Schmitz, Benedikt (2023)
Towards Compact Laser-Driven Neutron Sources: A numerical study of liquid leaf targets for high repetition rate laser experiments and neutron
production using deep learning.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00024335
Dissertation, Erstveröffentlichung, Verlagsversion
Kurzbeschreibung (Abstract)
More than 20 years ago, the first ion acceleration experiments were performed using Target Normal Sheath Acceleration (TNSA). Most TNSA experiments relied on low repetition rates and fixed foil targets. Recent technological advances have enabled the development of laser-plasma accelerators with high repetition rates. The core aspect of the high repetition rate is gas or liquid-based targets, of which the liquid leaf target seems particularly promising. Despite the potential to achieve high repetition rate laser pulses for TNSA, the predictive capabilities of the proposed Liquid Leaf target remain limited.
This research investigates the plasma generated by a Liquid Leaf target and its interaction with any laser using particle-in-cell simulations. A surrogate model is developed to predict the full spectrum of the generated beam and the maximum ion energy. This model also allows numerical optimization of the experiment. A surrogate model for the neutron yield is also developed based on Monte Carlo simulations and artificial neural networks. Combining these models makes it possible to determine the potential of a compact laser-driven neutron source.
Optimizing a laser plasma experiment is performed as an example for two different optimization conditions, focusing on maximizing the proton yield. The optimized solutions utilize an effect caused by different ions, oxygen, and hydrogen, propagating in the same expanding plasma. These ions interact, causing spectra to deviate from plasma expansion models. This effect can be predicted and applied to other applications outside neutron production.
The neutron model allows comparing different designs of compact neutron sources. This allows determining the particle numbers and repetition rates required to make laser-driven neutron sources competitive. Since simulations are insufficient for real-world applications, a new method is proposed to improve data analysis using radiochromic film stacks. The method uses algorithmic solutions to reduce operator inaccuracy while increasing the evaluation speed, allowing Big Data analysis of laser-plasma acceleration data to be performed soon.
Typ des Eintrags: | Dissertation | ||||
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Erschienen: | 2023 | ||||
Autor(en): | Schmitz, Benedikt | ||||
Art des Eintrags: | Erstveröffentlichung | ||||
Titel: | Towards Compact Laser-Driven Neutron Sources: A numerical study of liquid leaf targets for high repetition rate laser experiments and neutron production using deep learning | ||||
Sprache: | Englisch | ||||
Referenten: | Boine-Frankenheim, Prof. Dr. Oliver ; Roth, Prof. Dr. Markus | ||||
Publikationsjahr: | 2023 | ||||
Ort: | Darmstadt | ||||
Kollation: | x, 141 Seiten | ||||
Datum der mündlichen Prüfung: | 27 Juni 2023 | ||||
DOI: | 10.26083/tuprints-00024335 | ||||
URL / URN: | https://tuprints.ulb.tu-darmstadt.de/24335 | ||||
Kurzbeschreibung (Abstract): | More than 20 years ago, the first ion acceleration experiments were performed using Target Normal Sheath Acceleration (TNSA). Most TNSA experiments relied on low repetition rates and fixed foil targets. Recent technological advances have enabled the development of laser-plasma accelerators with high repetition rates. The core aspect of the high repetition rate is gas or liquid-based targets, of which the liquid leaf target seems particularly promising. Despite the potential to achieve high repetition rate laser pulses for TNSA, the predictive capabilities of the proposed Liquid Leaf target remain limited. This research investigates the plasma generated by a Liquid Leaf target and its interaction with any laser using particle-in-cell simulations. A surrogate model is developed to predict the full spectrum of the generated beam and the maximum ion energy. This model also allows numerical optimization of the experiment. A surrogate model for the neutron yield is also developed based on Monte Carlo simulations and artificial neural networks. Combining these models makes it possible to determine the potential of a compact laser-driven neutron source. Optimizing a laser plasma experiment is performed as an example for two different optimization conditions, focusing on maximizing the proton yield. The optimized solutions utilize an effect caused by different ions, oxygen, and hydrogen, propagating in the same expanding plasma. These ions interact, causing spectra to deviate from plasma expansion models. This effect can be predicted and applied to other applications outside neutron production. The neutron model allows comparing different designs of compact neutron sources. This allows determining the particle numbers and repetition rates required to make laser-driven neutron sources competitive. Since simulations are insufficient for real-world applications, a new method is proposed to improve data analysis using radiochromic film stacks. The method uses algorithmic solutions to reduce operator inaccuracy while increasing the evaluation speed, allowing Big Data analysis of laser-plasma acceleration data to be performed soon. |
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Alternatives oder übersetztes Abstract: |
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Status: | Verlagsversion | ||||
URN: | urn:nbn:de:tuda-tuprints-243356 | ||||
Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 530 Physik 600 Technik, Medizin, angewandte Wissenschaften > 621.3 Elektrotechnik, Elektronik |
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Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder > Beschleunigerphysik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Teilchenbeschleunigung und Theorie Elektromagnetische Felder |
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Hinterlegungsdatum: | 24 Jul 2023 12:02 | ||||
Letzte Änderung: | 25 Jul 2023 07:40 | ||||
PPN: | |||||
Referenten: | Boine-Frankenheim, Prof. Dr. Oliver ; Roth, Prof. Dr. Markus | ||||
Datum der mündlichen Prüfung / Verteidigung / mdl. Prüfung: | 27 Juni 2023 | ||||
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