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.
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Acknowledgements
This research was supported by project no. 020/RID/2018 /19 financed within the program of the Polish Ministry of Science and Higher Education “Regional Initiative of Excellence” (years 2019 - 2023, the amount of financing 12 000 000 PLN). This research was partly supported by the PLGrid infrastructure at ACK Cyfronet AGH in Krakow.
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Rojek, K., Wyrzykowski, R., Gepner, P. (2023). Chemical Mixing Simulations with Integrated AI Accelerator. In: Mikyška, J., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2023. ICCS 2023. Lecture Notes in Computer Science, vol 14074. Springer, Cham. https://doi.org/10.1007/978-3-031-36021-3_50
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