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Title: Tripled yield in direct-drive laser fusion through statistical modelling [Tripling the yield in direct-drive laser fusion via predictive statistical modeling]

Abstract

Focusing laser light onto a very small target can produce the conditions for laboratory-scale nuclear fusion of hydrogen isotopes. The lack of accurate predictive models, which are essential for the design of high-performance laser-fusion experiments, is a major obstacle to achieving thermonuclear ignition. Here we report a statistical approach that was used to design and quantitatively predict the results of implosions of solid deuterium–tritium targets carried out with the 30-kilojoule OMEGA laser system, leading to tripling of the fusion yield to its highest value so far for direct-drive laser fusion. When scaled to the laser energies of the National Ignition Facility (1.9 megajoules), these targets are predicted to produce a fusion energy output of about 500 kilojoules—several times larger than the fusion yields currently achieved at that facility. As a result, this approach could guide the exploration of the vast parameter space of thermonuclear ignition conditions and enhance our understanding of laser-fusion physics.

Authors:
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+ Show Author Affiliations
  1. Univ. of Rochester, Rochester, NY (United States)
  2. Univ. of Rochester, Rochester, NY (United States); Politecnico di Milano, Milan (Italy)
  3. Univ. of Rochester, Rochester, NY (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  4. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Publication Date:
Research Org.:
Univ. of Rochester, NY (United States). Lab. for Laser Energetics
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
Contributing Org.:
Laboratory for Laser Energetics, University of Rochester
OSTI Identifier:
1493448
Report Number(s):
2018-88, 1463
Journal ID: ISSN 0028-0836; 2018-88, 1463, 2420
Grant/Contract Number:  
NA0001944
Resource Type:
Accepted Manuscript
Journal Name:
Nature (London)
Additional Journal Information:
Journal Name: Nature (London); Journal Volume: 565; Journal Issue: 7741; Journal ID: ISSN 0028-0836
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY

Citation Formats

Gopalaswamy, V., Betti, R., Knauer, J. P., Luciani, N., Patel, D., Woo, K. M., Bose, A., Igumenshchev, I. V., Campbell, E. M., Anderson, K. S., Bauer, K. A., Bonino, M. J., Cao, D., Christopherson, A. R., Collins, G. W., Collins, T. J. B., Davies, J. R., Delettrez, J. A., Edgell, D. H., Epstein, R., Forrest, C. J., Froula, D. H., Glebov, V. Y., Goncharov, V. N., Harding, D. R., Hu, S. X., Jacobs-Perkins, D. W., Janezic, R. T., Kelly, J. H., Mannion, O. M., Maximov, A., Marshall, F. J., Michel, D. T., Miller, S., Morse, S. F. B., Palastro, J., Peebles, J., Radha, P. B., Regan, S. P., Sampat, S., Sangster, T. C., Sefkow, A. B., Seka, W., Shah, R. C., Shmyada, W. T., Shvydky, A., Stoeckl, C., Solodov, A. A., Theobald, W., Zuegel, J. D., Johnson, M. Gatu, Petrasso, R. D., Li, C. K., and Frenje, J. A. Tripled yield in direct-drive laser fusion through statistical modelling [Tripling the yield in direct-drive laser fusion via predictive statistical modeling]. United States: N. p., 2019. Web. doi:10.1038/s41586-019-0877-0.
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Gopalaswamy, V., Betti, R., Knauer, J. P., Luciani, N., Patel, D., Woo, K. M., Bose, A., Igumenshchev, I. V., Campbell, E. M., Anderson, K. S., Bauer, K. A., Bonino, M. J., Cao, D., Christopherson, A. R., Collins, G. W., Collins, T. J. B., Davies, J. R., Delettrez, J. A., Edgell, D. H., Epstein, R., Forrest, C. J., Froula, D. H., Glebov, V. Y., Goncharov, V. N., Harding, D. R., Hu, S. X., Jacobs-Perkins, D. W., Janezic, R. T., Kelly, J. H., Mannion, O. M., Maximov, A., Marshall, F. J., Michel, D. T., Miller, S., Morse, S. F. B., Palastro, J., Peebles, J., Radha, P. B., Regan, S. P., Sampat, S., Sangster, T. C., Sefkow, A. B., Seka, W., Shah, R. C., Shmyada, W. T., Shvydky, A., Stoeckl, C., Solodov, A. A., Theobald, W., Zuegel, J. D., Johnson, M. Gatu, Petrasso, R. D., Li, C. K., & Frenje, J. A. Tripled yield in direct-drive laser fusion through statistical modelling [Tripling the yield in direct-drive laser fusion via predictive statistical modeling]. United States. https://doi.org/10.1038/s41586-019-0877-0
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Gopalaswamy, V., Betti, R., Knauer, J. P., Luciani, N., Patel, D., Woo, K. M., Bose, A., Igumenshchev, I. V., Campbell, E. M., Anderson, K. S., Bauer, K. A., Bonino, M. J., Cao, D., Christopherson, A. R., Collins, G. W., Collins, T. J. B., Davies, J. R., Delettrez, J. A., Edgell, D. H., Epstein, R., Forrest, C. J., Froula, D. H., Glebov, V. Y., Goncharov, V. N., Harding, D. R., Hu, S. X., Jacobs-Perkins, D. W., Janezic, R. T., Kelly, J. H., Mannion, O. M., Maximov, A., Marshall, F. J., Michel, D. T., Miller, S., Morse, S. F. B., Palastro, J., Peebles, J., Radha, P. B., Regan, S. P., Sampat, S., Sangster, T. C., Sefkow, A. B., Seka, W., Shah, R. C., Shmyada, W. T., Shvydky, A., Stoeckl, C., Solodov, A. A., Theobald, W., Zuegel, J. D., Johnson, M. Gatu, Petrasso, R. D., Li, C. K., and Frenje, J. A. Wed . "Tripled yield in direct-drive laser fusion through statistical modelling [Tripling the yield in direct-drive laser fusion via predictive statistical modeling]". United States. https://doi.org/10.1038/s41586-019-0877-0. https://www.osti.gov/servlets/purl/1493448.
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@article{osti_1493448,
title = {Tripled yield in direct-drive laser fusion through statistical modelling [Tripling the yield in direct-drive laser fusion via predictive statistical modeling]},
author = {Gopalaswamy, V. and Betti, R. and Knauer, J. P. and Luciani, N. and Patel, D. and Woo, K. M. and Bose, A. and Igumenshchev, I. V. and Campbell, E. M. and Anderson, K. S. and Bauer, K. A. and Bonino, M. J. and Cao, D. and Christopherson, A. R. and Collins, G. W. and Collins, T. J. B. and Davies, J. R. and Delettrez, J. A. and Edgell, D. H. and Epstein, R. and Forrest, C. J. and Froula, D. H. and Glebov, V. Y. and Goncharov, V. N. and Harding, D. R. and Hu, S. X. and Jacobs-Perkins, D. W. and Janezic, R. T. and Kelly, J. H. and Mannion, O. M. and Maximov, A. and Marshall, F. J. and Michel, D. T. and Miller, S. and Morse, S. F. B. and Palastro, J. and Peebles, J. and Radha, P. B. and Regan, S. P. and Sampat, S. and Sangster, T. C. and Sefkow, A. B. and Seka, W. and Shah, R. C. and Shmyada, W. T. and Shvydky, A. and Stoeckl, C. and Solodov, A. A. and Theobald, W. and Zuegel, J. D. and Johnson, M. Gatu and Petrasso, R. D. and Li, C. K. and Frenje, J. A.},
abstractNote = {Focusing laser light onto a very small target can produce the conditions for laboratory-scale nuclear fusion of hydrogen isotopes. The lack of accurate predictive models, which are essential for the design of high-performance laser-fusion experiments, is a major obstacle to achieving thermonuclear ignition. Here we report a statistical approach that was used to design and quantitatively predict the results of implosions of solid deuterium–tritium targets carried out with the 30-kilojoule OMEGA laser system, leading to tripling of the fusion yield to its highest value so far for direct-drive laser fusion. When scaled to the laser energies of the National Ignition Facility (1.9 megajoules), these targets are predicted to produce a fusion energy output of about 500 kilojoules—several times larger than the fusion yields currently achieved at that facility. As a result, this approach could guide the exploration of the vast parameter space of thermonuclear ignition conditions and enhance our understanding of laser-fusion physics.},
doi = {10.1038/s41586-019-0877-0},
journal = {Nature (London)},
number = 7741,
volume = 565,
place = {United States},
year = {Wed Jan 30 00:00:00 EST 2019},
month = {Wed Jan 30 00:00:00 EST 2019}
}
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    • Physics of Plasmas, Vol. 26, Issue 6
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    Neutron backscatter edge: A measure of the hydrodynamic properties of the dense DT fuel at stagnation in ICF experiments
    journal, January 2020

    • Crilly, A. J.; Appelbe, B. D.; Mannion, O. M.
    • Physics of Plasmas, Vol. 27, Issue 1
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    Analysis of NIF scaling using physics informed machine learning
    journal, January 2020

    • Hsu, Abigail; Cheng, Baolian; Bradley, Paul A.
    • Physics of Plasmas, Vol. 27, Issue 1
    • DOI: 10.1063/1.5130585

    The Blind Implosion-Maker - Automated Inertial Confinement Fusion experiment design
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