Abstract
Identifying unknown components of an object that emits radiation is an important problem for national and global security. Radiation signatures measured from an object of interest can be used to infer object parameter values that are not known. This problem is called an inverse transport problem. An inverse transport problem may have multiple solutions and the most widely used approach for its solution is an iterative optimization method. This paper proposes a stochastic derivative-free global optimization algorithm to find multiple solutions of inverse transport problems. The algorithm is an extension of a multilevel single linkage (MLSL) method where a mesh adaptive direct search (MADS) algorithm is incorporated into the local phase. Numerical test cases using uncollided fluxes of discrete gamma-ray lines are presented to show the performance of this new algorithm.
Highlights
Inverse transport problems are problems in which radiation signatures are used to identify unknown components of a radioactive source/shield system
The algorithm is an extension of a multilevel single linkage (MLSL) method where a mesh adaptive direct search (MADS) algorithm is incorporated into the local phase
Where wEi ðr; X^ Þ is the angular flux of gamma rays of energy Ei at position r and angle X^ ðc Á cm À2 Á s À1Þ, REt i ðrÞ is the total macroscopic photon cross section at energy Ei and position r, qEi ðrÞ is the source rate density of gamma rays of energy Ei at position r ðc Á cm À3 Á s À1Þ, Ei is the energy line at index i, and Nl is the number of energy lines considered
Summary
Inverse transport problems are problems in which radiation signatures are used to identify unknown components of a radioactive source/shield system. Characteristics of inverse transport optimization problems include: (a) no assumption is made on the functions used to define the objective function, (b) no derivative information is available to be exploited, (c) only objective function values calculated by a computational code are used by an optimization algorithm during an iterative scheme, and (d) a computational code may return outputs that cannot be trusted even when feasible points are used in calculations (i.e., there are hidden constraints). We cannot include hidden constraints in the problem definition because these constraints exist due to the failure of the numerical integration method used to calculate the objective function values Since it is very difficult (or probably impossible) to make the numerical integration subroutine work for all feasible hidden constraint points, we apply the extreme barrier approach to handle the hidden constraints.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
