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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Spring-Summer Temperatures Since AD 1780 Reconstructed from Stable Oxygen Isotope Ratios in White Spruce Tree-Rings from the Mackenzie Delta, Northwestern Canada

High-latitude delta(exp 18)O archives deriving from meteoric water (e.g., tree-rings and ice-cores) can provide valuable information on past temperature variability, but stationarity of temperature signals in these archives depends on the stability of moisture source/trajectory and precipitation seasonality, both of which can be affected by atmospheric circulation changes. A tree-ring delta(exp 18)O record (AD 1780-2003) from the Mackenzie Delta is evaluated as a temperature proxy based on linear regression diagnostics. The primary source of moisture for this region is the North Pacific and, thus, North Pacific atmospheric circulation variability could potentially affect the tree-ring delta(exp 18)O-temperature signal. Over the instrumental period (AD 1892-2003), tree-ring delta(exp 18)O explained 29% of interannual variability in April-July minimum temperatures, and the explained variability increases substantially at lower-frequencies. A split-period calibration/verification analysis found the delta(exp 18)O-temperature relation was time-stable, which supported a temperature reconstruction back to AD 1780. The stability of the delta(exp 18)O-temperature signal indirectly implies the study region is insensitive to North Pacific circulation effects, since North Pacific circulation was not constant over the calibration period. Simulations from the NASA-GISS ModelE isotope-enabled general circulation model confirm that meteoric delta(exp 18)O and precipitation seasonality in the study region are likely insensitive to North Pacific circulation effects, highlighting the paleoclimatic value of tree-ring and possibly other delta(exp 18)O records from this region. Our delta(exp 18)O-based temperature reconstruction is the first of its kind in northwestern North America, and one of few worldwide, and provides a long-term context for evaluating recent climate warming in the Mackenzie Delta region.

Canada↗
DOE OSTI2022

MIONet: Learning Multiple-Input Operators via Tensor Product

As an emerging paradigm in scientific machine learning, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. Several neural operators have been recently developed. However, all the existing neural operators are only designed to learn operators defined on a single Banach space; i.e., the input of the operator is a single function. Here, for the first time, we study the operator regression via neural networks for multiple-input operators defined on the product of Banach spaces. We first prove a universal approximation theorem of continuous multiple-input operators. We also provide a detailed theoretical analysis including the approximation error, which provides guidance for the design of the network architecture. Based on our theory and a low-rank approximation, we propose a novel neural operator, MIONet, to learn multiple-input operators. MIONet consists of several branch nets for encoding the input functions and a trunk net for encoding the domain of the output function. Here, we demonstrate that MIONet can learn solution operators involving systems governed by ordinary and partial differential equations. In our computational examples, we also show that we can endow MIONet with prior knowledge of the underlying system, such as linearity and periodicity, to further improve accuracy.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

Cross-calibration of liquid and solid QCT calibration standards: corrections to the UCSF normative data

Quantitative computed tomography (QCT) has been shown to be a precise and sensitive method for evaluating spinal bone mineral density (BMD) and skeletal response to aging and therapy. Precise and accurate determination of BMD using QCT requires a calibration standard to compensate for and reduce the effects of beam-hardening artifacts and scanner drift. The first standards were based on dipotassium hydrogen phosphate (K2HPO4) solutions. Recently, several manufacturers have developed stable solid calibration standards based on calcium hydroxyapatite (CHA) in water-equivalent plastic. Due to differences in attenuating properties of the liquid and solid standards, the calibrated BMD values obtained with each system do not agree. In order to compare and interpret the results obtained on both systems, cross-calibration measurements were performed in phantoms and patients using the University of California San Francisco (UCSF) liquid standard and the Image Analysis (IA) solid standard on the UCSF GE 9800 CT scanner. From the phantom measurements, a highly linear relationship was found between the liquid- and solid-calibrated BMD values. No influence on the cross-calibration due to simulated variations in body size or vertebral fat content was seen, though a significant difference in the cross-calibration was observed between scans acquired at 80 and 140 kVp. From the patient measurements, a linear relationship between the liquid (UCSF) and solid (IA) calibrated values was derived for GE 9800 CT scanners at 80 kVp (IA = [1.15 x UCSF] - 7.32).(ABSTRACT TRUNCATED AT 250 WORDS).

NASA Program Space Physiology and Countermeasures↗
NASA NTRS2020

Very High Resolution, Altitude-Corrected, TMPA-Based Monthly Satellite Precipitation Product Over the CONUS

The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) product provided over 17 years of gridded precipitation datasets. However, the accuracy and spatial resolution of TMPA limits the applicability in hydrometeorological applications. We present a dataset that enhances the accuracy and spatial resolution of the TMPA monthly product (3B43). We resample the TMPA data to a 1 km grid and apply a correction function derived from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) to reduce bias in the data. We confirm a linear relationship between bias and elevation above 1,500 meters where TMPA underestimates measured precipitation, providing a proof-of-concept of how simple linear scaling can be used to augment existing satellite datasets. The result of the correction is the High-Resolution Altitude-Corrected Precipitation product (HRAC-Precip) for the CONUS. Using 9,200 precipitation stations from the Global Historical Climatology Network (GHCN), we compare the accuracy of TMPA 3B43 versus the new HRAC-Precip product. The results show an improvement of the mean absolute error of 12.98% on average.

Hossein Hashemi↗
DOE OSTI2022

Star–Galaxy Image Separation with Computationally Efficient Gaussian Process Classification

Abstract We introduce a novel method for discerning optical telescope images of stars from those of galaxies using Gaussian processes (GPs). Although applications of GPs often struggle in high-dimensional data modalities such as optical image classification, we show that a low-dimensional embedding of images into a metric space defined by the principal components of the data suffices to produce high-quality predictions from real large-scale survey data. We develop a novel method of GP classification hyperparameter training that scales approximately linearly in the number of image observations, which allows for application of GP models to large-size Hyper Suprime-Cam Subaru Strategic Program data. In our experiments, we evaluate the performance of a principal component analysis embedded GP predictive model against other machine-learning algorithms, including a convolutional neural network and an image photometric morphology discriminator. Our analysis shows that our methods compare favorably with current methods in optical image classification while producing posterior distributions from the GP regression that can be used to quantify object classification uncertainty. We further describe how classification uncertainty can be used to efficiently parse large-scale survey imaging data to produce high-confidence object catalogs.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1992

A statistical analysis of elevated temperature gravimetric cyclic oxidation data of 36 Ni- and Co-base superalloys based on an oxidation attack parameter

A large body of high temperature cyclic oxidation data generated from tests at NASA Lewis Research Center involving gravimetric/time values for 36 Ni- and Co-base superalloys was reduced to a single attack parameter, K(sub a), for each run. This K(sub a) value was used to rank the cyclic oxidation resistance of each alloy at 1000, 1100, and 1150 C. These K(sub a) values were also used to derive an estimating equation using multiple linear regression involving log(sub 10)K(sub a) as a function of alloy chemistry and test temperature. This estimating equation has a high degree of fit and could be used to predict cyclic oxidation behavior for similar alloys and to design an optimum high strength Ni-base superalloy with maximum high temperature cyclic oxidation resistance. The critical alloy elements found to be beneficial were Al, Cr, and Ta.

Barrett, Charles A.↗
NASA NTRS1988

Efficient load measurements using singular value decomposition

Various basic research was performed on efficient load measurement estimation techniques for aircraft structure analysis. An overview is presented of the load measurement problem. Two basic equivalent approaches to load measurement evaluations were considered. Under approach 1, the load values are modeled as depending linearly on the measured values. Under approach 2, the measured values depend linearly on the load values. By using the modern Singular Value Decomposition method, it was shown that under all conditions of the number of loads and number of gages, approach 1 is equivalent to approach 2. By using the conventional normal equation (linear regression) approach, approach 1 is only valid when the number of loads is equal to or greater than the number of gages, while approach 2 is the reverse. Furthermore, except for the case of the number of loads equals the number of gages, the load prediction formulas under the two approaches are not equivalent.

Yao, Kung↗
DOE OSTI2026

Meteoric 10Be Flux Calibration Data for the East River Watershed, Colorado, USA

This data package contains tabular and geospatial data used to quantify and model meteoric beryllium-10 fluxes in the East River watershed, Colorado, USA. The tabular component includes calibration-site data from five glacial moraine sites and includes environmental variables used to evaluate spatial controls on meteoric 10Be delivery, including elevation, mean annual precipitation (MAP), mean snow depth, and mean snow water equivalent (SWE). These site-level data were used to compare observed fluxes with environmental gradients across the watershed and to evaluate the effects of erosion correction on flux estimates. The package also includes supporting slope and curvature values used to assess topographic inputs to the erosion analysis. A second component of the data package contains updated manuscript tables and regression outputs used to summarize the relationships between meteoric 10Be flux and environmental predictors. These tables include meteoric 10Be sample information and AMS results, site-level environmental values, site-level meteoric 10Be inventory and flux values, watershed-averaged predicted fluxes, soil bulk density measurements, fine-fraction values, soil pH measurements, and regression statistics including slope, intercept, coefficient of determination, and p-value. The regression products include both standard linear regressions and regressions in which the intercept is constrained to pass through zero, and they support the analyses presented in the companion manuscript. Together, these tabular files provide the numerical basis for the manuscript tables and the regression-based interpretation of meteoric 10Be flux variability in a snow-dominated mountain watershed. The geospatial component of the package consists of GeoTIFF raster files used to generate the map products presented in Figures 2 and 6 of the companion manuscript. These rasters represent watershed-scale spatial layers for environmental variables and regression-based predictions of meteoric 10Be flux. This dataset contains comma-separated values files (.csv), Microsoft Excel files (.xlsx), GeoTIFF raster files (.tif), and upporting metadata files, including CSV data dictionaries and readme text files (.csv, .txt). The tabular files can be opened with standard spreadsheet software, and the raster files can be viewed and analyzed in GIS software such as ArcGIS Pro or QGIS. Together, these files document the numerical and spatial datasets used to calibrate and predict meteoric 10Be delivery in the East River watershed.

East River↗
DOE OSTI2024

Optical image analysis for graphene layer detection: Enhanced green channel methodology

Graphene, a material of increasing research interest, requires accurate layer identification due to its sensitivity to layer count. Existing methods for graphene layer number identification are either time-consuming or of low accuracy, with high-accuracy methods often requiring expensive processes. This paper aims to address this challenge by proposing a cost-effective and efficient approach. Specifically, the current work highlights only the green channel—one of the three primary color channels (red, green, blue) that make up an optical image—from images of exfoliated graphene flakes for layer count identification. A linear regression is performed between pixel position and substrate green channel value, and this effect is subtracted from the entire optical image to mitigate background effects. By storing the range of green channel values for each type of flake (monolayer, bilayer, or tri-layer) based on a few images, we establish thresholds for identifying different types of layers in a particular setup. Additionally, our methodology allows for flexible threshold tuning using a single reference image, enabling adjustment to changes in detection setup such as illumination level, magnification, or microscope used. Finally, demonstrating high accuracy and flexibility, this methodology presents a suitable technique for graphene layer number identification without the need for large datasets or expensive instruments.

2D materials↗
DOE OSTI2024

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Ascencio-Medina, Estefania↗
DOE OSTI2022

Peak Rain Rate Sensitivity to Observed Cloud Condensation Nuclei and Turbulence in Continental Warm Shallow Clouds During CACTI

Abstract Warm clouds strongly affect Earth's energy budget but remain imperfectly represented in climate models, partly due to the complexity and covariability of relevant processes influencing warm rain. This work presents a detailed analysis of different factors affecting rain rate peak intensity (RR) in continental warm clouds. Clouds were identified with vertically pointing radar and lidar observations and categorized via a temperature‐based cloud type classification algorithm from which warm clouds were isolated. Observations and retrievals of liquid water path (LWP), cloud condensation nuclei concentration (N CCN ), cloud depth, and cloud duration of more than 3,000 separate warm clouds sampled during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are analyzed in this work. Multiple linear regression (MLR) and random forest (RF) models are applied to assess the relative impact of these variables on RR. Overall, RR tends to increase as cloud depth, LWP, and cloud duration increase, or N CCN decreases. Cloud depth affects RR the most while N CCN impacts it the least. When considering over 170 warm clouds observed at least 1 hr in which in‐cloud turbulence is retrieved, the effect of N CCN on RR remains most likely suppressive, but it is not significant at a 75% level for MLR and is highly uncertain for RF. The impact of in‐cloud turbulence depends on the moment and location it is sampled. Cloud base turbulence around the time of RR suppresses RR, while cloud top turbulence effects are inconclusive. Possible difficulties in isolating robust CCN and turbulence effects on RR are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using leaf and stomatal traits to predict biomass production and water use efficiency in Populus

Climate change is reshaping ecosystems, driving plants to adapt through leaf-trait plasticity that reflects strategies for growth and water use. Predicting biomass production and intrinsic water use efficiency (iWUE) remains challenging because of genetic, taxonomic, and environmental variability. Here, we used eastern cottonwood and Populus hybrids as a model system to test whether easily measurable leaf traits can serve as reliable predictors of performance, and whether adding stomatal and biochemical traits improves predictive power. Across two field sites in Mississippi, leaf mass per area (LMA), biomass production, iWUE, leaf area, and foliar nitrogen ( N %) differed significantly among taxa and sites, while other traits were conserved. Factorial analysis of mixed data (FAMD) revealed distinct clustering of taxa and sites, indicating coordinated variation among leaf and stomatal traits. Pairwise correlations highlighted fundamental trade-offs, with biomass positively related to LMA and petiole length but negatively associated with iWUE, N %, and carbon isotopic ratios (δ 13 C). Leaf temperature and leaf angle varied among taxa and were significantly correlated with LMA and petiole length, suggesting mechanisms of heat dissipation and leaf movability that link simple traits to gas exchange and productivity. Weighted multiple linear regression models explained 80%–91% of variation in biomass production and iWUE. Models using only LMA, petiole length, and stomatal metrics performed nearly as well as those incorporating N %, and δ 13 C, with complex traits adding approximately 10% explanatory power. These results demonstrate that simple morphological traits capture integrated functional trade-offs, while complex traits refine predictions. This tiered approach provides an efficient framework for selecting high-yielding, water-efficient genotypes of Populus and other hardwood species, offering practical pathways to enhance carbon uptake and iWUE under climate change.

biomass production↗
DOE OSTI2025

Data and scripts associated with “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA"

This data package is associated with the publication “Allometric scaling of hyporheic respiration across basins in the Pacific Northwest USA” submitted to JGR-Biogeosciences (Regier et al. 2025).This study used reach-scale modeled estimates of hyporheic aerobic respiration made by the River Corridor Model (Fang et al. 2020) and watershed characteristics across the Willamette and Yakima River basins to explore potential allometric scaling (i.e., power-law relationships between size and function) of cumulative hyporheic respiration across catchment-to-basin scales. Scaling was explored quantitatively via the R2, slope, and y-intercept of relationships between cumulative hyporheic respiration and watershed area, divided into hyporheic exchange flux (HEF) quantiles. We also explored relationships between allometric scaling and other watershed characteristics through linear regression, spatial patterns, and mutual information analyses. Our results also suggest variability of hyporheic respiration allometry for middle exchange flux quantiles, and in relation to land-cover. Our findings provide initial evidence that allometric scaling may be useful for predicting hyporheic biogeochemical dynamics across watersheds from reach to basin scales. This data package is associated with the GitHub repository found at https://github.com/peterregier/rc_wrb_yrb_scaling. The data package is organized into several key directories. The “data” folder contains multiple CSV files, including landscape heterogeneity, scaling analysis, and watershed boundary data. The “figures” folder has all figure files in both PDF and PNG formats. Core analysis scripts and figure generation scripts are in the “scripts” directory, systematically numbered for sequential execution. The root directory includes essential project files; please see the file ending in “flmd.csv” for a list and description of all files contained in this data package and the file ending in “dd.csv” for data dictionaries used to describe tabular column headers.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

An Empirical Cumulus Parameterization Scheme for a Global Spectral Model

Realistic vertical heating and drying profiles in a cumulus scheme is important for obtaining accurate weather forecasts. A new empirical cumulus parameterization scheme based on a procedure to improve the vertical distribution of heating and moistening over the tropics is developed. The empirical cumulus parameterization scheme (ECPS) utilizes profiles of Tropical Rainfall Measuring Mission (TRMM) based heating and moistening derived from the European Centre for Medium- Range Weather Forecasts (ECMWF) analysis. A dimension reduction technique through rotated principal component analysis (RPCA) is performed on the vertical profiles of heating (Q1) and drying (Q2) over the convective regions of the tropics, to obtain the dominant modes of variability. Analysis suggests that most of the variance associated with the observed profiles can be explained by retaining the first three modes. The ECPS then applies a statistical approach in which Q1 and Q2 are expressed as a linear combination of the first three dominant principal components which distinctly explain variance in the troposphere as a function of the prevalent large-scale dynamics. The principal component (PC) score which quantifies the contribution of each PC to the corresponding loading profile is estimated through a multiple screening regression method which yields the PC score as a function of the large-scale variables. The profiles of Q1 and Q2 thus obtained are found to match well with the observed profiles. The impact of the ECPS is investigated in a series of short range (1-3 day) prediction experiments using the Florida State University global spectral model (FSUGSM, T126L14). Comparisons between short range ECPS forecasts and those with the modified Kuo scheme show a very marked improvement in the skill in ECPS forecasts. This improvement in the forecast skill with ECPS emphasizes the importance of incorporating realistic vertical distributions of heating and drying in the model cumulus scheme. This also suggests that in the absence of explicit models for convection, the proposed statistical scheme improves the modeling of the vertical distribution of heating and moistening in areas of deep convection.

Rajendran, K.↗
NASA NTRS2000

Estimation of EOP From VLBI: Direct Approach

The currently adopted strategy of Earth Orientation Parameters (EOP) estimation from Very Long Baseline Interferometry (VLBI) is to estimate six parameters: Universal Time 1 (UT1), UT1 rate, pole positions, and nutation offsets for each 24-hour session independently. Then the resulting time series of raw EOP are filtered and a regression analysis is performed to obtain nutation coefficients, polhode of the pole, and other physical parameters. Thus, the latter parameters are obtained indirectly in two stages. An alternative approach of direct estimation of the final EOP is presented. Pole coordinates and UT1 are considered as a sum of three components: the low-period component that is modeled by a cubic spline, the harmonic component that includes forced nutation, precession and sub-daily variations of EOP, and the stochastic component that is modeled by a linear spline with segment length 1-2 hours. All parameters are obtained in a single LSQ solution using all available data.

POLAR MOTION↗
NASA NTRS2018

Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis

The tropical rainforests are the largest reserves of terrestrial carbon and, therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and on-going drought in the Congo region for more than two decades, there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. However, there is no single ecosystem model that quantifies the relationship between vegetation health in these rainforests and climatic factors. Small scale studies have used statistical correlation measure and simple linear regression to model climate-vegetation interactions, but suffer from the lack of comprehensive data representation as well as simplistic assumptions about dependency of the target on the covariates. In this paper we use genetic programming (GP) based symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space to build individual models for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Our method GP-tree shows that the rainforests exhibit tremendous resiliency in the face of extreme climatic events by adapting to changing conditions.

genetic programming↗
DOE OSTI2023

Proper orthogonal descriptors for efficient and accurate interatomic potentials

Here, we present the proper orthogonal descriptors for efficient and accuracy representation of the potential energy surface. The potential energy surface is represented as a many-body expansion of parametrized potentials in which the potentials are functions of atom positions and parameters. The proper orthogonal decomposition is employed to decompose the parametrized potentials into a set of proper orthogonal descriptors (PODs). Because of the rapid convergence of the proper orthogonal decomposition, relevant snapshots can be sampled exhaustively to represent the atomic neighborhood environment accurately with a small number of descriptors. The proper orthogonal descriptors are used to develop interatomic potentials by using a linear expansion of the descriptors and determining the expansion coefficients from a weighted least-squares regression against a density functional theory (DFT) training set. We present a comprehensive evaluation of the POD potentials on previously published DFT data sets comprising Li, Mo, Cu, Ni, Si, Ge, and Ta elements. The data sets represent a diverse pool of metals, transition metals, and semiconductors. The accuracy of the POD potentials are comparable to that of state-of-the-art machine learning potentials such as the spectral neighbor analysis potential (SNAP) and the atomic cluster expansion (ACE).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

Resolving the percentage of component terrains within single resolution elements

An approximate maximum likelihood technique employing a widely available discriminant analysis program is discussed that has been developed for resolving the percentage of component terrains within single resolution elements. The method uses all four channels of Landsat data simultaneously and does not require prior knowledge of the percentage of components in mixed pixels. It was tested in five cases that were chosen to represent mixtures of outcrop, soil and vegetation which would typically be encountered in geologic studies with Landsat data. For all five cases, the method proved to be superior to single band weighted average and linear regression techniques and permitted an estimate of the total area occupied by component terrains to within plus or minus 6% of the true area covered. Its major drawback is a consistent overestimation of the pixel component percent of the darker materials (vegetation) and an underestimation of the pixel component percent of the brighter materials (sand).

Marsh, S. E.↗