ADVANCES IN INVERSE TRANSPORT METHODS
We present advances in inverse transport methods and demonstrate their application to neutron tomography problems that have significant scattering. The problem we consider is in-ference of the material distribution in an object by detection and analysis of the radiation exiting from it. Our approach combines both deterministic and stochastic optimization methods to find a material distribution that minimizes the difference between com-puted and measured detector responses. The main advances are dimension-reduction schemes that we have designed to take ad-vantage of known and postulated constraints. One key constraint is that the cross sections for a given region in the object must be the cross sections for a real material. We illustrate our ap-proach using a neutron tomography model problem on which we impose reasonable constraints, similar to those that in practice would come from prior information or engineering judgment. This problem shows that our method is capable of generating results that are much better than those of deterministic minimiza-tion methods and dramatically more efficient than those of typi-cal stochastic methods.
Zeyun Wu Marvin L.Adams
Department of Nuclear Engineering Texas A&M University College Station, TX 77840
国际会议
18th International Conference on Nuclear Engineering(第18届国际核能工程大会 ICONE 18)
西安
英文
1-7
2010-05-17（万方平台首次上网日期，不代表论文的发表时间）