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Research Papers

Optimization of Moderator Design for Explosive Detection by Thermal Neutron Activation Using a Genetic Algorithm

[+] Author and Article Information
Zafar Ullah Koreshi

Department of Mechatronics Engineering,
Air University,
Islamabad 44000, Pakistan
e-mail: zafar@mail.au.edu.pk

Hamda Khan

Department of Mechatronics Engineering,
Air University,
Islamabad 44000, Pakistan
e-mail: hamda.khan@mail.au.edu.pk

1Corresponding author.

Manuscript received February 27, 2015; final manuscript received January 31, 2016; published online June 17, 2016. Assoc. Editor: Brian Ikeda.

ASME J of Nuclear Rad Sci 2(3), 031018 (Jun 17, 2016) (7 pages) Paper No: NERS-15-1021; doi: 10.1115/1.4032702 History: Received February 27, 2015; Accepted January 31, 2016

An optimal design analysis is carried out for an explosives’ detection system (EDS) based on thermal neutron activation (TNA) of a sample under investigation. The objective of this work is to use a genetic algorithm (GA) to obtain the optimized moderator design that would yield the “best” signal in a detection system. In a preliminary analysis, a full Monte Carlo (MC) simulation is carried out to estimate the effectiveness of various moderators, namely, water, graphite, and beryllium with respect to radiative capture (n,γ) reactions in a sample under investigation. Since MC simulation is computationally “expensive,” it is generally not used for random-search-based optimization analysis. Thus, more efficient methods are required for the design of optimal nuclear systems, where neutron transport is accurately modeled and iteratively solved for estimating the effect of independent design parameters. This paper proposes a computational scheme in which GA is coupled with the two-group neutron diffusion equation (DE) for carrying out an optimization analysis. The coupled GA-DE optimization scheme is demonstrated for obtaining the optimal moderator design. It is found that with considerably less computational effort than in an elaborate MC computation, the GA-DE approach can be used for the optimal design of detection systems.

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Figures

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Fig. 1

Flowchart of the GA program

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Fig. 2

Point source at the origin surrounded by three concentric shells of moderator and one for the specimen under investigation (axes represent distance in cm)

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Fig. 3

Comparison of the finite exact, infinite exact, and finite-difference numerical solutions for the fast neutron flux φ1(r) in water with zone radii 4, 6, 8 cm and 100 mesh intervals

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Fig. 4

Comparison of the finite exact, infinite exact, and finite-difference numerical solutions for the thermal neutron flux φ2(r) in water with zone radii 4, 6, 8 cm and 100 mesh intervals

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Fig. 5

Comparison of the finite exact and finite-difference numerical solutions for the neutron fluxes φ1,2(r) in water with zone radii 4, 6, 8 cm and 100 mesh intervals

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Fig. 6

Neutron flux φ(u) (neutrons cm−2/lethargy interval) in water shells of radii 4 cm (zone 1), 6 cm (zone 2), 8 cm (zone 3) for a Cf252 source at origin (NPS=200,000)

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Fig. 7

Neutron flux φ(u) (neutrons/lethargy interval) in zones of radii 4 cm (inner zone: water), 6 cm (middle zone: graphite), 8 cm (outer zone: water) for a Cf252 source at origin (NPS=200,000)

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Fig. 8

Convergence of GA optimization results (water–graphite–water)

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Fig. 9

MCNP results for (n,γ) reactions in H in zone 3 per source neutron cm−3 versus radius of zone 1 water moderator: water–graphite–water

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Fig. 10

Pulse height tally in specimen cell for 5 cm wax moderator shell

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