Development of turbulent heat flux model for unsteady forced convective heat transfer of small-to-medium Prandtl-number fluids based on deep learning
LX Chen, C Yuan, HN Zhang, XB Li, Y Ma… - International Journal of …, 2022 - Elsevier
Turbulent heat flux (THF) models are used for the closure of the THF term when solving the
steady/unsteady Reynolds-averaged scalar transport equation to simulate the turbulent heat …
steady/unsteady Reynolds-averaged scalar transport equation to simulate the turbulent heat …
Direct calculation of the eddy viscosity operator in turbulent channel flow at Reτ= 180
This study aims to quantify how turbulence in a channel flow mixes momentum in the mean
sense. We applied the macroscopic forcing method (Mani & Park, Phys. Rev. Fluids, 2021 …
sense. We applied the macroscopic forcing method (Mani & Park, Phys. Rev. Fluids, 2021 …
Data-driven turbulent heat flux modeling with inputs of multiple fidelity
Data-driven RANS modeling is emerging as a promising methodology to exploit the
information provided by high-fidelity data. However, its widespread application is limited by …
information provided by high-fidelity data. However, its widespread application is limited by …
Development and application of turbulent heat flux model for lead-bismuth eutectic based on deep learning
LX Chen, C Yuan, JL Zhao, HN Zhang, XB Li… - Annals of Nuclear …, 2024 - Elsevier
Existing turbulent heat flux (THF) closure models in Reynolds-averaged Navier–Stokes
simulation of scalar transport are insufficient on accuracy for fluids with a low Prandtl number …
simulation of scalar transport are insufficient on accuracy for fluids with a low Prandtl number …
Research on performance predictions using single-hole film cooling based on PointNet
A PointNet-based data-driven neural network model is proposed, which takes the film hole
geometry variables and flow conditions as inputs to reconstruct the adiabatic cooling …
geometry variables and flow conditions as inputs to reconstruct the adiabatic cooling …