A CNN surrogate predicts wall radiative fluxes in a 2D furnace about 10,000 times faster than the DTRM solver, with 7 to 9 percent mean relative error, beating an MLP in accuracy and stability.
CRC press, 2020
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Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
A CNN surrogate predicts wall radiative fluxes in a 2D furnace about 10,000 times faster than the DTRM solver, with 7 to 9 percent mean relative error, beating an MLP in accuracy and stability.