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- W4382703681 abstract "In this work, we develop a method for integrating an AI model with a CFD solver to predict chemical mixing simulations’ output. The proposed AI model is based on a deep neural network with a variational autoencoder that is managed by our AI supervisor. We demonstrate that the developed method allows us to accurately accelerate the steady-state simulations of chemical reactions performed with the MixIT solver from Tridiagonal solutions. In this paper, we investigate the accuracy and performance of AI-accelerated simulations, considering three different scenarios: i) prediction in cases with the same geometry of mesh as used during training the model, ii) with a modified geometry of tube in which the ingredients are mixed, iii) with a modified geometry of impeller used to mix the ingredients. Our AI model is trained on a dataset containing 1500 samples of simulated scenarios and can accurately predict the process of chemical mixing under various conditions. We demonstrate that the proposed method achieves accuracy exceeding 90% and reduces the execution time up to 9 times." @default.
- W4382703681 created "2023-07-01" @default.
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- W4382703681 date "2023-01-01" @default.
- W4382703681 modified "2023-09-25" @default.
- W4382703681 title "Chemical Mixing Simulations with Integrated AI Accelerator" @default.
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- W4382703681 doi "https://doi.org/10.1007/978-3-031-36021-3_50" @default.
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