The discrete ordinates method is used to develop a solution to an inverse radiation problem of source term in one-dimensional semitransparent plane-parallel media with opaque and specularly reflecting boundaries. It i...The discrete ordinates method is used to develop a solution to an inverse radiation problem of source term in one-dimensional semitransparent plane-parallel media with opaque and specularly reflecting boundaries. It is assumed that, with the exception of the inhomogeneous source term, all aspects of the radiation transport problem are known. A method is developed to determine the inhomogeneous source term from specified incident radiation intensities on the boundaries. The inverse problem is solved using conjugate gradient method that minimizes the error between the incident radiation intensities calculated and the experimental data. The effects of singl-scattering albedo, scattering asymmetry parameter, wall emissivity, the diffuse fraction of reflectivity, and the optical thickness on the accuracy of the inverse are investigated. The results show that the source term can be estimated accurately, even with noisy data.展开更多
文摘The discrete ordinates method is used to develop a solution to an inverse radiation problem of source term in one-dimensional semitransparent plane-parallel media with opaque and specularly reflecting boundaries. It is assumed that, with the exception of the inhomogeneous source term, all aspects of the radiation transport problem are known. A method is developed to determine the inhomogeneous source term from specified incident radiation intensities on the boundaries. The inverse problem is solved using conjugate gradient method that minimizes the error between the incident radiation intensities calculated and the experimental data. The effects of singl-scattering albedo, scattering asymmetry parameter, wall emissivity, the diffuse fraction of reflectivity, and the optical thickness on the accuracy of the inverse are investigated. The results show that the source term can be estimated accurately, even with noisy data.
