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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Overlap Preservation Using Loosely-Coupled Boundary Conditions for Body-Fitted Structured Overset Grids

Several improvements are made to overlap preservation in the automated approach to generating overset structured meshes. A loosely-coupled boundary condition (LCBC) is introduced to maintain optimal overlap for surface meshes generated from overlapping initial curves. Extension of the LCBC scheme to three-dimensional volume meshing is also presented. Test cases include various rotary wing vehicles from NASA’s Revolutionary Vertical Lift Technology (RVLT) project, and the wing-body geometry from the NASA Juncture Flow Experiment. Results demonstrate improvements in donor stencil quality from the enhanced overlap between neighboring surface and volume meshes.

TTT↗
NASA NTRS2022

Full Spectrum Fitting Analysis of Bulk Elemental Composition Analyzer (BECA) Gamma Ray Data

The Bulk Elemental Composition Analyzer (BECA) is an instrument developed through NASA’s Development and Advancement of Lunar Instrumentation (DALI) program to measure the in situ bulk elemental composition of the lunar regolith from either a lander or rover platform. BECA uses active neutron and gamma ray spectroscopy to make these measurements and thus consists of a Pulsed Neutron Generator (PNG), a CeBr3 scintillator Gamma Ray Spectrometer (GRS), and two He-3 neutron detectors. As shown in Figure 1, the PNG emits 14.1 MeV neutrons that induce lunar materials to emit gamma rays with energies characteristic of the elements that produced them. The GRS measures the energy spectra of these gamma rays to identify the elements present and establish their absolute concentrations in the regolith. The analysis of BECA’s gamma ray spectral data yields quantitative elemental concentrations by mass (wt.%) in the material directly beneath the lunar lander or rover. A Compact Neutron Monitor (CNM) attached to the PNG precisely measures the output of 14.1 MeV neutrons to enable quantification of absolute elemental abundances.

Mauricio Ayllon Unzueta↗
NASA NTRSDate not supplied

Quantifying radiation quality for space relevant radiation types: Fitting excess risk models to three combined HZE-irradiated mouse datasets

Radiation health risks are predominantly derived from low linear energy transfer (LET) terrestrial exposures; however, space radiation includes exposure to high-LET and high-charge, high-energy (HZE) particles. Accurately quantifying the differences in radiation quality between the space and terrestrial radiation environments is important for assessing and predicting health risks for astronauts. Weil et al. 2009 and 2014 used two different inbred mouse strains to study differences in hepatocellular carcinoma (HCC) tumorigenesis after exposures to low- and high- LET radiation. More recently, Edmundson et al. 2020 provided valuable new tumor data in outbred mice that were exposed to low- and high-LET radiation. The present study aims to rigorously investigate a relative biological effectiveness (RBE) factor by leveraging the HCC tumor data from Weil et al. 2009, Weil et al. 2014, and Edmundson et al. 2020. The three experiments were similarly designed, allowing the raw data to be combined into a pooled dataset to estimate excess relative risk (ERR) and excess absolute risk (EAR) models using Bayesian Poisson regression. These effect estimates from the pooled data provide greater power to calculate a data driven RBE. Extensive sensitivity analyses test the robustness of RBE estimates to various model assumptions. The following questions will be explored through the sensitivity analyses: • Is the shape of the dose response different for low-LET radiation and HZE radiation, indicating that RBE is a function of dose? • Does attained age modify the effect estimates differently for low-LET radiation and HZE radiation, indicating RBE is a function of attained age? • Are the effect estimates and RBE estimates different for inbred mouse strains and outbred mouse strains? • Do assumptions about differences in ERR models and EAR models change the estimated RBE? Additional studies would be needed to validate the findings from these exploratory analyses.

Lori J. Chappell↗