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Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach

  • The development of compact neutron sources for applications is extensive and features many approaches. For ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for proton and deuteron beams with arbitrary energy distributions with kinetic energies from 3 MeV to 97 MeV. This model makes it possible to compare different ion-based neutron source concepts against each other quickly. This contribution derives a predictive model using Monte Carlo simulations (an order of 50,000 simulations) and deep neural networks. It is the first time a model of this kind has been developed. With this model, lengthy Monte Carlo simulations, which individually take a long time to complete, can be circumvented. A prediction of neutron spectra then takes some milliseconds, which enables fast optimization and comparison. The models’ shortcomings for low-energy neutrons (<0.1 MeV ) and the cut-off prediction uncertainty (±3 MeV ) are addressed, and mitigation strategies are proposed.

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Metadaten
Author:Benedikt SchmitzORCiD, Stefan ScheurenORCiD
URN:urn:nbn:de:hebis:ds114-opus4-4316
DOI:https://doi.org/10.3390/jne5020009
ISSN:2673-4362
Parent Title (English):Journal of Nuclear Engineering
Publisher:MDPI
Editor:Dan Gabriel Cacuci
Document Type:Article
Language:English
Year of Completion:2024
Publishing Institution:Hochschule Darmstadt
Release Date:2024/04/15
Tag:Monte Carlo; artificial neural network; bootstrapping; modeling; neutron; thick target yield
GND Keyword:-
Volume:5
Issue:2
First Page:114
Last Page:127
Note:
Originalveröffentlichung: Schmitz B, Scheuren S. Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach. Journal of Nuclear Engineering. 2024; 5(2):114-127. https://doi.org/10.3390/jne5020009
Institutes:Elektrotechnik und Informationstechnik
OpenAIRE:OpenAIRE
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International