Turbulence Modeling Through Deep Learning: An In-Depth Study of Wasserstein GANs

This research study compares the accuracy of different techniques based on deep learning (DL) for predicting turbulent flows. Different types of Generative Adversarial Networks (GANs) are examined in terms of their applicability to the study and simulation of turbulence. Next, we select Wasserstein...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 793 - 797
Main Authors Alghamdi, Wajdi, Mayakannan, S., Sivasankar, G A, Singh, Jagendra, Ravi Naik, B., Venkata Krishna Reddy, Ch
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Correlation
Deep learning
DNS
Generative adversarial networks
High Reynolds number
Numerical models
Numerical simulation
Training
Turbulence
WGANs
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Summary:This research study compares the accuracy of different techniques based on deep learning (DL) for predicting turbulent flows. Different types of Generative Adversarial Networks (GANs) are examined in terms of their applicability to the study and simulation of turbulence. Next, we select Wasserstein Gans (WGANs) to produce localized disturbances. Network features including the learning rate and loss function are examined as they pertain to the performance of the WGANs during training on turbulent data gleaned from high-resolution Direct Numerical Simulations (DNS). DNS input data and the generated turbulent structures are proven to agree qualitatively well. The projected turbulent fields are evaluated quantitatively and statistically.
DOI:10.1109/ICOSEC58147.2023.10275878