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DOE OSTI2023

Linear and Non-linear Optical Characterization of Glass and Sapphire for Optical Instrumentation of Advanced Fission Reactors

The linear and nonlinear optical properties of glass and sapphire are investigated to determine the material’s suitability to support optical instrumentation for advanced fission reactors. A post-irradiation examination experiment consisting of a Z-scan, measuring nonlinear optical absorption and refraction, and a spectrometer, measuring linear optical absorption was developed and used. Radiation-induced negative nonlinear optical absorption was observed for the first time in fused silica, sapphire, and borosilicate glass under both types of thermal annealing conditions and the respective effects of thermal annealing are reported.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1985

The HEAO-2 Guest Investigator Program: Non-linear growth of instabilities in line-driven stellar winds

The linear instability of line-driven stellar winds to take proper account of the dynamical effect of scattered radiation were analyzed. It is found that: (1) the drag effect of the mean scattered radiation does greatly reduce the contribution of scattering lines to the instability at the very base of the wind, but the instability growth rate associated with such lines rapidly increases as the flow moves outward from the base, reaching more than 50% of the growth rate for pure absorption lines within a stellar radius of the surface, and eventually reaching 80% of that rate at large radii; (2) perturbations in the scattered radiation field may be important for the propagation of wind disturbances, but they have little effect on the wind instability; and (3) the contribution of strongly shadowed lines to the wind instability is often reduced compared to that of unshadowed lines, but their overall effect is not one of damping in the outer parts of the wind. It is concluded that, even when all scattering effects are taken into account, the bulk of the flow in a line-driven stellar wind is still highly unstable.

Rybicki, G. B.↗

NASA NTRS2020

Evaluating impacts of snow, surface water, soil and vegetation on empirical vegetation and snow indices for the Utqiaġvik tundra ecosystem in Alaska with the LVS3 model

Satellite observations for the Arctic and boreal region may contain information of vegetation, soil, snow, snowmelt, and/or other surface water bodies. We investigated the impacts of vegetation, soil, snow and surface water on empirical vegetation/snow indices on a tundra ecosystem area located around Utqiaġvik (formerly Barrow) of Alaska with the Moderate Resolution Imaging Spectrometer (MODIS) images in 2001–2014. Empirical vegetation indices, such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), the index of near infrared of vegetation (NIRv), and modified EVI (EVI2), have been used to monitor vegetation. Normalized difference snow index (NDSI) has been widely applied to monitor snow. The vegetation cover fraction (VGCF), the soil cover fraction (SOILCF), the snow cover fraction (SNOWCF), the surface water body cover fraction (WaterBodyCF), the fractional absorption of photosynthetically active radiation (PAR) by vegetation chlorophyll (fAPARchl), the fractional absorption of PAR by non-chlorophyll components of the vegetation (fAPARnon-chl), and the fractional absorption of PAR by the entire canopy (fAPARcanopy) are retrieved with the MODIS images and a coupled Leaf-Vegetation-Soil-Snow-Surface water body radiative transfer model, LVS3. The vegetation indices (NDVI, EVI, EVI2 and NIRv) differ from VGCF, fAPARchl, fAPARnon-chl, and fAPARcanopy. In addition to vegetation, we find that soil, snow and surface water also have impacts on vegetation indices NDVI, EVI (EVI2), and NIRv. Presence of snow makes lower the observed values of NDVI, EVI2 and NIRv. After snowmelt is gone, the vegetation indices (NDVI, EVI, EVI2 and NIRv) linearly decrease with SOILCF and WaterBodyCF, and WaterBodyCF has stronger impacts on these vegetation indices than SOILCF. The relationship between EVI and snow is complicated. NDSI non-linearly increases with SNOWCF, but linearly increases with sum of SNOWCF and WaterBodyCF (sum = 0.5893 × NDSI +0.4342, R2 = 0.976). NDSI linearly decreases with VGCF, and the relationship between NDSI and SOILCF is complex. Retrievals of VGCF, fAPARchl, fAPARnon-chl and fAPARcanopy with the LVS3 model provide alternatives for vegetation monitoring and ecological modeling.

Qingyuan Zhang↗