Prediction and optimization of airfoil aerodynamic performance using deep neural network coupled Bayesian method
In this paper, we proposed an innovative Bayesian optimization (BO) coupled with deep
learning for rapid airfoil shape optimization to maximize aerodynamic performance of …
learning for rapid airfoil shape optimization to maximize aerodynamic performance of …
Physics-informed graph convolutional neural network for modeling fluid flow and heat convection
This paper introduces a novel surrogate model for two-dimensional adaptive steady-state
thermal convection fields based on deep learning technology. The proposed model aims to …
thermal convection fields based on deep learning technology. The proposed model aims to …
Surrogate modeling of heat transfers of nanofluids in absorbent tubes with fins based on deep convolutional neural network
In this paper, we propose and investigate a deep convolutional neural network-based
surrogate model for fast prediction of heat transfer of nanofluid in absorbent tubes with fins …
surrogate model for fast prediction of heat transfer of nanofluid in absorbent tubes with fins …
Physics-informed graph convolutional neural network for modeling geometry-adaptive steady-state natural convection
This paper presents a novel deep learning-based surrogate model for steady-state natural
convection problem with variable geometry. Traditional deep learning based surrogate …
convection problem with variable geometry. Traditional deep learning based surrogate …
Fast optimization of multichip modules using deep learning coupled with Bayesian method
In this study, we develop an approach based on deep learning and the Bayesian method for
fast optimization of the thermal placement of the multichip modules (MCMs). Specifically, a …
fast optimization of the thermal placement of the multichip modules (MCMs). Specifically, a …
Grid adaptive reduced-order model of fluid flow based on graph convolutional neural network
In the interdisciplinary field of data-driven models and computational fluid mechanics, the
reduced-order model for flow field prediction is mainly constructed by a convolutional neural …
reduced-order model for flow field prediction is mainly constructed by a convolutional neural …
Prediction of internal and external flow with sparse convolution neural network: A computationally effective reduced-order model
This paper presents a novel reduced-order model for internal and external flow field
estimations based on a sparse convolution neural network. Since traditional convolution …
estimations based on a sparse convolution neural network. Since traditional convolution …
Rapid optimization for inner thermal layout in horizontal annuli using genetic algorithm coupled graph convolutional neural network
The present study introduces a novel optimization framework that combines a Graph
Convolutional Neural Network surrogate model with Genetic Algorithms (GCN-GA). This …
Convolutional Neural Network surrogate model with Genetic Algorithms (GCN-GA). This …
Thermal performance estimation of nanofluid-filled finned absorber tube using deep convolutional neural network
Numerical simulations are usually used to analyze and optimize the performance of the
nanofluid-filled absorber tube with fins. However, solving partial differential equations …
nanofluid-filled absorber tube with fins. However, solving partial differential equations …
Reduced order modelling of natural convection of nanofluids in horizontal annular pipes based on deep learning
Natural convection of nanofluids in annular pipes has been investigated in many studies
due to its high occurrence in heat transfer systems. Solving natural convection problems …
due to its high occurrence in heat transfer systems. Solving natural convection problems …