Making inertial confinement fusion models more predictive

Gaffney, Jim A.; Brandon, Scott T.; Humbird, Kelli D.; Kruse, Michael K.  G.; Nora, Ryan C.; Peterson, J. Luc; Spears, Brian K.

doi:10.1063/1.5108667

Making inertial confinement fusion models more predictive

Journal Article · Mon Aug 12 00:00:00 EDT 2019 · Physics of Plasmas

DOI:https://doi.org/10.1063/1.5108667· OSTI ID:1597607

^[1]; Brandon, Scott T. ^[1]; Humbird, Kelli D. ^[1]; ^[1]; ^[1]; ^[1]; Spears, Brian K. ^[1]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Computer models of inertial confinement fusion (ICF) implosions play an essential role in experimental design and interpretation as well as our understanding of fundamental physics under the most extreme conditions that can be reached in the laboratory. Building truly predictive models is a significant challenge, with the potential to greatly accelerate progress to high yield and ignition. One path to more predictive models is to use experimental data to update the underlying physics in a way that can be extrapolated to new experiments and regimes. We describe a statistical framework for the calibration of ICF simulations using data collected at the National Ignition Facility (NIF). We perform Bayesian inferences for a series of laser shots using an approach that is designed to respect the physics simulation as much as possible and then build a second model that links the individual-shot inferences together. We show that this approach is able to match multiple X-ray and neutron diagnostics for a whole series of NIF “BigFoot” shots. Within the context of 2D radiation hydrodynamic simulations, our inference strongly favors a significant reduction in fuel compression over other known degradation mechanisms (namely, hohlraum issues and engineering perturbations). This analysis is expanded using a multifidelity technique to pick fuel-ablator mix from several candidate causes of the degraded fuel compression (including X-ray preheat and shock timing errors). Finally, we use our globally calibrated model to investigate the extra laser drive energy that would be required to overcome the inferred fuel compression issues in NIF BigFoot implosions.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1597607

Alternate ID(s):: OSTI ID: 1556809

Report Number(s):: LLNL-JRNL--770914; 961192

Journal Information:: Physics of Plasmas, Journal Name: Physics of Plasmas Journal Issue: 8 Vol. 26; ISSN 1070-664X

Publisher:: American Institute of Physics (AIP)Copyright Statement

Country of Publication:: United States

Language:: English

References (39)

Ensemble simulations of inertial confinement fusion implosions Nora, Ryan; Peterson, Jayson Luc; Spears, Brian Keith Statistical Analysis and Data Mining: The ASA Data Science Journal, Vol. 10, Issue 4 https://doi.org/10.1002/sam.11344	journal	May 2017
Stochastic Finite Elements: A Spectral Approach Ghanem, Roger G.; Spanos, Pol D. Springer New York, NY https://doi.org/10.1007/978-1-4612-3094-6	book	January 1991
Bayesian inference of inaccuracies in radiation transport physics from inertial confinement fusion experiments Gaffney, J. A.; Clark, D.; Sonnad, V. High Energy Density Physics, Vol. 9, Issue 3 https://doi.org/10.1016/j.hedp.2013.04.012	journal	September 2013
Laser Compression of Matter to Super-High Densities: Thermonuclear (CTR) Applications Nuckolls, John; Wood, Lowell; Thiessen, Albert Nature, Vol. 239, Issue 5368, p. 139-142 https://doi.org/10.1038/239139a0	journal	September 1972
Three-dimensional HYDRA simulations of National Ignition Facility targets Marinak, M. M.; Kerbel, G. D.; Gentile, N. A. Physics of Plasmas, Vol. 8, Issue 5 https://doi.org/10.1063/1.1356740	journal	May 2001
Equation of State Calculations by Fast Computing Machines Metropolis, Nicholas; Rosenbluth, Arianna W.; Rosenbluth, Marshall N. The Journal of Chemical Physics, Vol. 21, Issue 6 https://doi.org/10.1063/1.1699114	journal	June 1953
Point design targets, specifications, and requirements for the 2010 ignition campaign on the National Ignition Facility Haan, S. W.; Lindl, J. D.; Callahan, D. A. Physics of Plasmas, Vol. 18, Issue 5 https://doi.org/10.1063/1.3592169	journal	May 2011
Detailed implosion modeling of deuterium-tritium layered experiments on the National Ignition Facility Clark, D. S.; Hinkel, D. E.; Eder, D. C. Physics of Plasmas, Vol. 20, Issue 5 https://doi.org/10.1063/1.4802194	journal	May 2013
Zonal flow generation in inertial confinement fusion implosions Peterson, J. L.; Humbird, K. D.; Field, J. E. Physics of Plasmas, Vol. 24, Issue 3 https://doi.org/10.1063/1.4977912	journal	March 2017
Capsule physics comparison of National Ignition Facility implosion designs using plastic, high density carbon, and beryllium ablators Clark, D. S.; Kritcher, A. L.; Yi, S. A. Physics of Plasmas, Vol. 25, Issue 3 https://doi.org/10.1063/1.5016874	journal	March 2018
Comparison of plastic, high density carbon, and beryllium as indirect drive NIF ablators Kritcher, A. L.; Clark, D.; Haan, S. Physics of Plasmas, Vol. 25, Issue 5 https://doi.org/10.1063/1.5018000	journal	May 2018
The high velocity, high adiabat, “Bigfoot” campaign and tests of indirect-drive implosion scaling Casey, D. T.; Thomas, C. A.; Baker, K. L. Physics of Plasmas, Vol. 25, Issue 5 https://doi.org/10.1063/1.5019741	journal	May 2018
Deep learning: A guide for practitioners in the physical sciences Spears, Brian K.; Brase, James; Bremer, Peer-Timo Physics of Plasmas, Vol. 25, Issue 8 https://doi.org/10.1063/1.5020791	journal	August 2018
Mitigation of X-ray shadow seeding of hydrodynamic instabilities on inertial confinement fusion capsules using a reduced diameter fuel fill-tube MacPhee, A. G.; Smalyuk, V. A.; Landen, O. L. Physics of Plasmas, Vol. 25, Issue 5 https://doi.org/10.1063/1.5025183	journal	May 2018
Hydrodynamic instabilities seeded by the X-ray shadow of ICF capsule fill-tubes MacPhee, A. G.; Smalyuk, V. A.; Landen, O. L. Physics of Plasmas, Vol. 25, Issue 8 https://doi.org/10.1063/1.5037816	journal	August 2018
Three-dimensional modeling and hydrodynamic scaling of National Ignition Facility implosions Clark, D. S.; Weber, C. R.; Milovich, J. L. Physics of Plasmas, Vol. 26, Issue 5 https://doi.org/10.1063/1.5091449	journal	May 2019
An electron conductivity model for dense plasmas Lee, Y. T.; More, R. M. Physics of Fluids, Vol. 27, Issue 5 https://doi.org/10.1063/1.864744	journal	January 1984
A new quotidian equation of state (QEOS) for hot dense matter More, R. M.; Warren, K. H.; Young, D. A. Physics of Fluids, Vol. 31, Issue 10 https://doi.org/10.1063/1.866963	journal	January 1988
Density Estimation for Statistics and Data Analysis Dehnad, Khosrow Technometrics, Vol. 29, Issue 4 https://doi.org/10.1080/00401706.1987.10488295	journal	November 1987
Maximum entropy sampling Shewry, M. C.; Wynn, H. P. Journal of Applied Statistics, Vol. 14, Issue 2 https://doi.org/10.1080/02664768700000020	journal	January 1987
Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables Pourhabib, Arash; Huang, Jianhua Z.; Wang, Kan IIE Transactions, Vol. 47, Issue 2 https://doi.org/10.1080/0740817X.2014.917777	journal	September 2014
An indirect-drive non-cryogenic double-shell path to 1ω Nd-laser hybrid inertial fusion–fission energy Amendt, Peter; Milovich, Jose; Perkins, L. John Nuclear Fusion, Vol. 50, Issue 10 https://doi.org/10.1088/0029-5515/50/10/105006	journal	August 2010
Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF Gaffney, J. A.; Clark, D.; Sonnad, V. Nuclear Fusion, Vol. 53, Issue 7 https://doi.org/10.1088/0029-5515/53/7/073032	journal	June 2013
Monte Carlo sampling methods using Markov chains and their applications Hastings, W. K. Biometrika, Vol. 57, Issue 1 https://doi.org/10.1093/biomet/57.1.97	journal	April 1970
Fusion Energy Output Greater than the Kinetic Energy of an Imploding Shell at the National Ignition Facility Le Pape, S.; Berzak Hopkins, L. F.; Divol, L. Physical Review Letters, Vol. 120, Issue 24 https://doi.org/10.1103/PhysRevLett.120.245003	journal	June 2018
High-Performance Indirect-Drive Cryogenic Implosions at High Adiabat on the National Ignition Facility Baker, K. L.; Thomas, C. A.; Casey, D. T. Physical Review Letters, Vol. 121, Issue 13 https://doi.org/10.1103/PhysRevLett.121.135001	journal	September 2018
Deep Neural Network Initialization With Decision Trees Humbird, Kelli D.; Peterson, J. Luc; Mcclarren, Ryan G. IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, Issue 5 https://doi.org/10.1109/TNNLS.2018.2869694	journal	May 2019
Bayesian calibration of computer models Kennedy, Marc C.; O'Hagan, Anthony Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3 https://doi.org/10.1111/1467-9868.00294	journal	August 2001
The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations Xiu, Dongbin; Karniadakis, George Em SIAM Journal on Scientific Computing, Vol. 24, Issue 2 https://doi.org/10.1137/S1064827501387826	journal	January 2002
Combining Field Data and Computer Simulations for Calibration and Prediction Higdon, Dave; Kennedy, Marc; Cavendish, James C. SIAM Journal on Scientific Computing, Vol. 26, Issue 2 https://doi.org/10.1137/S1064827503426693	journal	January 2004
Computer Model Calibration Using High-Dimensional Output Higdon, Dave; Gattiker, James; Williams, Brian Journal of the American Statistical Association, Vol. 103, Issue 482 https://doi.org/10.1198/016214507000000888	journal	June 2008
Examples of Adaptive MCMC Roberts, Gareth O.; Rosenthal, Jeffrey S. Journal of Computational and Graphical Statistics, Vol. 18, Issue 2 https://doi.org/10.1198/jcgs.2009.06134	journal	January 2009
Density Estimation for Statistics and Data Analysis Silverman, Bernard W. https://doi.org/10.1201/9781315140919	book	January 1998
On Estimation of a Probability Density Function and Mode Parzen, Emanuel The Annals of Mathematical Statistics, Vol. 33, Issue 3 https://doi.org/10.1214/aoms/1177704472	journal	September 1962
Remarks on Some Nonparametric Estimates of a Density Function Rosenblatt, Murray The Annals of Mathematical Statistics, Vol. 27, Issue 3 https://doi.org/10.1214/aoms/1177728190	journal	September 1956
Bayesian Experimental Design: A Review Chaloner, Kathryn; Verdinelli, Isabella Statistical Science, Vol. 10, Issue 3 https://doi.org/10.1214/ss/1177009939	journal	August 1995
Equation of state calculations by fast computing machines Metropolis, Nicholas; Rosenbluth, Arianna W.; Rosenbluth, Marshall N. https://doi.org/10.2172/4390578	report	March 1953
Bayesian inference of inaccuracies in radiation transport physics from inertial confinement fusion experiments Gaffney, Jim A.; Clark, Dan; Sonnad, Vijay arXiv https://doi.org/10.48550/arxiv.1302.5743	text	January 2013
Development of a Bayesian method for the analysis of inertial confinement fusion experiments on the NIF Gaffney, Jim A.; Clark, Dan; Sonnad, Vijay arXiv https://doi.org/10.48550/arxiv.1302.5745	text	January 2013

Cited By (1)

Analysis of NIF scaling using physics informed machine learning Hsu, Abigail; Cheng, Baolian; Bradley, Paul A. Physics of Plasmas, Vol. 27, Issue 1 https://doi.org/10.1063/1.5130585	journal	January 2020

Figures / Tables (10)

Similar Records

Design and modeling of indirectly driven magnetized implosions on the NIF

Journal Article · Tue Sep 17 20:00:00 EDT 2024 · Physics of Plasmas · OSTI ID:2567733

Related Subjects

70 PLASMA PHYSICS AND FUSION TECHNOLOGY
Artificial neural networks
Bayesian inference
Calibration methods
Computing
Hydrodynamics simulations
Mathematics
Nuclear fusion
Physics

Making inertial confinement fusion models more predictive

Citation Formats

References (39)

Cited By (1)

Figures / Tables (10)

Similar Records

Related Subjects