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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS1943

The Strength of Thin-wall Cylinders of D Cross Section in Combined Pure Bending and Torsion

The results of tests of 56 cylinders of D cross section conducted in the Aeronautical Laboratory of the University of Maryland are presented in this report. These cylinders were subjected to pure bending and torsional moments of varying proportions to give the strength under combined loading conditions. The average buckling stress of these cylinders has been related to that of circumscribing circular cylinders for conditions of pure torsion and pure bending and the equation of the interaction curve has been determined for conditions of combined loading.

CYLINDERS, CIRCULAR - BUCKLING↗
NASA NTRS1996

Dependence of the Normalized Radar Cross Section of Water Waves on Bragg Wavelength-Wind Speed Sensitivity

Measurements of the normalized radar cross section (sigma(sup o)) made by the YSCAT ultrawideband scatterometer during an extended deployment on the Canada Centre for Inland Waters(CCIW) Research Tower located at Lake Ontario are analyzed and compared with anemometer wind measurements to study the sensitivity of (sigma(sup o)) to the wind speed as a function of the Bragg wavelength. This paper concentrates on upwind and downwind azimuth angles in the wind speed range of 4.5-12 m/s. While YSCAT collected measurements of sigma(sup o) at a variety of frequencies and incidence angles, this paper focuses on frequencies of 2.0, 3.05, 5.30, 10.02, and 14.0 GHz and incidence angles within the Bragg regime, 30-50 deg. Adopting a power law model to describe the relationship between sigma(sup o) and wind speed, both wind speed exponents and upwind/downwind (u/d) ratios of sigma(sup o) are found using least squares linear regression. The analysis of the wind speed exponents and u/d ratios show that shorter Bragg wavelengths (Lambda less than 4 cm) are the most sensitive to wind speed and direction. Additionally, vertical polarization (V-pol) sigma(sup o) is shown to be more sensitive to wind speed than horizontal polarization (H-pol) sigma(sup o), while the H-pol u/d ratio is larger than the V-pol u/d ratio.

Long, David G.↗
NASA NTRS2010

Standard Deviation of Spatially-Averaged Surface Cross Section Data from the TRMM Precipitation Radar

We investigate the spatial variability of the normalized radar cross section of the surface (NRCS or Sigma(sup 0)) derived from measurements of the TRMM Precipitation Radar (PR) for the period from 1998 to 2009. The purpose of the study is to understand the way in which the sample standard deviation of the Sigma(sup 0) data changes as a function of spatial resolution, incidence angle, and surface type (land/ocean). The results have implications regarding the accuracy by which the path integrated attenuation from precipitation can be inferred by the use of surface scattering properties.

Meneghini, Robert↗
DOE OSTI2022

Estimating mass-absorption cross-section of ambient black carbon aerosols: theoretical, empirical, and machine learning models

The mass-absorption cross-section of black carbon (MAC BC ) is an essential parameter to link the atmospheric concentration of black carbon (BC) with its radiative forcing. When a direct calculation of MAC BC based on observations of aerosol light absorption and BC mass concentration is impossible, we rely on modeling and simulations to estimate MAC BC , but currently, there is no consensus model that can be relied on for accurate predictions across all atmospheric environments when BC particles have different coating thicknesses. Here, we applied five MAC BC prediction models (including three light scattering theories, an empirical model based on observations of particle mass concentrations, and a machine learning model developed in our previous work) to aerosols from three Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) field campaigns. While many studies have found that increasing the complexity of the models helps to constrain biases of the estimated MAC BC , our effort is to evaluate the models based on the criteria of simplicity and accuracy. We find that our machine learning model (support vector machine for regression, SVM) generally performs well across all DOE ARM field campaign data, while the accuracy of core-shell Mie theory depends on the bias correction algorithm applied to filter-based light absorption data. Generally, the empirical model for internally-mixed particles that we considered tends to over-predict MAC BC , while Mie theory for externally-mixed particles tends to under-predict MAC BC . An examination of the influence of coating material on BC cores suggests that the performance of our current SVM model is degraded when the BC is thickly-coated (e.g., it has undergone aging and mixing with other materials in the atmosphere).

54 ENVIRONMENTAL SCIENCES↗