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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

A simplified method for the calculation of diurnally averaged photodissociation rates

Expressions are given for the calculation of the diurnally averaged transmission of solar flux from a series summation involving relatively few terms and readily available mathematical functions. It is shown that these expressions can be scaled to remove much of the dependence on latitude and solar declination so that at large optical depth, they become asymptotic to a function which depends only on the optical depth at noon and displays an inverse-square-root variation. From this variation, a simple analytic form containing one adjustable parameter is proposed. The functional dependence of this parameter on latitude and solar declination is determined by numerical fitting procedures. The accuracy of the analytic approximation is better than 4.5% for all altitudes, latitudes and seasons of the year.

Boughner, R. E.↗
NASA NTRS1991

Comparison of two atmospheric correction models for a vegetated Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene

Current atmospheric correction models applied to imaging spectroscopy data include such methods as residual (scene average) and flat field correction, regression method, and the LOWTRAN 7 method. Due to the limitations of using residual and flat field corrections on vegetated scenes, regression and LOWTRAN 7 are compared. Field measured targets taken at the time of the 13 April, 1989 AVIRIS overflight of Jasper Ridge, California (U.S.) were used to formulate the regression atmospheric correction. Assuming the regressed image represents ground truth, results show that the LOWTRAN 7 method with radiosonde data does not compensate as well for atmospheric water vapor as the regression method, but it may be easier to obtain a posteriori information to perform the LOWTRAN 7 atmospheric correction.

Vandenbosch, Jeannette Marie↗
NASA NTRS1997

Rainfall Estimation Over Tropical Oceans: Area Average Rain Rate - 1

Multichannel dual polarization microwave radiometer SSM/I observations over oceans do not contain sufficient information to differentiate quantitatively the rain from other hydrometeors on a scale comparable to the radiometer field of view (approx. 30 km). For this reason we have developed a method to retrieve average rain rate over a mesoscale grid box of approx. 300 x 300 sq km area over the TOGA COARE region where simultaneous radiometer and radar observations are available for four months (Nov. 92 to Feb. 93). The rain area in the grid box, inferred from the scattering depression due to hydrometeors in the 85 Ghz brightness temperature, constitutes a key parameter in this method. Then the spectral and polarization information contained in all the channels of the SSM/I is utilized to deduce a second parameter. This is the ratio S/E of scattering index S, and emission index E calculated from the SSM/I data. The rain rate retrieved from this method over the mesoscale area can reproduce the radar observed rain rate with a correlation coefficient of about 0.85. Furthermore monthly total rainfall estimated from this method over that area has an average error of about 15%.

Cuddapah, Prabhakara↗
NASA NTRSDate not supplied

Exploring Unstructured Mesh Adaptation for Hybrid Reynolds-Averaged Navier–Stokes/Large Eddy Simulation

Mesh adaptation methods for the Reynolds-averaged Navier–Stokes (RANS) equations are rapidly maturing and beginning to impact the design of aerospace vehicles. RANS turbulence modeling improvements have slowed and may stagnate. Wall-modeled large eddy simulation (LES) and hybrid RANS/LES (HRLES) may provide an improved modeling capability but require specialized expertise to construct appropriate meshes and are considered be too ex-pensive for routine practical use. The realization of the CFD Vision 2030 Study includes improving geometry linkage, mesh generation/adaptation, and turbulence modeling/resolving methods for automated management of errors and uncertainties of physics-based, predictive modeling that can set the stage for ensuring a vehicle is in compliance with a regulation or specification (i.e., certification or qualification by analysis). An exploration of mesh adaptation for HRLES is performed to document synergies and challenges between mesh adaptation and HRLES. Vortex breakdown over a delta wing is examined to show the improvement of HRLES over RANS turbulence modeling approaches. A high lift configuration is shown to demonstrate complex geometry capability. Research and development opportunities are identified to advocate for continuing investments that may allow HRLES to enter routine practical use as a tool for aerospace vehicle analysis and design.

Michael A Park↗
DOE OSTI2026

Quantifying mean, variability, and uncertainty in indoor radon exposure in Pennsylvania using random forest and quantile regression forest models

Radon is a naturally occurring radioactive gas that poses a serious health risk as the primary cause of lung cancer in non-smokers. Despite the well-known adverse association with health outcomes, current radon exposure assessments are limited to county-level or average-level estimates, which fail to capture regional variability. This study uses Machine Learning models, including Random Forest (RF) and Quantile Regression Forest (QRF), to estimate the indoor radon concentrations at the ZCTA (Zip code tabulation area)-level and characterize uncertainties in model estimates. Incorporating geological, meteorological, and building-specific data, the models aim to improve radon risk assessment by capturing mean exposure, variability, and extreme concentration levels. Processed radon test data (n = 718,111) were analyzed using average, variability, and quantile prediction methods. Models that estimate the average radon exposure at the ZCTA-level can yield promising model-fit results, but they do not capture the underlying variability of indoor radon exposure within a ZCTA. We utilize volatility analyses to identify characteristics indicative of high variability of indoor radon exposure. We also show that a QRF model can be used to estimate upper quantiles of residential radon exposure, thereby uncovering localized areas of elevated exposure that were not apparent in mean estimates. The results highlighted the need for a deep characterization of exposure risk and show that regions with moderate average exposure levels could still harbor extreme outliers with implications for evaluating health risks. Utilizing multiple radon exposure models allows for a deeper characterization of radon risk within a geographic area and can better identify high-risk areas. The results from this study provide a foundation for developing mitigation strategies and examining associations between radon exposure and health outcomes at fine scales. Future research should extend the geographic scope and incorporate additional environmental risk factors to establish a comprehensive framework for risk assessment.

Lee, Heechan [ORNL]↗
NASA NTRS1991

Comparison of three atmospheric correction models for a vegetated airborne visible/infrared imaging spectrometer (AVIRIS) scene

Current atmospheric correction models applied to imaging spectroscopy data include such methods as residual or scene average, flat field correction, regression method or empirical line algorithm, the continuum interpolated band ratio (CIBR) derivation and the LOWTRAN 7 method. Due to the limitations of using residual and flat field corrections on vegetated scenes, three methods will be compared: regression, CIBR derivation and LOWTRAN 7. Field-measured bright and dark targets taken at the time of the 13 April, 1989 AVIRIS overflight of Jasper Ridge, California were used to formulate the regression method atmospheric correction. Using this corrected scene as 'ground truth', the CIBR derivation and the LOWTRAN 7 method with both input models are compared on the vegetated Jasper Ridge scene. Although representing a qualitative approach, this is a first approximation and shows the need for more quantitative analysis.

Van Den Bosch, J. M.↗
NASA NTRS1992

Sea ice classification using fast learning neural networks

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Dawson, M. S.↗
NASA NTRS1996

Estimating Engine Airflow in Gas-Turbine Powered Aircraft with Clean and Distorted Inlet Flows

The P404-GF-400 Powered F/A-18A High Alpha Research Vehicle (HARV) was used to examine the impact of inlet-generated total-pressure distortion on estimating levels of engine airflow. Five airflow estimation methods were studied. The Reference Method was a fan corrected airflow to fan corrected speed calibration from an uninstalled engine test. In-flight airflow estimation methods utilized the average, or individual, inlet duct static- to total-pressure ratios, and the average fan-discharge static-pressure to average inlet total-pressure ratio. Correlations were established at low distortion conditions for each method relative to the Reference Method. A range of distorted inlet flow conditions were obtained from -10 deg. to +60 deg. angle of attack and -7 deg. to +11 deg. angle of sideslip. The individual inlet duct pressure ratio correlation resulted in a 2.3 percent airflow spread for all distorted flow levels with a bias error of -0.7 percent. The fan discharge pressure ratio correlation gave results with a 0.6 percent airflow spread with essentially no systematic error. Inlet-generated total-pressure distortion and turbulence had no significant impact on the P404-GE400 engine airflow pumping. Therefore, a speed-flow relationship may provide the best airflow estimate for a specific engine under all flight conditions.

Williams, J. G.↗
NASA NTRS1989

Numerical modeling of turbulent supersonic reacting coaxial jets

The paper considers the mixing and subsequent combustion within turbulent reacting shear layers. A computer program was developed to solve the axisymmetric Reynolds averaged Navier-Stokes equations. The numerical method integrates the Reynolds averaged Navier-Stokes equations using a finite volume approach while advancing the solution forward in time using a Runge-Kutta scheme. Three separate flowfields are investigated and it is found that no single turbulence model considered could accurately predict the degree of mixing for all three cases.

Eklund, Dean R.↗
NASA NTRS1991

The application of cost averaging techniques to robust control of the benchmark problem

A method is presented for the synthesis of robust controllers for linear time invariant systems with parameterized uncertainty structures. The method involves minimizing the average quadratic (H2) cost over the parameterized system. Bonded average cost implies stability over the set of systems. The average cost functional is minimized to derive robust fixed-order dynamic compensators. The robustness properties of these controllers are demonstrated on the sample problem.

Hagood, Nesbitt W.↗
NASA NTRS1995

CFD Methods and Tools for Multi-Element Airfoil Analysis

This lecture will discuss the computational tools currently available for high-lift multi-element airfoil analysis. It will present an overview of a number of different numerical approaches, their current capabilities, short-comings, and computational costs. The lecture will be limited to viscous methods, including inviscid/boundary layer coupling methods, and incompressible and compressible Reynolds-averaged Navier-Stokes methods. Both structured and unstructured grid generation approaches will be presented. Two different structured grid procedures are outlined, one which uses multi-block patched grids, the other uses overset chimera grids. Turbulence and transition modeling will be discussed.

Rogers, Stuart E.↗
NASA NTRSDate not supplied

F-ANG+: A 3-D Augmented-Stencil Face-Averaged Nodal-Gradient Cell-Centered Finite-Volume Method for Hypersonic Flows

We describe the extension of a 2-D simplified face-averaged nodal-gradient (F-ANG) method to 3-D and demonstrate that the 3-D simplified F-ANG method is accomplished by augmenting the nodecentered gradient least squares stencil. This augmented stencil F-ANG method is shown to result in advection and diffusion schemes that are stable for hexahedral, prismatic, pyramidal and tetrahedral cells without having to resort to cell-averaged nodal gradients. In addition, we describe the modifications to the augmented stencil required to support the use of wall function boundary conditions. Finally we describe a consistent, face-stencil based multi-dimensional limiter procedure (MLP), and show it to be fully consistent and compatible with the linearity-preserving unstructured- MUSCL (LP-U-MUSCL) scheme for all values of kappa. These methods and schema are implemented in the cell-centered finite-volume code VULCAN-CFD, which is then used to investigate whether the robustness improvements demonstrated in 2-D carry over to 3-D by computing hypersonic flows using mixed-element grids as well as highly adapted tetrahedral grids.

Weighted Least-Squares↗
NASA NTRSDate not supplied

Regional Variations in the Diurnal Cycle of Tropical Precipitation as Represented by IMERG, ERA5, and Spaceborne Ku Radar

The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing effects that can be highly localized. The diurnal cycle in high precipitation regions such as the Amazon and the Maritime Continent are of particular interest, especially due to the complex coastal effects which take place over the Maritime Continent. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset, is used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-year (2000 – 2018) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on precipitation averaged for each half-hour of the day at 0.1°x0.1° spatial resolution. We first introduce an objective method of identifying locations where the diurnal signals are robust and strong. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broad scale patterns of diurnal variability but does not capture all the fine scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

L J Hayden↗
NASA NTRSDate not supplied

A 3-D Nodal-Averaged Gradient Approach for Unstructured-Grid Cell-Centered Finite-Volume Methods for Application to Turbulent Hypersonic Flow

A 2-D nodal weighted least-squares gradient method and a related face-averaged nodal gradient approach that were developed for use with triangular grids are extended to 3-D for use with tetrahedral grids. In addition, a method, developed in 2-D, to stabilize the iterative convergence of these methods on quadrilateral cells is described and extended to 3-D and remedies are investigated to determine the nodal gradient averaging approach most suitable for use with grids made up of hexahedral, prismatic, pyramidal and tetrahedral cells. Moreover, due to an interest in hypersonic flow, a robust multidimensional gradient limiter procedure that is consistent with the stencil used to construct the nodal gradients is described. Finally, we demonstrate that the resulting 3-D methods are sufficiently robust for use in scramjet computations through the solution of three canonical turbulent hypersonic flow problems as well as a physically realistic 3-D scramjet inlet geometry.

Jeffery A White↗
NASA NTRS2014

Areas of Polar Coronal Holes from 1996 Through 2010

Polar coronal holes (PCHs) trace the magnetic variability of the Sun throughout the solar cycle. Their size and evolution have been studied as proxies for the global magnetic field. We present measurements of the PCH areas from 1996 through 2010, derived from an updated perimeter-tracing method and two synoptic-map methods. The perimeter tracing method detects PCH boundaries along the solar limb, using full-disk images from the SOlar and Heliospheric Observatory/Extreme ultraviolet Imaging Telescope (SOHO/EIT). One synoptic-map method uses the line-of-sight magnetic field from the SOHO/Michelson Doppler Imager (MDI) to determine the unipolarity boundaries near the poles. The other method applies thresholding techniques to synoptic maps created from EUV image data from EIT. The results from all three methods suggest that the solar maxima and minima of the two hemispheres are out of phase. The maximum PCH area, averaged over the methods in each hemisphere, is approximately 6 % during both solar minima spanned by the data (between Solar Cycles 22/23 and 23/24). The northern PCH area began a declining trend in 2010, suggesting a downturn toward the maximum of Solar Cycle 24 in that hemisphere, while the southern hole remained large throughout 2010.

Corona↗
DOE OSTI2024

Comparison of methods for the NMR measurement of motionally averaged dipolar couplings

Motionally averaged dipolar couplings are an important tool for understanding the complex dynamics of catalysts, polymers, and biomolecules. While there is a plethora of solid-state NMR pulse sequences available for their measurement, in can be difficult to gauge the methods’ strengths and weaknesses. In particular, there has not been a comprehensive comparison of their performance in natural abundance samples, where 1H homonuclear dipolar couplings are important and the use of large MAS rotors may be required for sensitivity reasons. In this work, we directly compared some of the more common methods for measuring C–H dipolar couplings in natural abundance samples using L-alanine (L-Ala) and the N-formyl-L-methionyl-L-leucyl-L-phenylalanine (fMLF) tripeptide as model systems. Here, we evaluated their performance in terms of accuracy, resolution, sensitivity, and ease of implementation. We found that, despite the presence of 1H homonuclear dipolar interactions, all methods, with the exception of REDOR, were able to yield the reasonable dipolar coupling strengths for both mobile and static moieties. Of these methods, PDLF provides the most convenient workflow and precision at the expense of low sensitivity. In low-sensitivity cases, MAS-PISEMA and DIPSHIFT appear to be the better options.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

Hoffbeck, Joseph P.↗
DOE OSTI2022

Paw-Net: Stacking ensemble deep learning for segmenting scanning electron microscopy images of fine-grained shale samples

Segmentation of scanning electron microscopy (SEM) images is critical yet time-consuming for geological analyses, as it needs to differentiate the boundaries for different mineral objects to facilitate subsequent analyses, such as porosity calculation. Recently, various machine learning methods, especially convolutional neural networks (CNNs), have been explored to segment SEM images of fine-grained shale samples. However, we found that general CNNs do not yield optimal performance due to insufficient training data and imbalanced objects in SEM images. This work has revised the U-Net architecture, a popular approach for biomedical image analyses, by incorporating a loss function that addresses the imbalance issue. Furthermore, we used the ensemble learning method to train multiple models and combined the results to improve the overall performance of segmentation. We prepared 2162 sub-images from raw SEM images in our experiments and divided them into training, validation, and testing datasets. The overall results show that our method improves the average Intersection over Union (IOU) of mineral objects from 0.49 to 0.58, compared to the original U-Net model. Our method can clearly distinguish each object from others with boundaries, even in highly imbalanced images. Training our models takes less than three minutes using a single GPU, while manual labeling can take up to three hours for each image. Furthermore, the method helps geoscientists gain insights quickly and effectively by building neural network models from a small dataset of SEM images.

58 GEOSCIENCES↗