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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Importance of Weather and Climate to Energy Systems: A Workshop on Next Generation Challenges in Energy–Climate Modeling

More than 80 international participants, representing weather, climate, and energy systems research, joined two 4-hour remote sessions to highlight and prioritize ongoing and future challenges in energy-climate modelling. The workshop had two primary goals: to build a deeper engagement across the “energy” and “climate” research communities, and to identify and begin to address the scientific challenges associated with modelling climate risk in energy systems.

17 WIND ENERGY↗
DOE OSTI2014

Hybrid Energy System Modeling in Modelica

In this paper, a Hybrid Energy System (HES) configuration is modeled in Modelica. Hybrid Energy Systems (HES) have as their defining characteristic the use of one or more energy inputs, combined with the potential for multiple energy outputs. Compared to traditional energy systems, HES provide additional operational flexibility so that high variability in both energy production and consumption levels can be absorbed more effectively. This is particularly important when including renewable energy sources, whose output levels are inherently variable, determined by nature. The specific HES configuration modeled in this paper include two energy inputs: a nuclear plant, and a series of wind turbines. In addition, the system produces two energy outputs: electricity and synthetic fuel. The models are verified through simulations of the individual components, and the system as a whole. The simulations are performed for a range of component sizes, operating conditions, and control schemes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Assessing the behavioral realism of energy system models in light of the consumer adoption literature

Effective policymaking to achieve net zero greenhouse gas emissions demands an understanding of the complex drivers of, and barriers to, consumer adoption behavior via behaviorally realistic energy system models. Existing models tend to oversimplify by focusing on homogenized financial factors while neglecting consumer heterogeneity and non-monetary influences. This study develops and applies a comprehensive framework for evaluating the behavioral realism of consumer adoption models, informed by the adoption literature. It introduces a typology for factors influencing low-carbon technology adoption decisions: monetary and non-monetary factors relating to household characteristics, psychology, technological attributes, and contextual conditions. Next, reviews of the consumer adoption and decision-making literature identify the most influential adoption factor categories for distributed solar photovoltaics, electric vehicles, and air-source heat pumps. Finally, the extent to which a selection of energy system models accounts for these adoption factors is assessed. Existing models predominantly emphasize the economic aspects of technology, which are generally identified as the most important factors. Where the models fall short — in considering moderately important factor categories — sector-specific and agent-based models can offer more behaviorally realistic insights. This study sheds light on which types of factors are most important for consumer adoption decisions and investigates how well current models rise to the challenge of behavioral realism. The end-to-end analysis presented enables internally consistent comparisons across models and energy technologies. This research advances timely conversations on consumer adoption. It could inform more behaviorally realistic energy system modeling, and thereby more effective decarbonization policymaking.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Electric train energy consumption modeling

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error of 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Quantitative modeling of energy dissipation in Arabidopsis thaliana

In photosynthesis, solar energy is absorbed and converted into chemical energy. Chlorophyll embedded in proteins absorb light and transfer excitation energy to reaction centers where charge separation occurs. However, the solar flux incident on photosynthetic organisms is highly variable, requiring complex feedback systems to regulate the excitation pressure on reaction centers and prevent excess absorbed energy from causing damage. During periods of transient high light, excess absorbed energy is dissipated as heat. This is routinely observed as the quenching of chlorophyll fluorescence, and often broadly referred to as non-photochemical quenching (NPQ). Understanding the mechanisms through which photosynthetic systems dissipate excess energy and regulate excitation pressure in response to variable light conditions requires extensive quantitative modeling of the photosynthetic system and energy dissipation to interpret experimental observations. This review discusses efforts to model energy dissipation, or quenching, in Arabidopsis thaliana and their connections to models of regulatory systems that control quenching. We begin with a review of theory used to describe energy transfer and experimental data obtained to construct energy transfer models of the photosynthetic antenna system that underlie the interpretation of chlorophyll fluorescence quenching. Second, experimental evidence leading to proposed molecular mechanisms of quenching and the implications for modeling are discussed. The initial incorporation of depictions of proposed mechanisms into quantitative energy transfer models is reviewed. Finally, the necessity of connecting energy transfer models that include molecular models of quenching mechanisms with regulatory models is discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Renewable Energy Potential Model: Hawaii Geothermal Supply Curves

This dataset extends the development of the Renewable Energy Potential (reV) model to include geothermal energy, with a specific focus on Hawaii. Provided here are the results of two scenarios that were modeled for geothermal energy in Hawaii: binary enhanced geothermal systems (EGS) at a depth of 2.5 km and hydrothermal binary systems at a depth of 1.5 km. The resource data for both scenarios were derived from Lautze and Haskins (2024) using an exponential method. The PFA probability of heat map was used as a look up table for which temperature gradient to use (Lautze and Haskins, 2024). The dataset provides geospatial and techno-economic details for evaluating geothermal energy potential. It includes spatial coordinates, estimated capacity factors, developable area, resource potential, and annual energy production metrics. Economic details such as levelized cost of electricity (LCOE), site development costs, transmission costs, and fixed-charge rates are also included. The reV model, originally developed for wind and solar energy, incorporates these variables to evaluate deployment constraints related to land use, environmental and cultural factors, and grid integration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Predictions of Yield Strength Evolution Due to Physical Aging of 828 DGEBA/DEA using the Simplified Potential Energy Clock Model

The capability of the Simplified Potential Energy Clock Model (SPEC) to represent the uniaxial compression yield strength evolution under isothermal aging conditions is evaluated for a widely used epoxy thermoset encapsulation material, 828 DGEBA/DEA. A baseline model calibration is used. We note that this calibration did not consider yield strength behavior close to the glass transition temperature ( T g ), but in this work, the model is exercised in this temperature range to evaluate the ability to predict changes in material response with aging as equilibrium is approached. Some model alterations were needed to remove negative Prony weights in the thermal and bulk relaxation function ( f 1 ), which is chiefly responsible for aging in this analysis, but otherwise, the model was not altered. Model predictions of yield stress evolution are quantitatively different compared with experiments, but the rate of change of yield stress with respect to aging time is in reasonable agreement with respect to experiments for the first 1000 hours of aging. After this aging time, the measured yield stress stops evolving, but the model continues to evolve for several more decades in time. Parametric studies and model alterations are considered to investigate how yield strength evolution predictions are affected by modeling choices. It is clear that the baseline calibration must be re-examined in order to represent the aging data quantitatively.

42 ENGINEERING↗
NASA NTRS1979

An energy-balance model of glaciation cycles

A one dimensional energy balance model is presented which contains a time lagged albedo to account for the delayed dependence of continental ice sheets on temperature; it also includes a smoothing of temperature gradients in the tropics to account for the effect of the Hadley circulation on the strong flattening of meridional temperature profiles there. The model exhibits finite amplitude, sustained free oscillations; these oscillations are triggered by a change in the insulation parameter and occur in the absence of any external forcing. This model behavior strongly suggests the possibility of an almost-intransitive mechanism playing a role in glaciation cycles. This behavior also occurs on shorter time scales which might be relevant to the interannual variability of the atmosphere.

Ghil, M.↗
DOE OSTI2017

Open-Source Integrated Design-Analysis Environment For Nuclear Energy Advanced Modeling & Simulation Final Scientific/Technical Report

The framework created through the Open-Source Integrated Design-Analysis Environment (IDAE) for Nuclear Energy Advanced Modeling & Simulation grant has simplify and democratize advanced modeling and simulation in the nuclear energy industry that works on a range of nuclear engineering applications. It leverages millions of investment dollars from the Department of Energy's Office of Nuclear Energy for modeling and simulation of light water reactors and the Office of Nuclear Energy's research and development. The IDEA framework enhanced Kitware’s Computational Model Builder (CMB) while leveraging existing open-source toolkits and creating a graphical end-to-end umbrella guiding end-users and developers through the nuclear energy advanced modeling and simulation lifecycle. In addition, the work deliver strategic advancements in meshing and visualization for ensembles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Final Energy Savings Analysis of the Proposed NYStretch-Energy Code 2018

This study was conducted by the Pacific Northwest National Laboratory (PNNL) in support of the stretch energy code development led by the New York State Energy Research and Development Authority (NYSERDA). In 2017 NYSERDA developed its 2016 Stretch Code Supplement to the 2016 New York State Energy Conservation Construction Code (hereinafter referred to as “NYStretch-Energy”). NYStretch-Energy is intended as a model energy code for statewide voluntary adoption that anticipates other code advancements culminating in the goal of a statewide Net Zero Energy Code by 2028. Since then, NYSERDA continues to develop the NYStretch-Energy Code 2018 edition (NYSERDA 2018). To support the effort, PNNL conducted energy simulation analysis to quantify the energy savings of proposed commercial provisions of the NYStretch-Energy Code 2018 in New York. The focus of this project is to significantly improve the energy efficiency over existing commercial model energy codes. A key requirement of the proposed stretch code is that it be ‘adoptable’ as an energy code, meaning that it must align with current code scope and limitations, and primarily impact building components that are currently regulated by local building departments. It is largely limited to prescriptive measures, which are what most building departments and design projects are most familiar with. This report describes a set of energy-efficiency measures (EEMs) that demonstrate significant energy savings over ANSI/ASHRAE/IES Standard 90.1-2013 (ASHRAE 2013) across a broad range of commercial building types and all three climate zones in New York. In collaboration with New Building Institute, the EEMs were developed from national model codes and standards, high-performance building codes and standards, regional energy codes, and measures being proposed as part of the on-going code development process. PNNL analyzed these measures using whole building energy models for selected prototype commercial buildings and multifamily buildings representing buildings in New York.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Energy-Storage Modeling: State-of-the-Art and Future Research Directions

Given its physical characteristics and the range of services that it can provide, energy storage raises unique modeling challenges. This paper summarizes capabilities that operational, planning, and resource-adequacy models that include energy storage should have and surveys gaps in extant models. Existing models that represent energy storage differ in fidelity of representing the balance of the power system and energy-storage applications. Modeling results are sensitive to these differences. The importance of capturing chronology can raise challenges in energy-storage modeling. Some models ‘decouple’ individual operating periods from one another, allowing for natural decomposition and rendering the models relatively computationally tractable. Energy storage complicates such a modeling approach. Improving the representation of the balance of the system can have major effects in capturing energy-storage costs and benefits.

25 ENERGY STORAGE↗
DOE OSTI2024

Impacts of Model Building Energy Codes

The Department of Energy (DOE) Building Energy Codes Program (BECP) periodically evaluates national and state-level impacts associated with energy codes in residential and commercial buildings. Pacific Northwest National Laboratory (PNNL), funded by DOE, conducted an assessment of the prospective impacts of national model building energy codes from 2010 through 2040. A previous PNNL study evaluated the impact of the Building Energy Codes Program. A 2016 study looked more broadly at overall code impacts and this report describes the methodology used for the assessment and presents the impacts in terms of energy savings, consumer cost savings, and reduced emissions at the state level and at aggregated levels. In 2021, DOE conducted an interim and limited update to its 2016 study to evaluate potential building code updates using the 2016 methodology. That interim update includes estimated savings resulting from updates to the model energy codes, including the ANSI/ASHRAE/IES Standard 90.1-2016 (ASHRAE 90.1-2016) and 2019 editions, as well as the 2018 and 2021 International Energy Conservation Code (IECC). In 2023, DOE developed a fully updated report that includes code updates (ASHRAE 90.1-2019 and 2021 IECC), as well as additional enhancements and updates, including updated energy prices, annual floorspace additions, state code adoption dates, and emission factors, among others. This current version is another fully updated report that includes code updates (ASHRAE 90.1-2022 and 2024 IECC), as well as additional enhancements and updates, including updated energy prices, state code adoption dates, emission factors, and renewable energy contribution among others. Energy codes follow a three-phase cycle that starts with the development of a new model code, proceeds with the adoption of the new code by states and local jurisdictions, and finishes when the new code is implemented and builders, architects, and engineers are required to comply with the new provisions. The development of new model code editions creates the potential for increased energy savings. After a new model code is adopted, potential savings are realized in the field when new buildings (or additions and alterations) are constructed to comply with the new code. The contributions of all three phases are crucial to the overall impact of codes and are considered in this assessment. Figure ES.1 schematically describes the analysis framework. Energy savings are expressed in terms of energy use intensity (EUI) in the figure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Impacts of Model Building Energy Codes

The U.S. Department of Energy (DOE) Building Energy Codes Program (BECP) periodically evaluates national and state-level impacts associated with energy codes in residential and commercial buildings. Pacific Northwest National Laboratory (PNNL), funded by DOE, conducted an assessment of the prospective impacts of national model building energy codes from 2010 through 2040. A previous PNNL study evaluated the impact of the Building Energy Codes Program; this study looked more broadly at overall code impacts. This report describes the methodology used for the assessment and presents the impacts in terms of energy savings, consumer cost savings, and reduced CO 2 emissions at the state level and at aggregated levels. This analysis does not represent all potential savings from energy codes in the U.S. because it excludes several states which have codes which are fundamentally different from the national model energy codes or which do not have state-wide codes. Energy codes follow a three-phase cycle that starts with the development of a new model code, proceeds with the adoption of the new code by states and local jurisdictions, and finishes when buildings comply with the code. The development of new model code editions creates the potential for increased energy savings. After a new model code is adopted, potential savings are realized in the field when new buildings (or additions and alterations) are constructed to comply with the new code. Delayed adoption of a model code and incomplete compliance with the code’s requirements erode potential savings. The contributions of all three phases are crucial to the overall impact of codes, and are considered in this assessment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Avista’s Shared Energy Economy Model Pilot: A Techno-economic Assessment

As part of the second round of the Washington Clean Energy Fund, Avista Corp received a $3.5 million matching grant in support of a shared energy economy project to test the integration of energy assets–from rooftop solar and battery storage to building energy management systems–that can be shared and used for multiple purposes. The goal of this project is to demonstrate how both the customer and the utility can benefit from this shared energy economy model and demonstrate that the electric grid can become more reliable, efficient, resilient, and flexible. Pacific Northwest National Laboratory was engaged by the U.S. Department of Energy and the Washington State Department of Commerce to work with Avista in assessing the benefits of the shared energy economy model. This report documents the techno-economic assessment of the shared energy economy model, including the definition of use cases and applications, collection and preparation of data and input parameters, development of modeling and optimization methods, and case studies and analysis results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

China Vehicle Fleet Model: Estimation of Vehicle Stocks, Usage, Emissions, and Energy Use - Model Description, Technical Documentation, and User Guide

With fast economic growth, urbanization, and industrialization, China has experienced rapid expansion in the on-road transportation sector in the past three decades. The highway vehicle population in China increased from 1.65 million in 1980 to 208 million in 2017 with an annual average growth rate (AAGR) of about 14% .

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Collisional-energy-cascade model for nonthermal velocity distributions of neutral atoms in plasmas

Nonthermal velocity distributions with much greater tails than a Maxwellian have been observed for radical atoms in plasmas for a long time. Historically, such velocity distributions have been modeled by a two-temperature Maxwell distribution. In this paper, I propose a model based on collisional energy cascade, which has been studied in the field of granular materials. In the collisional energy cascade, a particle ensemble undergoes energy input at the high-energy region, entropy production by elastic collisions among particles, and energy dissipation. For radical atoms, energy input may be caused by the Franck-Condon energy of molecular dissociation or charge-exchange collision with hot ions, and the input energy is eventually dissipated by collisions with the walls. Here, I show that the steady-state velocity distribution in the collisional energy cascade is approximated by the generalized Mittag-Leffler distribution, which is a one-parameter extension of the Maxwell distribution. This parameter indicates the degree of the nonthermality and is related to the relative importance of energy dissipation over entropy production. This model is compared with a direct molecular dynamics simulation for simplified gaseous systems with energy input, as well as some experimentally observed velocity distributions of light radicals in plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

EV-ELM (Electric Vehicle Policies with the Energy Language Model) [SWR-25-156]

Electric Vehicle Policies with the Energy Language Model (EV-ELM) leverages previous work using Large Language Models (LLMs) to find, download, and parse policy information related to energy infrastructure. In this application, we use LLMs to find policy documents related to the permitting and installation of electric vehicle charging infrastructure. This software contains the code to find, download, and parse these documents, while a related data record in the Open Energy Data Initiative (OEDI) will include the resulting output dataset that can be used for downstream analysis. The EV-ELM repository contains code for the EV-ELM project, which focuses on retrieving and processing EV permitting processes using large language models. The project is composed of two pipelines: (1) a web scraping pipeline for discovering and downloading EV permitting documents, and (2) a document parsing and extraction pipeline that processes the downloaded files to produce structured data. The web scraping pipeline is designed to extract relevant information from various websites, while the document parsing pipeline processes and analyzes the extracted documents to derive meaningful insights. Both pipelines depend on the NLR elm repository, which provides essential tools and functionalities for handling and processing the data. The web scraping pipeline is a modified version of the ordinance_gpt example within the elm repository. It has been adapted to fit the specific requirements of the EV-ELM project, ensuring that it effectively captures and processes the necessary information related to EV permitting.

Olson, Reid [National Laboratory of the Rockies (N↗
DOE OSTI2026

Dataset of Generative AI Workload Power Profiles

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model . This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems. The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison. The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment. If you use this dataset, please cite the associated publication: Vercellino et al., “Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning,” arXiv:2604.07345 (2026).

97 MATHEMATICS AND COMPUTING↗