Validation of the Monte Carlo code MONK for thermal 232Th/233U systems using the JEF-2.2 and JEFF-3.1 libraries

Peakman, Aiden; Owen, Hywel; Abram, Timothy J.

doi:10.1016/j.anucene.2020.107919

Annals of Nuclear Energy

2021

DOI: 10.1016/j.anucene.2020.107919

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Validation of the Monte Carlo code MONK for thermal 232Th/233U systems using the JEF-2.2 and JEFF-3.1 libraries

Aiden Peakman

Hywel Owen

Timothy J. Abram³

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Cited by 3 publications

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Analysis of the LatticeNet neural network framework’s performance using prediction-calculated temperature coefficients in PWR assemblies

Furlong,

Watson

2024

Annals of Nuclear Energy

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Validation of the Monte Carlo code MONK for thermal 232Th/233U systems using the JEF-2.2 and JEFF-3.1 libraries

Cited by 3 publications

References 19 publications

Analysis of the LatticeNet neural network framework’s performance using prediction-calculated temperature coefficients in PWR assemblies

Analysis of the LatticeNet neural network framework’s performance using prediction-calculated temperature coefficients in PWR assemblies

Core design and fuel behaviour of a small modular pressurised water reactor using (Th,U)O2 fuel for commercial marine propulsion

Investigation of Monte Carlo trained CNNs for neutronics predictions of typical and atypical PWR assemblies

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