Multi-objective optimization for the National Ignition facility's Gamma reaction History diagnostic

George R. Labaria; Judith A. Liebman; Daniel B. Sayre; Hans W. Herrmann; Essex J. Bond; Jennifer A. Church

doi:10.1117/12.2009047

18 February 2013 Multi-objective optimization for the National Ignition Facility's Gamma Reaction History diagnostic

George R. Labaria, Judith A. Liebman, Daniel B. Sayre, Hans W. Herrmann, Essex J. Bond, Jennifer A. Church

Proceedings Volume 8602, High Power Lasers for Fusion Research II; 86020C (2013) https://doi.org/10.1117/12.2009047
Event: SPIE LASE, 2013, San Francisco, California, United States

Abstract

The National Ignition Facility (NIF) is producing experimental results for the study of Inertial Confinement Fusion (ICF). The Gamma Reaction History (GRH) diagnostic at NIF can detect gamma rays to measure fusion burn parameters such as fusion burn width, bang time, neutron yield, and areal density of the compressed ablator for cryogenic deuterium-tritium (DT) implosions. Gamma-ray signals detected with this diagnostic are inherently distorted by hardware impulse response functions (IRFs) and gains, and are comprised of several components including gamma rays from laser-plasma interactions (LPI). One method for removing hardware distortions to approximate the gamma-ray reaction history is deconvolution. However, deconvolution of the distorted signal to obtain the gamma-ray reaction history and its associated parameters presents an ill-posed inverse problem and does not separate out the source components of the gamma-ray signal. A multi-dimensional parameter space model for the distorted gamma-ray signal has been developed in the literature. To complement a deconvolution, we develop a multi-objective optimization algorithm to determine the model parameters so that the error between the model and the collected gamma-ray data is minimized in the least-squares sense. The implementation of the optimization algorithm must be suffciently robust to be used in automated production software. To achieve this level of robustness, impulse response signals must be carefully processed and constraints on the parameter space based on theory and experimentation must be implemented to ensure proper convergence of the algorithm. In this paper, we focus on the optimization algorithm's theory and implementation.

Citation Download Citation

George R. Labaria, Judith A. Liebman, Daniel B. Sayre, Hans W. Herrmann, Essex J. Bond, and Jennifer A. Church "Multi-objective optimization for the National Ignition Facility's Gamma Reaction History diagnostic", Proc. SPIE 8602, High Power Lasers for Fusion Research II, 86020C (18 February 2013); https://doi.org/10.1117/12.2009047

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