A system of materials composition and geometry arrangement for fast neutron beam thermalization: An MCNP study

Uhlář, Radim; Alexa, Petr; Pištora, Jaromı́r

doi:10.1016/j.nimb.2013.01.032

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…For thermal neutron activation analysis purposes, it is required to consider the TDs surrounding a fast neutron source. In the design of the TD, an arrangement of materials including moderators, collimators, and reflectors with different dimensions has then been investigated using MCNPX2.6 code 17 – 23 . Following our recent work 21 , the best materials can be selected based on a smaller thermal neutron capture cross section (< 10 −2 barn) among BeO, BeD 2 , Plexiglas, borated paraffin, carbon, polyethylene (PE), Teflon (CF 2 ), and heavy water (D 2 O).…”

Section: Methodsmentioning

confidence: 99%

“…However, neutron beams generated by such sources should be thermalized for PGNAA facility based on the thermal neutron capture reaction 17 – 21 . Thermalization devices (TDs) provide thermal neutron beams with minimum non-thermal neutron flux.…”

Section: Introductionmentioning

confidence: 99%

“…Since the properties of the obtained neutron beam depend on both materials and dimensions, TD optimization can accordingly be taken into account as a multi-parameter problem. Also, conventional computational codes such as MCNP 17 – 23 are considerably time-consuming nevertheless; therefore, sequential optimization of dimensions in different parts of the TD is preferred, i.e., the characteristics of each layer (including appropriate materials and optimal dimensions) of TD against the neutron source should be selected through a large number of simulations. The next layers should be also optimized according to the properties of the previous layers.…”

Section: Introductionmentioning

confidence: 99%

“…The next layers should be also optimized according to the properties of the previous layers. For example, the optimal TDs based on D-T neutron generator have been designed as a multi-parameter problem after the simulation of multiple programs using MCNP code for PGNAA 17 , 18 . Researchers also selected the appropriate materials and optimized the dimensions of two moderators and the collimator of the TD based on a 20 MeV electron Linac and spherical tungsten as electron photo-neutron converter using MCNPX code to achieve the maximum thermal neutron flux at the aperture of TD for PGNAA 21 .…”

Section: Introductionmentioning

confidence: 99%

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Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network

Zolfaghari

Masoudi

Rahmani

et al. 2022

Sci Rep

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As a powerful, non-destructive analysis tool based on thermal neutron capture reaction, prompt gamma neutron activation analysis (PGNAA) indeed requires the appropriate neutron source. Neutrons produced by electron Linac-based neutron sources should be thermalized to be appropriate for PGNAA. As a result, thermalization devices (TDs) are used for the usual fast neutron beam to simultaneously maximize the thermal neutron flux and minimize the non- thermal neutron flux at the beam port of TD. To achieve the desired thermal neutron flux, the optimized geometry of TD including the proper materials for moderators and collimator, as well as the optimized dimensions are required. In this context, TD optimization using only Monte Carlo approaches such as MCNP is a multi-parameter problem and time-consuming task. In this work, multilayer perceptron (MLP) neural network has been applied in combination with Q-learning algorithm to optimize the geometry of TD containing collimator and two moderators. Using MLP, both thickness and diameter of the collimator at the beam port of TD have first been optimized for different input electron energies of Linac as well as for moderators’ thickness values and the collimator. Then, the MLP has been learned by the thermal and non-thermal neutron flux simultaneously at the beam port of TD calculated by MCNPX2.6 code. After selecting the optimized geometry of the collimator, a combination of Q-learning algorithm and MLP artificial neural network have been used to find the optimal moderators’ thickness for different input electron energies of Linac. Results verify that the final optimum setup can be obtained based on the prepared dataset in a considerably smaller number of simulations compared to conventional calculation methods as implemented in MCNP.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network

Zolfaghari

Masoudi

Rahmani

et al. 2022

Sci Rep

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show abstract

“…According to the simulation results, we set the thicknesses of t1, t2, t3 to 1 cm, 20 cm, and 15 cm respectively. The materials of W, WC, W 2 C and Cu had a better effect compared with other materials, but their weight and price were higher, so we chose lead as the material of the reflector [29][30][31][32].…”

Section: Reflector and Moderator Calculationsmentioning

confidence: 99%

Simulation of the cement measurement based on the pulse DT neutron generator: A Monte Carlo study

Gao

et al. 2021

PLoS ONE

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The PGNAA system for the cement measurement was simulated based on Monte Carlo method. The sizes of the moderator and reflector for the 14 MeV DT neutron generator were optimized for fast and thermal neutron outputs. The DT neutron generator was simulated at the pulse mode, and the gamma-ray detector was set as LaBr3(Ce) scintillator. The characteristic peaks of the major elements (Ca, Si, Al, Fe) can be identified from the gamma-ray spectra which induced at the different time intervals of the neutron radiation. For the different thicknesses of the cement sample the ratios of the gamma-ray peaks were observed, and the result showed that when the thickness was between 20 to 30 cm, the ratios became stable. With the ratios, we can calculate the iron modulus, silica modulus and lime saturation factor.

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Investigation of interaction parameters of gamma radiation, neutron and charge particles in selected thermoplastic polymers for radiation protection

Abdel-Monem

Echeweozo²

2023

J Mater Sci: Mater Electron

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A system of materials composition and geometry arrangement for fast neutron beam thermalization: An MCNP study

Cited by 11 publications

References 15 publications

Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network

Thermal neutron beam optimization for PGNAA applications using Q-learning algorithm and neural network

Simulation of the cement measurement based on the pulse DT neutron generator: A Monte Carlo study

Investigation of interaction parameters of gamma radiation, neutron and charge particles in selected thermoplastic polymers for radiation protection

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