022-01-01) -
An implicit factorized transformer with applications to fast prediction of three-dimensional turbulence
by: Huiyu Yang, et al.
Published: (2024-11-01) -
Turbulent Flow Analysis with Banach and Sobolev Spaces in the LES Method Incorporating the Smagorinsky Subgrid-Scale Model
by: Rômulo Damasclin Chaves dos Santos, et al.
Published: (2023-09-01)