Liu Linhua

Inverse radiation problem of temperature field in three-dimensional rectangular furnaces

The discrete ordinates method is used to developed a solution to an inverse radiation problem of temperature field in rectangular furnaces. It is assumed that, with the exception of the inhomogeneous temperature field, all aspects of the radiation transport problem are known. A method is developed to determine the inhomogeneous temperature field from specified incident radiation heat fluxes at the centers of boundary walls. The inverse problem is solved using conjugate gradient method that minimize the error between the incident radiation heat fluxes calculated and the experimental data. The results of temperature estimation show that the temperature field can be estimated accurately, even with noisy data.

Inverse radiation problem in one-dimensional semitransparent plane-parallel media with opaque and specularly reflecting boundaries

Abstract 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 single-scattering albedo, scattering asymmetry parameter, wall emissivity, the diffuse fraction of reflectivity, and the optical thickness on the accuracy of the inverse analyses are investigated. The results show that the source term can be estimated accurately, even with noisy data.

Inverse Radiation Problem of Boundary Incident Radiation Heat Flux in Semitransparent Planar Slab with Semitransparent Boundaries

An inverse method is presented for estimating the unknown boundary incident radiation heat flux on one side of one-dimensional semitransparent planar slab with semitransparent boundaries from the knowledge of the radiation intensities exiting from the other side. The inverse problem is solved using conjugate gradient method of minimization based on discrete ordinates method (DOM) of radiative transfer equation. The equations of sensitivity coefficients are derived and easily solved by DOM, with the result that the complicated numerical differentiation commonly used in solving sensitivity coefficients is avoided. The effects of anisotropic scattering, absorption coefficient, scattering coefficient, boundary reflectivity, fluid temperature outside the boundaries, convection heat transfer coefficients, conduction coefficient of semitransparent media and slab thickness on the accuracy of the inverse analysis are investigated. The results show that the boundary incident radiation heat flux can be estimated accurately, even with noisy data.

Inverse radiation problem in one-dimensional semitransparent plane-parallel media

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 single-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.