Modified UNet with attention gate and dense skip connection for flow field information prediction with porous media

Yu, Yang; Chen, Sheng; Wei, Heng

doi:10.1016/j.flowmeasinst.2022.102300

Cited by 12 publications

(2 citation statements)

References 31 publications

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“…The trained model serves to expedite the optimization of architectural wind fields and exhibited high prediction efficiency and accuracy. Yu et al [41] proposed a method to predict the saturation variation and flow velocity profile of two-dimensional multiphase flow in porous media and found that the prediction is consistent with the CFD results. Mokhtar et al [42] used conditional generative adversarial networks to rapidly reconstruct the wind speed field of pedestrian height, but were unable to predict the velocity distribution under different inflow speeds.…”

Section: Introductionmentioning

confidence: 75%

Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method

You,

Yu,

Mao

2024

Applied Sciences

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CFD offers advantages over wind tunnel experiments in the prediction and optimization of building wind environment; however, the computational costs associated with optimizing architectural wind environment remain a challenge. In this study, an approach that combines deep learning techniques with CFD simulations is proposed for the prediction and optimization of the architectural wind environment efficiently. A dataset of wind field is constructed using CFD simulation, considering various wind directions, wind speeds, and building spacing. Subsequently, a U-net deep learning model is trained as a surrogate model to rapidly predict the architectural wind field under different conditions. The results indicate that the model can accurately predict the wind field in buildings. The prediction time of building wind field is only 1/900 of that of CFD simulations, making it a viable surrogate model for wind environment optimization. Furthermore, considering all the building layouts and inflow conditions examined in this study, the maximum and minimum uniform wind speed area ratios Auni are 0.84 and 0.13, respectively. Under a single inflow speed, the maximum improvement in the Auni is 0.4, with an improvement rate of 48%. The results demonstrate the effectiveness of the proposed method as an efficient approach for optimizing architectural wind environment.

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Section: Introductionmentioning

confidence: 75%

Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method

You,

Yu,

Mao

2024

Applied Sciences

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“…The VOF method is widely used in the study of multiphase transport process in GDL [6,[16][17][18][19], mainly because the pore diameter of GDL is generally about tens of microns, and most of the porous areas meet the condition that the Nurson number is less than 0.01. Therefore, it can be considered that these porous areas belong to the category of continuous media and meet the assumption of continuous media.…”

Section: Volume Of Fluid Methods (Vof)mentioning

confidence: 99%

Numerical study on the effect of microporous layer crack changes on water management in gas diffusion layer of proton exchange membrane fuel cell

Chen

Wei

2023

International Journal of Heat and Mass Transfer

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Advances in the modeling of multiphase flows and their application in nuclear engineering—A review

Wu,

Zhang,

Gui

et al. 2024

Exp. Comput. Multiph. Flow

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Modified UNet with attention gate and dense skip connection for flow field information prediction with porous media

Cited by 12 publications

References 31 publications

Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method

Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method

Numerical study on the effect of microporous layer crack changes on water management in gas diffusion layer of proton exchange membrane fuel cell

Advances in the modeling of multiphase flows and their application in nuclear engineering—A review

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