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- W4361025990 abstract "The present study proposes a deep learning regression approach for parametric modeling of thermal fluid flows based on a combination of convolutional auto-encoders (CAE) and neural networks (NN). Steady-state mixed convection inside concentric annulus with rotating outer wall is selected as the test case. Computational fluid dynamics (CFD) is used to generate the training dataset. The suggested regression model comprises of three steps. In the first step, the thermo-fluid field is dimensionally reduced by the convolutional encoder. Afterward, a multi-layer NN is connected to the encoded layer to generalize the reduced space with respect to the given Rayleigh (Ra) and Reynolds (Re) numbers. In the last step, the reconstruction process is completed by transforming back the generalized encoded data to the original dimension by the convolutional decoder. Such an approach can be interpreted as an estimation model Z which predicts the steady-state thermal field F as a function of input parameters, Ra and Re numbers, i.e. F=Z(Ra,Re). The model shows promising potential in predicting details of temperature and vorticity fields for a range of 103≤Ra≤106 and 0≤Re≤600. In average, there is 0.085 root-mean-squared error between the simulation (ground truth) and the predicted wall Nusselt numbers." @default.
- W4361025990 created "2023-03-30" @default.
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- W4361025990 date "2023-08-01" @default.
- W4361025990 modified "2023-09-27" @default.
- W4361025990 title "A convolutional auto-encoder regression for parametric modeling of mixed convection in concentric annulus" @default.
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- W4361025990 doi "https://doi.org/10.1016/j.ijthermalsci.2023.108293" @default.
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