دورية أكاديمية
3D reconstruction of an inertial-confinement fusion implosion with neural networks using multiple heterogeneous data sources.
| العنوان: | 3D reconstruction of an inertial-confinement fusion implosion with neural networks using multiple heterogeneous data sources. |
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| المؤلفون: | Kunimune JH; Plasma Science and Fusion Center, Massachusetts Institute of Technology, 167 Albany St., Cambridge, Massachesetts 02139, USA., Casey DT; Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA., Kustowski B; Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA., Geppert-Kleinrath V; Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, New Mexico 87545, USA., Divol L; Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA., Fittinghoff DN; Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA., Volegov PL; Los Alamos National Laboratory, P.O. Box 1663, Los Alamos, New Mexico 87545, USA., Kruse MKG; Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA., Gaffney JA; Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA., Nora RC; Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, California 94550, USA., Frenje JA; Plasma Science and Fusion Center, Massachusetts Institute of Technology, 167 Albany St., Cambridge, Massachesetts 02139, USA. |
| المصدر: | The Review of scientific instruments [Rev Sci Instrum] 2024 Jul 01; Vol. 95 (7). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: American Institute Of Physics Country of Publication: United States NLM ID: 0405571 Publication Model: Print Cited Medium: Internet ISSN: 1089-7623 (Electronic) Linking ISSN: 00346748 NLM ISO Abbreviation: Rev Sci Instrum Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Publication: 1933- : Woodbury, N.Y. : American Institute Of Physics Original Publication: 1930-1932 : Menasha, WI : Optical Society of America |
| مستخلص: | 3D asymmetries are major degradation mechanisms in inertial-confinement fusion implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed and reconstructed with the neutron imaging system (NIS) on three lines of sight around the NIF target chamber. Conventional tomographic reconstructions are used to reconstruct the 3D morphology of the implosion using NIS [Volegov et al., J. Appl. Phys. 127, 083301 (2020)], but the problem is ill-posed with only three imaging lines of sight. Asymmetries can also be diagnosed with the real-time neutron activation diagnostics (RTNAD) and the neutron time-of-flight (nToF) suite. Since the NIS, RTNAD, and nToF each sample a different part of the implosion using different physical principles, we propose that it is possible to overcome the limitations of too few imaging lines of sight by performing 3D reconstructions that combine information from all three heterogeneous data sources. This work presents a new machine learning-based reconstruction technique to do just this. By using a simple physics model and group of neural networks to map 3D morphologies to data, this technique can easily account for data of multiple different types. A simple proof-of-principle is presented, demonstrating that this technique can accurately reconstruct a hot-spot shape using synthetic primary neutron images and a hot-spot velocity vector. In particular, the hot-spot's asymmetry, quantified as spherical harmonic coefficients, is reconstructed to within ±4% of the radius in 90% of test cases. In the future, this technique will be applied to actual NIS, RTNAD, and nToF data to better understand 3D asymmetries at the NIF. (© 2024 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).) |
| تواريخ الأحداث: | Date Created: 20240703 Latest Revision: 20240703 |
| رمز التحديث: | 20240703 |
| DOI: | 10.1063/5.0205656 |
| PMID: | 38958513 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Scitation. | تدمد: | 1089-7623 |
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| DOI: | 10.1063/5.0205656 |