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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7

Nano-Transistor Modeling: Two Dimensional Green's Function Method

Two quantum mechanical effects that impact the operation of nanoscale transistors are inversion layer energy quantization and ballistic transport. While the qualitative effects of these features are reasonably understood, a comprehensive study of device physics in two dimensions is lacking. Our work addresses this shortcoming and provides: (a) a framework to quantitatively explore device physics issues such as the source-drain and gate leakage currents, DIBL (Drain Induced Barrier Lowering), and threshold voltage shift due to quantization, and b) a means of benchmarking quantum corrections to semiclassical models (such as density-gradient and quantum-corrected MEDICI).

Svizhenko, Alexei↗

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗

Shortgrass prairie spectral measurements

The spectral methods of vegetation analysis not only measure herbage biomass on a nondestructive basis but also can be adapted to aircraft and satellite devices to map the spatial distribution over an area in an efficient and economical fashion. This study reviews the ground-based in situ field spectrometry in the 0.350-0.800 micron region of the spectrum. A statistical analysis of in situ spectroreflectance data from sample plots of the shortgrass prairie shows that green biomass, chlorophyll concentration, and leaf water content are directly interrelated to that composite property of the plot which is called functioning green biomass. Spectrocorrelation data indicate the spectral regions of optimum sensitivity for a remote estimation of the green biomass, chlorophyll, and leaf water content. The near-infrared region of the spectrum shows a high positive spectrocorrelation to these three sample parameters, regardless of the amount of standing dead vegetation.

Tucker, C. J.↗