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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Burning rate for steel-cased, pressed binderless HMX

The burning behavior of pressed binderless HMX laterally confined in 6.4 mm i.d. steel cases was measured over the pressure range 1.45 to 338 MPa in a constant pressure strand burner. The measured regression rates are compared to those reported previously for unconfined samples. It is shown that lateral confinement results in a several-fold decrease in the regression rate for the coarse particle size HMX above the transition to super fast regression. For class E samples, confinement shifts the transition to super fast regression from low pressure to high pressure. These results are interpreted in terms of the previously proposed progressive deconsolidation mechanism. Preliminary holographic photography and closed bomb tests are also described. Theoretical one dimensional modeling calculations were carried out to predict the expected flame height (particle burn out distance) as a function of particle size and pressure for binderless HMX burning by a progressive deconsolidation mechanism.

Fifer, R. A.↗
NASA NTRS2009

Solid Rocket Booster Large Main and Drogue Parachute Reliability Analysis

The parachutes on the Space Transportation System (STS) Solid Rocket Booster (SRB) are the means for decelerating the SRB and allowing it to impact the water at a nominal vertical velocity of 75 feet per second. Each SRB has one pilot, one drogue, and three main parachutes. About four minutes after SRB separation, the SRB nose cap is jettisoned, deploying the pilot parachute. The pilot chute then deploys the drogue parachute. The drogue chute provides initial deceleration and proper SRB orientation prior to frustum separation. At frustum separation, the drogue pulls the frustum from the SRB and allows the main parachutes that are mounted in the frustum to unpack and inflate. These chutes are retrieved, inspected, cleaned, repaired as needed, and returned to the flight inventory and reused. Over the course of the Shuttle Program, several improvements have been introduced to the SRB main parachutes. A major change was the replacement of the small (115 ft. diameter) main parachutes with the larger (136 ft. diameter) main parachutes. Other modifications were made to the main parachutes, main parachute support structure, and SRB frustum to eliminate failure mechanisms, improve damage tolerance, and improve deployment and inflation characteristics. This reliability analysis is limited to the examination of the SRB Large Main Parachute (LMP) and drogue parachute failure history to assess the reliability of these chutes. From the inventory analysis, 68 Large Main Parachutes were used in 651 deployments, and 7 chute failures occurred in the 651 deployments. Logistic regression was used to analyze the LMP failure history, and it showed that reliability growth has occurred over the period of use resulting in a current chute reliability of R = .9983. This result was then used to determine the reliability of the 3 LMPs on the SRB, when all must function. There are 29 drogue parachutes that were used in 244 deployments, and no in-flight failures have occurred. Since there are no observed drogue chute failures, Jeffreys Prior was used to calculate a reliability of R =.998. Based on these results, it is concluded that the LMP and drogue parachutes on the Shuttle SRB are suited to their mission and changes made over their life have improved the reliability of the parachute.

Clifford, Courtenay B.↗
DOE OSTI2018

Genetic polymorphisms of GRIN2A and GRIN2B modify the neurobehavioral effects of low-level lead exposure in children

Highlights: • Genetic polymorphisms may increase adverse effects of Pb on neurobehavioral processes. • We studied 330 participants of the Casa Pia Dental Amalgams Trial in Children. • Adverse interactions were found with variants of GRIN2A and GRIN2B in boys. • Our results implicate genetic factors in defining susceptibility to Pb neurotoxicity. Lead (Pb) is neurotoxic and children are highly susceptible to this effect, particularly within the context of continuous low-level Pb exposure. A current major challenge is identification of children who may be uniquely susceptible to Pb toxicity because of genetic predisposition. Learning and memory are among the neurobehavioral processes that are most notably affected by Pb exposure, and modification of N-methyl-D-aspartate receptors (NMDAR) that regulate these processes during development are postulated to underlie these adverse effects of Pb. We examined the hypothesis that polymorphic variants of genes encoding glutamate receptor, ionotropic, NMDAR subunits 2A and 2B, GRIN2A and GRIN2B, exacerbate the adverse effects of Pb exposure on these processes in children. Participants were subjects who participated as children in the Casa Pia Dental Amalgam Clinical Trial and for whom baseline blood Pb concentrations and annual neurobehavioral test results over the 7 year course of the clinical trial were available. Genotyping assays were performed for variants of GRIN2A (rs727605 and rs1070503) and GRIN2B (rs7301328 and rs1806201) on biological samples acquired from 330 of the original 507 trial participants. Regression modeling strategies were employed to evaluate the association between genotype status, Pb exposure, and neurobehavioral test outcomes. Numerous significant adverse interaction effects between variants of both GRIN2A and GRIN2B, individually and in combination, and Pb exposure were observed particularly among boys, preferentially within the domains of Learning & Memory and Executive Function. In contrast, very few interaction effects were observed among similarly genotyped girls with comparable Pb exposure. These findings support observations of an essential role of GRIN2A and GRIN2B on developmental processes underlying learning and memory as well as other neurological functions in children and demonstrate, further, modification of Pb effects on these processes by specific variants of both GRIN2A and GRIN2B genes. These observations highlight the importance of genetic factors in defining susceptibility to Pb neurotoxicity and may have important public health implications for future strategies aimed at protecting children and adolescents from potential health risks associated with low-level Pb exposure.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Periodic Korteweg–de Vries soliton potentials generate quasisymmetric magnetic field strength in a finite plasma- β equilibrium

Quasisymmetry (QS) is a hidden symmetry of the magnetic field strength, B , that enables effective confinement of charged particles in a fully three-dimensional (3D) toroidal plasma equilibrium. Such equilibria are typically modeled by the ideal magnetohydrostatic (MHS) equations. The nonlinear, overdetermined nature of the QS MHS equations severely complicates our understanding of the interplay between 3D shaping, equilibrium properties such as pressure and rotational transform, and B . Progress has been made through expansions near the magnetic axis; however, a more comprehensive theory is desirable. Using a combination of analysis and regression on a large dataset of numerically optimized quasisymmetric stellarators, we demonstrate that there is a hidden lower dimensionality of B on a magnetic flux surface with connections to the theory of periodic solitons. We show that B on a flux surface is determined by three or at most four flux functions, each of which determines a critical value of the derivative of B along the field line. While being consistent with the near-axis models, our results are global and hold even on the last closed flux surface.

Differential geometry↗
DOE OSTI2015

The Geant4 physics validation repository

The Geant4 collaboration regularly performs validation and regression tests. The results are stored in a central repository and can be easily accessed via a web application. In this article we describe the Geant4 physics validation repository which consists of a relational database storing experimental data and Geant4 test results, a java API and a web application. Lastly, the functionality of these components and the technology choices we made are also described

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Investigating the effects of local environment on nitrogen vacancies in high-entropy metal nitrides

High-entropy metal nitrides are an important material class in a variety of applications, and the role of nitrogen vacancies is of great importance for understanding their stability and mechanical properties. Here, we study six different high-entropy nitrides with eight different metal species to build a predictive model of the nitrogen-vacancy formation energy. We construct sets of supercells that maximize the number of unique nitrogen environments for a given chemistry, and then use density-functional theory to calculate the energy density for all nitrogen sites, and the vacancy formation energies for the highest, lowest, and a median subset based on the energy densities. The energy density of nitrogen sites correlates with the vacancy formation energies, for binary, ternary, and high-entropy nitrides. A linear regression model predicts the vacancy formation energies using only the nearest-neighbor composition; across our eight metals, we find the largest vacancy formation energies next to Hf, then Zr, Ti, V, Cr, Ta, Nb, and the lowest near Mo. Additionally, we see that binary nitride data show qualitatively similar vacancy formation energy trends for high-entropy nitrides; however, the binary data alone are insufficient to predict the complex nitride behavior. Our model is both predictive and easily interpretable, and correlates with experimental data.

DeSilva, Charith R. [Univ. of Illinois at Urbana-C↗
DOE OSTI2021

Efficacy of a portable, moderate-resolution, fast-scanning differential mobility analyzer for ambient aerosol size distribution measurements

Abstract. Ambient aerosol size distributions obtained with a compact scanning mobility analyzer, the “Spider” differential mobility analyzer (DMA), are compared to those obtained with a conventional mobility analyzer, with specific attention to the effect of mobility resolution on the measured size distribution parameters. The Spider is a 12 cm diameter radial differential mobility analyzer that spans the 10–500 nm size range with 30 s mobility scans. It achieves its compact size by operating at a nominal mobility resolution R=3 (sheath flow = 0.9 L min−1; aerosol flow = 0.3 L min−1) in place of the higher ratio of sheath flow to aerosol flow commonly used. The question addressed here is whether the lower resolution is sufficient to capture key characteristics of ambient aerosol size distributions. The Spider, operated at R=3 with 30 s up- and downscans, was co-located with a TSI 3081 long-column mobility analyzer, operated at R=10 with a 360 s sampling duty cycle. Ambient aerosol data were collected over 26 consecutive days of continuous operation, in Pasadena, CA. Over the 17–500 nm size range, the two instruments exhibit excellent correlation in the total particle number concentrations and geometric mean diameters, with regression slopes of 1.13 and 1.00, respectively. Our results suggest that particle sizing at a lower resolution than typically employed may be sufficient to obtain key properties of ambient size distributions, at least for these two moments of the size distribution. Moreover, it enables better counting statistics, as the wider transfer function for a given aerosol flow rate results in a higher counting rate.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Verification studies of Seasat-A satellite scatterometer /SASS/ measurements

Two comparisons between Seasat-A satellite scatterometer (SASS) data and surface truth, obtained from the Gulf of Alaska Seasat Experiment and the Joint Air-Sea Interaction program, have been made to determine the behavior of SASS and its algorithms. The performance of SASS was first evaluated irrespective of the algorithms employed to convert the SASS data to geophysical parameters, which was done by separating the backscatter measurements into small bins of incidence and azimuth angles and polarity and regression against wind speed measurements. The algorithms were then tested by comparing their predicted slopes and y intercepts with those derived from the regressions, and by comparing each SASS backscatter measurement with the backscatter derived from the algorithms, and the given wind velocity from the observations. It was shown that SASS was insensitive to winds at high incidence angles for horizontal polarizations. Fairly high correlations were found between backscatter and wind speeds. The algorithms functioned well at mid-ranges of incidence angle and backscattering coefficient.

Halberstam, I.↗
NASA NTRS1981

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Barnett, T. L.↗
DOE OSTI2024

PhILMs: Collaboratory on Mathematics and Physics-Informed Learning Machines for Multiscale and Multiphysics Problems

The landscape of computational science and engineering is continually evolving, with the challenge of high-dimensional regression problems standing as a significant hurdle in numerous scientific endeavors. Addressing this challenge, our research, funded by this award, has led to the development of an innovative computational framework known as Probabilistic Partition of Unity Networks (PPOU-Nets). This initiative represents a collaborative effort to harness the potential of mathematics and physics-informed machine learning in tackling multiscale and multiphysics problems prevalent in high-dimensional spaces. Through this work, we have proposed a novel methodology that seamlessly integrates adaptive dimensionality reduction and a mixture of experts model, thereby facilitating a more efficient and accurate approximation of complex functions. This research effort has not only advanced the state of computational science but also opened new avenues for exploration in quantum computing and beyond. This report outlines the motivation, methodology, key findings, and implications of our work, underscoring our contributions to the broader scientific community and the potential pathways for future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Data-driven search for promising intercalating ions and layered materials for metal-ion batteries

The rise in demand for lithium-ion batteries has led to a large-scale search for electrode materials and intercalating ion species to meet the demands of next-generation energy technologies. Recent efforts largely focus on searching for cathodes that can accommodate large amounts of intercalating ions, but similar work on anodes is relatively limited. This study utilizes machine learning methods to find alternative two-dimensional (2D) materials and intercalating ions beyond Li for metal-ion batteries with high-power efficiencies. The approach first uses density functional theory (DFT) calculations to estimate the theoretical capacities and voltages of various metal ions on 2D materials. The DFT-generated data also provide insights into the local structural accommodation upon ion intercalation on various 2D materials. Significant changes to the lattice can result in irreversible changes to the bonding environments in the anode material, resulting in poor cycling stability. Next, this study develops a binding energy and structural accommodation-based classification model to screen anode materials for next-generation batteries. The classification model selects intercalating ions and 2D material pairs suitable for batteries based on the calculated voltage and volumetric changes in the 2D material upon intercalation. Finally, this study builds a regression model to accurately predict the binding energies of the various intercalating ions on 2D materials. The approach highlights the importance of different elemental and structural features for classification and regression tasks. In conclusion, the insights gained from this study on the role of involved features, such as electronegativities of the constituent ions and the presence of unfilled electronic levels, will help to streamline further studies towards the search for future layered battery materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Constraining primordial non-Gaussianity from DESI quasar targets and Planck CMB lensing

We detect the cross-correlation between 2.7 million DESI quasar targets across 14,700 deg 2 (180 quasars deg -2 ) and Planck 2018 CMB lensing at ~30σ. We use the cross-correlation on very large scales to constrain local primordial non-Gaussianity via the scale dependence of quasar bias. The DESI quasar targets lie at an effective redshift of 1.51 and are separated into four imaging regions of varying depth and image quality. We select quasar targets from Legacy Survey DR9 imaging, apply additional flux and photometric redshift cuts to improve the purity and reduce the fraction of unclassified redshifts, and use early DESI spectroscopy of 194,000 quasar targets to determine their redshift distribution and stellar contamination fraction (2.6%). Due to significant excess large-scale power in the quasar autocorrelation, we apply weights to mitigate contamination from imaging systematics such as depth, extinction, and stellar density. We use realistic contaminated mocks to determine the greatest number of systematic modes that we can fit, before we are biased by overfitting and spuriously remove real power. We find that linear regression with one to seven imaging templates removed per region accurately recovers the input cross-power, f NL and linear bias. As in previous analyses, our fNL constraint depends on the linear primordial non-Gaussianity bias parameter, b Φ = 2(b - p)δ c assuming universality of the halo mass function. We measure f NL = -26 +45 -40 with p = 1.6 (f NL = -18 +29 -27 with p = 1.0), and find that this result is robust under several systematics tests. Future spectroscopic quasar cross-correlations with Planck lensing can tighten the f NL constraints by a factor of 2 if they can remove the excess power on large scales in the quasar auto power spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗
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 NTRS1984

Empirical models of the eddy heat flux and vertical shear on short time scales

An intimate relation exists between the vertical shear and the horizontal eddy heat flux within the atmosphere. In the present investigation empirical means are employed to provide clues concerning the relationship between the shear and eddy heat flux. In particular, linear regression models are applied to individual and joint time series of the shear and eddy heat flux. These discrete models are used as a basis to infer continuous models. A description is provided of the observed relationship between the flux and the shear, taking into account means, standard deviations, and lag correction functions.

Ghan, S. J.↗
NASA NTRS2001

A semi-automatic method for left ventricle volume estimate: an in vivo validation study

This study aims to the validation of the left ventricular (LV) volume estimates obtained by processing volumetric data utilizing a segmentation model based on level set technique. The validation has been performed by comparing real-time volumetric echo data (RT3DE) and magnetic resonance (MRI) data. A validation protocol has been defined. The validation protocol was applied to twenty-four estimates (range 61-467 ml) obtained from normal and pathologic subjects, which underwent both RT3DE and MRI. A statistical analysis was performed on each estimate and on clinical parameters as stroke volume (SV) and ejection fraction (EF). Assuming MRI estimates (x) as a reference, an excellent correlation was found with volume measured by utilizing the segmentation procedure (y) (y=0.89x + 13.78, r=0.98). The mean error on SV was 8 ml and the mean error on EF was 2%. This study demonstrated that the segmentation technique is reliably applicable on human hearts in clinical practice.

NASA Discipline Cardiopulmonary↗
DOE OSTI2018

Differentiation in stem/progenitor cells along fetal or adult hepatic stages requires transcriptional regulators independently of oscillations in microRNA expression

Highlights: • The role of microRNAs in differentiation of stem cells is not well understood. • This study found microRNA levels oscillated during differentiation of hepatocytes. • Many functions but not differentiation were regulated by microRNAs in hepatocytes. • Regulators of gene networks independently maintained hepatic differentiation. • These insights will advance how stem cells may contribute in disease mechanisms and therapies. Understanding mechanisms in lineage differentiation is critical for organ development, pathophysiology and oncogenesis. To determine whether microRNAs (miRNA) may serve as drivers or adjuncts in hepatic differentiation, we studied human embryonic stem cell-derived hepatocytes and primary hepatocytes representing fetal or adult stages. Model systems were used for hepatic lineage advancement or regression under culture conditions with molecular assays. Profiles of miRNA in primary fetal and adult hepatocytes shared similarities and distinctions from pluripotent stem cells or stem cell-derived early fetal-like hepatocytes. During phenotypic regression in fetal or adult hepatocytes, miRNA profiles oscillated to regain stemness-associated features that had not been extinguished in stem cell-derived fetal-like hepatocytes. These oscillations in stemness-associated features were not altered in fetal-like hepatocytes by inhibitory mimics for dominantly-expressed miRNA, such as hsa-miR-99b, -100, -214 and -221/222. The stem cell-derived fetal-like hepatocytes were permissive for miRNA characterizing mature hepatocytes, including mimics for hsa-miR-122, -126, -192, -194 and -26b, although transfections of the latter did not advance hepatic differentiation. Examination of genome-wide mRNA expression profiles in stem cell-derived or primary fetal hepatocytes indicated targets of highly abundant miRNA regulated general processes, e.g., cell survival, growth and proliferation, functional maintenance, etc., without directing cell differentiation. Among upstream regulators of gene networks in stem cell-derived hepatocytes included HNF4A, SNAI1, and others, which affect transcriptional circuits directing lineage development or maintenance. Therefore, miRNA expression oscillated in response to microenvironmental conditions, whereas lineage-specific transcriptional regulators, such as HNF4A, were necessary for directing hepatic differentiation. This knowledge will be helpful for understanding the contribution of stem cells in pathophysiological states and oncogenesis, as well as for applications of stem cell-derived hepatocytes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Predictors of Liver Toxicity Following Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma

Purpose: To identify risk factors associated with a decline in liver function after stereotactic body radiation therapy (SBRT) for hepatocellular carcinoma. Methods and Materials: Data were analyzed from patients with hepatocellular carcinoma treated on clinical trials of 6-fraction SBRT. Liver toxicity was defined as an increase in Child-Pugh (CP) score ≥2 three months after SBRT. Clinical factors, SBRT details, and liver dose-volume histogram (DVH) parameters were tested for association with toxicity using logistic regression. CP class B patients were analyzed separately. Results: Among CP class A patients, 101 were evaluable, with a baseline score of A5 (72%) or A6 (28%). Fifty-three percent had portal vein thrombus. The median liver volume was 1286 cc (range, 766-3967 cc), and the median prescribed dose was 36 Gy (range, 27-54 Gy). Toxicity was seen in 26 patients (26%). Thrombus, baseline CP of A6, and lower platelet count were associated with toxicity on univariate analysis, as were several liver DVH-based parameters. Absolute and spared liver volumes were not significant. On multivariate analysis for CP class A patients, significant associations were found for baseline CP score of A6 (odds ratio [OR], 4.85), lower platelet count (OR, 0.90; median, 108 × 10{sup 9}/L vs 150 × 10{sup 9}/L), higher mean liver dose (OR, 1.33; median, 16.9 Gy vs 14.7 Gy), and higher dose to 800 cc of liver (OR, 1.11; median, 14.3 Gy vs 6.0 Gy). With 13 CP-B7 patients included or when dose to 800 cc of liver was replaced with other DVH parameters (eg, dose to 700 or 900 cc of liver) in the multivariate analysis, effective volume and portal vein thrombus were associated with an increased risk. Conclusions: Baseline CP scores and higher liver doses (eg, mean dose, effective volume, doses to 700-900 cc) were strongly associated with liver function decline 3 months after SBRT. A lower baseline platelet count and portal vein thrombus were also associated with an increased risk.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Combining data and theory for derivable scientific discovery with AI-Descartes

Abstract Scientists aim to discover meaningful formulae that accurately describe experimental data. Mathematical models of natural phenomena can be manually created from domain knowledge and fitted to data, or, in contrast, created automatically from large datasets with machine-learning algorithms. The problem of incorporating prior knowledge expressed as constraints on the functional form of a learned model has been studied before, while finding models that are consistent with prior knowledge expressed via general logical axioms is an open problem. We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression. We demonstrate these concepts for Kepler’s third law of planetary motion, Einstein’s relativistic time-dilation law, and Langmuir’s theory of adsorption. We show we can discover governing laws from few data points when logical reasoning is used to distinguish between candidate formulae having similar error on the data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗