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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Dark Energy Survey Year 3 results: Curved-sky weak lensing mass map reconstruction

ABSTRACT We present reconstructed convergence maps, mass maps, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (primarily dark matter) in the foreground of the observed galaxies. We use four reconstruction methods, each is a maximum a posteriori estimate with a different model for the prior probability of the map: Kaiser–Squires, null B-mode prior, Gaussian prior, and a sparsity prior. All methods are implemented on the celestial sphere to accommodate the large sky coverage of the DES Y3 data. We compare the methods using realistic ΛCDM simulations with mock data that are closely matched to the DES Y3 data. We quantify the performance of the methods at the map level and then apply the reconstruction methods to the DES Y3 data, performing tests for systematic error effects. The maps are compared with optical foreground cosmic-web structures and are used to evaluate the lensing signal from cosmic-void profiles. The recovered dark matter map covers the largest sky fraction of any galaxy weak lensing map to date.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

SALT2 versus SALT3: updated model surfaces and their impacts on type Ia supernova cosmology

ABSTRACT For the past decade, SALT2 has been the most common model used to fit Type Ia supernova (SN Ia) light curves for dark energy analyses. Recently, the SALT3 model was released, which upgraded a number of model features but has not yet been used for measurements of dark energy. Here, we evaluate the impact of switching from SALT2 to SALT3 for a SN cosmology analysis. We train SALT2 and SALT3 on an identical training sample of 1083 well-calibrated Type Ia supernovae, ensuring that any differences found come from the underlying model framework. We publicly release the results of this training (the SALT ‘surfaces’). We then run a cosmology analysis on the public Dark Energy Survey 3-Yr Supernova data sample (DES-SN3YR), and on realistic simulations of those data. We provide the first estimate of the SN + CMB systematic uncertainty arising from the choice of SALT model framework (i.e. SALT2 versus SALT3), Δw = + 0.001 ± 0.005 – a negligible effect at the current level of dark energy analyses. We also find that the updated surfaces are less sensitive to photometric calibration uncertainties than previous SALT2 surfaces, with the average spectral energy density dispersion reduced by a factor of two over optical wavelengths. This offers an opportunity to reduce the contribution of calibration errors to SN cosmology uncertainty budgets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Magnetic properties of a staggered S = 1 chain with an alternating single-ion anisotropy direction

Materials composed of spin-1 antiferromagnetic (AFM) chains are known to adopt complex ground states that are sensitive to the single-ion-anisotropy (SIA) energy ( D ), and intrachain ( J 0 ) and interchain ( J 1 , 2 ′ ) exchange energy scales. While theoretical and experimental studies have extended this model to include various other energy scales, the effect of the lack of a common SIA axis is not well explored. Here we investigate the magnetic properties of Ni ( pyrimidine ) ( H 2 O ) 2 ( NO 3 ) 2 , a chain compound where the tilting of Ni octahedra leads to a twofold alternation of the easy-axis directions along the chain. Muon-spin relaxation measurements indicate a transition to long-range order at T N = 2.3 K and the magnetic structure is initially determined to be antiferromagnetic and collinear using elastic neutron diffraction experiments. Inelastic neutron scattering measurements were used to find J 0 = 5.107 ( 7 ) K , D = 2.79 ( 1 ) K , J 1 ′ = 0.00 ( 5 ) K , J 2 ′ = 0.18 ( 3 ) K , and a rhombic anisotropy energy E = 0.19 ( 9 ) K . Mean-field modeling reveals that the ground state structure hosts spin canting of ϕ ≈ 6 . 5 ∘ , which is not detectable above the noise floor of the elastic neutron diffraction data. Monte Carlo simulation of the powder-averaged magnetization, M ( H ) , is then used to confirm these Hamiltonian parameters, while single-crystal M ( H ) simulations provide insight into features observed in the data. Published by the American Physical Society 2025

Vaidya, S. (ORCID:000000033975497X)↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data---including electric line utilization, nodal price, and available generation flexibility---in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

RAVIS: Resource Forecast and Ramp Visualization for Situational Awareness of Variable Renewable Generation: Preprint

The Resource Forecast and Ramp Visualization for Situational Awareness (RAVIS) is an innovative, open-source tool for visualizing variable renewable resource forecasts and alerts for significant up- and down-ramps in renewable generation and the consequent system net load. The modular dashboard of RAVIS contains configurable panes for viewing probabilistic time-series forecasts, ramp event alerts on the look-ahead timeline, and spatially resolved resource sites, and forecasts. For comprehensive situational awareness, the tool can add additional data layers from simulation and independent system operator (ISO) market clearing data—including electric line utilization, nodal price, and available generation flexibility—in response to continuously updated renewable forecasts. This paper introduces the RAVIS technology suite employed to provide optimum visualization and flexible design characteristics. The paper also illustrates some use cases of the tool using site-specific solar power forecast data obtained from the IBM Watt-Sun forecasting platform for the California ISO and Midcontinent ISO footprints. The source code for RAVIS is public (https://github.com/ravis-nrel/ravis), and the intended users are forecasters, utility planners, ISO operators, and researchers.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

MBX V1.2: Accelerating Data-Driven Many-Body Molecular Dynamics Simulations

The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these potential energy functions integrate physics-based and machine-learned many-body terms trained on electronic structure data calculated at the "gold standard" coupled-cluster level of theory. Recent advancements in MBX have focused on optimizing its performance, resulting in the release of MBX v1.2. While the inherently many-body nature of MB-pol and MB-nrg ensures high accuracy, it poses computational challenges. MBX v1.2 addresses these challenges with significant performance improvements, including enhanced parallelism that fully harnesses the power of modern multicore CPUs. In conclusion, these advancements enable simulations on nanosecond time scales for condensed-phase systems, significantly expanding the scope of high-accuracy, predictive simulations of complex molecular systems powered by data-driven many-body potential energy functions.

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

Empirical evidence that mean soil contaminant concentration is not a conservative indicator of external exposure to wildlife

Adequate information on external exposure is often a limiting factor in ecological risk assessments because of a lack of exposure data for free-ranging animals as they interact with a heterogeneous distribution of contaminants. Instead, most wildlife exposure data are estimated from model simulations. However, empirical exposure data are rarely available to verify model results, or to test fundamental assumptions inherent in exposure assessments. We used novel, GPS-coupled radiation dosimeters on reindeer (Rangifer tarandus) to quantify their external exposure for five months in a Chernobyl fallout affected mountainous area of Norway. The exposure data were then used to test a fundamental assumption in screening-level risk assessments: Mean soil contaminant concentrations can be used to conservatively estimate chronic external exposures to representative animals. External exposure and GPS coordinates were obtained hourly and compared to model simulations of exposure based on airborne surveys of 137 Cs soil contamination. Our objectives were to determine 1) if modelled dose estimates based on mean 137 Cs soil concentrations were conservative, and 2) if modelled dose estimates could be improved by adjusting for spatial–temporal heterogeneity of contaminants using 137 Cs concentrations within reindeers’ GPS-defined home ranges and core areas. We found that external exposure modelled from mean soil contaminant concentrations underestimated exposure by 70% compared to empirical measurements from GPS-dosimeter collars worn on individual animals. Using area-weighted mean contaminant levels within the animals’ home range improved the model estimates, but still underestimated the field dosimeter data by 53%. The best estimate of external exposure was obtained when high-use areas within their home range, along with spatial heterogeneity of the contaminant, was accounted for by using area-weighted mean soil contaminant levels within GPS-derived core areas. Accounting for temporal use of core areas within animals’ home range provided a 24% improvement. Here, a comparison of our analyses to similar work performed on Chernobyl wolves found the two studies concur that risk assessors should be cautious in relying on the paradigm of using averaged soil contaminant levels to provide conservative estimates of external exposure to representative individuals. Results of modelled external exposure based on mean soil contaminant concentrations could misguide assessors and cause inappropriate management decisions.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

RINO: Renormalization Group Invariance with No Labels

A common challenge with supervised machine learning (ML) in high energy physics (HEP) is the reliance on simulations for labeled data, which can often mismodel the underlying collision or detector response. To help mitigate this problem of domain shift, we propose RINO (Renormalization Group Invariance with No Labels), a self-supervised learning approach that can instead pretrain models directly on collision data, learning embeddings invariant to renormalization group flow scales. In this work, we pretrain a transformer-based model on jets originating from quantum chromodynamic (QCD) interactions from the JetClass dataset, emulating real QCD-dominated experimental data, and then finetune on the JetNet dataset -- emulating simulations -- for the task of identifying jets originating from top quark decays. RINO demonstrates improved generalization from the JetNet training data to JetClass data compared to supervised training on JetNet from scratch, demonstrating the potential for RINO pretraining on real collision data followed by fine-tuning on small, high-quality MC datasets, to improve the robustness of ML models in HEP.

Hao, Zichun [Caltech] (ORCID:0000000256244907)↗
DOE OSTI2021

Multi-fidelity modeling to predict the rheological properties of fiber suspensions

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and time-consuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multi -fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce high-fidelity data, we perform direct numerical simulations to model the fibers as one-dimensional inextensible slender bodies that obey the Euler- Bernoulli beam equation. The Navier-Stokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The low-fidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multi-fidelity network is used compared to the single-fidelity network.

Boodaghidizaji, Miad↗
DOE OSTI2023

Automated identification and calculation of prompt effects in kinetic mechanisms using statistical models

The kinetics of prompt dissociation involves rovibrationally excited species (generally formed by exothermic reactions) which may dissociate or isomerize prior to thermalization via collisions with the bath gas. Treating such rovibrationally excited species (so-called "hot" species) with standard kinetic phenomenology may result in incorrect macroscopic representation of their reactivity. Here this work presents the first fully automated methodology for the calculation of prompt effects of a chosen species in a kinetic mechanism, including (i) reaction selection; (ii) theoretical calculation of rate constants and prompt branching fractions; and (iii) final rate constant fitting. The energy partition between hot fragments is estimated using a variety of statistical models, including a new physically sound microcanonical statistical model based on the rovibrational density of states of the fragments. The methodology is validated against literature data for the prompt dissociations of HCO and C 3 H 7 radicals. The microcanonical statistical model is in better agreement with trajectory simulations for larger species and is thus applicable for practical systems that typically involve large molecules, for which direct dynamics calculations are impractical. The automated workflow is applied to the evaluation of the effects of prompt dissociation for two isomeric radicals C 4 H 7 1-3 (1-methylallyl) and C 4 H 7 1-4 (3-buten-1-yl). Twelve H-atom abstraction reactions are selected and the corresponding rate constants are computed with first principles theory. The microcanonical statistical model predicts that prompt dissociations of C 4 H 7 1-3 and C 4 H 7 1-4 are already significant at 1000K, resulting in differences of up to an order of magnitude at 2000K with respect to the phenomenological thermal rate constants. To illustrate the effects of prompt dissociation on simulations of experimental data, the calculated prompt rate constants are implemented in both CRECK and C3MechV3.3 kinetic mechanisms. Simulations of experimental flame data illustrate the noticeable impact of prompt dissociation kinetics on the high-temperature combustion reactivity of C 4 H 8 -1 and C 4 H 8 -2.

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

Characterization of turbulence under different stability conditions using lidar scanning data

The spatiotemporal scales of turbulence in the boundary layer flow vary with the static stability of the atmosphere. For example, turbulence structures in unstable conditions are often found to be large and coherent, whereas the turbulent structures in stable conditions are relatively small and sporadic. To better understand the behavior of such turbulent structures under different atmospheric stability conditions in the lower atmosphere, herein, we use scanning lidar data collected over several months during the US Department of Energy's second Wind Forecast Improvement Project (WFIP 2) in 2016. The radial velocity data from scanning lidar were gathered along the horizontal plane (PPI mode) and were analyzed using proper orthogonal decomposition (POD) and spectral energy methods. The results from these analyses show that the turbulent structures in daytime unstable conditions are different from those present during nighttime stable conditions in terms of amount of energy and coherent structures. The POD energy is distributed more evenly in all the higher spatial modes in the daytime unstable case compared to the nighttime stable case. Besides characterizing the turbulence in the flow, the lidar data were also used to compare the simulated flow field generated from the WRF-LES framework (Weather Research and Forecasting - large-eddy simulation) with the scanning lidar data. The results from both simulated and lidar scanned data show similar structures orienting the streak-like structure along the mean wind. This indicates that the streak-like structures can exist in the real atmosphere during unstable atmospheric condition cases.

Rai, Raj K.↗
DOE OSTI2022

Data and code for: "Soil respiration response to simulated precipitation change depends on ecosystem type and study duration"

Data, code, and figures from our meta-analysis of the effect of precipitation change on soil respiration, with an emphasis on ecosystem variability and the effect of study duration. We combined the results of 81 separate studies to determine the effect of altered rainfall on the amount of carbon that leaves soil in the form of carbon dioxide, a major component of global carbon cycling. In addition we looked at how long the changes lasted, and how different soil properties and the intensity of precipitation changes affected the study results. We found that more precipitation resulted in greater amounts of carbon dioxide leaving the soil, and less precipitation resulted in less. However, the changes weakened over time in ecosystems that typically receive plenty of rainfall (e.g., forests), in contrast to ecosystems that typically receive little rainfall (e.g., deserts) where changes strengthened over time. Changes in the amount of carbon dioxide that left the soil were also affected by the amount of carbon in the soil, which impacts how much water soil can hold.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

To Derive or Not to Derive: I/O Libraries Take Charge of Derived Quantities Computation

The ever-increasing volume of data produced by HPC simulations necessitates scalable methods for data exploration and knowledge extraction. Scientific data analysis often involves complex queries across distributed datasets, requiring manipulation of multiple primary variables and generating derived data that needs to be handled efficiently, creating challenges for applications that need to parse many large datasets. Relying on individual applications to handle all intermediate data generally leads to redundant computations across studies and unnecessary data transfers. In this paper, we investigate the performance of different approaches where applications define derived variables as quantities of interest (QoIs) and offload the computation and transfer of these QoIs to the I/O library. This significantly reduces redundancy and optimizes data movement across the distributed storage and processing infrastructure by allowing control over when and where derived variables are computed. We present a detailed analysis of the performance-storage trade-offs associated with different solutions and showcase results for our study on two large-scale datasets created from climate and combustion simulations.

Gainaru, Ana↗
DOE OSTI2022

Dynamic responses of submerged components in advanced reactors: experimental and numerical studies

The seismic design of an advanced nuclear reactor must consider the interaction of vessel internal components with the surrounding coolant: fluid–structure interaction (FSI). Available analytical solutions for FSI of submerged components do not accommodate multiple-component, intense seismic inputs and complex reactor and internal geometries. Physical testing of reactor vessels and internals to inform seismic design is impractical and cost-prohibitive, leaving the use of verified and validated, robust numerical models as the only plausible option for analysis and design. Physical data that could be used for validating such numerical models for multi-component shaking are not available. This article describes an experimental program performed on a 6-degree-of-freedom earthquake simulator to generate data that could support validation of seismic FSI numerical models for submerged components in commercial finite element codes. A scaled model of a base-supported reactor vessel with simplified representations of submerged internals was tested to generate submerged-component response histories for a range of seismic inputs. The generated data were used to validate numerical models in the finite element code LS-DYNA. Numerical models were validated for calculation of hydrodynamic pressure responses on internals, in-water frequencies of internals, and acceleration responses of internals. The generated data and the analysis recommendations could aid engineering analysts designing submerged components and systems for seismic effects.

Engineering↗
DOE OSTI2024

VALIDATION, VERIFICATION, AND CALIBRATION THROUGH A CAUSAL LENS

This paper presents an alternative method based on causal inference to perform validation, verification, and calibration of simulation models. While classical validation and verification approaches focus on the identification of the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on the identification of causal relationships between data elements. Statistical and machine learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between datasets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, then the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles it is known as a directed acyclic graph (DAG). A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and from experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts have a means to identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Boosting H I -Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations

After reionization, neutral hydrogen (${\rm H\, \small {I}}$) traces the large-scale structure (LSS) of the Universe, enabling ${\rm H\, \small {I}}$ intensity mapping (IM) to capture the LSS in 3D and constrain key cosmological parameters. We present a new framework utilizing higher-order cross-correlations to study ${\rm H\, \small {I}}$ clustering around galaxies, tested using real-space data from the IllustrisTNG300 simulation. This approach computes the joint distributions of k-nearest neighbor (kNN) optical galaxies and the ${\rm H\, \small {I}}$ brightness temperature field smoothed at relevant scales (the kNN-field framework), providing sensitivity to all higher-order cross-correlations, unlike two-point statistics. To simulate ${\rm H\, \small {I}}$ data from actual surveys, we add random thermal noise and apply a simple foreground cleaning model, filtering out Fourier modes of the brightness temperature field with k ∥ < k min,∥ . Under current levels of thermal noise and foreground cleaning, typical of a Canadian Hydrogen Intensity Mapping Experiment (CHIME)-like survey, the ${\rm H\, \small {I}}$-galaxy cross-correlation signal in our simulations, using the kNN-field framework, is detectable at >30σ across r = [3, 12] h –1 Mpc. In contrast, the detectability of the standard two-point correlation function (2PCF) over the same scales depends strongly on the foreground filter: a sharp k ∥ filter can spuriously boost detection to 8σ due to position-space ringing, whereas a less sharp filter yields no detection. Nonetheless, we conclude that kNN-field cross-correlations are robustly detectable across a broad range of foreground filtering and thermal noise conditions, suggesting their potential for enhanced constraining power over 2PCFs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

MARIAH PCAP data for Validation Demonstration

This dataset holds simulated PCAP (packet capture) data from the SCEPTRE validation demonstration model as a set of pairwise communications between devices via specific protocols. All connections should be assumed to be symmetric, as this data is an aggregation of the true PCAP. A mapping is also provided associating each IP address with its true device type.

cyber-physical system↗
DOE OSTI2022

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗