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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Overcoming significant challenges in extracting off-stoichiometric thermodynamics using the compound energy formalism through complementary use of experimental and first principles data: A case study of Ba 1-x Sr x FeO 3-δ

The compound energy formalism (CEF) is a powerful framework to describe the thermodynamics of metal oxides as a function of off-stoichiometry, temperature, and composition. The thermodynamic properties are crucial materials design attributes in metal oxide-based oxygen-exchange chemical processes. Despite the richness of information an accurate CEF model provides, a method to determine a unique and accurate fit for oxygen exchange materials remains elusive. This contribution details a method for fitting the CEF model that overcomes the current fitting challenges through three innovations: 1) the combination of density functional theory calculations with experimental data decorrelates excess terms and delineates the enthalpic/entropic contributions to the Gibbs free energy; 2) a systematic determination of the important CEF model terms, removing thermodynamic predetermining human intervention; 3) a self-consistent solution of the starting oxygen offstoichiometry (δ 0 ) of thermogravimetric measurements. Thus, our method enables the reliable extraction of off-stoichiometric metal oxide thermodynamic properties and facilitates rapid materials compositional screening, and reliable process design of systems dependent on off-stoichiometric redox-active metal oxides. We apply this method to a Ba x Sr 1-x FeO 3-δ test case. We find by systematically examining the performance of the CEF model fit with and without each innovation that all three innovations are necessary for an accurate fit. We determined that reduction enthalpy is higher and more sensitive to off-stoichiometry when the Sr fraction is large (139.5 and 185.3 kJ/mol O 2 for SrFeO 3 at δ = 0 and δ = 0.5, respectively vs. nearly constant 83 kJ/mol O 2 for BaFeO 3 ). However, the reduction entropy is mostly insensitive to Sr fraction, but highly dependent on δ suggesting larger contributions in the non-configurational entropy parameters.

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

Calibration of urban building energy model using smart meter data for district peak load prediction

Urban building energy modeling (UBEM) is a powerful approach to assessing baseline building energy performance and retrofits with new technologies across building stocks in cities. However, the accuracy of UBEM is often constrained by the limited availability of reliable data about building characteristics and operations, such as envelope efficiency levels, HVAC system performance, and end-use load patterns. Existing research has performed UBEM calibration using annual or monthly energy consumption data, which falls short when higher-resolution time series applications are needed, such as peak load prediction for utility operation planning. This study presents a new framework for calibrating building energy models at urban scale using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6 % during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Using the calibrated models, we analyzed the demand flexibility potential of the district building stock as an application of UBEM calibration. The findings affirm the appropriate use of UBEM for peak electric load forecasting and demand side management at the utility distribution system level.

AMI data↗
DOE OSTI2022

Enabling Principles for Dual Participation by Energy Storage as a Transmission and Market Asset

Because the electric transmission system is sized to meet the highest levels of customer demand, much of its designed capacity goes unused during average operating conditions. As a uniquely flexible and scalable asset capable of providing multiple grid services, energy storage can be deployed to support the transmission system and then also provide services to energy markets when not needed for transmission. This paper reviews the technical barriers in transmission planning practices and energy market design that prevent the realization of dual-use energy storage projects that provide both regulated transmission and competitive market services. Current practices create barriers against the identification of energy storage in the transmission planning process and against the participation of a regulated transmission asset in competitive energy markets. A review of regional transmission plans identifies best practices for including energy storage in the planning process by directing planners to proactively identify such projects or by providing clear mechanisms for stakeholders to propose them. Creating a market participation model for dual-use energy storage assets requires establishing when an asset will be allowed to participate in the market, how it will participate in the market, and where it will recover its costs. In answering these questions, key principles must be satisfied, such as prioritizing reliability, incrementally building on existing market design, and balancing competing objectives. Several policy options exist for satisfying those principles, giving grid operators and stakeholders significant flexibility in developing a dual-use participation model in accordance with regional policies and conditions. By objectively framing the issue and identifying policy options, this paper may inform subsequent proceedings on dual-use energy storage and be a resource to regional market operators, utilities, developers, regulators, policymakers, and other stakeholders as they collectively work on this complex issue.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

ResStock Dataset 2024.1 Documentation

Public ResStock datasets provide credible, relevant, and accessible information on energy use and related non-energy metrics to a variety of stakeholders in the residential buildings space. The current public datasets include baseline building characteristics, timeseries (15-minute) energy consumption, and timeseries carbon emissions for the baseline (existing) U.S. housing stock and the U.S. housing stock with 10 "what-if" energy measure packages applied. This report documents a new public ResStock dataset to complement and build upon the existing public datasets. This dataset is specifically intended to be a resource for state and local decision-makers considering options for energy retrofits for their housing stock to reduce carbon emissions, energy use, and/or utility bills. These data consist of housing stock characteristics and modeled full-year energy consumption, carbon emission, energy bill, and energy burden data for the baseline U.S. housing stock as well as the U.S. housing stock with 260 "what-if" energy measure packages applied. These measure packages include measures related to the building envelope, appliances, pools and spas, lighting, water heating, and HVAC (including efficiency improvements and fuel switching with equipment at a range of performance levels) in a variety of combinations. This report provides methodology information on the generation of this dataset and serves as a key part of the dataset's public documentation.

buildings↗
DOE OSTI2023

Geological Thermal Energy Storage Using Solar Thermal and Carnot Batteries: Techno-Economic Analysis: Preprint

Energy storage is increasingly necessary as Variable Renewable Energy (VRE) technologies are deployed. Seasonal energy storage can shift energy generation from the summer to the winter, but these technologies must have extremely large energy capacities and low costs. Geological Thermal energy storage (GeoTES) is proposed as a solution for long-term energy storage. Excess thermal energy can be stored in permeable reservoirs such as aquifers and depleted hydrocarbon reservoirs for several months. In this article, we describe a techno-economic model that has been developed to evaluate GeoTES systems. The models are developed by combining the output of specialist models which enables the performance and cost of both the subsurface and surface systems to be captured. Off-design models are developed so that the performance can be evaluated at each hour of the year. GeoTES can be charged with two different energy sources: (1) concentrating solar thermal and (2) renewable electricity using heat pumps (henceforth known as a "Carnot Battery"). The stored thermal energy can be used to generate electricity and - uniquely - also directly produce heat that can be used by industrial processes. Furthermore, Carnot-Battery-GeoTES can also be used to form a cold storage reservoir. Preliminary results that quantify the technical and economic performance of these two GeoTES systems are presented.

carnot battery↗
DOE OSTI2025

Dehumidification energy storage using a stratified liquid desiccant tank

Liquid desiccants can play an important role in reducing dehumidification energy requirements in the built environment. Because they are in a liquid state, the desiccant can be easily stored and then used to dehumidify buildings during peak energy consumption periods. By maintaining stratification between concentrated and diluted desiccant solutions, a single tank can be used to store liquid desiccant for energy storage purposes. Using a stratified tank instead of separate tanks for dilute and concentrated solutions will reduce storage costs and increase energy storage densities for liquid desiccant systems. This paper describes the experimental validation and one-dimensional modeling of a stratified liquid desiccant tank. The stratified tank prototype developed achieved 80 % of the theoretical energy storage density based on an imposed desiccant concentration change. Here, the stratified tank model was able to reasonably reproduce the experimental results. Using this model, the impact of varying operational conditions on the energy density of the stratified liquid desiccant energy storage was evaluated. Depending on the operating conditions, stratified liquid desiccant energy storage using aqueous LiCl up to 40 wt% can achieve energy storage densities in excess of 330 kWh/m 3 .

25 ENERGY STORAGE↗
DOE OSTI2021

Consumer Guide to Energy-Efficient Lighting

Learn how to reduce energy use from lighting and choose energy efficient light bulbs, as well as why it pays to make energy efficient lighting choices. This fact sheet from Energy Saver includes information on the history of lighting, today's LED lights and how to choose the best ones for your needs, and how to reduce your energy use from lighting.

lighting, light bulbs, LED lights, lumens, dayligh↗
DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Lighting and visual comfort performance of commercially available tubular daylight devices

Tubular daylight devices (TDDs) require a much smaller roof opening than conventional skylights and, because of their highly reflective tube, they can deliver daylight farther away from the building envelope. This can provide lighting energy savings, increasing resilience in new and existing buildings. Different types and configurations are available amongst commercially available TDDs, including domes/diffusers with varying optical properties and the diameter of the TDD. This paper presents a comprehensive experimental evaluation of the lighting and visual comfort performance of multiple configurations of commercially available TDDs, varying dome type (prismatic and clear), diffuser type (Fresnel and prismatic), and diameter (53 and 35 cm), under a range of environmental conditions (different times of day/year, sky cover). Based on illuminance measurements, estimated lighting energy use is also presented. Results indicate that, for clear sky, light levels increase and energy use decreases with solar altitude (e.g., 16 Wh/m 2 daily energy use intensity on a high maximum daily solar altitude (MDSA) day and 34 Wh/m 2 on a low MDSA day) and TDD diameter (e.g., 34 Wh/m 2 and 69 Wh/m 2 for 53 cm and 35 cm TDDs, respectively, for low MDSA). The daily illuminance profile is more rounded for prismatic domes and has higher peaks for clear domes; this translates into a somewhat higher average daily useful daylight illuminance (DUDI) for prismatic domes (86 %) when compared to clear domes (80 %). No clear impact of diffuser type was apparent. In conclusion, measurements indicated no discomfort glare for any of the conditions tested.

14 SOLAR ENERGY↗
DOE OSTI2025

A comprehensive academic and industrial survey of blockchain technology for the energy sector using fuzzy Einstein decision-making

The global energy sector is undergoing a significant transformation driven by decarbonization and digitalization, leading to the emergence of Distributed Ledger Technology (DLT) — particularly blockchain — as a promising tool for enhancing transparency, security, and efficiency in modern power systems. This study aims to provide a comprehensive academic and industrial survey of blockchain applications in the energy sector and develop a robust decision-making framework to identify and prioritize the most promising real-world use cases based on multidisciplinary criteria. A three-stage methodology was adopted: (i) a literature and market review encompassing over 300 academic publications and commercial blockchain initiatives in energy, (ii) an in-depth evaluation of the evolution and viability of blockchain initiatives in energy with the help of expert surveys, and (iii) a novel decision-making model using a q-rung orthopair fuzzy Multi-Attributive Border Approximation (q-ROF-MABAC) method under the Einstein operator. The results were compared with existing decision models to validate consistency and robustness. Nine key blockchain use case categories were identified and ranked based on technical, economic, and governance dimensions. The results demonstrated that integrating expert insights into a fuzzy logic framework helps filter out overhyped claims in the literature and prioritize realistic and high-impact applications such as green certificates, grid services, and peer-to-peer energy trading. The model’s rankings remained stable across varying weight configurations, confirming the robustness of the methodology. This study provides an evidence-based decision-support tool for researchers, industry stakeholders, and policymakers to better understand, evaluate, and adopt blockchain technologies in the energy sector.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2021

Baseline predictions of elliptic flow and fluctuations for the RHIC Beam Energy Scan using response coefficients

Currently the RHIC Beam Energy Scan is exploring a new region of the Quantum Chromodynamic phase diagram at large baryon densities that approaches nuclear astrophysics regimes. This provides an opportunity to study relativistic hydrodynamics in a regime where the net conserved charges of baryon number, strangeness, and electric charge play a role, which will significantly change the theoretical approach to simulating the baryon-dense Quark-Gluon Plasma. In this work, we detail many of the important changes needed to adapt both initial conditions and the medium to baryon-rich matter. Then, we make baseline predictions for the elliptical flow and fluctuations based on extrapolating the physics at LHC and top RHIC energies to support future analyses of where and how the new baryon-dense physics causes these extrapolations to break down. First we compare eccentricities across beam energies, exploring their underlying assumptions; we find the the extrapolated initial state is predicted to be nearly identical to that at AuAu $\sqrt{s_{NN}}$ = 200 GeV. Then the final flow harmonic predictions are based on linear+cubic response. We discuss preliminary STAR results in order to determine the implications that they have for linear+cubic response coefficients at the lowest beam energy of AuAu $\sqrt{s_{NN}}$ = 7 GeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Energy storage using an REP with an engine

An energy storage system includes a power plant configured to generate an exhaust gas comprising carbon dioxide. The energy storage system further includes a first fuel cell configured to operate in reverse as an electrolyzer. The first fuel cell is configured to receive at least a portion of the exhaust gas from the power plant. An anode is configured to receive carbon dioxide via the exhaust gas and methane from a separate feed, and the anode is configured to output a hydrogen-containing gas mixture. The energy storage system further includes a reformer configured to methanate the hydrogen-containing gas mixture to convert substantially all of the carbon monoxide in the hydrogen-containing gas mixture to methane. The energy storage system further includes a second fuel cell operating in reverse as a hydrogen pump, the second fuel cell configured to separate hydrogen from a gas mixture output by the reformer.

25 ENERGY STORAGE↗
DOE OSTI2020

Simulating Buildings While the Design is Still in the Architect's Imagination

Very early in the design process, prior to drawing a single line, architects get information from their client or prospective client trying to understand the nature and requirements of the building that needs to be designed. The design of the building is completely fluid, as the architect considers many options in their imagination. The overall massing of the building, the general shape and form, are being considered during this early step. Even for a simple box-shaped building, the aspect ratio between the east-west and the north-south dimensions of the building can greatly impact energy consumption due to daylighting availability. The prevailing winds and building orientation can impact natural ventilation. The locations of high and low population density spaces can also impact energy use. Deciding the floor area per office workstation or the size of the auditorium will impact energy use. Does making a building two stories instead of one reduce energy impacts by reducing roof area or does the additional volume for stairs and elevators result in more energy use. Since certain types of spaces require more windows for occupants than others, the locations of those spaces will determine how much glazing each building orientation has impacting daylighting as well as heating and cooling energy. The project was an attempted first step toward creating a system tailored toward architects for this very early stage in the design process to help them understand the energy impacts of design options. The approach was not to be based on drawing the building, and instead, the main inputs would have been the size of each type of space in the building. This report describes information gathered during the development that might be of use to others developing similar software as well as some of the software that was developed during the project. Phase II funding to continue this work was not pursued due to intellectual property issues discovered during the development of the commercialization plan.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Editorial: Using high energy density plasmas for nuclear experiments relevant to nuclear astrophysics

Thermonuclear reaction rates and nuclear processes have traditionally been explored by means of accelerator experiments, which are difficult to execute at conditions relevant to nucleosynthesis. High energy density (HED) plasmas generated using lasers, such as the inertial confinement fusion (ICF) platform, more closely mimic astrophysical environments in several ways, including with thermal distributions of reacting ions as opposed to mono-energetic ions impinging on a cold target; stellar-relevant plasma temperatures and densities; and neutron flux densities not found anywhere else on earth. The most extreme conditions can currently be achieved at the National Ignition Facility (NIF) laser in the US, where densities of 10 3 g/cm 3 and neutron fluxes up to 5∙10 27 neutrons/cm/s have been demonstrated over a time period of a few tens of picoseconds. The HED platform is emerging as an interesting complement to accelerator experiments.

charged-particle-induced reactions↗
DOE OSTI2020

Practical quantum computation of chemical and nuclear energy levels using quantum imaginary time evolution and Lanczos algorithms

Various methods have been developed for the quantum computation of the ground and excited states of physical and chemical systems, but many of them require either large numbers of ancilla qubits or high-dimensional optimization in the presence of noise. The quantum imaginary-time evolution (QITE) and quantum Lanczos (QLanczos) methods proposed in Motta et al. (2020) eschew the aforementioned issues. In this study, we demonstrate the practical application of these algorithms to challenging quantum computations of relevance for chemistry and nuclear physics, using the deuteron-binding energy and molecular hydrogen binding and excited state energies as examples. With the correct choice of initial and final states, we show that the number of timesteps in QITE and QLanczos can be reduced significantly, which commensurately simplifies the required quantum circuit and improves compatibility with NISQ devices. We have performed these calculations on cloud-accessible IBM Q quantum computers. With the application of readout-error mitigation and Richardson error extrapolation, we have obtained ground and excited state energies that agree well with exact results obtained from diagonalization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Automated model generation and parameter estimation of building energy models using an ontology-based framework

This study presents a methodology for automated model generation and parameter estimation of building energy models using semantic modeling and Bayesian estimation. Semantic modeling techniques are used to represent the system components and their interactions, facilitating the automatic generation of a simulation model from dynamic component models. The proposed approach is applied to a case study of a ventilation system where a simulation model is generated, calibrated, and assessed through different performance metrics. These metrics demonstrate the accuracy and reliability of both model point estimates and probabilistic prediction intervals across all model outputs. Overall, the proposed methodology offers a systematic and automated approach to model development and calibration in building energy systems, with potential applications in building performance analysis, monitoring, and optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Dynamic Life Cycle Assessment for Evaluating the Global Warming Potential of Geothermal Energy Production Using Inactive Oil and Gas Wells for District Heating in Tuttle, Oklahoma

Repurposing abandoned oil and gas infrastructure for geothermal energy production has great potential to reduce greenhouse gas (GHG) emissions. This study quantified the life cycle global warming potential of geothermal energy production using four inactive oil and gas wells repurposed for district heating in Tuttle, Oklahoma. A cradle-to-grave prospective life cycle assessment was performed to compare GHG emissions between the geothermal district heating system and conventional natural gas-fired heating system from 2020 to 2050. For initial implementation of the geothermal system, we investigated two approaches: 1) repurposing abandoned infrastructure from a nearby oil and gas well site, and 2) production and injection well drillings including new construction of a central heat exchange station. Environmental impacts from the geothermal system were estimated for five scenarios where a natural gas peaking boiler is incorporated to supply peak heat demand. The prospective results indicated that cumulative reduction in GHG emissions from transitioning to the geothermal district heating system increase over time as a function of future renewable resource penetration and technological advancements within electricity, fuel, and steel production. Over 30 years, the global warming potential associated with the district heating demand will have been reduced by up to 24 % with the repurposed system. These results imply that repurposing existing oil and gas infrastructure for geothermal energy systems of district heating will bring future climate benefits.

abandoned oil and gas wells↗