Abstract
Non-gray gas radiation plays an important role in combustion system, and the spectral-line-based weighted-sum-of-gray-gases (SLW) is a popular model to treat the gas radiative properties. In practical applications, the inverse calculations of the cross-sections from a specified F are time demanding. The neural network method is used here to develop inverse absorption line blackbody distribution function (ALBDF) to efficiently and accurately calculate the cross-sections from F. Results show that the neural network based SLW can predict the inverse ALBDF very well and the relative errors for the radiative heat source and heat flux for the radiation transfer are less than 2%. The computational time of the neural network based SLW is only 1/10th of the original SLW model.
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