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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A comparison of cosmological models using strong gravitational lensing galaxies

Strongly gravitationally lensed quasar-galaxy systems allow us to compare competing cosmologies as long as one can be reasonably sure of the mass distribution within the intervening lens. In this paper, we assemble a catalog of 69 such systems from the Sloan Lens ACS and Lens Structure and Dynamics surveys suitable for this analysis, and carry out a one-on-one comparison between the standard model, ΛCDM, and the R{sub h}=ct universe, which has thus far been favored by the application of model selection tools to other kinds of data. We find that both models account for the lens observations quite well, though the precision of these measurements does not appear to be good enough to favor one model over the other. Part of the reason is the so-called bulge-halo conspiracy that, on average, results in a baryonic velocity dispersion within a fraction of the optical effective radius virtually identical to that expected for the whole luminous-dark matter distribution modeled as a singular isothermal ellipsoid, though with some scatter among individual sources. Future work can greatly improve the precision of these measurements by focusing on lensing systems with galaxies as close as possible to the background sources. Given the limitations of doing precision cosmological testing using the current sample, we also carry out Monte Carlo simulations based on the current lens measurements to estimate how large the source catalog would have to be in order to rule out either model at a ∼99.7% confidence level. We find that if the real cosmology is ΛCDM, a sample of ∼200 strong gravitational lenses would be sufficient to rule out R{sub h}=ct at this level of accuracy, while ∼300 strong gravitational lenses would be required to rule out ΛCDM if the real universe were instead R{sub h}=ct. The difference in required sample size reflects the greater number of free parameters available to fit the data with ΛCDM. We point out that, should the R{sub h}=ct universe eventually emerge as the correct cosmology, its lack of any free parameters for this kind of work will provide a remarkably powerful probe of the mass structure in lensing galaxies, and a means of better understanding the origin of the bulge-halo conspiracy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Capturing the full benefits of bifacial modules to approach an LCOE of 3c/kWh through a regional optimization of the electrical architecture (Final Technical Report)

This is the final technical report for the Cypress Creek Renewables project focused on bifacial plant performance. The project consisted of building a heavily instrumented Bifacial test sites with several cell technologies; instrumenting >1 MW scale commercial sites with both monofacial and bifacial arrays in order to understand real-world differences; and to engage with the performance modeling and financial stakeholder community to improve accuracy and increase investor confidence. The data from the test and commercial sites has been used to validate and improve the accuracy of both physics-based and reduced order modeling software. The heavily instrumented nature has been used to understand how light contributions from the sky and reflected from the ground impact different portions of the module and array while the data from the commercial sites has directly impacted how independent engineers model production from bifacial systems. The latter point has led to an increase in over 2% compared to earlier predictions. Even this modest increase has had significant downward pressure on project LCOE. As the data and results are both public, this refined modeling and empirical evidence for a higher bifacial gain than modeled, the benefits to the broader US solar community are assured.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Airborne hyperspectral imaging of cover crops through radiative transfer process-guided machine learning

Cover cropping between cash crop growing seasons is a multifunctional conservation practice. Timely and accurate monitoring of cover crop traits, notably aboveground biomass and nutrient content, is beneficial to agricultural stakeholders to improve management and understand outcomes. Currently, there is a scarcity of spatially and temporally resolved information for assessing cover crop growth. Remote sensing has a high potential to fill this need, but conventional empirical regression operated with coarse-resolution multispectral data has large uncertainties. Therefore, this study utilized airborne hyperspectral imaging techniques and developed new process-guided machine learning approaches (PGML) for cover crop monitoring. Specifically, we deployed an airborne hyperspectral system covering visible to shortwave-infrared wavelengths (400–2400 nm) to acquire high spatial (0.5 m) and spectral (3–5 nm) resolution reflectance over 23 cover crop fields across Central Illinois in March and April of 2021. Airborne hyperspectral surface reflectance with high spectral and spatial resolution can be well matched with field data to quantify cover crop traits. Furthermore, the PGML models were pre-trained by synthetic data from soil-vegetation radiative transfer modeling (one million records), and then fine-tuned with field data of cover crop biomass and nutrient content. Results show that airborne hyperspectral data with PGML can achieve high accuracy to predict cover crop aboveground biomass (R 2 = 0.72, relative RMSE = 15.16%) and nitrogen content (R 2 = 0.69, relative RMSE = 16.59%) through leave-one-field-out cross-validation. Unlike the pure data-driven approach (e.g., partial least-squares regression), PGML incorporated radiative transfer knowledge and obtained higher predictive performance with fewer field data. Meanwhile, with field data for model fine-tuning, PGML predicted biomass more accurately than the inversion of radiative transfer models. Here we also found that the red edge has a high contribution in quantifying aboveground biomass and nitrogen content, followed by green and shortwave spectra. This study demonstrated the first attempt of utilizing hyperspectral remote sensing to accurately quantify cover crop traits. We highlight the strength of PGML in exploiting sensing data to quantify ecosystem variables to advance agroecosystem monitoring for sustainable agricultural management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Expanded Intercomparison of Nuclear Data Libraries Using Jupiter and Jupiter High-240 Experiments

There is a limited availability of plutonium experiments with sensitivity to lead in the ICSBEP (International Handbook of Evaluated Criticality Safety Benchmark Experiments) Handbook. The Jupiter and Jupiter High-240 experiments were performed at the National Criticality Experiments Research Center as a collaborative effort between Los Alamos National Laboratory and the Japan Atomic Energy Agency to assess lead void coefficients in a plutonium-lead system containing weapons- and reactor-grade plutonium, respectively. Concurrent with benchmark development, an intercomparison of calculations using different nuclear data libraries has been performed to assess the usability of the experimental data for nuclear data adjustment in a “softer-that-fast” neutron energy spectrum. Eigenvalue calculations using MCNP with the ENDF/B-VIII.0 and TENDL-2021 nuclear data libraries calculate closest to the benchmark values for Jupiter. Calculations using JENDL-5 and ENDF/B-VIII.1 match best with the Jupiter High-240 values. Lead void worth calculations using the various nuclear data libraries are all within 3σ of their respective measured values. Perturbation studies between ENDF/B-VIII.0 and ENDF/B-VIII.1 demonstrate an approximate increase in calculated eigenvalues for the Jupiter series experiments by ~240 pcm for plutonium (mostly 239 Pu) and ~120 pcm for lead accompanied by a decrease contributed by ~113 pcm for copper and ~13 pcm for stainless steel. Nuclear data sensitivities and uncertainties investigated using Whisper show slightly lower sensitivity to scatter than a lead-reflected plutonium sphere but greater sensitivity to neutron capture. The sensitivities between Jupiter and Jupiter High-240 for lead are very similar for both ENDF/B-VIII.0 and ENDF/B-VIII.1 nuclear data. These benchmarks are more sensitive to neutron capture in lead than other plutonium benchmark experiments and would be useful for both lead and 240 Pu validation. In conclusion, with the high degree of compensating effects between copper, lead, and plutonium cross sections, additional isolated Pb-Pu and Cu-Pu benchmarks would be beneficial in improving these nuclear data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Predictive coarse-graining

We propose a data-driven, coarse-graining formulation in the context of equilibrium statistical mechanics. In contrast to existing techniques which are based on a fine-to-coarse map, we adopt the opposite strategy by prescribing a probabilistic coarse-to-fine map. This corresponds to a directed probabilistic model where the coarse variables play the role of latent generators of the fine scale (all-atom) data. From an information-theoretic perspective, the framework proposed provides an improvement upon the relative entropy method and is capable of quantifying the uncertainty due to the information loss that unavoidably takes place during the coarse-graining process. Furthermore, it can be readily extended to a fully Bayesian model where various sources of uncertainties are reflected in the posterior of the model parameters. The latter can be used to produce not only point estimates of fine-scale reconstructions or macroscopic observables, but more importantly, predictive posterior distributions on these quantities. Predictive posterior distributions reflect the confidence of the model as a function of the amount of data and the level of coarse-graining. The issues of model complexity and model selection are seamlessly addressed by employing a hierarchical prior that favors the discovery of sparse solutions, revealing the most prominent features in the coarse-grained model. A flexible and parallelizable Monte Carlo – Expectation–Maximization (MC-EM) scheme is proposed for carrying out inference and learning tasks. A comparative assessment of the proposed methodology is presented for a lattice spin system and the SPC/E water model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data Validation for Hosting Capacity Analyses: Executive Summary

The usefulness of Hosting Capacity Analysis (HCA) is dependent on users' confidence that the results accurately reflect grid conditions. This report provides the findings and recommendations from research, conducted by The National Renewable Energy Lab (NREL) and The Interstate Renewable Energy Council (IREC), into HCA data validation best practices. The goal is to provide utilities, regulators, and stakeholders best practices for HCA data validation procedures so that future HCA deployments avoid uncertainties and potential mistakes from earlier rollouts and provide useful and accurate data from the day they are published.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2012

Modeling of the ITER-like wide-angle infrared thermography view of JET

Infrared (IR) thermography systems are mandatory to ensure safe plasma operation in fusion devices. However, IR measurements are made much more complicated in metallic environment because of the spurious contributions of the reflected fluxes. This paper presents a full predictive photonic simulation able to assess accurately the surface temperature measurement with classical IR thermography from a given plasma scenario and by taking into account the optical properties of PFCs materials. This simulation has been carried out the ITER-like wide angle infrared camera view of JET in comparing with experimental data. The consequences and the effects of the low emissivity and the bidirectional reflectivity distribution function used in the model for the metallic PFCs on the contribution of the reflected flux in the analysis are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Tuning Electrical Properties through Control of TiSe 2 Thickness in (BiSe) 1+δ (TiSe 2 ) n Compounds

A series of (BiSe) 1+δ (TiSe 2 )n compounds where n was varied from two to four were synthesized and electrically characterized to explore the extent of charge transfer from the BiSe layer to the TiSe 2 layers. These kinetically stable heterostructures were prepared using the modulated elemental reactants (MER) method, in which thin amorphous elemental layers are deposited in an order that mimics the nanostructure of the desired product. X-ray diffraction (XRD), X-ray area diffraction, and scanning transmission electron microscopy (STEM) data show that the precursors formed the desired products. Specular diffraction scans contain only 00l reflections, indicating that the compounds are crystallographically aligned with the c-axis perpendicular to the substrate. The c-axis lattice parameter increases by 0.604(3) nm with each additional TiSe 2 layer. In-plane diffraction scans contain reflections that can be indexed as the (hk0) of the BiSe and TiSe 2 constituents. Area diffraction scans are also consistent with the samples containing only BiSe and TiSe 2 constituents. Rietveld refinement of the 00l XRD data was used to determine the positions of atomic planes along the c-axis. STEM data supports the structures suggested by the diffraction data and associated refinements but also shows that antiphase boundaries occur approximately 1/3 of the time in the BiSe layers. All samples showed metallic behavior for the temperature-dependent electrical resistivity between 20 K and room temperature. Electrical measurements indicated that charge is transferred from the BiSe layer to the TiSe 2 layer. The measured Hall coefficients were all negative indicating that electrons are the majority carrier and are systematically decreased as n was increased. Assuming a single parabolic band model, carrier concentration decreased when the number of TiSe 2 layers is increased, suggesting that the amount of charge donated by the BiSe layer to the TiSe 2 layers is constant. Seebeck coefficients were negative for all of the (BiSe) 1+δ (TiSe 2 ) n compounds studied, indicating that electrons are the majority carrier, and decreased as n increased. Lastly, the effective mass of the carriers was calculated to be 5–6 m e for the series of compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Asymmetry in serial femtosecond crystallography data

Serial crystallography is an increasingly important approach to protein crystallography that exploits both X-ray free-electron laser (XFEL) and synchrotron radiation. Serial crystallography recovers complete X-ray diffraction data by processing and merging diffraction images from thousands of randomly oriented non-uniform microcrystals, of which all observations are partial Bragg reflections. Random fluctuations in the XFEL pulse energy spectrum, variations in the size and shape of microcrystals, integrating over millions of weak partial observations and instabilities in the XFEL beam position lead to new types of experimental errors. The quality of Bragg intensity estimates deriving from serial crystallography is therefore contingent upon assumptions made while modeling these data. Here it is observed that serial femtosecond crystallography (SFX) Bragg reflections do not follow a unimodal Gaussian distribution and it is recommended that an idealized assumption of single Gaussian peak profiles be relaxed to incorporate apparent asymmetries when processing SFX data. The phenomenon is illustrated by re-analyzing data collected from microcrystals of the Blastochloris viridis photosynthetic reaction center and comparing these intensity observations with conventional synchrotron data. The results show that skewness in the SFX observations captures the essence of the Wilson plot and an empirical treatment is suggested that can help to separate the diffraction Bragg intensity from the background.

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

Temperature-dependent diffuse reflectance measurements of ceramic powders in the near- and mid-infrared spectra

Radiative properties are critical to quantify radiative energy fluxes between surfaces and in participating media. However, there is limited experimental data on temperature-dependent radiative properties of materials. Herein, this work focuses on experimentally measuring temperature-dependent diffuse reflectance in the near- and mid-infrared spectra (1–20 μm) for ceramic particles with applications as heat-transfer and thermal-storage media in concentrated solar power (CSP) plants. Specifically, a commercially available sintered bauxite proppant, ACCUCAST ID80, and its primary chemical constituents—alumina (Al 2 O 3 ) and silica (SiO 2 )—are measured in powder form using a Fourier transform infrared spectrometer (FTIR) coupled with a specialized diffuse reflectance accessory and a heated stage. Room-temperature diffuse reflectance measurements show increased absorption in tests with greater mass fractions of the ceramic samples. There is a strong correlation in the measured reflectance spectra of ACCUCAST with alumina and silica in the spectral range 2000–500 cm –1 (5–20 μm). Whereas, for shorter wavelengths (< 5 μm), the absorptance for ACCUCAST is greater than the absorptance for alumina and silica, indicating contributions from other chemical species present in the composite material. For the first time, temperature-dependent diffuse reflectance measurements are reported for ACCUCAST up to 1000 °C. These results are compared against those of alumina and silica through the calculation of a thermal emittance. All three materials exhibit a calculated emittance of ~0.9 at room temperature. However, this value decreases to ~0.6 for ACCUCAST and drops to less than 0.4 for alumina and silica at 1000 °C. Thermal cycling in air at 1000 °C resulted in a visible color change from dark grey to light orange for ACCUCAST and a subsequent larger increase in reflectance for wavelengths less than 5 μm as compared to ACCUCAST thermally cycled at 1000 °C in vacuum. Alumina and silica spectra proved to be largely unaffected by thermal cycling under atmospheric conditions. Overall, this study establishes a powerful technique for the characterization of radiative properties of particulate materials as a function of temperature and presents a detailed case study of ACCUCAST, a candidate for next-generation particle-based CSP.

14 SOLAR ENERGY↗
DOE OSTI2019

ENDF/B-VIII.0 Covariance Data Development and Testing for Advanced Reactors

This report documents the ENDF/B-VIII.0 covariance data development and testing performed at Oak Ridge National Laboratory performed under the auspices of the US Department of Energy Office of Nuclear Energy. This work supports modeling and simulation of advanced reactor designs in the US by analyzing, validating and improving the nuclear data that provide underlying physics for calculations. This report assesses nuclear data uncertainty information for neutron cross sections, provided in the form of covariance matrices, in the 2018 ENDF/B-VIII.0 library. A review and interpretation of some of the issues with the current covariance library is provided. A new approach to augment the ENDF/B-VIII.0 covariance matrix to accurately reflect uncertainties in the results of modeling and simulation reliant on the current nuclear data library is documented. Rigorous testing of the ENDF/B-VIII.0 covariance data is documented. Finally, the performance of the ENDF/B-VIII.0 covariance data for advanced reactor applications is assessed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Meteorological Conditions in Urban Sites in Knoxville, USA. 2025

This 2025 dataset, which contains five csv files, reports hourly air temperature, wind speed, solar radiation, incident short wave, reflected short wave, incident long wave, emitted long wave, net radiation, and photosynthetically active radiation (PAR) data measured in urban parks in Knoxville, Tennessee, USA. The sites include Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEEED), West Hills Park (WHP), West View Park (WVP), and Victor Ashe Park (VAP). Air temperature, wind speed, and solar radiation data were obtained from a METER ATMOS 41 All-in-One Weather Station (Pullman, Washington, USA). Incident and emitted radiation (shortwave and longwave) measurements were made using an Apogee (Logan, Utah, USA) net radiometer (Model SN-500-SS). The SQ-521 Full-Spectrum Quantum sensor (Apogee Instruments, Inc) recorded the PAR. The measurement data for all the sites started on January 1, 2025. and all measurement ended on December 31, 2025. This work is a part of a larger study which investigates the impact of soil moisture and plant evapotranspiration on ambient temperature and relative humidity in several city parks in Knoxville, Tennessee.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗
DOE OSTI2023

Ocean Energy: Markets - Currency - Impact. Dimension of & Choices in the Technology Development Space: Preprint

This paper presents considerations of the employment of ocean wave energy to support different energy demand side applications. The key aspect in these considerations is the wave energy supported achievable positive impact and associated tangible contribution in service of common societal good and of the natural commons. The level of impact that can be delivered is dependent on both, the level of contribution of the supported energy use application, and the compatibility and unique suitability of the wave energy resource and its characteristics with the needs of the application. Thus, a variety of ocean wave energy markets, the key value indicators or "currency' in which these markets trade the value delivered and the achievable positive impact, are reflected upon. Ocean wave energy supported acquisition of high quality ocean system data across a wide spectrum of system properties is identified as a highly impactful application enabling and/or improving a comprehensive range of impactful ocean system activities. The technology development process towards these markets and desired impacts requires relevant technology development progress guidance and metrics. Going beyond technology readiness levels and technology performance levels, the notion of further technology development progress scales towards high impact and high contribution are proposed. These scales and the associated technology properties can be regarded as additional technology development dimensions to span-up the technology development space in which desired system capability and functional requirement choices and subsequent ideation, innovation, research and technology development decisions can and are to be made.

data market↗
DOE OSTI2020

Prestack Waveform Inversion at GC 955: Trials and sensitivity of PWI to high-resolution seismic data (Data Report)

Understanding the spatial extent and variability of hydrate deposits has important implications for potential economic production, climate change, and assessing natural hazards risks. Seismic reflection techniques are capable of determining the extent of gas hydrate deposits and can shed light on hydrate concentrations and properties. Using high resolution prestack time migrated seismic data collected during a 2013 US Geological Survey expedition and prestack waveform inversion (PWI) techniques, we produce highly resolved velocity models and compare them to co-located well logs. Coupling our PWI results with velocity-porosity relationships and nearby well control, we compare hydrate properties at site 955 in the Green Canyon and site 313 at Walker Ridge.

03 NATURAL GAS↗