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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Measured and satellite-derived albedo data for estimating bifacial photovoltaic system performance

The albedo of the ground surface is an important factor in the cost-effectiveness of a bifacial photovoltaic (PV) system. To improve the availability of reliable albedo data, datasets of ground albedo and associated meteorological data were developed by using existing measurement network data and data measured by the PV industry. The measured datasets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Satellite-derived values of albedo are available from the National Solar Radiation Data Base (NSRDB). The NSRDB albedos were compared to the measured albedos for Surface Radiation budget (SURFRAD) network locations for the period 2001–2017, and the mean bias difference results were from -0.044 to +0.056. Overall, these differences are greater than the albedo measurement uncertainty of ±0.02; consequently, the NSRDB albedos should be used with caution for estimating the performance of bifacial PV systems. Differences between SURFRAD and NSRDB albedos are attributed to the NSRDB method for determining albedo and to the ground surfaces within the NSRDB 4 km spatial resolution pixel consisting of a mixture of surface types rather than just the single surface types viewed by the albedometers at the SURFRAD stations.

14 SOLAR ENERGY↗
DOE OSTI2025

srlife : A software tool for estimating the life of high temperature concentrating solar receivers. Part II – Ceramic receivers

As Concentrating Solar Power (CSP) technologies aim for higher operating temperatures to enhance efficiency and meet industrial process heat demands, high-temperature metallic materials, including nickel-based superalloys, face challenges in maintaining structural integrity. Advanced ceramics offer a promising alternative due to their superior high-temperature strength. However, accurately assessing the performance of ceramic components requires a fundamentally different approach from that used for metallic components. This Part II of a two-part paper describes the integration of ceramic statistical failure models within srlife – an open-source tool for predicting the life of high-temperature CSP receivers. These models account for the inherent variability in ceramic strength, as well as the effects of subcritical crack growth (SCG) under high temperature cyclic loads. Here, the paper includes an example problem that demonstrate the process of evaluating ceramic receivers using srlife. Part I details the life estimation process for metallic receivers (i.e. creep-fatigue life) along with input and output data structure, thermohydraulic analysis, and structural analysis. The complete tool is available as open-source software at https://github.com/srlife-project/srlife and can be installed via the PyPi package manager (https://pypi.org). By supporting both ceramic and metallic receiver analyses, srlife facilitates fair comparisons between competing metallic and ceramic designs, enabling accurate evaluations of plant efficiency and the economic benefits of ceramic solar receivers and other components.

High temperature ceramic receivers↗
DOE OSTI2020

Estimating region-specific fuel economy in the United States from real-world driving cycles

Here we describe a method for estimating region-specific real-world light-duty vehicle fuel economy in the United States that is unique in both the size and representativeness of real-world driving that was considered, and for its ability to model regional variations in driving patterns. Over one million miles of national driving data were used to select real-world cycles representative of observed trip categories. The six cycles were compared to U.S. legislative cycles, revealing some key differences. Finally, a set of cycle weighting factors for 533 separate U.S. regions was derived from annual traffic statistics. Applying this method, it was found that regional fuel economy varies due to differences in driving patterns alone and that rural driving patterns lead to improved fuel economy (for conventional vehicles). The driving cycles and regional weighting factors described here are useful for testing and simulation studies, specifically those sensitive to regional variations in driving patterns.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Developing a heavy-duty vehicle activity database to estimate start and idle emissions

Heavy-duty vehicle start and idling activities were characterized from two datasets to improve emission estimates in the MOtor Vehicle Emission Simulator (MOVES): 1. Fleet DNA from the National Renewable Energy Laboratory (NREL) and 2. A dataset collected by the University of California, Riverside for the California Air Resources Board. Furthermore, the combined dataset includes 564 commercial vehicles, over 23,000 vehicle days of operation and covers seven of the nine heavy-duty source types defined by MOVES. The start and idle activities are characterized and illustrated across MOVES source types, vocations, fleets, days, and hours. This study provides the most comprehensive analysis yet made publicly available to characterize start and idle activity for heavy-duty vehicles within the United States. The results also show there is significant uncertainty in the average heavy-duty idle and start activity due to the large variation in activity across fleets and vocations, and sparsity of nation-wide vehicle population data by vocation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

AutonomieAI: An efficient and deployable vehicle energy consumption estimation toolkit

Here, this paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.

Autonomie↗
DOE OSTI2022

Estimating the population level impact of a gonococcal vaccine candidate: Predictions from a simple mathematical model

Neisseria gonorrhoeae cross-protection was suggested in a New Zealand meningitis B vaccine. We modeled the potential impact of similar vaccines on gonorrhea prevalence in heterosexuals in the United States. Here, our mathematical model incorporated infection, behavior, and vaccination dynamics. Approximate Bayesian Computation calibrated our model to US prevalence. Primary analyses assumed New Zealand vaccine characteristics: 30% efficacy and 2-year duration of protection. We estimated impact under two vaccine coverages (20%, 50%). Reduction in gonorrhea prevalence ranged from 4.8 to 39.4%, depending on vaccine coverage. Vaccine impact was correlated with both size of the highly sexually active subpopulation and sexual mixing between high and low activity subpopulations. A meningitis vaccine providing low efficacy cross-protection against gonorrhea acquisition and short duration of protection could result in a large reduction in gonorrhea prevalence in the United States. Potential dual protective effects can be considered when making vaccine recommendations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Protocol to detect dilution cycles in chemostat experiments and estimate growth rate slopes with linear modeling with R software chemostat_regression

Chemostat growth chambers measure optical density over time and require manual calculation of growth rates. Here, we present chemostat_regression, R software that enables users to automatically identify chemostat cycles and estimate growth rate using a linear regression approach. We describe steps for creating requisite software environment(s), formatting input data, executing the software via command line/RStudio/R-Shiny, interpreting results, assessing the validity of results, and modifying input parameters.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Prevalence of Americans reporting a family history of cancer indicative of increased cancer risk: Estimates from the 2015 National Health Interview Survey

The collection and evaluation of family health history in a clinical setting presents an opportunity to discuss cancer risk, tailor cancer screening recommendations, and identify people with an increased risk of carrying a pathogenic variant who may benefit from referral to genetic counseling and testing. National recommendations for breast and colorectal cancer screening indicate that men and women who have a first-degree relative affected with these types of cancers may benefit from talking to a healthcare provider about starting screening at an earlier age and other options for cancer prevention. The prevalence of reporting a first-degree relative who had cancer was assessed among adult respondents of the 2015 National Health Interview Survey who had never had cancer themselves (n = 27,999). We found 35.6% of adults reported having at least one first-degree relative with cancer at any site. Significant differences in reporting a family history of cancer were observed by sex, age, race/ethnicity, educational attainment, and census region. Nearly 5% of women under age 50 and 2.5% of adults under age 50 had at least one first-degree relative with breast cancer or colorectal cancer, respectively. We estimated that 5.8% of women had a family history of breast or ovarian cancer that may indicate increased genetic risk. A third of U.S. adults who have never had cancer report a family history of cancer in a first-degree relative. Furthermore, this finding underscores the importance of using family history to inform discussions about cancer risk and screening options between healthcare providers and their patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Flux and estimated spectra from a low-intensity laser-driven X-ray source

Laser-driven X-rays as probes for high-energy-density physics spans an extremely large parameter space with laser intensities varying by 8 orders of magnitude. We have built and characterized a soft X-ray source driven by a modest intensity laser of 4 × 10 13 W/cm 2 . Emitted X-rays were measured by diamond radiation detectors and a filtered soft X-ray camera. A material-dependence study on Al, Ti, stainless steel alloy 304, Fe, Cu and Sn targets indicated 5-μm-thick Cu foils produced the highest X-ray yield. X-ray emission in the laser direction and emission in the reverse direction depend strongly on the foil material and the thickness due to the opacity and hydrodynamic disassembly time. The time-varying X-ray signals are used to measure the material thinning rate and is found to be ~1.5 μm/ns for the materials tested implying thermal temperature around 0.6 eV. The X-ray spectra from Cu targets peaks at ~2 keV with no emission >4 keV and was estimated using images with eight different foil filters. One-dimensional hydrodynamic and spectral calculations using HELIOS-CR provide qualitative agreement with experimental results. Modest intensity lasers can be an excellent source for nanosecond bursts of soft X-rays.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Atom Equivalent Energies for the Rapid Estimation of the Heat of Formation of Explosive Molecules from Density Functional Tight Binding Theory

Atom equivalent energies have been derived from which the gas-phase heat of formation of explosive molecules can be estimated from fast, semiempirical density functional tight binding total energy calculations. The root-mean-square deviation and maximum deviation of the heats of formation from the experimental values for the set of 45 energetic molecules compiled by Byrd and Rice [ J. Phys. Chem. A, 2006, 110, 1005–1013] are 10.4 and 25.5 kcal/mol, respectively, using 4 atom equivalent energies and 7.4 and 15.0 kcal/mol, respectively, using 7 atom equivalent energies. These errors are around a factor of 2–3 larger than those obtained from density functional theory calculations but are smaller than those obtained from other semiempirical electronic structure methods. Heats of formation calculated with density functional tight binding theory using the 4 and 7 atom equivalent energies, the Byrd and Rice scheme, and the atom pair contribution method for a new set of 531 energetic molecules that contain only carbon, hydrogen, nitrogen, and oxygen are provided.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between them of 0.50 kcal/mol. The correlation between packages is very good, with the lowest Spearman’s, Pearson’s and Kendall’s tau correlation coefficients being 0.92, 0.91, and 0.76, respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Optimized Quantum Phase Estimation for Simulating Electronic States in Various Energy Regimes

While quantum algorithms for simulation exhibit better asymptotic scaling than their classical counterparts, they currently cannot be implemented on real-world devices. Instead, chemists and computer scientists rely on costly classical simulations of these quantum algorithms. In particular, the quantum phase estimation (QPE) algorithm is among several approaches that have attracted much attention in recent years for its genuine quantum character. However, it is memory-intensive to simulate and intractable for moderate system sizes. Herein, this paper discusses the performance and applicability of QPESIM, a new simulation of the QPE algorithm designed to take advantage of modest computational resources. In particular, we demonstrate the versatility of QPESIM in simulating various electronic states by examining the ground and core-level states of H 2 O. For these states, we also discuss the effect of the active-space size on the quality of the calculated energies. For the high-energy core-level states, we demonstrate that new QPE simulations for active spaces defined by 15 active orbitals significantly reduce the errors in core-level excitation energies compared to earlier QPE simulations using smaller active spaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Ultrafast Correlation Energy Estimator

A virtually no-cost method is proposed that can compute the correlation energies of general, covalently bonded, organic, and inorganic molecules (including conjugated π-electron systems) with a well-defined dominant Lewis structure at the accuracy of 99.5% of the near-exact values determined by the coupled-cluster singles, doubles, and perturbative triples [CCSD(T)] in the complete-basis-set (CBS) limit. This Correlation Energy Per Bond (CEPB) method assigns a partial correlation energy to each bond type (characterized by the identities of the two atoms forming the bond and its integer bond order) and to a lone pair, regardless of the bond length, bond angle, sp-hybridization, π-electron conjugation, ionicity, noncovalent interactions, etc. At its current stage, the method is mainly suitable for near-equilibrium geometries. The correlation energies per bond are determined by a fit to the CCSD(T)/CBS benchmarks. It can neither improve the equilibrium structures nor discern conformers or positional isomers, yet its accuracy for reaction energies rivals that of the second-order Møller–Plesset perturbation theory, which is far more expensive. Its promising performance underscores the possibility that surprisingly compact, chemically intuitive molecular fragments exist into which correlation energies can be partitioned, leading to various ultrafast correlation-energy estimators tailored to different purposes.

Binding energy↗
DOE OSTI2026

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗
DOE OSTI2023

Cost Estimates of Roll-to-Roll Production of Organic Light Emitting Devices for Lighting

White organic light emitting devices (WOLEDs) offerdistinct advantages as solid state lighting sources, including high energy efficiency, superior color quality,and a flexible, thin profile that can accommodate a vast array of possible fixture designs. Recent advances in device lifetime and high speed deposition suggest that mass production of WOLED panels is approaching an inflection point. To understand the tradeoffs in the volume manufacturing of WOLED lighting panels, in this work, we estimate the cost of white WOLED panel production based on vacuum and vapor phase deposition in a roll-to-roll production line envisioned to yield an annual capacity of at least 6 × 10 6 m 2 . Assuming a WOLED operating luminance at 10 klm/m 2 , we anticipate a $\$12.5$ /klm cost of a WOLED light engine that includes the cost of the current driverand packaging. With incremental reduction in material and driver costs and improved luminance, the costof WOLED lighting can be reduced to $\$6.3$ /klm in thenear term, potentially positioning WOLEDs for use innumerous premium lighting applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Estimating the Impact of Seep Methane Oxidation on Ocean pH and Dissolved Inorganic Radiocarbon Along the U.S. Mid-Atlantic Bight

Ongoing ocean warming can release methane (CH 4 ) currently stored in ocean sediments asfree gas and gas hydrates. Once dissolved in ocean waters, this CH 4 can be oxidized to carbon dioxide(CO 2 ). While it has been hypothesized that the CO 2 produced from aerobic CH4oxidation could enhanceocean acidification, a previous study conducted in Hudson Canyon shows that CH 4 oxidation has a smallshort-term influence on ocean pH and dissolved inorganic radiocarbon. Here we expand upon thatinvestigation to assess the impact of widespread CH 4 seepage on CO 2 chemistry and possible accumulationof this carbon injection along 234 km of the U.S. Mid-Atlantic Bight. Consistent with the estimates fromHudson Canyon, we demonstrate that a small fraction of ancient CH 4 -derived carbon is being assimilatedinto the dissolved inorganic radiocarbon (mean fraction of 0.5 ± 0.4%). The areas with the highest fractionsof ancient carbon coincide with elevated CH 4 concentration and active gas seepage. This suggests thataerobic CH 4 oxidation has a greater influence on the dissolved inorganic pool in areas where CH 4 concentrations are locally elevated, instead of displaying a cumulative effect downcurrent from widespreadgroupings of CH 4 seeps. Afirst-order approximation of the input rate of ancient-derived dissolved inorganiccarbon (DIC) into the waters overlying the northern U.S. Mid-Atlantic Bight further suggests thatoxidation of ancient CH 4 -derived carbon is not negligible on the global scale and could contribute todeepwater acidification over longer time scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Simplified Green-Ampt Model, Imbibition-Based Estimates of Permeability, and Implications for Leak-off in Hydraulic Fracturing

Predicting water imbibition into porous materials is important in a wide variety of fields, and hydraulic fracturing of low permeability hydrocarbon reservoirs has emerged as an application that is imposing a large water footprint. Furthermore, reliable predictions of imbibition are needed to better manage water use, yet are challenging because of uncertainties in both the permeability and capillary pressure driving force. Here, this uncertainty is reduced through evaluating correlations between the permeability and the effective capillary pressure associated with the wetting front, P c,f . These correlations allow elimination of P c,f from the Green and Ampt equation and concentrate all uncertainties in fluxes on the effective permeability k . Over a wide range of k and porosities n , imbibition scales approximately with k 1/3 . Although Leverett k 1/4 scaling for predicting P c,f is shown to be inferior when tested with data spanning a wide range of n , it nevertheless predicted imbibition fairly well. From simple imbibition measurements, both the empirical and Leverett scaling approaches allow estimates of k that have root-mean-square deviations of about one order of magnitude relative to measurements that ranged over 10 orders of magnitude in k .

58 GEOSCIENCES↗