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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Three Artificial Spintronic Leaky Integrate-and-Fire Neurons

We report that due to their non-volatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of an external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient, and the third uses shape variations of the DW track.

neural network crossbar↗
DOE OSTI2024

Exploring layer thinning of exfoliated β-tellurene and room temperature photoluminescence with large exciton binding energy revealed in β-TeO 2

Due to its tunable bandgap, anisotropic behavior, and superior thermoelectric properties, device applications using layered tellurene (Te) are becoming more attractive. Here, we report a thinning technique for exfoliated tellurene nanosheets using thermal annealing in an oxygen environment. We characterize different thinning parameters, including temperature and annealing time. Based on our measurements, we show that controlled layer thinning occurs in the narrow temperature range of 325–350 °C. We also show a reliable method to form β-tellurene oxide (β-TeO 2 ), which is an emerging wide bandgap semiconductor with promising electronic and optoelectronic properties. This wide bandgap semiconductor exhibits a broad photoluminescence (PL) spectrum with multiple peaks covering the range of 1.76–2.08 eV. This PL emission, coupled with Raman spectra, is strong evidence of the formation of 2D β-TeO 2 . We discuss the results obtained and the mechanisms of Te thinning and β-TeO 2 formation at different temperature regimes. We also discuss the optical bandgap of β-TeO 2 and show the existence of pronounced excitonic effects evident by the large exciton binding energy in this 2D β-TeO 2 system that reach 1.54–1.62 eV for bulk and monolayer, respectively. Our work can be utilized to have better control over the Te nanosheet thickness. It also sheds light on the formation of well-controlled β-TeO 2 layered semiconductors for electronic and optoelectronic applications.

42 ENGINEERING↗
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 OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗
DOE OSTI2020

Light-nuclei production and search for the QCD critical point

We discuss the potential of light-nuclei measurements in heavy-ion collisions at intermediate energies for the search of the hypothetical QCD critical end-point. A previous proposal based on neutron density fluctuations has brought appealing experimental evidences of a maximum in the ratio of the number of tritons times protons, divided over deuterons square, $\mathcal{O}_{tpd}$. However these results are difficult to reconcile with the state-of-the-art statistical thermal model predictions. Based on the idea that the QCD critical point can lead to a substantial attraction among nucleons, we propose new light-nuclei multiplicity ratios involving 4 He in which the maximum would be more noticeable. We argue that the experimental extraction is feasible by presenting these ratios formed from actual measurements of total and differential yields at low and high collision energies from FOPI and ALICE experiments, respectively. We also illustrate the possible behavior of these ratios at intermediate energies applying a semiclassical method based on flucton paths using the preliminary NA49 and STAR data for $\mathcal{O}_{tpd}$ as input.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1975

Crack growth in bonded elastic half planes

Two solutions were developed for the two dimensional problem of bonded linearly elastic half-planes. For each solution, numerical results are presented for the stress intensity factors, strain energy release rate, stresses, and displacements. The behavior predicted by the studies was investigated experimentally using polymers for the material pairs. Close agreement was found for the critical stress intensity factor at fracture for the perpendicular crack near the interface. Fracture along the interface proved to be inconclusive due to difficulties in obtaining a brittle bond. Some interesting and predictable behavior regarding the potential for the crack to cross the interface was observed and is discussed.

Goree, J. G.↗
DOE OSTI2025

Threat Hunt Guide for BESS Environments

The rapid digitalization of the electric grid - driven by the integration of inverter-based resources (IBRs), battery energy storage systems (BESS), and advanced grid control platforms - has significantly enhanced grid efficiency, visibility, and flexibility. However, this evolution also introduces new cybersecurity risks, particularly through supply chain dependencies and operational blind spots at the grid edge. To address these challenges, Idaho National Laboratory (INL), through the Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) Rapid Risk initiative, conducted a series of rapid risk assessment engagements with energy organizations across the United States. Drawing on lessons learned from these engagements, INL developed the following threat hunting guide for asset owners and operators (AOOs) to enhance their cybersecurity visibility within BESS and IBR systems. The guide demonstrates how to use passive network monitoring to baseline device behavior, detect adversarial activity, and investigate anomalies without disrupting operations. By implementing these practices, energy sector stakeholders can improve coordination between cybersecurity and operations teams and strengthen the resilience of distributed energy resources (DERs) within the modern power grid. Prior to implementing any network monitoring, packet capture, or threat hunting activity described in this guide, AOOs are strongly advised to review applicable governance frameworks, legal requirements, and organizational policies. This guide is intended for informational and educational purposes only. It does not replace compliance with any federal, state, or local cybersecurity mandates or industry standards. Implementation of described configurations, technologies, or analytic workflows is performed at the discretion and responsibility of the asset owner and operator.

25 - ENERGY STORAGE↗
DOE OSTI2021

Review of Release Behavior of Hydrogen & Natural Gas Blends from Pipelines

Hydrogen can be used to reduce carbon emissions by blending into other gaseous energy carriers, such as natural gas. However, hydrogen blending into natural gas has important implications for safety which need to be evaluated. Hydrogen has different physical properties than natural gas, and these properties affect safety evaluations concerning a leak of the blended gas. The intent of this report is to begin to investigate the safety implications of blending hydrogen into the natural gas infrastructure with respect to a leak event from a pipeline. A literature review was conducted to identify existing data that will better inform future hazard and risk assessments for hydrogen/natural gas blends. Metrics with safety implications such as heat flux and dispersion behavior may be affected by the overall blend ratio of the mixture. Of the literature reviewed, there was no directly observed separation of the hydrogen from the natural gas or methane blend. No literature was identified that experimentally examined unconfined releases such as concentration fields or concentration at specific distances. Computational efforts have predicted concentration fields by modified versions of existing engineering models, but the validation of these models is limited by the unavailability of literature data. There are multiple literature sources that measured flame lengths and heat flux values, which are both relevant metrics to risk and hazard assessments. These data can be more directly compared to the outputs of existing engineering models for validation.

03 NATURAL GAS↗
DOE OSTI2021

Measurement of the energy dependence of the e+e- → $ B\overline{B} $, $ B{\overline{B}}^{\ast } $ and $ {B}^{\ast }{\overline{B}}^{\ast } $ exclusive cross sections

We report the first measurement of the exclusive cross sections e + e - → $B\overline{B}$, e + e - → $B{\overline{B}}^{\ast }$, and e + e - → ${B}^{\ast }{\overline{B}}^{\ast }$ in the energy range from 10.63 GeV to 11.02 GeV. The B mesons are fully reconstructed in a large number of hadronic final states and the three channels are identified using a beam-constrained-mass variable. The shapes of the exclusive cross sections show oscillatory behavior with several maxima and minima. The results are obtained using data collected by the Belle experiment at the KEKB asymmetric-energy e + e - collider.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Pathways for Electron Transfer at MgO–Water Interfaces from Ab Initio Molecular Dynamics

The nature of electron transfer across metal oxide–water interfaces depends significantly on the band gap of the oxide and its band edge energies relative to the potentials of relevant aqueous redox couples. Here we focus on the water interface with MgO, a prototypical wide band gap oxide whose conduction band edge is close in energy to that of water. We investigate the behavior of an excess electron at and out of equilibrium near the interface using ab initio molecular dynamics based on hybrid density functional theory. Our simulations show that under equilibrium conditions the excess electron (donated by an Al impurity in MgO) localizes to a midgap defect state comparable in energy and shape to a hydrated electron in bulk water. To characterize the electron transfer from the conduction band of MgO to interfacial product states, we dope near-equilibrium configurations of the pristine MgO–water system with Al and run short trajectories of these instantaneously out-of-equilibrium systems. Here we observe two distinct products associated with the excess electron: a surface-localized electron (e surf –) and an aqueous hydrogen radical (H • ). The H • pathway exhibits a much higher activation barrier despite being more exoergic, making e surf – the kinetic product. Our characterization of the pathways on the basis of Marcus theory is consistent with the poor observed utility of MgO for water radiolysis. Moreover, we anticipate that the computational framework employed here will be broadly applicable to assessing electron transfer mechanisms at aqueous, photocatalytic interfaces.

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

ComStock Measure Documentation: High-Efficiency Rooftop Unit

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - high-efficiency rooftop unit (RTU) - and briefly introduces key results. The full public data set can be accessed on the Comstock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ComStock Measure Documentation: Variable-Speed Pumps

Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy's ResStock and ComStock models, this work produces national data sets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the commercial building stock across the United States. The "baseline" model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass adoption impact on the baseline building stock. "Measures" refers to various "what-if" scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public data sets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario - variable speed pumps - and briefly introduces key results. The full public data set can be accessed on the ComStock data lake or via the Data Viewer at comstock.nlr.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Household Transportation Energy Affordability by Region and Socioeconomic Factors

Transportation fuel is an important component of household budgets, as 3.3% of total household expenditures are for vehicle fuel nationwide and over 50% of annual household expenditures on energy are for transportation. These average values vary geographically, and higher energy cost burdens are faced by households with lower incomes. Defining transportation energy affordability burden as the percentage of annual household income spent on vehicle fuel, this study aims to quantify affordability as a function of household characteristics and geography. Here, through analysis at the census tract level, this study (i) projects annual household vehicle miles traveled (VMT) based on demographic factors using machine-learning techniques, (ii) estimates local differences in vehicle fuel economy and fuel price, and (iii) quantifies resulting transportation energy affordability by census tract. This study found that the average burden by tract varies from 0.15% to 8%. The variation in affordability can be largely explained by income level and vehicle fuel efficiency. Suburban and rural households spend more on transportation energy compared with urban households because of the usage of less fuel-efficient vehicle technologies and higher annual VMT. Lower-income groups have a wide distribution of the percentage of income spent on transportation energy, 1.2% to 8%, whereas the range for the highest income group ($125,000+) is from 0.15% to 3.9%. This detailed transportation energy affordability analysis provides a better understanding of regional variations in household travel behavior, helps to determine where fuel-efficient vehicle technologies are more likely to be used, and improves estimates of vehicle ownership costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

ComStock Measure Documentation: Fan Static Pressure Reset for Multizone Variable Air Volume Systems

This report assesses the potential for nationwide adoption of a duct static pressure reset in MZ VAV systems in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario—Fan Static Pressure Reset for Multizone Variable Air Volume (VAV) Systems—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use case (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ComStock Measure Documentation: Thermostat Setbacks During Unoccupied Periods

This report assesses the potential for nationwide adoption of thermostat setbacks in appropriate applications. Building on the 3-year End-Use Load Profiles project to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models, this work produces national datasets that enable cities, states, utilities, and other stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses various data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual sub-hourly energy consumption of the commercial building stock across the United States. The “baseline” model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology of the baseline model is discussed in the ComStock Reference Documentation. The goal of this work is to develop energy efficiency and demand flexibility measures that cover market-ready technologies and study their mass-adoption impact on the baseline building stock. “Measures” refers to various “what-if” scenarios that can be applied to buildings. The results for the baseline and measure scenario simulations are published in public datasets that provide insights into building stock characteristics, operational behaviors, utility bill impacts, and annual and sub-hourly energy usage by fuel type and end use. This report describes the modeling methodology for a single ComStock measure scenario— Thermostat Setbacks During Unoccupied Periods—and briefly introduces key results. The full public dataset can be accessed on the ComStock data lake or via the Data Viewer at comstock.nrel.gov. The public dataset enables users to create custom aggregations of results for their use cases (e.g., filter to a specific county or building type).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

TEAMER Technical Support for Optimal Control of an Oscillating Surge Wave Energy Converter (CRADA Final Report)

This project will focus on running experiments that evaluate the benefits of using model predictive control (MPC) to optimize power absorbed by a laboratory-scale oscillating surge wave energy converter (OSWEC). MPC is a promising technique to optimize wave energy converter (WEC) behavior while applying system constraints that can help promote structural integrity and device survivability, but there are few studies that experimentally test this control scheme on WECs. Therefore, the Participant is proposing a series of tests that will assess the benefits of MPC experimentally in response to a variety of sea states. For these tests, the Participant will provide the OSWEC device and Data Acquisition (DAQ) system, and request support from the Contractor to use and operate the wave tank for experiments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Active Site Engineering via Optimizing the Heterogeneous Support Structure for Single-Atom Catalysis

Supported single-atom catalysts show a large range of activities and selectivities that depend on the local environment of the catalytic sites. Here a theory-based optimization strategy is presented that is based on a density functional theory determination of the transition states and intermediates for a low-dimensional coordinate representation of the heterogeneity of the active sites. The approach is applied to a vanadium catalyst on an amorphous SiO 2 support that involves a large kinetic network described using a full chemistry model. Without assuming a priori scaling relations or mechanism reduction, the optimal state of heterogeneity is found to lie at atomic configurations where the activation energies for two distinct key chemical processes are equal. It is found a posteriori that the behavior of the system is consistent with linear free energy scaling relations in the randomness parameters. The energetic span theory proves quite useful in reducing the full chemistry model to a small number of key reactions. The use of a nonlinear optimization algorithm in combination with energetic span theory provides significant simplification in treating disordered systems.

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

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗