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Predictive modeling of a radiative shock system

Author

Listed:
  • Holloway, James Paul
  • Bingham, Derek
  • Chou, Chuan-Chih
  • Doss, Forrest
  • Paul Drake, R.
  • Fryxell, Bruce
  • Grosskopf, Michael
  • van der Holst, Bart
  • Mallick, Bani K.
  • McClarren, Ryan
  • Mukherjee, Ashin
  • Nair, Vijay
  • Powell, Kenneth G.
  • Ryu, D.
  • Sokolov, Igor
  • Toth, Gabor
  • Zhang, Zhanyang

Abstract

A predictive model is constructed for a radiative shock experiment, using a combination of a physics code and experimental measurements. The CRASH code can model the radiation hydrodynamics of the radiative shock launched by the ablation of a Be drive disk and driven down a tube filled with Xe. The code is initialized by a preprocessor that uses data from the Hyades code to model the initial 1.3ns of the system evolution, with this data fit over seven input parameters by a Gaussian process model. The CRASH code output for shock location from 320 simulations is modeled by another Gaussian process model that combines the simulation data with eight field measurements of a CRASH experiment, and uses this joint model to construct a posterior distribution for the physical parameters of the simulation (model calibration). This model can then be used to explore sensitivity of the system to the input parameters. Comparison of the predicted shock locations in a set of leave-one-out exercises shows that the calibrated model can predict the shock location within experimental uncertainty.

Suggested Citation

  • Holloway, James Paul & Bingham, Derek & Chou, Chuan-Chih & Doss, Forrest & Paul Drake, R. & Fryxell, Bruce & Grosskopf, Michael & van der Holst, Bart & Mallick, Bani K. & McClarren, Ryan & Mukherjee, , 2011. "Predictive modeling of a radiative shock system," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1184-1193.
    • Handle: RePEc:eee:reensy:v:96:y:2011:i:9:p:1184-1193
    DOI: 10.1016/j.ress.2010.08.011
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    References listed on IDEAS

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    1. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
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