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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

digital twin↗
DOE OSTI2020

A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Reliable, non-intrusive, short-term (of up to 12 hours ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. Here, we propose a novel low-complexity non-intrusive approach that improves the predictive accuracy of the state-of-the-art by up to ~10%. The backbone of our approach is a K-nearest neighbours search method, that exploits the demand pattern of the most similar historical days, and incorporates appropriate time-series pre-processing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Assessment of Performance of Density Functionals for Predicting Potential Energy Curves in Hydrogen Storage Applications

We report the availability of accurate computational tools for modeling and simulation is vital to accelerate the discovery of materials capable of storing hydrogen (H 2 ) under given parameters of pressure swing and temperature. Previously, we compiled the H2Bind275 data set consisting of equilibrium geometries and assessed the performance of 55 density functionals over this data set (Veccham, S. P.; Head-Gordon, M. J. Chem. Theory Comput. 2020, 16, 4963-4982). As it is crucial for computational tools to accurately model the entire potential energy curve (PEC), in addition to the equilibrium geometry, we extended this data set with 389 new data points to include two compressed and three elongated geometries along 78 PECs for H 2 binding, forming the H2Bind78 × 7 data set. By assessing the performance of 55 density functionals on this significantly larger and more comprehensive H2Bind78 × 7 data set, we identified the best performing density functionals for H 2 binding applications: PBE0-DH, ωB97X-V, ωB97M-V, and DSD-PBEPBE-D3(BJ). The addition of Hartree-Fock exchange improves the performance of density functionals, albeit not uniformly throughout the PEC. We recommend the usage of ωB97X-V and ωB97M-V density functionals as they offer good performance for both geometries and energies. In addition, we also identified B97M-V and B97M-rV as the best semilocal density functionals for predicting H 2 binding energy at its equilibrium geometry.

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

Fifth-degree elastic energy for predictive continuum stress–strain relations and elastic instabilities under large strain and complex loading in silicon

Materials under complex loading develop large strains and often phase transformation via an elastic instability, as observed in both simple and complex systems. Here, we represent a material (exemplified for Si I) under large Lagrangian strains within a continuum description by a 5th-order elastic energy found by minimizing error relative to density functional theory (DFT) results. The Cauchy stress—Lagrangian strain curves for arbitrary complex loadings are in excellent correspondence with DFT results, including the elastic instability driving the Si I → II phase transformation (PT) and the shear instabilities. PT conditions for Si I → II under action of cubic axial stresses are linear in Cauchy stresses in agreement with DFT predictions. Such continuum elastic energy permits study of elastic instabilities and orientational dependence leading to different PTs, slip, twinning, or fracture, providing a fundamental basis for continuum physics simulations of crystal behavior under extreme loading.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fifth-degree elastic energy for predictive continuum stress-strain relations and elastic instabilities under large strain and complex loading in silicon

Materials under complex loading develop large strains and often phase transformation via an elastic instability, as observed in both simple and complex systems. Here, we represent a material (exemplified for Si I) under large Lagrangian strains within a continuum description by a 5th-order elastic energy found by minimizing error relative to density functional theory (DFT) results. The Cauchy stress—Lagrangian strain curves for arbitrary complex loadings are in excellent correspondence with DFT results, including the elastic instability driving the Si I → II phase transformation (PT) and the shear instabilities. PT conditions for Si I → II under action of cubic axial stresses are linear in Cauchy stresses in agreement with DFT predictions. Such continuum elastic energy permits study of elastic instabilities and orientational dependence leading to different PTs, slip, twinning, or fracture, providing a fundamental basis for continuum physics simulations of crystal behavior under extreme loading.

Levitas, Valery↗
DOE OSTI2020

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Incentivizing Cold-Climate Efficiency in Juneau (Final Report)

This is the final technical report for the DOE EERE BTO project Incentivizing Cold-Climate Efficiency in Juneau. The project implemented a community energy campaign to deploy heat pumps and efficiency for residents of Juneau, in Southeast Alaska. The project helped Juneau make progress towards its renewable energy goal of reaching 80% renewable energy for space heating by 2045. The Incentivizing Cold-Climate Efficiency in Juneau (ICE-Juneau) project began in 2020 and concluded three years later in 2023. During that time, a group of implementation partners and a research advisory team instituted a beneficial electrification campaign to promote energy savings and carbon reduction in residences of Juneau, Alaska. The campaign, Thermalize Juneau, focused on the installation of single head ductless mini-split heat pumps along with other efficiency upgrades. Thermalize Juneau was the first campaign of its kind in Alaska. After several months of planning, registration opened to the public in early 2021, and over the course of six months 164 participants enrolled. Campaign staff provided education to homeowners on heat pumps and efficiency, and each participant received a one-on-one heat pump assessment using a custom Microsoft Excel-based calculator that estimated energy savings for their residence based on building characteristics and past utility bills. Participants could also obtain a free energy audit from one of the two local energy auditors to further inform their decision. A heat pump installer, electrician, and builder were selected via a competitive RFP process. Participants who felt ready to install a heat pump or other efficiency upgrades received a site visit and custom quote from each of these contractors free of charge, and if they still felt energy upgrades were right for them, could move forward with an individual contract. Participants received a $400 heat pump installation rebate, offered by the installer if 40 heat pump installations occurred through the campaign. Overall, the campaign facilitated 75 heat pump installations (including participants that went with another contractor or heat pump model) and 30 efficiency upgrades (including participants that went with another builder or did DIY upgrades). The campaign created 3 new jobs as the heat pump installer hired an administrative assistant and two apprentices over the course of the campaign. It also helped Juneau work toward achieving its renewable energy goal of 80% renewable energy for space heating by 2045 by upgrading houses from fuel oil to heat pumps powered by the hydropower electric grid. Researchers conducted four surveys to inform Thermalize Juneau and future energy campaigns. The first surveyed existing and prospective heat pump owners in Juneau to identify barriers the campaign could address and inform recruitment efforts. Entry and exit surveys provided information on participant demographics, goals, outcomes, and suggestions for improving future campaigns. And a final survey of community members who had not participated in Thermalize Juneau gave insight on ways future campaigns could include a greater diversity of participants so they could realize similar benefits. Researchers also used pre-campaign energy modeling to predict energy savings, which was then compared to the savings estimated through aggregation of heat pump assessments and energy audits, and later to actual energy savings of 10 participants who installed a heat pump and were able to provide complete energy use data sets. In addition to energy savings, and to assist the electric utility in future planning efforts, researchers analyzed the overall change in electric use across participants with energy data and heat pump installations. They also completed a life cycle cost analysis, showing positive net present values for those displacing a fuel oil appliance, and a more mixed case for those switching from electric baseboard. This project proved the feasibility of energy campaigns, with a goal of beneficial electrification, in cold, remote locations. The Thermalize Juneau team compiled a Guidebook to Thermalize Campaigns, available online. In addition to documenting what occurred in Alaska’s first thermalize campaign, it provides tips and resources for other communities wishing to implement a similar program.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Spectroscopy of the fractal Hofstadter energy spectrum

Hofstadter’s butterfly, the predicted energy spectrum for non-interacting electrons confined to a two-dimensional lattice in a magnetic field, is one of the most remarkable fractal structures in nature. At rational ratios of magnetic flux quanta per lattice unit cell, this spectrum shows self-similar distributions of energy levels that reflect its recursive construction. For most materials, Hofstadter’s butterfly is predicted under experimental conditions that are unachievable using laboratory-scale magnetic fields. More recently, electrical transport studies have provided evidence for Hofstadter’s butterfly in materials engineered to have artificially large lattice constants, such as those with moiré superlattices. Yet, so far, direct spectroscopy of the fractal energy spectrum predicted by Hofstadter nearly 50 years ago has remained out of reach. Here, in this work, we use high-resolution scanning tunnelling microscopy/spectroscopy (STM/STS) to investigate the flat electronic bands in twisted bilayer graphene (TBG) near the predicted second magic angle, an ideal setting for spectroscopic studies of Hofstadter’s spectrum. Our study shows the fractionalization of flat moiré bands into discrete Hofstadter subbands and discerns experimental signatures of self-similarity of this spectrum. Moreover, our measurements uncover a spectrum that evolves dynamically with electron density, showing phenomena beyond that of Hofstadter’s original model owing to the combined effects of strong correlations, Coulomb interactions and the quantum degeneracy of electrons in TBG.

electronic properties and materials↗
DOE OSTI2024

Predicting Adhesion Energies of Metal Nanoparticles to Support Surfaces, Which Determines Metal Chemical Potential versus Particle Size and Thus Catalyst Performance

Improved catalysts and electrocatalysts composed of transition metal nanoparticles dispersed on high-area supports are essential for energy and environmental technologies. The chemical potential of the metal atoms in these supported nanoparticles is an important descriptor that correlates with both their catalytic activity and deactivation rate. This descriptor (μ M ) is predictably determined by the particle size and the adhesion energy per unit area at the metal/support interface (E adh ). We show here that the adhesion energies for different metals on a given support scale linearly with a simple property of the metal: for oxides, it is proportional to the metal oxophilicity, and for the carbon support, it increases linearly with metal carbophilicity (both divided by the area per metal atom). Furthermore, these relationships allow predicting E adh for other metal/support combinations, thus allowing estimation of μ M versus particle size and thereby better structure-based predictions of catalysts’ performance, which can aid in designing improved catalysts.

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

ExaWind: Predictive Wind Energy Simulations

This presentation describes the ExaWind project and the team's progress in creating a suite of performance-portable codes designed for predictive simulations of wind farms on next-generation exascale-class supercomputers. Such simulations will require the resolution of scales spanning many orders of magnitude, from blade boundary layers to wind farm flow structures. In the U.S., the first exascale systems will be GPU accelerated, and different GPU manufacturers have been chosen for the different systems. At the heart of the ExaWind software is a hybrid-solver approach based on the codes Nalu-Wind and AMR-Wind, which are computational fluid dynamics solvers for the incompressible Navier-Stokes equations. Nalu-Wind is an unstructured-grid code used to resolve wind turbine geometry and blade boundary layers, whereas AMR-Wind is a structured-grid background solver for atmospheric turbulent flow and turbine wake propagation. The models are coupled with overset meshes and global linear systems are approximated through a loose-coupling algorithm. Results will include validation-quality high-fidelity simulations and strong/weak scaling results from the Summit supercomputer.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2021

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2023

Identifying limitations in screening high-throughput photocatalytic bimetallic nanoparticles with machine-learned hydrogen adsorptions

The Sabatier principle is of fundamental importance to computational catalyst discovery, saving researchers time and expense by predicting catalytic activity in silico at scale. However, as polycrystalline and nanoscale catalysts increasingly dominate industry, computational screening tools must be adapted to these uses. In this work, we demonstrate the effectiveness of computational adsorption energy screening in nanocatalysis by comparing a multisite adsorption energy prediction workflow against a large experimental dataset of hydrogen evolution activities over bimetallic nanoparticles. Comparing 16 million hydrogen adsorption energy predictions with the hydrogen evolution activity of 5300 experiments across 84 monometallic and bimetallic systems, we discover that favorable adsorption energies are a necessary condition for experimental activity, but other factors often determine trends in practice. Finally, about half of the bimetallic search space can be excluded from experimental screens using hydrogen adsorption predictions, but this method may become significantly more powerful when combined with other screening tools.

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

ScaleML: Machine Learning based Heap Memory Object Scaling Prediction

Memory subsystem contributes 28-40% of total energy consumption. Several studies investigated energy prediction and consumption via profiling memory object access patterns. However, such profiling leads to higher energy consumption due to intense memory object-level profiling to achieve high prediction accuracy. Further, memory object access pattern prediction has been considered through analyzing the variation between memory object access patterns, referred to as scaling rate. The existing techniques for scaling rate prediction, such as Linear Scaling Rate (LSR), suffer from a high error rate in prediction with changes in access patterns, which leads to a high error rate of energy consumption prediction. In this paper, we compare and evaluate several memory object access pattern prediction models including LSR and machine learning (ML) models. Further, we propose SCALEML, a heap memory object scaling rate prediction mechanism that employs an ML model to achieve high access pattern prediction accuracy with variations in memory object access patterns. We evaluate SCALEML using various application benchmarks. The experimental results show that SCALEML achieves about 20% higher accuracy than the LSR model for predicting the scaling rate of object access patterns and energy estimation.

Park, Joongeon↗
DOE OSTI2020

Predicting Excitation Energies of Twisted Intramolecular Charge-Transfer States with the Time-Dependent Density Functional Theory: Comparison with Experimental Measurements in the Gas Phase and Solvents Ranging from Hexanes to Acetonitrile

Electronically excited states characterized by intramolecular charge transfer play an essential role in many biological processes and optical devices. The ability to make quantitative ab initio predictions of the relative energetics involved is a challenging yet desirable goal, especially for large molecules in solution. In this work, we present a data set of 61 experimental measurements of absorption and emission processes, both in the gas phase and in solvents representing a broad range of polarities, which involve intramolecular charge transfer mediated by a nonzero, "twisted" dihedral angle between one or more donor and acceptor subunits. Among a variety of density functionals investigated within the framework of linear-response theory, the "optimally tuned" LRC-ωPBE functional, which utilizes a system-specific yet nonempirical procedure to specify the range-separation parameter, emerges as the preferred choice. For the entire set of excitation energies, involving changes in dipole moment ranging from 4 to >20 Debye, the mean signed and absolute errors are 0.02 and 0.18 eV, respectively (compared, e.g., to -0.30 and 0.30 for PBE0, 0.44 and 0.47 for LRC-ωPBEh, 0.83 and 0.83 for ωB97X-V). We analyze the performance of polarizable continuum solvation models available in Q-Chem that partition the solvent response into fast and slow time scales, and clear trends emerge when measurements corresponding to the four small 4-(dimethylamino)benzonitrile (DMABN)-like molecules and a charged species are excluded. We make the case that the large errors found only for small molecules in the gas phase and weak solvents cannot be expected to improve via the optimal tuning procedure, which enforces a condition that is exact only in the well-separated donor-acceptor limit, and present empirical evidence implicating the outsized importance for small donor-acceptor systems of relaxation effects that cannot be accounted for by the linear-response time-dependent density functional theory within the adiabatic approximation. Finally, we demonstrate the utility of the optimally tuned density functional approach by targeting the charge-transfer states of a large biomimetic model system for light-harvesting structures in Photosystem II.

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

Predicting High Energy Arcing Fault Zones of Influence for Aluminum Using an Arc Flash Modeling Approach: Evaluation of a model bias, uncertainty, parameter sensitivity and zone of influence estimation

This report documents the development of an arc flash hazard model to calculate the incident energy and zone of influence from high energy arcing faults involving aluminum. The NRC has identified the potential for (HEAFs) involving aluminum to increase the damage zone beyond what is currently postulated in fire probabilistic risk assessment (PRA) methodologies. To estimate the hazard from HEAFs involving aluminum an arc flash model was developed. Differences between the initial model and nuclear power plant (NPP) fire PRA scenarios were identified. Modification of the initial model established from existing literature and test data was used to minimize these differences. The developed model was evaluated against NRC datasets to understand the model prediction and relative uncertainties. Finally, a range of fire PRA zone of influences (ZOI) were developed based on the developed model, target fragility estimates and update HEAF PRA methodology. The results were developed to support an NRC LIC-504 evaluation in tandem with other modeling efforts. The report documents the effort and provides a reference for any future advancements in arc flash modeling.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗