Application of Neutron Multiplicity Counting Experiments to Optimal Cross-Section Adjustments

Abstract

CLARK, ALEXANDER RICH. Application of Neutron Multiplicity Counting Experiments to Optimal Cross Section Adjustments. (Under the direction of John Mattingly.) This dissertation presents the first application of model calibration to neutron multiplicity counting (NMC) experiments for cross section optimization that utilizes adjoint-based sensitivity analysis (SA) and first-order uncertainty quantification (UQ). We summarize previous work on SA applied to NMC and describe notable additions. We give the procedure for first-order UQ and Bayesian-inference-based parameter estimation (PE). We then discuss model calibration applied to NMC of a 4.5-kg sphere of weapons-grade, alpha-phase plutonium metal with a neutron multiplicity counter. For bare and polyethylene-reflected configurations of the plutonium sphere, we discuss the sensitivity of the first-and second-moment detector responses (i.e. first and second moments of the NMC distribution, respectively) to the cross sections. We describe the sources of uncertainty in the measured and simulated responses. Specifically, uncertainty in the measured responses is due to both random and systematic sources of uncertainty. Uncertainty in the simulated responses is due to cross section covariances. We describe in detail the adjustment to the cross sections and cross section covariances due to the optimization. Due to the contribution of systematic uncertainties to the measured response uncertainties, the adjustment to the cross sections is similar in trend but larger in magnitude compared to that recommended by previous work. We compare the measured responses to responses simulated with nominal and optimized cross sections, demonstrating that the best-estimate cross sections produce simulations of NMC experiments that are more accurate with reduced uncertainty. We verify that the response variance due to the cross sections computed with sampling-based uncertainty quantification (UQ) is well-approximated by that estimated with first-order UQ.

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