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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Asteroid Shape Reconstruction From Radar Observations

I estimate near-Earth asteroid 1992 SK's physical properties from radar delay-Doppler images, Doppler-only echo spectra and optical lightcurves. The images are not very strong, but place up to 20 (40 m by 160 m) pixels on the asteroid. The radar tracks are confined to subradar latitudes between 20 and 40 degrees but have complete rotational phase coverage. The echo spectra and optical lightcurves span approx.80 degrees of sky motion, providing geometric leverage to constrain the pole direction. The optical lightcurves are essential to accurate determination of the asteroid's shape and spin state. The asteroid is approx.1.4 km in maximum extent and mildly asymmetric, with an elongation of approx.1.5 and relatively subdued topography. The radar albedo is about 0.13 and the optical albedo about 0.3. The circular polarization ratio for the object is about 0.34, implying typical cm-scale surface roughness. I estimate the asteroid's period to be 7.3182+/-0.0003 hours and its pole direction as (99deg+/-5deg,-3deg+/-5deg) in ecliptic coordinates. The radar-refined orbital solution accurately predicts planetary close approaches between the years 826 and 2690. I have used my model to predict salient characteristics of radar images and optical lightcurves obtainable during the asteroid's March 2006 approach.

Busch, Michael J.↗
NASA NTRS2010

When Weather Matters: Science and Service to Meet Critical Societal Needs

The goal of weather prediction is to provide information people and organizations can use to reduce weather-related losses and enhance societal benefits, including protection of life and property, public health and safety, and support of economic prosperity and quality of life. In economic terms, the benefit of the investment in public weather forecasts and warnings is substantial: the estimated annualized benefit is about $31.5 billion, compared to the $5.1 billion cost of generating the information. Between 1980 and 2009, 96 weather disasters in the United States each caused at least $1 billion in damages, with total losses exceeding $700 billion. Between 1999 and 2008, there were an average of 629 direct weather fatalities per year. The annual impacts of adverse weather on the national highway system and roads are staggering: 1.5 million weather-related crashes with 7,400 deaths, more than 700,000 injuries, and $42 billion in economic losses.

Weather forecasting↗
NASA NTRS2020

Status of Analysis and Manufacturability of Superconducting Wires with Low AC Losses

Superconductors can carry an order-of-magnitude higher current than room temperature copper wires and can do so with two orders-of-magnitude lower alternating current (AC) electrical losses. These advantages underlie our estimates that turboelectric propulsion of large aircraft can be enabled by superconducting machines. However, even the much smaller losses of superconductors pose thermal management issues at the low temperatures required for superconductivity, and predicting those losses and validating the predictions has been a developing process at NASA Glenn Research Center over the last decade. Since Glenn’s earliest assessments of the feasibility of fully superconducting machines for turboelectric propulsion, the available models of AC losses in superconductors have changed significantly, as well as the state of development of superconducting wire for the coils of electric machines. While the models available to us have improved significantly, the value of the AC losses predicted by these models have only increased as more fidelity was developed. The fabrication of medium temperature superconducting wires (Tc near 40 K) has advanced, and wires can be produced with finer filaments and tighter twisting than a decade ago. However, the ideal wire configurations, developed decades ago for low temperature superconductors (Tc < 25 K), still elude manufacturers of medium and high temperature (Tc > 77 K) superconductors. This report discusses the developments, the current limitations, and the expectations that future configurations of superconducting wire can yet be produced that will provide suitably low AC losses for the aircraft propulsion application. The report presents a basic discussion of the types of AC losses in superconductors, followed by a discussion of the evolution of our understanding of the practical consequences of those AC losses. Next, there is a discussion of the superconducting wire configurations that have been developed, which were partially guided by that understanding. A brief discussion of the modes of removing the heat produced by the losses is presented. Lastly, a comparison is presented between losses in currently available MgB2 wire and other important cases, including copper at room temperature, copper at liquid hydrogen temperature, and expected future MgB2 wire. Room temperature copper has 100 times the loss of today’s MgB2 and 300 times the expected loss of future MgB2. That implies we can expect much higher efficiency from fully superconducting machines than from machines with copper stators, and high efficiency targets remain the driver behind investment in MgB2 development and medium temperature superconductor research.

superconducting motors↗
DOE OSTI2023

Reducing Uncertainty in Offshore Wind Energy Yield Estimates via a Metocean Reference Site

The offshore wind industry is burgeoning in the coastal waters of the United States, specifically along the Atlantic. For wind energy to be successful, reliable observations and model simulations are needed for resource assessment and forecasting. While many of these activities have already begun, there is currently an absence of observations at hub-height in these waters, with the closest available hub-height measurements usually taken onshore. Deployment of floating lidars has occurred through various federally funded projects, but only encapsulates time periods of a couple of years at best. Private industry is also beginning to leverage floating lidars, but this data is often proprietary, and not shared with the general public. In this work, we make the case for a metocean reference site for long-term offshore wind energy. Specifically, we quantify the impact of having a metocean reference site compared to other methods of determining hub-height winds and energy production. We use an offshore floating lidar to directly measure the wind resource, and compare these measurements to predictions derived from other widely-available surface meteorological variables. These prediction methods (vertical extrapolation, machine learning, and NWP output) produce a variety of vertical wind speed profiles, of which produce different energy yield estimates for a reference offshore turbine (Figure 1). While some methods perform reasonably well against the lidar, the uncertainty in these energy yield estimates has financial implications, further illustrating the need for long-term measurements in coastal waters.

machine learning↗
NASA NTRS1980

LACIE - An application of meteorology for United States and foreign wheat assessment

This paper describes the overall Large Area Crop Inventory Experiment technical approach utilizing the global weather-reporting network and the Landsat satellite to make a quasi-operational application of existing research results, and the accomplishments of this cooperative experiment in utilizing the weather information. Global weather data were utilized in preparing timely yield estimates for selected areas of the U.S. Great Plains, the U.S.S.R. and Canada. Additionally, wheat yield models were developed and pilot tested for Brazil, Australia, India and Argentina. The results of the work show that heading dates for wheat in North America can be predicted with an average absolute error of about 5 days for winter wheat and 4 days for spring wheat. Independent tests of wheat yield models over a 10-year period for the U.S. Great Plains produced a root-mean-square error of 1.12 quintals per hectare (q/ha) while similar tests in the U.S.S.R. produced an error of 1.31 q/ha. Research designed to improve the initial capability is described as is the rationale for further evolution of a capability to monitor global climate and assess its impact on world food supplies.

Hill, J. D.↗
DOE OSTI2021

Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers

Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications such as image recognition, drug discovery, and materials design. The Boltzmann probability distribution is used as a model to identify network parameters by optimizing the likelihood of predicting an output given hidden states trained on available data. Training such networks often requires sampling over a large probability space that must be approximated during gradient based optimization. Quantum annealing has been proposed as a means to search this space more efficiently which has been experimentally investigated on D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization. Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave hardware during neural network training by maximizing the likelihood or minimizing the Shannon entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental validation on an image recognition problem. Neural network image reconstruction errors are evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude lower image reconstruction error using the maximum likelihood over manually optimizing the hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the Shannon entropy for image reconstruction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Effects of plasma on physical properties of water: nanocrystalline-to-amorphous phase transition and improving produce washing

Recently is was discovered in various applications that many physical and chemical properties of water change their temperature dependence between about 35 and 60 degrees Celsius. In particular, heat conductance, light absorption, and surface tension all change their temperature dependence. These drastic changes were associated with water gradually changing its mesoscopic structure: while at the higher temperatures water is a uniform media (amorphous state), at the temperatures below transition it consists of many nano-to-micro-scale clusters (crystalline state). This transition is similar to the second order phase transition. In the present paper we show that treating water with non-thermal plasma (adding plasma-created active compounds) can lower the temperature of the transition and thus cause a significant change in such physical quantities as surface tension, viscosity, freezing rate, and wettability and washability. We present analytical estimates of the transition temperature shift based on the Debye-Hückel theory. We discuss the produce-washing experiments that illustrate the predicted effects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

The work on Comet Halley's orbit at ESOC

The predominant error source in the final navigation for the Giotto Comet fly-by of Halley's nucleus is due to uncertainties in the comet's ephemeris. Efforts are being undertaken at ESOC to improve the prediction of the comet's state at the time of the encounter. Some of the aspects involved in the problem discussed include ways to improve old observations by re-measurement or re-reduction and encouragement to observers to take fine quality astrometric observations in the future, especially at critical times. Difficulties in the mathematical modelling of the non-gravitational forces and possible offsets of the comet's center-of-light from its center-of-mass are addressed. The available estimation techniques are also examined including one based upon selecting optimum observations.

Morley, T.↗
NASA NTRS2002

Numerical Simulations of Vortex Generator Vanes and Jets on a Flat Plate

Numerical simulations of a single low-profile vortex generator vane, which is only a small fraction of the boundary-layer thickness, and a vortex generating jet have been performed for flows over a flat plate. The numerical simulations were computed by solving the steady-state solution to the Reynolds-averaged Navier-Stokes equations. The vortex generating vane results were evaluated by comparing the strength and trajectory of the streamwise vortex to experimental particle image velocimetry measurements. From the numerical simulations of the vane case, it was observed that the Shear-Stress Transport (SST) turbulence model resulted in a better prediction of the streamwise peak vorticity and trajectory when compared to the Spalart-Allmaras (SA) turbulence model. It is shown in this investigation that the estimation of the turbulent eddy viscosity near the vortex core, for both the vane and jet simulations, was higher for the SA model when compared to the SST model. Even though the numerical simulations of the vortex generating vane were able to predict the trajectory of the stream-wise vortex, the initial magnitude and decay of the peak streamwise vorticity were significantly under predicted. A comparison of the positive circulation associated with the streamwise vortex showed that while the numerical simulations produced a more diffused vortex, the vortex strength compared very well to the experimental observations. A grid resolution study for the vortex generating vane was also performed showing that the diffusion of the vortex was not a result of insufficient grid resolution. Comparisons were also made between a fully modeled trapezoidal vane with finite thickness to a simply modeled rectangular thin vane. The comparisons showed that the simply modeled rectangular vane produced a streamwise vortex which had a strength and trajectory very similar to the fully modeled trapezoidal vane.

Allan, Brian G.↗
NASA NTRS2013

Three Dimensional Numerical Simulation and Characterization of Crack Growth in the Weld Region of a Friction Stir Welded Structure

Friction stir welding (FSW) fabrication technology is being adopted in aerospace applications. The use of this technology can reduce production cost, lead-times, reduce structural weight and need for fasteners and lap joints, which are typically the primary locations of crack initiation and multi-site fatigue damage in aerospace structures. FSW is a solid state welding process that is well-suited for joining aluminum alloy components; however, the process introduces residual stresses (both tensile and compressive) in joined components. The propagation of fatigue cracks in a residual stress field and the resulting redistribution of the residual stress field and its effect on crack closure have to be estimated. To insure the safe insertion of complex integral structures, an accurate understanding of the fatigue crack growth behavior and the complex crack path process must be understood. A life prediction methodology for fatigue crack growth through the weld under the influence of residual stresses in aluminum alloy structures fabricated using FSW will be detailed. The effects and significance of the magnitude of residual stress at a crack tip on the estimated crack tip driving force are highlighted. The location of the crack tip relative to the FSW and the effect of microstructure on fatigue crack growth are considered. A damage tolerant life prediction methodology accounting for microstructural variation in the weld zone and residual stress field will lead to the design of lighter and more reliable aerospace structures

Seshadri, Banavara R.↗
NASA NTRS2018

Prediction and Predictability of the Madden Julian Oscillation in the NASA GEOS-5 Seasonal-to-Subseasonal System

In this study, we examine the prediction skill and predictability of the Madden Julian Oscillation (MJO) in a recent version of the NASA GEOS-5 atmosphere-ocean coupled model run at at 1/2 degree horizontal resolution. The results are based on a suite of hindcasts produced as part of the NOAA SubX project, consisting of seven ensemble members initialized every 5 days for the period 1999-2015. The atmospheric initial conditions were taken from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the ocean and the sea ice were taken from a GMAO ocean analysis. The land states were initialized from the MERRA-2 land output, which is based on observation-corrected precipitation fields. We investigated the MJO prediction skill in terms of the bivariate correlation coefficient for the real-time multivariate MJO (RMM) indices. The correlation coefficient stays at or above 0.5 out to forecast lead times of 26-36 days, with a pronounced increase in skill for forecasts initialized from phase 3, when the MJO convective anomaly is located in the central tropical Indian Ocean. A corresponding estimate of the upper limit of the predictability is calculated by considering a single ensemble member as the truth and verifying the ensemble mean of the remaining members against that. The predictability estimates fall between 35-37 days (taken as forecast lead when the correlation reaches 0.5) and are rather insensitive to the initial MJO phase. The model shows slightly higher skill when the initial conditions contain strong MJO events compared to weak events, although the difference in skill is evident only from lead 1 to 20. Similar to other models, the RMM-index-based skill arises mostly from the circulation components of the index. The skill of the convective component of the index drops to 0.5 by day 20 as opposed to day 30 for circulation fields. The propagation of the MJO anomalies over the Maritime Continent does not appear problematic in the GEOS-5 hindcasts implying that the Maritime Continent predictability barrier may not be a major concern in this model. Finally, the MJO prediction skill in this version of GEOS-5 is superior to that of the current seasonal prediction system at the GMAO; this could be partly attributed to a slightly better representation of the MJO in the free running version of this model and partly to the improved atmospheric initialization from MERRA-2.

Achuthavarier, Deepthi↗
DOE OSTI2024

Prediction of Redox Potentials for Ac, Th, and Pa in Aqueous Solution

Density functional theory in conjunction with small core pseudopotentials and the associated basis sets was used to calculate potentials for multiple redox couples, covering a range of oxidation states for Ac (0 to III), Th (0 to IV), and Pa (0 to V) in aqueous solution. Solvation effects were incorporated using a supermolecule-continuum approach, with 30 water molecules representing two solvation shells, and the COSMO and SMD implicit solvation models. The calculated geometries for Ac(III), Th(IV), and Pa(V) were in reasonable agreement with the available experimental data. Using the COSMO model with the B3LYP functional, the calculated redox potentials were within ± 0.2 V from experiment for most redox couples. Several pathways were explored for the Pa(V/IV) redox couple for different forms of Pa(V) and Pa(IV). Most Pa(V/IV) redox couples have very similar potentials, ranging from 0 to -0.4 V up to a pH of 1.4. At pH = 1.4, the potentials shift to values that are more negative than -0.7 V, reflecting the growing unfavorable nature of the redox process at higher pH levels. The calculated values for An(III/II) potentials were consistent with prior estimates and the available experimental data. The predicted redox potentials for An(II/I) were highly negative, as expected. For An(I/0) potentials, Th and Pa exhibited positive values, contrasting with the negative values calculated for Ac. Furthermore, the An +m /An(0) potentials agreed better with the experimental data when using the COSMO solvation model as compared to the SMD model.

Chemical calculations↗
NASA NTRS2001

A Proposal for an Integrated Geophysical Strategy to "Follow the Water" on Mars

The search for subsurface water has become a primary focus of Mars exploration. Its abundance and distribution (both as ground ice and groundwater) have important implications for understanding the geologic, hydrologic, and climatic evolution of the planet; the potential origin and continued survival of life; and the accessibility of a critical in situ resource for sustaining future human explorers. For these reasons, a principal goal of the Mars science, astrobiology, and the HEDS programs is to determine the 3-D distribution and state of subsurface H2O, at a resolution sufficient to permit reaching any desired volatile target by drilling. The three targets most often discussed are: groundwater, massive deposits of near-surface ground ice (associated with the ponded discharge of the outflow channels or the relic of a former ocean), and ice-saturated frozen ground. Based on the present best estimates of mean annual surface temperature, crustal thermal conductivity, geothermal heat flow, and groundwater freezing temperature, the mean thickness of frozen ground on Mars is expected to vary from approx. = 2.5 - 5 km at the equator to approx. = 6.5 - 13 km at the poles. However, natural variations in both crustal heat flow and thermal conductivity are likely to result in significant local departures from these predicted values. The recent discovery of "young" fluvial-like features, emanating from the slopes of local scarps, raises the possibility that liquid water may also exist episodically at shallow (approx. = 100 - 500 m) depth; however, the true nature and absolute age of these features remains highly uncertain. Although the belief that Mars is water-rich is supported by a wide variety geologic evidence, our ignorance about the heterogeneous nature and thermal evolution of the planet's crust effectively precludes geomorphic or theoretical attempts to quantitatively assess the current geographic and subsurface vertical distribution of ground ice and groundwater . For this reason, any exploration activity (such as drilling) whose success is contingent on the presence of subsurface water, must be preceded by a comprehensive high-resolution geophysical survey capable of assessing whether local reservoirs of water and ice actually exist. Terrestrial experience has demonstrated that the accurate identification of such targets is likely to require the application of multiple geophysical techniques. In this abstract we propose an integrated strategy for the geophysical exploration of Mars that we believe represents the fastest, most cost-effect, and technically capable approach to identifying the state and distribution of subsurface water. Additional information is contained in the original extended abstract.

Clifford, S. M.↗
DOE OSTI2025

Permeability scaling relationships of volcanic tuff from core to field scale measurements

A recent chemical explosive test in P-Tunnel at the Nevada National Security Site, Nevada, USA, was conducted to better understand how signals propagate from explosions in the subsurface. A primary signal of interest is the migration of gases that can be used to differentiate chemical from nuclear explosions. Gas migration is highly dependent on the rock permeability which is notoriously difficult to determine experimentally in the field due to a potentially large dependence on the scale over which measurements are made. Here, we present pre-explosion permeability estimates to characterize the geologic units surrounding the recent test. Permeability measurements were made at three scales of increasing size: core samples (≈2 cm), borehole packer system tests (≈1 m), and a pre-shot cavity pressurization test (> 10 m) across ten tuff units. Permeability estimates based on core measurements showed little difference from borehole packer tests. However, permeability in most rock units calibrated from cavity pressurization tests resulted in higher permeability estimates by up to two orders of magnitude. Here, we demonstrate that the scale of the measurement significantly impacts the characterization efforts of hydraulic properties in volcanic tuff, and that local-scale measurements (< 10 m scale) do not incorporate enough heterogeneity to accurately predict field-scale flow and mass transport.

Environmental sciences↗
DOE OSTI2022

Addressing delayed case reporting in infectious disease forecast modeling

Infectious disease forecasting is of great interest to the public health community and policymakers, since forecasts can provide insight into disease dynamics in the near future and inform interventions. Due to delays in case reporting, however, forecasting models may often underestimate the current and future disease burden. In this paper, we propose a general framework for addressing reporting delay in disease forecasting efforts with the goal of improving forecasts. We propose strategies for leveraging either historical data on case reporting or external internet-based data to estimate the amount of reporting error. We then describe several approaches for adapting general forecasting pipelines to account for under- or over-reporting of cases. We apply these methods to address reporting delay in data on dengue fever cases in Puerto Rico from 1990 to 2009 and to reports of influenza-like illness (ILI) in the United States between 2010 and 2019. Through a simulation study, we compare method performance and evaluate robustness to assumption violations. Our results show that forecasting accuracy and prediction coverage almost always increase when correction methods are implemented to address reporting delay. Some of these methods required knowledge about the reporting error or high quality external data, which may not always be available. Provided alternatives include excluding recently-reported data and performing sensitivity analysis. This work provides intuition and guidance for handling delay in disease case reporting and may serve as a useful resource to inform practical infectious disease forecasting efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Mesoscale Linear Elastic Modeling and Homogenization of Marine Energy Composites

The design of fiber-reinforced composite (FRC)-based components for marine energy applications necessitates a fundamental understanding of material properties and the resulting geometry to predict long-term performance. In this work, we present a modeling workflow to predict linear elastic and diffusive bulk properties at the mesoscale for an idealized geometry based on knowledge of fiber and resin properties. A parametric study was performed to identify the key model input parameters that influence bulk properties. Furthermore, we demonstrate how bulk properties can be leveraged in high-fidelity image-based simulations, where imperfections in tow geometry and voids captured during X-ray computed tomography imaging are explicitly represented within the simulation. Bulk properties of interest include moduli, Poisson’s ratios, hygroscopic swelling, diffusivity, and moisture uptake, which are key parameters for characterizing FRC performance within marine environments. Modeling predictions agreed well with experimental data, except for estimating swelling coefficients, likely due to crack accumulation as a function of moisture uptake. The mesoscale modeling workflow ultimately highlights a versatile framework for understanding the influence of material and geometric properties, which can be leveraged to rapidly assess new FRC-based components.

computational mechanics↗
DOE OSTI2024

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

Xie, Yu↗