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.
Author: | Benedikt SchmitzORCiD, Stefan ScheurenORCiD |
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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): | Creative Commons - CC BY - Namensnennung 4.0 International |