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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Additional Data Focused on Phase 1 Geared Toward Computational Fluid Dynamics (CFD) validation

A new validation campaign was developed within the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) to better understand the complex interactions between components of a floating wind system (e.g., columns, pontoons, etc.) in a more generic sense, and to validate modeling approaches for a variety of floating designs. A set of cylinders based on the Offshore Code Comparison Collaboration Continuation (OC4)-DeepCwind semidesign were tested under wave loading, both individually and in different combinations (including the attachment of heave plates of different sizes). The cylinders were held rigid, and pressure sensors were used to measure the distributed loading on the structures, while load cells measured the total integrated hydrodynamic loads.

17 WIND ENERGY↗
DOE OSTI2020

STOCHASTIC OPTIMIZATION FOR LONG TERM CAPITAL STRUCTURES, SYSTEMS, AND COMPONENTS REFURBISHMENT AND REPLACEMENT

As commercial nuclear power plants (NPPs) pursue extended plant operations in the form of Second License Renewals (SLRs), opportunities exist for these plants to provide capital investments to ensure long-term, safe, and economic performance. Several utilities have already announced their intention to pursue extended operations for one or more of their NPPs via SLR2. The goal of this research is to develop a riskinformed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions in NPP operations. In order to prioritize project selection via a riskinformed approach we developed a single decision-making tool that integrates safety/reliability, cost, and stochastic optimization models to provide users with data analysis capabilities to more cost effectively manage plant assets. Both stochastic analysis methods—such as Monte Carlo-based sampling strategies—and multi-stage stochastic optimization strategies are employed to provide priority lists to decisionmakers in support of risk-informed decisions. We applied the proposed method to a trial application of projected replacement/refurbishment expenditures for plant capital assets (i.e., Structures, Systems, and Components [SSCs]). The objective is to optimize the SSC replacement/refurbishment schedule in terms of economic constraints, data uncertainties, and SSC reliability data, as well to generate a priority list for maximizing returns on investment.

42 ENGINEERING↗
DOE OSTI2023

AL-ASMR: Active Learning of Atomistic Surrogate Models for Rare Events

Atomistic simulation with artificial intelligence (AI) is an emerging tool for understanding materials' properties and behaviors and predicting novel materials with optimized/targeted properties. Neural network potentials (NNPs) are outstanding in this field as they have shown a comparable accuracy to ab initio electronic structure calculations for reproducing potential energy surfaces while being several orders of magnitude faster. However, such NNPs can perform poorly outside of their training domain and typically fail catastrophically in the prediction of rare events in molecular dynamics (MD) simulations. For effective AL loops to distinguish the informative data from enhanced sampled configurations, we developed a decision engine by configurational similarity and uncertainty quantification (UQ) with data augmentation.

Jung, Gang Seob↗
DOE OSTI2026

Multiprobe constraints on early and late time dark energy

We perform a multiprobe analysis combining cosmic microwave background (CMB) data from Planck and the Atacama Cosmology Telescope (ACT), ACT CMB lensing, and large-scale structure (LSS) measurements from the Dark Energy Spectroscopic Instrument (DESI), including DESI Legacy Imaging Survey (LS) galaxies and baryon acoustic oscillations (BAOs). We present the first 5×2 pt analysis of ACT DR6 lensing, DESI LS, and Planck Integrated Sachs-Wolfe (ISW). Within Λ⁢CDM, this yields 𝑆 8 =𝜎 8 ⁢(Ω 𝑚 /0.3) 0.5 =0.819 ±0.016, in good agreement with primary CMB inferences and provides a sound-horizon-free Hubble constant constraint of 𝐻 0 =70.0±4.4 km s −1 Mpc −1 . Then, combining with CMB primary and BAO, we reconfirm a CMB–BAO discrepancy in the Ω 𝑚 –$\frac{𝐷𝑣}{𝑟𝑑}$ plane, which is heightened when combining BAO with the 5 ×2 pt data vector. We explore two dark-energy extensions that may reconcile this: an early-time modification, early dark energy (EDE), and late-time dynamical dark energy (DDE) parametrized by 𝑤 0 ⁢𝑤 𝑎 . For CMB primary +BAO +5 ×2 pt, we find a 3.3⁢𝜎 preference for DDE over Λ⁢CDM, while EDE is modestly favored at 2.3⁢𝜎. The models address different shortcomings of Λ⁢CDM: DDE relaxes the neutrino mass bound (𝑀 𝜈 <0.17 eV vs <0.050 eV under Λ⁢CDM), making it compatible with neutrino oscillation measurements, while EDE raises the Hubble constant to 𝐻 0 =70.5±1.2 km s −1 Mpc −1 , easing the discrepancy with SH0ES. However, neither model resolves both issues simultaneously. Our analysis indicates that both DDE and EDE remain viable extensions of Λ⁢CDM within current uncertainties and demonstrates the capacity of combined probes to place increasingly stringent constraints on cosmological parameters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Data-driven reevaluation of f t values in superallowed β decays

We present a comprehensive reevaluation of the ft values in superallowed nuclear β decays crucial for the precise determination of V ud and low-energy tests of the electroweak standard model. It consists of the first, fully data-driven analysis of the nuclear β decay form factor, that utilizes isospin relations to connect the nuclear charged weak distribution to the measurable charge distributions. This prescription supersedes previous shell-model estimations, and allows for a rigorous quantification of theory uncertainties in f which is absent in the existing literature. Furthermore, our new evaluation shows an overall downward shift of the central values of f at the level of 0.01%.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Targeted Adaptive Design

Modern advanced manufacturing and advanced materials design often require searches of relatively high-dimensional process control parameter spaces for settings that result in optimal structure, property, and performance parameters. The mapping from the former to the latter must be determined from noisy experiments or from expensive simulations. Here, we abstract this problem to a mathematical framework in which an unknown function from a control space to a design space must be ascertained by means of expensive noisy measurements, which locate control settings generating desired design features within specified tolerances, with quantified uncertainty. We describe targeted adaptive design (TAD), a new algorithm that performs this sampling task efficiently. TAD creates a Gaussian process surrogate model of the unknown mapping at each iterative stage, proposing a new batch of control settings to sample experimentally and optimizing the updated expected log-predictive probability density of the target design. TAD either stops upon locating a solution with uncertainties that fit inside the tolerance box or uses a measure of expected future information to determine that the search space has been exhausted with no solution. TAD thus embodies the exploration-exploitation tension in a manner that recalls, but is essentially different from, Bayesian optimization and optimal experimental design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Structurally flexible cloud microphysics, observationally constrained at all scales via ML-accelerated Bayesian inference

We discuss the challenge of developing observationally informed parameterizations of microphysics for use at a hierarchy of modeling scales. Our proposed approach is applicable to any domain that suffers from a two-fold parameterization problem, where physical processes are not resolved at the model scale (the first problem), and those processes are uncertain at any scale (the second problem). For such problems, a physical approach facilitates modeling across scales, as well as systematic observational inference accelerated by machine learning (ML) surrogate models, and quantification of physical uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Getting Started with Evaluations with Means and Uncertainties (EMU 3.0)

One of the most fundamental quantities in nuclear physics is the reaction cross section. A cross section represents the probability that a nuclear reaction will resolve through a given channel given a target nucleus and a projectile with a certain energy. A nuclear evaluation is a set of discrete data and interpolation rules to convert those discrete nuclear reaction data—such as the cross section—into a continuous function at arbitrary energies. Evaluated nuclear data files that can appear in Evaluated Nuclear Data File (ENDF) and Generalized Nuclear Data Structure (GNDS) formats, storing a “most-complete” discretized representation of nuclear data, based on both experimental measurements and theory models.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Enabling robust offline active learning for machine learning potentials using simple physics-based priors

Abstract Machine learning surrogate models for quantum mechanical simulations have enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning (AL) and uncertainty estimates. When starting with small datasets, convergence of AL approaches is a major outstanding challenge which has limited most demonstrations to online AL. In this work we demonstrate a Δ-machine learning (ML) approach that enables stable convergence in offline AL strategies by avoiding unphysical configurations with initial datasets as little as a single data point. We demonstrate our framework’s capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70%–90%. The approach is incorporated and developed alongside AMP torch , an open-source ML potential package, along with interactive Google Colab notebook examples.

Shuaibi, Muhammed↗
DOE OSTI2023

Cosmology from weak lensing, galaxy clustering, CMB lensing, and tSZ – I. 10 × 2pt modelling methodology

ABSTRACT The overlap of galaxy surveys and cosmic microwave background (CMB) experiments presents an ideal opportunity for joint cosmological data set analyses. In this paper we develop a halo model-based method for the first joint analysis combining these two experiments using 10 correlated two-point functions (10 × 2pt) derived from galaxy position, galaxy shear, CMB lensing convergence, and Compton-y fields. We explore this method using the Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and the Simons Observatory (SO) as examples. We find such LSS × CMB joint analyses lead to significant improvement in Figure-of-Merit of Ωm and S8 over the constraints from using LSS-only probes within Λ cold dark matter (ΛCDM) model. We identify that the shear–y and y–y correlations are the most valuable additions when thermal Sunyaev–Zel’dolvich (tSZ) is included. We further identify the dominant sources of halo model uncertainties in the small-scale modelling, and investigate the impact of halo self-calibration due to the inclusion of small-scale tSZ information.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

MrHyDE v.1.0

SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2024

Accurate field-level weak lensing inference for precision cosmology

We present miko, a catalog-to-cosmology pipeline for general flat-sky field-level inference, which provides access to cosmological information beyond the two-point statistics. In the context of weak lensing, we identify several new field-level analysis systematics (such as aliasing, Fourier mode-coupling, and density-induced shape noise), quantify their impact on cosmological constraints, and correct the biases to a percent level. Next, we find that model misspecification can lead to both absolute bias and incorrect uncertainty quantification for the inferred cosmological parameters in realistic simulations. The Gaussian map prior infers unbiased cosmological parameters, regardless of the true data distribution, but it yields overconfident uncertainties. The log-normal map prior quantifies the uncertainties accurately, although it requires careful calibration of the shift parameters for unbiased cosmological parameters. Here, we demonstrate systematics control down to the 2% level for both models, making them suitable for ongoing weak lensing surveys.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Photo-z outlier self-calibration in weak lensing surveys

Calibrating photometric redshift errors in weak lensing surveys with external data is extremely challenging. In this work, we show that both Gaussian and outlier photo-z parameters can be self-calibrated from the data alone. This comes at no cost for the neutrino masses, curvature and dark energy equation of state w0, but with a 65% degradation when both w0 and wa are varied. We perform a realistic forecast for the Vera Rubin Observatory (VRO) Legacy Survey of Space and Time (LSST) 3× 2 analysis, combining cosmic shear, projected galaxy clustering and galaxy - galaxy lensing. We confirm the importance of marginalizing over photo-z outliers. We examine a subset of internal cross-correlations, dubbed "null correlations", which are usually ignored in 3× 2 analyses. Despite contributing only ~ 10% of the total signal-to-noise, these null correlations improve the constraints on photo-z parameters by up to an order of magnitude. Using the same galaxy sample as sources and lenses dramatically improves the photo-z uncertainties too. Together, these methods add robustness to any claim of detected new Physics, and reduce the statistical errors on cosmology by 15% and 10% respectively. Finally, including CMB lensing from an experiment like Simons Observatory or CMB-S4 improves the cosmological and photo-z posterior constraints by about 10%, and further improves the robustness to systematics. To give intuition on the Fisher forecasts, we examine in detail several toy models that explain the origin of the photo-z self-calibration. Our Fisher code LaSSI (Large-Scale Structure Information), which includes the effect of Gaussian and outlier photo-z, shear multiplicative bias, linear galaxy bias, and extensions to LCDM, is publicly available at \href{https://github.com/EmmanuelSchaan/LaSSI}{https://github.com/EmmanuelSchaan/LaSSI}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The Effect of Warm Dark Matter on Early Star Formation Histories of Massive Galaxies: Predictions from the CROC Simulations

Several massive (M * > 108 M ⊙ ), high-redshift (z = 8–10) galaxies have recently been discovered to contain stars with ages of several hundred million years, pushing the onset of star formation in these galaxies back to z ~ 15. The very existence of stars formed so early may serve as a test for cosmological models with suppressed small-scale power (and, hence, late formation of cosmic structure). We explore the ages of the oldest stars in numerical simulations from the Cosmic Reionization On Computers project with cold dark matter (CDM) and two warm dark matter (WDM) cosmologies with 3 and 6 keV particles. There are statistically significant differences of ~5 Myr between average stellar ages of massive galaxies in CDM and 3 keV WDM, while CDM and 6 keV WDM are statistically indistinguishable. Even this 5 Myr difference, however, is much less than current observational uncertainties on the stellar population properties of high-redshift galaxies. The age distributions of all galaxies in all cosmologies fail to produce a substantial Balmer break, although uncertainties in dust attenuation are a potentially significant factor. Finally, we assess the convergence of our simulation predictions and find that the systematic uncertainties on individual galaxy properties are comparable to the differences between cosmologies, suggesting these differences may not be numerically robust.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Imaging the crust and uppermost mantle structure of Portugal (West Iberia) with seismic ambient noise

SUMMARY We present a new high-resolution 3-D shear wave velocity (Vs) model of the crust and uppermost mantle beneath Portugal, inferred from ambient seismic noise tomography. We use broad-band seismic data from a dense temporary deployment covering the entire Portuguese mainland between 2010 and 2012 in the scope of the WILAS project. Vertical component data are processed using phase correlation and phase weighted stack to obtain empirical Green functions (EGFs) for 2016 station pairs. Further, we use a random sampling and subset stacking strategy to measure robust Rayleigh-wave group velocities in the period range 7–30 s and associated uncertainties. The tomographic inversion is performed in two steps: First, we determine group-velocity lateral variations for each period. Next, we invert them at each grid point using a new trans-dimensional inversion scheme to obtain the 3-D shear wave velocity model. The final 3-D model extends from the upper crust (5 km) down to the uppermost mantle (60 km) and has a lateral resolution of ∼50 km. In the upper and middle crusts, the Vs anomaly pattern matches the tectonic units of the Variscan Massif and Alpine basins. The transition between the Lusitanian Basin and the Ossa Morena Zone is marked by a contrast between moderate- and high-velocity anomalies, in addition to two arched earthquake lineations. Some faults, namely, the Manteigas–Vilariça–Bragança fault and the Porto–Tomar–Ferreira do Alentejo fault, have a clear signature from the upper crust down to the uppermost mantle (60 km). Our 3-D shear wave velocity model offers new insights into the continuation of the main tectonic units at depth and contributes to better understanding the seismicity of Portugal.

Silveira, Graça (ORCID:0000000221102554)↗
DOE OSTI2020

Density modification of cryo-EM maps

Density modification uses expectations about features of a map such as a flat solvent and expected distributions of density in the region of the macromolecule to improve individual Fourier terms representing the map. This process transfers information from one part of a map to another and can improve the accuracy of a map. Here, the assumptions behind density modification for maps from electron cryomicroscopy are examined and a procedure is presented that allows the incorporation of model-based information. Density modification works best in cases where unfiltered, unmasked maps with clear boundaries between the macromolecule and solvent are visible, and where there is substantial noise in the map, both in the region of the macromolecule and the solvent. It also is most effective if the characteristics of the map are relatively constant within regions of the macromolecule and the solvent. Model-based information can be used to improve density modification, but model bias can in principle occur. Here, model bias is reduced by using ensemble models that allow an estimation of model uncertainty. A test of model bias is presented that suggests that even if the expected density in a region of a map is specified incorrectly by using an incorrect model, the incorrect expectations do not strongly affect the final map.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Multi-resolution Arctic Shrub Cover Dataset Derived from UAS and Airborne SfM and LiDAR (2013-2025)

We synthesized 177 unoccupied aerial system flights and 77 airborne flights across the Arctic and created a multi-resolution benchmark data of low-to-tall shrub fractional cover leveraging Structure-from-Motion and Light Detection and Ranging. The resulting dataset covered a total of 1899 km2 across Alaska, Western Canada, Sweden, and Siberian Arctic, including key sites from the Oro Arctic to the High Arctic. The dataset is organized into 6 primary data collection directories (“Abisko,” “AWI,” “ERE,” “Fairbanks,” “NGEE,” “Toolik”), each containing site and flight subdirectories. Flight directories include shrub cover rasters (*.tifs) at 1 m, 5 m, and 30 m resolution, the canopy height model at 1 m resolution (*.tifs), and a bounding box *.kml file. For the AWI, Abisko, NGEE, and Fairbanks collections, we also include the GCC raster at 1 m resolution (*.tif). Files are organized by Collection > Site > Flight Name > Data Files. Flight rasters are in the local UTM zone and the .kml files are in the geographic coordinate system EPSG 4326. We also include a .csv file that details the source datasets for every flight. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

canopy height model↗