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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Framework for Identifying Building Energy Models of Localized Utility Service Areas Using Smart Meter Data

Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Assessing Impacts of Waves on Hub-Height Winds off the U.S. West Coast Using Lidar Buoys and Coupled Modeling Approaches

Given the importance of offshore wind energy development to the U.S. clean energy targets, it is vital to be able to characterize the wind resource in that environment accurately. Toward that end, two Bureau of Ocean Energy Management buoys equipped with Doppler lidar are being maintained by Pacific Northwest National Laboratory on behalf of the Department of Energy and deployed to regions of potential offshore wind development. In addition to standard meteorological and oceanographic measurements, the buoys document the wind profile between about 40 m and 250 m above the sea surface through Doppler lidar retrievals. After a multiyear deployment of two buoys along the U.S. East Coast, the buoys were redeployed to the U.S. West coast from 2020 – 2022 to locations near the Humboldt and Morro Bay lease areas. The buoys provide nearly continuous, multiyear datasets that can be used to evaluate predictions of hub-height (~100 m) wind speed for standard atmospheric models in the region. In the absence of measurements at the study site, offshore wind developers rely on model-based data to assess site conditions. Potential sources of model error in this environment include under-resolution or misrepresentation of coastal topographically forced flows, marine boundary layer dynamics and the evolution of their associated cloud and turbulence fields, the role of upwelling and other currents on surface heat fluxes into the boundary layer, and the impact of wave fields on surface momentum fluxes and thus the wind speed profile. In particular, most predictive models of wind speed do not predict wave fields at all, relying on parameterizations to represent their effects. In thus study, we focus on evaluating the role of wind / wave interactions on modeled hub-height wind speed and error by using the Coupled Ocean–Atmosphere–Wave–Sediment–Transport Modeling System to capture two-way interactions between an atmospheric model (Weather Research and Forecasting (WRF)) and a wave model (WAVEWATCHIII (WW3)) and compare to both stand-alone WRF and one-way coupled WRF / WW3 configurations. Our approach is similar to that used in Gaudet et al. (2022) to evaluate wind / wave coupling over the U.S. East Coast, but applied to the very different environment of the U.S. West Coast. We show examples for two cases, a cold-season frontal case and a warm-season low-level jet case. We find that wind / wave coupling makes little impact on model error for these cases at the location of the lidar buoys, for which other misrepresentations of model physics seems to be responsible for model-observation discrepancies. However, domain-wide evaluations, which also make use of the National Buoy Data Center network, show that a two-way coupling approach is less prone to systematic errors in the hub-height wind field than the one-way coupled approach. WRF resolution of kilometer-scale or less is needed to properly capture the sharp wind speed gradients that can be found along the coastline, and WW3 simulations driven by the downscaled WRF produce better bulk and spectral wave fields when compared to observations. Implications of the results for wind resource characterization are then discussed.

17 WIND ENERGY↗
DOE OSTI2022

Evaluation of a quasi-steady-state approximation of the cloud droplet growth equation (QDGE) scheme for aerosol activation in global models using multiple aircraft data over both continental and marine environments

This research introduces a numerically efficient aerosol activation scheme and evaluates it by using stratus and stratocumulus cloud data sampled during multiple aircraft campaigns in Canada, Chile, Brazil, and China. The scheme employs a quasi-steady-state approximation of the cloud droplet growth equation (QDGE) to efficiently simulate aerosol activation, the vertical profile of supersaturation, and the activated cloud droplet number concentration (CDNC) near the cloud base. The calculated maximum supersaturation values using the QDGE scheme were compared with multiple parcel model simulations under various aerosol and environmental conditions. The differences are all below 0.18 %, indicating good performance and accuracy of the QDGE scheme. We evaluated the QDGE scheme by specifying observed environmental thermodynamic variables and aerosol information from 31 cloud cases as input and comparing the simulated CDNC with cloud observations. The average of mean relative error ($\overline{MRE}$) of the simulated CDNC for cloud cases in each campaign ranges from 17.30 % in Brazil to 25.90 % in China, indicating that the QDGE scheme successfully reproduces observed variations in CDNC over a wide range of different meteorological conditions and aerosol regimes. Additionally, we carried out an error analysis by calculating the maximum information coefficient (MIC) between the MRE and input variables for the individual campaigns and all cloud cases. MIC values were then sorted by aerosol properties, pollution level, environmental humidity, and dynamic condition according to their relative importance to MRE. Based on the error analysis, we found that the magnitude of MRE is more relevant to the specification of input aerosol pollution level in marine regions and aerosol hygroscopicity in continental regions than to other variables in the simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

CERES : a cryo-EM re-refinement system for continuous improvement of deposited models

The field of electron cryomicroscopy (cryo-EM) has advanced quickly in recent years as the result of numerous technological and methodological developments. This has led to an increase in the number of atomic structures determined using this method. Recently, several tools for the analysis of cryo-EM data and models have been developed within the Phenix software package, such as phenix.real_space_refine for the refinement of atomic models against real-space maps. Also, new validation metrics have been developed for low-resolution cryo-EM models. To understand the quality of deposited cryo-EM structures and how they might be improved, models deposited in the Protein Data Bank that have map resolutions of better than 5 Å were automatically re-refined using current versions of Phenix tools. The results are available on a publicly accessible web page (https://cci.lbl.gov/ceres). The implementation of a Cryo-EM Re-refinement System ( CERES ) for the improvement of models deposited in the wwPDB, and the results of the re-refinements, are described. Based on these results, contents are proposed for a `cryo-EM Table 1', which summarizes experimental details and validation metrics in a similar way to `Table 1' in crystallography. The consistent use of robust metrics for the evaluation of cryo-EM models and data should accompany every structure deposition and be reported in scientific publications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Neural Network‐Based Methods for Ocean Surface Wave Measurement Using Submarine Distributed Acoustic Sensing (DAS)

Two new data-driven models for estimating ocean surface waves from distributed acoustic sensing (DAS) submarine cable strain rate are developed using supervised machine learning on a 10-day data set collected offshore of Oliktok Point, Alaska. The new models were trained on target data from seafloor pressure moorings at three sites spaced evenly along 27.1 km of cable and were benchmarked against an empirical transfer function method previously used to estimate waves from DAS. A model which uses convolutional neural networks to transform 2-km frequency-wavenumber strain spectra to seafloor pressure spectra outperforms the benchmark in wave height prediction (RMSE of 0.15 vs. 0.41 m) and period prediction (0.29 vs. 0.37 s) when evaluated on a held-out test data set. When applied to a DAS data set collected on the same cable 2 years prior, the CNN-based model maintained similar significant wave height performance (RMSE = 0.23 m) relative to available satellite altimetry data. A two-hidden-layer, fully connected neural network which transforms 1-D strain spectra to seafloor pressure spectra also outperforms the benchmark in wave height prediction (RMSE of 0.19 vs. 0.41 m), but does not generalize as well to the prior data. Regression-based machine learning is useful for estimating waves from DAS data when the pressure-strain relationship varies temporally and spatially across different wave conditions. Models can be applied to DAS data to measure waves with higher spatial resolution and longer temporal coverage than traditional methods, which often measure waves only at a single point.

Davis, Jacob R. [Univ. of Washington, Seattle, WA ↗
DOE OSTI2025

In Situ Data Analysis Through Physics-informed Tensor Decompositions (LDRD Final Report)

We introduce a new low-dimensional model of high-dimensional numerical simulation data based on low-rank tensor decompositions. Our new model aims to minimize differences between the model data and simulation data as well as functions of the model data and functions of the simulation data. This novel approach to dimensionality reduction of simulation data provides a means of directly incorporating quantities of interests and invariants associated with conservation principles associated with the simulation data into the low-dimensional model, thus enabling more accurate analysis of the simulation without requiring access to the full set of high-dimensional data. Computational results of applying this approach to two standard low-rank tensor decompositions of data arising from simulation of combustion and plasma physics are presented.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. Further, the accuracy of the force field is demonstrated by calculating rate constants for the H + HO 2 → H 2 + 3 O 2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.

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

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines

Neutrinoless double-beta decay ($0\nu\beta\beta$) is a rare nuclear process that, if observed, will provide insight into the nature of neutrinos and help explain the matter-antimatter asymmetry in the Universe. The large enriched germanium experiment for neutrinoless double-beta decay (LEGEND) will operate in two phases to search for $0\nu\beta\beta$. The first (second) stage will employ 200 (1000) kg of High-Purity Germanium (HPGe) enriched in 76 Ge to achieve a half-life sensitivity of 10 27 (10 28 ) years. In this study, we present a semi-supervised data-driven approach to remove non-physical events captured by HPGe detectors powered by a novel artificial intelligence model. We utilize affinity propagation to cluster waveform signals based on their shape and a support vector machine to classify them into different categories. We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. We demonstrate that our model yields a maximum sacrifice of physics events of $0.024 ^{+0.004}_{-0.003} \%$ after data cleaning. Our model is being used to accelerate data cleaning development for LEGEND-200 and will serve to improve data cleaning procedures for LEGEND-1000.

artificial intelligence↗
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗
DOE OSTI2021

Biomembrane Structure and Material Properties Studied With Neutron Scattering

Cell membranes and their associated structures are dynamical supramolecular structures where different physiological processes take place. Detailed knowledge of their static and dynamic structures is therefore needed, to better understand membrane biology. The structure–function relationship is a basic tenet in biology and has been pursued using a range of different experimental approaches. In this review, we will discuss one approach, namely the use of neutron scattering techniques as applied, primarily, to model membrane systems composed of lipid bilayers. An advantage of neutron scattering, compared to other scattering techniques, is the differential sensitivity of neutrons to isotopes of hydrogen and, as a result, the relative ease of altering sample contrast by substituting protium for deuterium. This property makes neutrons an ideal probe for the study of hydrogen-rich materials, such as biomembranes. In this review article, we describe isotopic labeling studies of model and viable membranes, and discuss novel applications of neutron contrast variation in order to gain unique insights into the structure, dynamics, and molecular interactions of biological membranes. We specifically focus on how small-angle neutron scattering data is modeled using different contrast data and molecular dynamics simulations. We also briefly discuss neutron reflectometry and present a few recent advances that have taken place in neutron spin echo spectroscopy studies and the unique membrane mechanical data that can be derived from them, primarily due to new models used to fit the data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Leaf 13 C data constrain the uncertainty of the carbon dynamics of temperate forest ecosystems

Stable carbon isotope discrimination occurred in plant biophysical and biogeochemical processes can help understand plant physiology and soil biogeochemistry with respect to carbon cycling. Here, we incorporated the photosynthetic carbon isotope discrimination into a process-based land surface model (iTEM) to test if stable carbon isotope composition (δ 13 C) can impose additional constraint on model parameters. Sequential data assimilation was implemented at six eddy covariance flux tower sites using carbon flux observations including gross primary productivity (GPP) and net ecosystem exchange (NEE) with and without considering foliar δ 13 C (δ 13 C f ) measurement constraints, respectively. Our model-data fusion showed that δ 13 C f can provide useful constraint on photosynthetic (V cmax25 , the maximum rate of carboxylation at 25°C) and stomatal (g1, the slope of stomatal function) parameters as well as the posterior carbon fluxes. When including δ 13 C f measurement, g1 spatially varies among the six sites and is significantly correlated with annual precipitation. We incorporated the statistical relationship between g1 and annual precipitation into iTEM, which is then used to quantify the regional carbon dynamic in temperate forest ecosystems of the Northern Hemisphere. Compared with the simulation only conditioned on carbon flux observations, regional carbon flux estimations performed slightly better against the FLUXCOM products and the uncertainties of modeled carbon fluxes were reduced by 27%. Our study demonstrated that δ 13 C f data constrains carbon flux uncertainties across space.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Coupled Long-Term Experiment and Model Investigation of the Differential Response of Plants and Soil Microbes in a Changing Permafrost Tundra Ecosystem

The major research goal of this project was to understand and quantify the fate of carbon stored in permafrost ecosystems using a combination of field and laboratory experiments to measure and model isotope ratios and carbon fluxes in a tundra ecosystem exposed to experimental warming. Field measurements centered on a two-factor experimental warming to increase air and soil temperatures alone, and in combination, at a tundra field site in Alaska. A second manipulation of water table was embedded within the warming treatment such that both major environmental factors (temperature, moisture) controlling ecosystem carbon dynamics were experimentally altered. The experiment was interfaced with modeling activities using both data assimilation and forward modeling approaches. Models were used to make forecasts of ecosystem carbon dynamics beyond the time frame and environmental space of the experiment itself. This project was a follow-on to previous work that made use of the same field manipulation; here we continued the experimental manipulation in order to test new hypotheses regarding long-term effects of warming and permafrost thaw and subsequent changes in moisture availability on ecosystem carbon dynamics. These results were linked with ongoing synthesis and model intercomparison activities through the Permafrost Carbon Network, which led to additional synthesis publications as a result of collaboration and data sharing from this project. As key outcomes, this project has produced new papers published in the peer-reviewed literature, archived datasets used for model-data intercomparisons, trained graduate students and postdoctoral researchers, and has raised awareness about the vulnerability of permafrost carbon to a wider audience.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A ModEx Framework for Watershed Subsurface Investigation With Limited Geophysical Data Using Machine Learning and Hydrologic Modeling

Abstract Subsurface heterogeneity influences watershed hydrology strongly but remains difficult to characterize at catchment scales with sparse and costly field data. Geophysical surveys such as electromagnetic induction (EMI) provide local spatial subsurface images yet scaling them to watershed scales and converting EMI‐derived resistivity into hydraulic properties remains a challenge. We present a Model–Experiment (ModEx) framework that integrates limited EMI data with machine learning (ML) and hydrologic modeling to improve process representation and guide field investigations. Sparse EMI surveys were scaled to the catchment scale using a Random Forest model, and the resulting resistivity fields were combined with nearby borehole constraints to parameterize a hydrologic model. The EMI‐informed hydrological simulations improved predictions of streamflow sustained by subsurface flow and shallow saturation patterns. By combining EMI data and ML with hydrologic modeling, the ModEx framework guides future subsurface surveys, providing a transferable and efficient strategy for data–model integration across diverse watersheds. Plain Language Summary Mapping the underground network of soil and rock that controls water is essential for predicting floods and droughts, but seeing underground is difficult and expensive. We cannot drill everywhere, so scientists use geophysical tools to scan broad areas. There are two key challenges: these geophysical scans are often sparse across the whole watershed, and the geophysical data is hard to translate into water‐related properties. We used artificial intelligence to solve these problems. We taught a computer to find patterns linking the limited geophysical data to the land surface properties. This allowed it to fill in the gaps and create a complete, useful subsurface map for the entire watershed. This new map improves hydrologic simulations, leading to more accurate predictions of water movement in the watershed. It also helps scientists build better models with less data and generates a priority map showing where to measure next, making future investigations more efficient. Key Points Limited EMI scaled with ML improves catchment‐scale subsurface parameterization for hydrologic models The framework integrates hydrologic modeling with limited geophysical data to support subsurface investigation design ModEx framework offers a transferable data–model integration strategy that quantifies and reduces uncertainty guiding watershed studies

Chen, Hang↗
DOE OSTI2024

Evaluation of the EarthSHAB Stratospheric Solar Hot Air Balloon Flight Prediction Model Using Balloon Trajectory Data

Abstract The heliotrope is a solar balloon design which is constructed out of painter’s plastic, and the exterior is coated in charcoal powder. Darkening the plastic gives the balloon a high solar absorptance, which allows it to ascend into the lower stratosphere and float for hours at a time. The balloons have previously been used to lift scientific instruments into the stratosphere to study chemical explosions, earthquakes, and stratospheric aerosols. They have also been proposed as a platform for planetary exploration. Flight predictions are crucial to preflight planning to reduce safety risks and meet flight objectives. However, there exists a wide range of possible flight paths due to varying environmental conditions and solar balloon configurations. EarthSHAB is one such software that was designed to support flight planning using the weather forecasts and balloon properties to predict the flight path of a solar balloon. We compare EarthSHAB-simulated flight paths to a set of observed flight paths for the 3.5-m diameter heliotrope design called the “Cloudskimmer.” Using the criteria that the modeled paths must fall within 5% of the observations to be considered successful, we found that EarthSHAB successfully predicted the Cloudskimmer ascent rate and average float altitude 10% and 90% of the time, respectively. We also found that the average difference in the observed and predicted landing locations was 97 km and landing times were 54 ± 38 min. Significant deviations between the observed and predicted ascent rates and excursions at float were found to be associated with heavy payloads and convective cloud development, respectively. Significance Statement Solar balloons are used to lift scientific instruments into the lower stratosphere for hours at a time to study chemical explosions, earthquakes, stratospheric aerosols, and more. The flight paths of solar balloons can be difficult to predict due to variability in their design and surrounding environment. We evaluate the accuracy of EarthSHAB, a software that predicts the altitude profile and horizontal trajectory of a solar balloon using inputs such as the weather, balloon size, and balloon mass. Our results suggest that for the balloon design used in this study, EarthSHAB is best suited for modeling the behavior of balloons with lightweight payloads that do not fly within or directly above clouds.

Lien, Jessica M. [Sandia National Laboratories, Al↗
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗