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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2021 .Net)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗
DOE OSTI2022

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2022 .Net)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗
DOE OSTI2023

Greenhouse gases, Regulated Emissions, and Energy use in Technologies Model ® (2023 .Net)

To fully evaluate energy and emission impacts of advanced vehicle technologies and new transportation fuels, the fuel cycle from wells to wheels and the vehicle cycle through material recovery and vehicle disposal need to be considered. Sponsored by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE), Argonne has developed a full life-cycle model called GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies). It allows researchers and analysts to evaluate various vehicle and fuel combinations on a full fuel-cycle/vehicle-cycle basis. The first version of GREET was released in 1996. Since then, Argonne has continued to update and expand the model. GREET.Net provides the user with an easy to use and fully graphical toolbox to perform life cycle analysis simulations of alternative transportation fuels and vehicle technologies in a matter of a few clicks. It provides a comprehensive, life-cycle-based approach to compare the energy use and emissions of conventional and advanced vehicle technologies. The tool includes the data of both fuel-cycle (fuel production and vehicle operation) and vehicle-cycle (vehicle material recovery and production, vehicle component fabrication, vehicle assembly, and vehicle disposal/recycling). This public domain model is available free of charge for anyone to use.

Wang, Michael↗
DOE OSTI2025

Quality Guidelines for Energy System Studies: Process Modeling Design Parameters

The National Energy Technology Laboratory (NETL) conducts systems analysis studies that require a large number of inputs, from ambient conditions to parameters for Aspen Plus ® (Aspen) process blocks. The sheer number of assumptions required makes it impractical to document all of them in each issued report. The purpose of the Quality Guidelines for Energy System Studies (QGESS) is to document the assumptions most commonly used in system analysis studies and the basis for those assumptions. In order to develop the systems analysis models presented in various NETL reports, significant vendor data have been obtained, and these data enhance the model outputs. Much of the vendor data obtained are considered proprietary and not suitable for public release or attribution to a specific vendor. As such, several sub-systems common in NETL reports and their process parameter data are not reported in this document to protect proprietary vendor information. The values and ranges of values presented in this report represent assumptions that have been made in previous studies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Miscellaneous Electric Loads: Characterization and Energy Savings Potential

Over time, miscellaneous electric loads (MELs) are expected to increase both in magnitude and share of residential and commercial building energy consumption. This trend is most apparent in North America, but it is also occurring in Japan and Europe. However, the contribution of MELs to building energy use is not currently well understood, both because the products in this category are transforming rapidly and the definition and classification of MELs is ambiguous. This study estimated the national energy consumption of 36 MELs using best-available data and found them to comprise 12% of delivered electricity to the U.S. residential and commercial building sectors. If 26 of these MELs were replaced with the most energy-efficient product models available on the market, their energy consumption could be halved to 6% of delivered electricity. National energy models will better account for building energy consumption by incorporating the MELs data collected and analyzed for this study, leading to improved policy decisions.

Miscellaneous electric loads, Plug loads, Taxonomy↗
NASA NTRS1993

The embedded young stars in the Taurus-Auriga molecular cloud. I - Models for spectral energy distributions

We describe radiative transfer calculations of infalling, dusty envelopes surrounding pre-main-sequence stars and use these models to derive physical properties for a sample of 21 heavily reddened young stars in the Taurus-Auriga molecular cloud. The density distributions needed to match the FIR peaks in the spectral energy distributions of these embedded sources suggest mass infall rates similar to those predicted for simple thermally supported clouds with temperatures about 10 K. Unless the dust opacities are badly in error, our models require substantial departures from spherical symmetry in the envelopes of all sources. These flattened envelopes may be produced by a combination of rotation and cavities excavated by bipolar flows. The rotating infall models of Terebey et al. (1984) models indicate a centrifugal radius of about 70 AU for many objects if rotation is the only important physical effect, and this radius is reasonably consistent with typical estimates for the sizes of circumstellar disks around T Tauri stars.

Kenyon, Scott J.↗
NASA NTRS2003

Two-Pole Caustic Model for High-Energy Lightcurves of Pulsars

We present a new model of high-energy lightcurves from rotation powered pulsars. The key ingredient of the model is the gap region (i.e. the region where particle acceleration is taking place and high-energy photons originate) which satisfies the following assumptions: i) the gap region extends from each polar cap to the light cylinder; ii) the gap is thin and confined to the surface of last open magnetic-field lines; iii) photon emissivity is uniform within the gap region. The model lightcurves are dominated by strong peaks (either double or single) of caustic origin. Unlike in other pulsar models with caustic effects, the double peaks arise due to crossing two caustics, each of which is associated with a different magnetic pole. The generic features of the lightcurves are consistent with the observed characteristics of pulsar lightcurves: 1) the most natural (in terms of probability) shape consists of two peaks (separated by 0.4 to 0.5 in phase for large viewing angles); 2) the peaks possess well developed wings; 3) there is a bridge (inter-peak) emission component; 4) there is a non-vanishing off-pulse emission level; 5) the radio pulse occurs before the leading high-energy peak. The model is well suited for four gamma-ray pulsars - Crab, Vela, Geminga and B1951+32 - with double-peak lightcurves exhibiting the peak separation of 0.4 to 0.5 in phase. Hereby, we apply the model to the Vela pulsar. Moreover, we indicate the limitation of the model in accurate reproducing of the lightcurves with single pulses and narrowly separated (about 0.2 in phase) pulse peaks. We also discuss the optical polarization properties for the Crab pulsar in the context of the two-pole caustic model.

Dyks, J.↗
DOE OSTI2026

A Comprehensive Comparison of Methods for Evaluating Dispatch of Long-Duration Energy Storage in Power Systems Models

Long-duration energy storage (LDES) could play a pivotal role in the transformation of electricity grids with high shares of variable renewable energy (VRE) such as solar and wind. However, the weather-dependent nature of VRE introduces challenges for grid balancing and stability, which LDES - along with short-duration energy storage (SDES) - can help address. However, modeling LDES in production cost models (PCMs) is particularly challenging due to the need for high temporal resolution over extended optimization windows while preserving chronology, which ensures the alignment of energy storage operations with VRE generation over multi-day periods. This report compares traditional dispatch methods with advanced LDES dispatch strategies, such as the extended horizon approach, across different PCM platforms and examines tradeoffs and scalability. The comparison reveals that the traditional 1-day optimization horizon within the PCM leads to inefficient utilization of LDES. In contrast, extending the optimization horizon as much as possible significantly reduces curtailment and improves storage dispatch, especially in renewable-dense systems. There is also promise in using state-of-charge or end volume targets set by an external model, however this requires an additional modeling set and generally increases computational burden. This paper presents a comparison of these various methods in a number of power systems, showing algorithms initially in small test systems and scaling up to large, country-wide simulations. Overall, the research presents the trade-offs of various computational methods and illustrates how LDES may play an essential role in power systems of the future.

14 SOLAR ENERGY↗
DOE OSTI2024

Incorporating energy justice and equity objectives in power system models

Ensuring an equitable energy transition requires models and tools that can account for equity and energy justice goals. Power system models (PSMs) are widely used throughout industry, government, and academia to simulate or optimize the operations and planning of current and future electricity systems under different scenarios, parameter assumptions and policy frameworks. These models are important tools that allow users to understand how the power system may evolve under different future conditions, but importantly, they are also used to inform policy implementation and investment decisions across all aspects of the power system. However, existing models seldom include energy justice considerations and therefore energy justice priorities are not reflected in the policies and other decision-making processes that are informed by these models. The purpose of this review is to provide a framework that energy modelers can draw upon to integrate energy justice and equity goals into PSMs. To this end, 99 papers that examine the intersection of energy justice and power system models are summarized and ten core aspects of the power system that can impact energy justice outcomes, and therefore require new modeling approaches, are identified. This review then establishes key current practices, challenges, and opportunities associated with capturing energy justice considerations in power system models across these ten aspects. This review concludes by proposing four key research directions that should be pursued to improve the representation of energy justice and equity in power system modeling. Finally, this review also addresses challenges raised by United Nations Sustainable Development Goal 7, which aims to ensure affordable energy access to everyone and Sustainable Development Goal 13, which aims to take urgent action to address climate change.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Perturbation theory for modeling galaxy bias: validation with simulations of the Dark Energy Survey

We describe perturbation theory (PT) models of galaxy bias for applications to photometric galaxy surveys. We model the galaxy-galaxy and galaxy-matter correlation functions in configuration space and validate against measurements from mock catalogs designed for the Dark Energy Survey (DES). We find that an effective PT model with five galaxy bias parameters provides a good description of the 3D correlation functions above scales of 4 Mpc/$h$ and $z < 1$. Our tests show that at the projected precision of the DES-Year 3 analysis, two of the non-linear bias parameters can be fixed to their co-evolution values, and a third (the $k^2$ term for higher derivative bias) set to zero. The agreement is typically at the 2 percent level over scales of interest, which is the statistical uncertainty of our simulation measurements. To achieve this level of agreement, our {\it fiducial} model requires using the full non-linear matter power spectrum (rather than the 1-loop PT one). We also measure the relationship between the non-linear and linear bias parameters and compare them to their expected co-evolution values. We use these tests to motivate the galaxy bias model and scale cuts for the cosmological analysis of the Dark Energy Survey; our conclusions are generally applicable to all photometric surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗
DOE OSTI2023

The winter central Arctic surface energy budget: A model evaluation using observations from the MOSAiC campaign

This study evaluates the simulation of wintertime (15 October, 2019, to 15 March, 2020) statistics of the central Arctic near-surface atmosphere and surface energy budget observed during the MOSAiC campaign with short-term forecasts from 7 state-of-the-art operational and experimental forecast systems. Five of these systems are fully coupled ocean-sea ice-atmosphere models. Forecast systems need to simultaneously simulate the impact of radiative effects, turbulence, and precipitation processes on the surface energy budget and near-surface atmospheric conditions in order to produce useful forecasts of the Arctic system. This study focuses on processes unique to the Arctic, such as, the representation of liquid-bearing clouds at cold temperatures and the representation of a persistent stable boundary layer. It is found that contemporary models still struggle to maintain liquid water in clouds at cold temperatures. Given the simple balance between net longwave radiation, sensible heat flux, and conductive ground flux in the wintertime Arctic surface energy balance, a bias in one of these components manifests as a compensating bias in other terms. This study highlights the different manifestations of model bias and the potential implications on other terms. Three general types of challenges are found within the models evaluated: representing the radiative impact of clouds, representing the interaction of atmospheric heat fluxes with sub-surface fluxes (i.e., snow and ice properties), and representing the relationship between stability and turbulent heat fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Distributed Trust Model Simulator for Energy Grid of Things Distributed Energy Resource Management System

The evolution of networks into more distributed, self-reliant nodes has mitigated single-point failures that plagued traditional centralized networks. Applied to power grids, distributed systems can increase the integrity and availability of grid services while also offering a power management solution. However, while distributed networks provide scalability, security, and sustainability compared to centralized networks, their distributed nature makes them harder for anomaly detection and prevention. Incorporating a Distributed Trust Model (DTM) System into an Energy Grid of Things Distributed Energy Resource Management System (EGOT DERMS) allows grid participants to be characterized and their communication to be analyzed for possible attacks. A Trust Model simulator is needed to evaluate and improve the DTM System.Trustworthiness is calculated using a Trust Model. While many trust models exist, most only consider 2-3 matrices to evaluate trust. The TM proposed in this thesis uses a Metric Vector of Trust (MVoT) monitoring 17 parameters when assessing trust. Moreover, unlike standard trust models, the proposed trust model establishes a method to test the trust between various actors within the network and probe the trust model itself. Using a Trust Model Simulator, MVoT calaculations, initial values, and parameters are fine-tuned to achieve high-confidence message classifications and minimize false positives. The DTM System and Trust Mode Simulation Suite allow for distributed trust evaluation with a real-time classification of EGOT DERMS actors, providing additional security for distributed systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1983

The electrical behavior of GaAs-insulator interfaces - A discrete energy interface state model

The relationship between the electrical behavior of GaAs Metal Insulator Semiconductor (MIS) structures and the high density discrete energy interface states (0.7 and 0.9 eV below the conduction band) was investigated utilizing photo- and thermal emission from the interface states in conjunction with capacitance measurements. It was found that all essential features of the anomalous behavior of GaAs MIS structures, such as the frequency dispersion and the C-V hysteresis, can be explained on the basis of nonequilibrium charging and discharging of the high density discrete energy interface states.

Kazior, T. E.↗
DOE OSTI2021

Hardware-Based Emulator with Deep Learning Model for Building Energy Control and Prediction Based on Occupancy Sensors’ Data

Heating, ventilation, and air conditioning (HVAC) is the largest source of residential energy consumption. Occupancy sensors’ data can be used for HVAC control since it indicates the number of people in the building. HVAC and sensors form a typical cyber-physical system (CPS). In this paper, we aim to build a hardware-based emulation platform to study the occupancy data’s features, which can be further extracted by using machine learning models. In particular, we propose two hardware-based emulators to investigate the use of wired/wireless communication interfaces for occupancy sensor-based building CPS control, and the use of deep learning to predict the building energy consumption with the sensor data. We hypothesize is that the building energy consumption may be predicted by using the occupancy data collected by the sensors, and question what type of prediction model should be used to accurately predict the energy load. Another hypothesis is that an in-lab hardware/software platform could be built to emulate the occupancy sensing process. The machine learning algorithms can then be used to analyze the energy load based on the sensing data. To test the emulator, the occupancy data from the sensors is used to predict energy consumption. The synchronization scheme between sensors and the HVAC server will be discussed. We have built two hardware/software emulation platforms to investigate the sensor/HVAC integration strategies, and used an enhanced deep learning model—which has sequence-to-sequence long short-term memory (Seq2Seq LSTM)—with an attention model to predict the building energy consumption with the preservation of the intrinsic patterns. Because the long-range temporal dependencies are captured, the Seq2Seq models may provide a higher accuracy by using LSTM architectures with encoder and decoder. Meanwhile, LSTMs can capture the temporal and spatial patterns of time series data. The attention model can highlight the most relevant input information in the energy prediction by allocating the attention weights. The communication overhead between the sensors and the HVAC control server can also be alleviated via the attention mechanism, which can automatically ignore the irrelevant information and amplify the relevant information during CNN training. Our experiments and performance analysis show that, compared with the traditional LSTM neural network, the performance of the proposed method has a 30% higher prediction accuracy.

Ye, Zhijing↗
DOE OSTI2023

Simulink Model of a New Energy EVG-005 5kW Hydropower Turbine

Simulink model for a New Energy 5kW hydropower turbine. ADCP data ("ds_streamwise_7_13.nc") and DC voltage, DC current, and rotor rotation observed from the New Energy EVG-005 Current Energy Converter (CEC) ("electrical_7_13_10ohms.nc") were collected at the Tanana River Test Site (TRTS) near Nenana Alaska. - Data was collected on July 13th, 2023 with a constant 10 ohms resistance applied with a DC load bank. - Simulink model is meant to resemble the electrical setup at the TRTS. - Model is initialized by running the "NewEnergy_2023_10hz.m" Matlab script. Then the Simulink model ("New_Energy_Model_PMSM.slx") can be run. - Results are processed with the "NewEnergy_2023_processing.m" Matlab code. The Matlab results are also saved in the "New_Energy_7_13_model_validation_results.mat" Matlab file. This can be directly loaded into the Matlab Workspace using the "Load()" command. - The timetable variables "electrical_model_downsampled" is the model results and the "electrical_limited" is the experimental data from the TRTS.

16 TIDAL AND WAVE POWER↗