Schmitz, Benedikt ; Scheuren, Stefan (2024)
Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach.
In: Journal of Nuclear Engineering, 2024, 5 (2)
doi: 10.26083/tuprints-00027131
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach |
Language: | English |
Date: | 14 May 2024 |
Place of Publication: | Darmstadt |
Year of primary publication: | 31 March 2024 |
Place of primary publication: | Basel |
Publisher: | MDPI |
Journal or Publication Title: | Journal of Nuclear Engineering |
Volume of the journal: | 5 |
Issue Number: | 2 |
DOI: | 10.26083/tuprints-00027131 |
Corresponding Links: | |
Origin: | Secondary publication DeepGreen |
Abstract: | 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. |
Uncontrolled Keywords: | neutron, thick target yield, artificial neural network, modeling, Monte Carlo, bootstrapping |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-271315 |
Classification DDC: | 500 Science and mathematics > 530 Physics |
Divisions: | 05 Department of Physics > Institute of Nuclear Physics |
Date Deposited: | 14 May 2024 13:59 |
Last Modified: | 20 Aug 2024 13:44 |
SWORD Depositor: | Deep Green |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/27131 |
PPN: | 520784251 |
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