Image Upsampling of Low Resolution Turbulent CFD Domains with U-Net
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Author / Producer
Date
2025
Publication Type
Conference Paper
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yes
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Abstract
The modelling of turbulent airflow with CFD is a computationally expensive task, yet vital for assessing architectural and urban ventilation concepts. Simplified airflow prediction methods exist, however at the cost of lacking accuracy and/or precision. This paper therefore proposes a hybrid simulation and deep learning approach. We utilize images of low resolution CFD domains as input to a U-Net neural network. The generated output is a high resolution upsampled image. As training data and application case, we use turbulent indoor flow with forced convection. Results are promising, as the generated flow fields can recreate higher level of details from the low resolution inputs as when compared to bicubic interpolation. However, the approach leaves room for improvement especially with respect to generated image sharpness.
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Publication status
published
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Book title
Multiphysics and Multiscale Building Physics. Proceedings of the 9th International Building Physics Conference (IBPC 2024). Volume 4: Indoor Air Quality (IAQ), Lighting and Acoustics
Journal / series
Volume
555
Pages / Article No.
48 - 54
Publisher
Springer
Event
9th International Building Physics Conference (IBPC 2024)
Edition / version
Methods
Software
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Date created
Subject
Deep Learning; Convolutional Neural Network; Upsampling; Computational Fluid Dynamics; Large Eddy Simulation
Organisational unit
08060 - FCL / FCL
03902 - Schlüter, Arno / Schlüter, Arno