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

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
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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