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
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Schmitz, Benedikt ; Scheuren, Stefan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach |
| Sprache: | Englisch |
| Publikationsjahr: | 14 Mai 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 31 März 2024 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Journal of Nuclear Engineering |
| Jahrgang/Volume einer Zeitschrift: | 5 |
| (Heft-)Nummer: | 2 |
| DOI: | 10.26083/tuprints-00027131 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27131 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | neutron, thick target yield, artificial neural network, modeling, Monte Carlo, bootstrapping |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-271315 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 530 Physik |
| Fachbereich(e)/-gebiet(e): | 05 Fachbereich Physik 05 Fachbereich Physik > Institut für Kernphysik |
| Hinterlegungsdatum: | 14 Mai 2024 13:59 |
| Letzte Änderung: | 16 Mai 2024 15:29 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach. (deposited 14 Mai 2024 13:59) [Gegenwärtig angezeigt]
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