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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

A Ground Validation Network for the Global Precipitation Measurement Mission

A prototype Validation Network (VN) is currently operating as part of the Ground Validation System for NASA's Global Precipitation Measurement (GPM) mission. The VN supports precipitation retrieval algorithm development in the GPM prelaunch era. Postlaunch, the VN will be used to validate GPM spacecraft instrument measurements and retrieved precipitation data products. The period of record for the VN prototype starts on 8 August 2006 and runs to the present day. The VN database includes spacecraft data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and coincident ground radar (GR) data from operational meteorological networks in the United States, Australia, Korea, and the Kwajalein Atoll in the Marshall Islands. Satellite and ground radar data products are collected whenever the PR satellite track crosses within 200 km of a VN ground radar, and these data are stored permanently in the VN database. VN products are generated from coincident PR and GR observations when a significant rain event occurs. The VN algorithm matches PR and GR radar data (including retrieved precipitation data in the case of the PR) by calculating averages of PR reflectivity (both raw and attenuation corrected) and rain rate, and GR reflectivity at the geometric intersection of the PR rays with the individual GR elevation sweeps. The algorithm thus averages the minimum PR and GR sample volumes needed to "matchup" the spatially coincident PR and GR data types. The result of this technique is a set of vertical profiles for a given rainfall event, with coincident PR and GR samples matched at specified heights throughout the profile. VN data can be used to validate satellite measurements and to track ground radar calibration over time. A comparison of matched TRMM PR and GR radar reflectivity factor data found a remarkably small difference between the PR and GR radar reflectivity factor averaged over this period of record in stratiform and convective rain cases when samples were taken from high in the atmosphere. A significant difference in PR and GR reflectivity was found in convective cases, particularly in convective samples from the lower part of the atmosphere. In this case, the mean difference between PR and corrected GR reflectivity was -1.88 dBZ. The PR-GR bias was found to increase with the amount of PR attenuation correction applied, with the PR-GR bias reaching -3.07 dBZ in cases where the attenuation correction applied is greater than 6 dBZ. Additional analysis indicated that the version 6 TRMM PR retrieval algorithm underestimates rainfall in case of convective rain in the lower part of the atmosphere by 30%-40%.

Schwaller, Mathew R.↗
NASA NTRS1989

Parametric study of statistical bias in laser Doppler velocimetry

Analytical studies have often assumed that LDV velocity bias depends on turbulence intensity in conjunction with one or more characteristic time scales, such as the time between validated signals, the time between data samples, and the integral turbulence time-scale. These parameters are presently varied independently, in an effort to quantify the biasing effect. Neither of the post facto correction methods employed is entirely accurate. The mean velocity bias error is found to be nearly independent of data validation rate.

Gould, Richard D.↗
DOE OSTI2023

A general framework for removing point-spread function additive systematics in cosmological weak lensing analysis

ABSTRACT Cosmological weak lensing measurements rely on a precise measurement of the shear two-point correlation function (2PCF) along with a deep understanding of systematics that affect it. In this work, we demonstrate a general framework for detecting and modelling the impact of PSF systematics on the cosmic shear 2PCF and mitigating its impact on cosmological analysis. Our framework can detect PSF leakage and modelling error from all spin-2 quantities contributed by the PSF second and higher moments, rather than just the second moments, using the cross-correlations between galaxy shapes and PSF moments. We interpret null tests using the HSC Year 3 (Y3) catalogs with this formalism and find that leakage from the spin-2 combination of PSF fourth moments is the leading contributor to additive shear systematics, with total contamination that is an order-of-magnitude higher than that contributed by PSF second moments alone. We conducted a mock cosmic shear analysis for HSC Y3 and find that, if uncorrected, PSF systematics can bias the cosmological parameters Ωm and S8 by ∼0.3σ. The traditional second moment-based model can only correct for a 0.1σ bias, leaving the contamination largely uncorrected. We conclude it is necessary to model both PSF second and fourth moment contaminations for HSC Y3 cosmic shear analysis. We also reanalyse the HSC Y1 cosmic shear analysis with our updated systematics model and identify a 0.07σ bias on Ωm when using the more restricted second moment model from the original analysis. We demonstrate how to self-consistently use the method in both real space and Fourier space, assess shear systematics in tomographic bins, and test for PSF model overfitting.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Reversible and Selective CO 2 to HCO 2 − Electrocatalysis near the Thermodynamic Potential

Abstract Reversible catalysis is a hallmark of energy‐efficient chemical transformations, but can only be achieved if the changes in free energy of intermediate steps are minimized and the catalytic cycle is devoid of high transition‐state barriers. Using these criteria, we demonstrate reversible CO 2 /HCO 2 − conversion catalyzed by [Pt(depe) 2 ] 2+ (depe=1,2‐ bis (diethylphosphino)ethane). Direct measurement of the free energies associated with each catalytic step correctly predicts a slight bias towards CO 2 reduction. We demonstrate how the experimentally measured free energy of each step directly contributes to the <50 mV overpotential. We also find that for CO 2 reduction, H 2 evolution is negligible and the Faradaic efficiency for HCO 2 − production is nearly quantitative. A free‐energy analysis reveals H 2 evolution is endergonic, providing a thermodynamic basis for highly selective CO 2 reduction.

Cunningham, Drew W.↗
DOE OSTI2019

Reversible and Selective CO 2 to HCO 2 - Electrocatalysis near the Thermodynamic Potential

Reversible catalysis is an emblem of energy-efficient chemical transformations, but can only be achieved if the changes in free energy of intermediate steps are minimized and the catalytic cycle is devoid of high transition-state barriers. Using these criteria, we show reversible CO 2 /HCO 2 - conversion catalyzed by [Pt(depe) 2 ] 2+ (depe=1,2- bis (diethylphosphino)ethane). Direct measurement of the free energies associated with each catalytic step correctly predicts a slight bias towards CO 2 reduction. We demonstrate how the experimentally measured free energy of each step directly contributes to the <50mV overpotential. We also find that for CO 2 reduction, H 2 evolution is negligible and the Faradaic efficiency for HCO 2 - production is nearly quantitative. A free-energy analysis reveals H 2 evolution is endergonic, providing a thermodynamic basis for highly selective CO 2 reduction.

10 SYNTHETIC FUELS↗
DOE OSTI2020

Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. Finally, the validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

42 ENGINEERING↗
DOE OSTI2026

Limiter-aware adaptive excitation control for hydropower generators with legacy voltage regulation systems

Hydropower units often rely on legacy excitation systems with fixed automatic voltage regulator (AVR) settings, but parameter drift, changing operating conditions, and evolving grid-service demands can degrade terminal-voltage regulation over time. This problem should be addressed because poor voltage regulation affects reactive-power support, system stability, and reliable plant operation, and replacing installed AVR and limiter logic is costly and disruptive in practice. To address this issue, this paper proposes a retrofit-friendly enhancement for hydropower generator excitation systems that combines adaptive tuning of the AVR middle lead–lag branch with a small proportional-integral correction for removing steady bias and slow drift. The adaptive laws are normalized, projection-bounded, and equipped with leakage, while adaptation is enabled only in the middle branch. Thus, the intent of the underexcitation and overexcitation limiters, as well as legacy current and rate limits, remains unchanged. Stability is analyzed under incremental performance assumptions, yielding boundedness and asymptotic tracking in the absence of saturation. The method is evaluated using real operational data from a utility-scale hydropower unit at Rocky Reach Dam, Unit C-8. Compared with the fixed AVR baseline, the proposed method reduces terminal-voltage mean squared error by about 70%, removes long periods of steady-state bias, and improves transient tracking while coexisting with limiter actions. These results show that substantial voltage-control improvement can be achieved on existing hydropower units with minimal integration effort and without replacing the installed AVR structure.

ESST5B model↗
DOE OSTI2023

Directionally supervised cellular automaton for the initial peopling of Sahul

Reconstructing the patterns of Homo sapiens expansion out of Africa and across the globe has been advanced using demographic and travel-cost models. However, modelled routes are ipso facto influenced by migration rates, and vice versa. We combined movement ‘superhighways’ with a demographic cellular automaton to predict one of the world's earliest peopling events — Sahul between 75000 and 50000 years ago. Novel outcomes from the superhighways-weighted model include (i) an approximate doubling of the predicted time to continental saturation (~10,000 years) compared to that based on the directionally unsupervised model (~5000 years), suggesting that rates of migration need to account for topographical constraints in addition to rate of saturation; (ii) a previously undetected movement corridor south through the centre of Sahul early in the expansion wave based on the scenarios assuming two dominant entry points into Sahul; and (iii) a better fit to the spatially de-biased, Signor-Lipps-corrected layer of initial arrival inferred from dated archaeological material. Our combined model infrastructure provides a data-driven means to examine how people initially moved through, settled, and abandoned different regions of the globe.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Aridity-Dependent Land Surface Skin Temperature Biases in CMIP5/6

Land surface skin temperature, a critical indicator of climate change, connects the water and energy cycles between the land and the atmosphere. Here, in this study, we evaluate the simulations of land surface skin temperature from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models with satellite-based datasets and reanalysis. We find systematic cold skin temperature biases over arid regions in CMIP5/CMIP6 simulations. Over arid and semi-arid regions, latent heat biases drive skin temperature biases by evaporative cooling. Over humid regions, surface downward shortwave and albedo biases are relatively more critical. Spatial patterns of biases remain similar in the latest CMIP6 simulations, suggesting systematic biases in land-atmosphere interactions. These biases need to be corrected or considered while using models for future projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Liquid to crystal Si growth simulation using machine learning force field

Machine learning force field (ML-FF) has emerged as a potential promising approach to simulate various material phenomena for large systems with ab initio accuracy. However, most ML-FFs have been used to study the phenomena relatively close to the equilibrium ground states. In this work, we have studied a far from equilibrium system of liquid to crystal Si growth using ML-FF. Here, we found that our ML-FF based on ab initio decomposed atomic energy can reproduce all the aspects of ab initio simulated growth, from local energy fluctuations to transition temperatures, to diffusion constant, and growth rates. We have also compared the growth simulation with the Stillinger-Weber classical force field and found significant differences. A procedure is also provided to correct a systematic fitting bias in the ML-FF training process, which exists in all training models, otherwise critical results like transition temperature will be wrong.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here, in this study, we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Private Tabular Survey Data Products through Synthetic Microdata Generation

We propose two synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a pseudo posterior mechanism that downweights by-record likelihood contributions with weights ∈[0,1] based on their identification disclosure risks to producing tabular products for survey data. Our method applied to an observed survey database achieves an asymptotic global probabilistic differential privacy guarantee. Our two approaches synthesize the observed sample distribution of the outcome and survey weights, jointly, such that both quantities together possess a privacy guarantee. The privacy-protected outcome and survey weights are used to construct tabular cell estimates (where the cell inclusion indicators are treated as known and public) and associated standard errors to correct for survey sampling bias. Through a real data application to the Survey of Doctorate Recipients public use file and simulation studies motivated by the application, we demonstrate that our two microdata synthesis approaches to construct tabular products provide superior utility preservation as compared to the additive noise approach of the Laplace Mechanism. Moreover, our approaches allow the release of microdata to the public, enabling additional analyses at no extra privacy cost.

Mathematical Methods In Social Sciences↗
DOE OSTI2022

Optical and X-ray GRB Fundamental Planes as cosmological distance indicators

ABSTRACT Gamma-ray bursts (GRBs), can be employed as standardized candles, extending the distance ladder beyond Type Ia supernovae (SNe Ia, z = 2.26). We standardize GRBs using the three-dimensional (3D) Fundamental Plane relation (the Dainotti relation) among the rest-frame end time of the X-ray plateau emission, its corresponding luminosity, and the peak prompt luminosity. Combining SNe Ia and GRBs, we constrain ΩM = 0.299 ± 0.009 assuming a flat Λ cold dark matter (ΛCDM) cosmology with and without correcting GRBs for selection biases and redshift evolution. Using a 3D optical Dainotti correlation, we find this sample is as efficacious in the determination of ΩM as the X-ray sample. We trimmed our GRB samples to achieve tighter planes to simulate additional GRBs. We determined how many GRBs are needed as stand-alone probes to achieve a comparable precision on ΩM to the one obtained by SNe Ia only. We reach the same error measurements derived using SNe Ia in 2011 and 2014 with 142 and 284 simulated optical GRBs, respectively, considering the error bars on the variables halved. These error limits will be reached in 2038 and in 2047, respectively. Using a doubled sample (obtained by future machine learning approaches allowing a light-curve reconstruction and the estimates of GRB redshifts when z is unknown) compared to the current sample, with error bars halved we will reach the same precision as SNe Ia in 2011 and 2014, now and in 2026, respectively. If we consider the current SNe precision, this will be reached with 390 optical GRBs by 2054.

Dainotti, M. G.↗
DOE OSTI2025

Accurate shear estimation with fourth-order moments

ABSTRACT As imaging surveys progress in exploring the large-scale structure of the Universe through the use of weak gravitational lensing, achieving sub-per cent accuracy in estimating shape distortions caused by lensing, or shear, is imperative for precision cosmology. In this paper, we extend the Fourier power function shapelets (FPFS) shear estimator using fourth-order shapelet moments and combine it with the original second-order shear estimator to reduce galaxy shape noise. We calibrate this novel shear estimator analytically to a sub-per cent level-accuracy using the AnaCal framework. This higher order shear estimator is tested with realistic image simulations, and after analytical correction for the detection/selection bias and noise bias, the multiplicative shear bias $|m|$ is below $3\times 10^{-3}$ (99.7 per cent confidence interval) for both isolated and blended galaxies. Once combined with the second-order FPFS shear estimator, the shape noise is reduced by $\sim 35~{{\ \rm per\ cent}}$ for isolated galaxies in simulations with Hyper Suprime-Cam and Vera C. Rubin Observatory Legacy Survey of Space and Time observational conditions. However, for blended galaxies, the effective number density does not significantly improve with the combination of the two estimators. Based on these results, we recommend exploration of how this framework can further reduce the systematic uncertainties in shear due to point spread function leakage and modelling error, and potentially provide improved precision in shear inference in high-resolution space-based images.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Many-body electronic structure of LaScO 3 by real-space quantum Monte Carlo

We present real space quantum Monte Carlo (QMC) calculations of the scandate LaSc0 3 that proved to be challenging for traditional electronic structure approaches due to strong correlation effects resulting in inaccurate band gaps from DFT and GW methods when compared with existing experimental data. Besides calculating an accurate QMC band gap corrected for supercell size biases and in agreement with numerous experiments, we also predict the cohesive energy of the crystal using the standard fixed-node QMC without any empirical or non-variational parameters. We show that promotion (optical) gap and fundamental gap agree with each other illustrating a clear absence of significant excitonic effects in the ideal crystal. We obtained these results in perfect consistency in two independent tracks that employ different basis sets (plane wave vs. localized gaussians), different codes for generating orbitals (QUANTUM ESPRESSO vs. CRYSTAL), different QMC codes (QMCPACK vs. QWALK) and different high-accuracy pseudopotentials (ccECPs vs. Troullier-Martins) presenting the maturity and consistency of QMC methodology and tools for studies of strongly correlated problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Mixture density network estimation of continuous variable maximum likelihood using discrete training samples

Abstract Mixture density networks (MDNs) can be used to generate posterior density functions of model parameters $$\varvec{\theta }$$ θ given a set of observables $${\mathbf {x}}$$ x . In some applications, training data are available only for discrete values of a continuous parameter $$\varvec{\theta }$$ θ . In such situations, a number of performance-limiting issues arise which can result in biased estimates. We demonstrate the usage of MDNs for parameter estimation, discuss the origins of the biases, and propose a corrective method for each issue.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Measurements of higher-order cumulants of multiplicity and net-electric charge distributions in inelastic proton-proton interactions by NA61/SHINE

This paper presents the energy dependence of multiplicity and net-electric charge fluctuations in p+p interactions at beam momenta 20, 31, 40, 80, and 158 \(\text{ Ge }\hspace{-1.00006pt}\text{ V }\!/\!c\). Results are corrected for the experimental biases and quantified with the use of cumulants and factorial cumulants. Cumulant ratios are an essential tool in the search for the critical point of strongly interacting matter in heavy ion collisions. Measurements performed in p+p interactions provide a vital baseline estimation in these studies. The measured signals are compared with the string hadronic models Epos1.99 and FTFP-BERT.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Multiplicity and net-electric charge fluctuations in central Ar+Sc interactions at 13 A , 19 A , 30 A , 40 A , 75 A , and 150 A GeV/c beam momenta measured by NA61/SHINE at the CERN SPS

This paper presents results on multiplicity fluctuations of positively and negatively charged hadrons as well as net-electric charge fluctuations measured in central Ar+Sc interactions at beam momenta 13 A , 19 A , 30 A , 40 A , 75 A , and 150 A GeV/c. The fluctuation analysis is one of the tools to search for the predicted critical point of strongly interacting matter. Results are corrected for the experimental biases and quantified using cumulant ratios. In most instances, multiplicity and net-charge distributions appear narrower than the corresponding Poisson or Skellam distributions. Cumulant ratios are compared with the EPOS1.99 model predictions, which provide a qualitative description that aligns with observations for positively and negatively charged particles. The obtained results are also compared to earlier NA61/SHINE results from inelastic p+p interactions in the same analysis acceptance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗