All relevant sources of error in the numerical solution of the radiative transport equation are considered. Common spatial discretization methods are discussed for completeness. The application of these methods to the radiative transport equation is not substantially different than for any other partial differential equation. Several of the most prevalent angular approximations within the heat transfer community are implemented and compared. Three model problems are proposed. The relative accuracy of each of the angular approximations is assessed for a range of optical thickness and scattering albedo. The model problems represent a range of application spaces. The quantified comparison of these approximations on the basis of accuracy over such a wide parameter space is one of the contributions of this work.
The major original contribution of this work involves the treatment of errors associated with the energy-dependence of intensity. The full spectrum correlated-k distribution (FSK) method has received recent attention as being a good compromise between computational expense and accuracy. Two approaches are taken towards quantifying the error associated with the FSK method. The Multi-Source Full Spectrum k–Distribution (MSFSK) method makes use of the convenient property that the FSK method is exact for homogeneous media. It involves a line-by-line solution on a coarse grid and a number of k-distribution solutions on subdomains to effectively increase the grid resolution. This yields highly accurate solutions on fine grids and a known rate of convergence as the number of subdomains increases.
The stochastic full spectrum k-distribution (SFSK) method is a more general approach to estimating the error in k-distribution solutions. The FSK method relies on a spectral reordering and scaling which greatly simplify the spectral dependence of the absorption coefficient. This reordering is not necessarily consistent across the entire domain which results in errors. The SFSK method involves treating the absorption line blackbody distribution function not as deterministic but rather as a stochastic process. The mean, covariance, and correlation structure are all fit empirically to data from a high resolution spectral database. The standard deviation of the heat flux prediction is found to be a good error estimator for the k-distribution method. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/23247 |
| Date | 18 February 2014 |
| Creators | Tencer, John Thomas |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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