A commentary on the impact of modelling results to inform mission planning and shield design

Abstract

A correspondence has been received in reference to a recently published article titled “On the decision making criteria for cis-lunar reference scenarios”. The intent of the paper was to demonstrate: (i) a novel methodology for calculating the dose from solar particle events (SPEs), and (ii) the impact of the SPE parametric model, shield thickness, dose metric, and radiation transport code on choosing a worst-case scenario. This effort assumed a spherical, aluminum spacecraft with an internal diameter of 3.8 m and with varying wall thickness ranging from 2 to 10 cm. A brief component of this article compared the dose from several solar particle events (SPEs) inside the spherical spacecraft geometry as calculated with Monte Carlo radiation transport code MCNPX and the on-line tool OLTARIS. In this comparison, the MCNPX simulation parameters assumed a volume-averaged dose while OLTARIS calculations assumed a point-dose estimate at the center of the spherical geometry. These modeling assumptions were detailed in the initial publication. The differences in the neutron, proton, and light-ion fluences and doses obtained between both codes were generally attributed to differences transport methodologies, nuclear physics models, boundary condition setup and detector regions. The commentary received demonstrated when both codes used a point-detector geometry and/or volume-averaged geometries, the two would yield similar proton fluences. This is a worthwhile observation that further emphasizes the impact of modeling assumption. The commentary further suggested however that the volume-averaged dose results “artificially reduced” estimates and that it was both “misleading” and “not-applicable” for use in storm shelter design. The response presented here will reiterate the context of the initial assumptions made, demonstrate the variability in point-dose estimates relative to a volume-averaged dose estimate, state why a volume-averaged estimate is equally applicable in this context, and lastly reference other factors that can give rise to increased uncertainty.