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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 433 records · Page 24

Validating Simulated Models of Energy Consumption by a Battery Electric Motorcoach: A real-world deployment in a harsh climate.

Many efforts have been made to simulate energy consumption of battery electric buses (BEBs) to optimize their deployment into existing fleets. The models produced, however, are rarely validated against real-world consumption data, limiting their generalizability and widespread application to fleets around the US. Furthermore, a major concern specific to BEBs is the effects of harsh climates on their performance. We build upon the state-of-the-art energy consumption modeling techniques developed for BEBs and apply them to a unique geographic context and a unique electrified vehicle. This geography, climate, and vehicle further the existing understanding of the factors affecting medium- and heavy-duty electric vehicles (MHDEVs) by allowing for new relationships to be tested and by assessing the generalizability of known relationships to new contexts. We find that temperature is less predictive of energy consumption for the battery electric motorcoach (BEM) in the case study environment than it is for BEBs in other studies. A mitigating factor that we presume to be working on the relationship between temperature and energy consumption is the fact that the BEM route does not stop between origin and destination to exchange passengers, and in turn, conditioned cabin air. Our model also incorporates wind speed and direction relative to travel, which is a novel contribution of our methodology. Results from our study are helpful for transit service planners, fleet operators, and logistics firms for improving their ability to predict performance of potential deployments of MHDEVs into existing operations.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗

Hydropower representation in water and energy system models: a review of divergences and call for reconciliation

Abstract Reservoir-based hydropower systems represent key interactions between water and energy systems and are being transformed under policy initiatives driven by increasing water and energy demand, the desire to reduce environmental impacts, and interacting effects of climate change. Such policies are often guided by complex system models, whereby divergence in system representations can potentially translate to incompatible planning outcomes, thereby undermining any planning that may rely on them. We review different approaches and assumptions in hydropower representation in water and energy systems. While the models and issues are relevant globally, the review focuses on applications in California given its extensive development of energy and water models for policy planning, but discusses the extent to which these observations apply to other regions. Structurally, both water-driven and energy-driven management models are similar. However, in energy models, hydropower is often represented as a single-priority output. Water management models typically allocate water for competing priorities, which are generally uninformed by dynamic electricity load demand, and often result in a lower priority for hydropower. In water models, constraints are increasingly resolved for non-energy components (e.g. inflow hydrology and non-energy water demand); few analogues exist for energy models. These limitations may result in inadequate representations of each respective sector, and vastly different planning outcomes for the same facilities between the two different sectors. These divergent modeling approaches manifest themselves in California where poorly reconciled outcomes may affect decisions in hydropower licensing, electricity grid flexibility and decarbonization, and planning for environmental water. Fully integrated water-energy models are computationally intensive and specific to certain regions, but better representation of each domain in respective efforts would help reconcile divergences in planning and management efforts related to hydropower across energy and water systems.

Rheinheimer, David E. (ORCID:0000000315259069)↗

Model America: Data and Models for every U.S. Building

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1". Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2". This dataset contains OSM and IDF zip files for every U.S. county. Each zip file contains the generated buildings from that county. The .csv input data contains the following data fields: 1. ID - the Unique Building Identifier (UBID), generated using the Pacific Northwest National Laboratory (PNNL) BuildingID framework 2. Centroid - building center location in latitude/longitude (from Footprint2D) 3. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 4. State_abbr - state name 5. Area - estimate of total conditioned floor area (ft2) 6. Area2D - footprint area (ft2) 7. Height - building height (ft) 8. NumFloors - number of floors (above-grade) 9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 10. CZ - ASHRAE Climate Zone designation 11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 12. Standard - building vintage This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). Update (September 23, 2025): We corrected the ID field in all state-level.csv input files to ensure one-to-one consistency with the corresponding .osm and .idf output files. The schema and file structure are unchanged; only the values in the ID column were modified. No files were added or removed, and the .zip bundles (containing .osm / .idf) are unchanged. The corrected .csv inputs were re-extracted in March 2025 from the original data generated ~ 2021 (Theta supercomputer runs), and published here to align input IDs with model outputs. Update (September 6, 2026): The Model America dataset was updated to replace the previous building ID field with the Unique Building Identifier (UBID), using the Pacific Northwest National Laboratory (PNNL) BuildingID framework. UBIDs provide standardized, location-based identifiers for individual building footprints and improve interoperability with other building and geospatial datasets. The data files containing the previous building identifiers were updated to include UBIDs. This update standardizes building identification; the underlying Model America building characteristics and energy simulation results were not recomputed as part of this update.

54 ENVIRONMENTAL SCIENCES↗

Figures and scripts from Taylor, et al's "A multi-scale calibration approach for process-oriented aggregated building energy demand models"

This dataset includes all figures that were generated in the course of writing Taylor, et al's "A multi-scale calibration approach for process-oriented aggregated building energy demand models." It includes the figures and tables from the paper, a large number of additional figures that were not included in the paper, and the underlying data and R scripts that were used to generate the figures.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Model America - data and models of every U.S. building

The 5-year goal of the 'Model America' concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) - OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus(v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy's (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). 'Model America - data and models of every U.S. building. [Data set].' Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

24 POWER TRANSMISSION AND DISTRIBUTION↗

Multiphysics Meshfree Degradation Modeling of Energy Storage Materials with Kernel Enrichment

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. Reference: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

25 ENERGY STORAGE↗

High-Level Model Articulation with BuildingSync and OpenStudio

The use of the BuildingSync schema for describing the contents of buildings is becoming more common with its recent integration into the Audit Template tool as well as ASHRAE’s Building Energy Quotient (bEQ) web portal. Although BuildingSync was initially created to store and transfer data related to building energy audits (as defined by ASHRAE Standard 211), it has since been expanded to store the data needed to articulate fully defined physics-based building energy models. BuildingSync combined with abstracted high-level input methods defined in OpenStudio’s Standards project and the newly developed BuildingSync gem allows BuildingSync eXtensible Markup Language (XML) (Bray, Paoli, Sperberg-McQueen, Maler, Eve (Sun Microsystems, & Francois, 2008) document to be converted into OpenStudio models. Automatic generation of a building energy model from data collected during an audit 1) eliminates the need to separately generate an energy model, 2) improves consistency between model generation, and 3) simplifies evaluation of various energy efficiency measures. This manuscript will discuss this open-source project, including: development of the validation infrastructure necessary to provide formalized expectations of informational requirements for BuildingSync documents; development of the BuildingSync gem for translation of BuildingSync documents to OpenStudio models as well as example implementations; and finally, the manuscript will elaborate on the advantages and disadvantages of using high-level models generated from BuildingSync as surrogates to detailed models.

30 DIRECT ENERGY CONVERSION↗

Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuels High Impact Problem: Coordinate Multiscale U 3 Si 2 Modeling

Since the events at the Fukushima-Daiichi nuclear power plant in March 2011 significant research has unfolded at national laboratories, universities and other institutions into alternative materials that have potential enhanced accident tolerance when compared to traditional \uo~fuel zircaloy clad fuel rods. One of the potential replacement fuels is uranium silicide (\usi) for its higher thermal conductivity and uranium density. The lower melting temperature is of potential concern during postulated accident conditions. Another disadvantage for \usi~ is the lack of experimental data under power reactor conditions. Due to the aggressive development schedule for inserting some of the potential materials into lead test assemblies or rods by 2022~\cite{bragg-sitton_2014} multiscale multiphysics modeling approaches have been used to provide insight into these materials. \\ \noindent The purpose of this letter report is to highlight the multiscale modeling effort for \usi~fuel as part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Accident Tolerant Fuel (ATF) High Impact Problem (HIP). This fiscal year two new models for \usi~fuel have been incorporated into the BISON fuel performance code~\cite{Williamson2012} based upon lower length scale simulations using the GRASS-SST rate theory code. The models are briefly described in the following sections.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Google/Makani Energy Kite Modeling (CRADA CRD-18-00569 Final Report)

In support of Makani’s energy kite development program, Makani engaged with NREL to develop, verify, and document a multiphysics engineering model of a megawatt-scale tethered energy kite (named KiteFAST). After the original development of KiteFAST was completed, Makani further engaged NREL to develop a software enhancement of KiteFAST to enable the dynamics modeling of an energy kite tethered to a floating offshore platform (named KiteFAST-OS). Before this project, the capability to model the aero-hydro-servo-elastic dynamics of an airborne wind energy (AWE) system with electricity generation on the flying device (fly gen) and crosswind flight operation did not exist. Such physics-based modeling capability is needed for loads analysis, structural design, and certification of energy kites. Throughout the project, an exclusive license kept KiteFAST and KiteFAST-OS available only to Makani, NREL, and its counterparts. With the ending of Makani, the KiteFAST and KiteFAST-OS software were merged into a single code base (named KiteFAST), the exclusive license was terminated, and the source code, documentation, and example models were released publicly.

17 WIND ENERGY↗

Prototype-Wise Sensitivity Analysis of Urban Building Energy Simulation Surrogate Modeling Accuracy

Urban Building Energy Modeling (UBEM) is an important reference for urban energy-related policymaking. Because of the significant impact of urban microclimates on the energy simulation, UBEM requires simulations of many microclimate-prototype pairs. Surrogate modeling is commonly used to reduce the cost of simulation computations. In UBEM surrogate modeling, it is important to determine the percentage of microclimates related to a prototype used for generating surrogate model training data. This study analyzes the prototype-wise variations and sensitivities of surrogate model estimation accuracy to the microclimate sampling ratios. The results of the study can help determine the number of simulations used for generating surrogate modeling data, avoid redundant simulations, and reduce the computational cost for UBEM surrogate modeling and its time.

Pan, Xiyu↗

Climate sensitivity with a seasonal cycle energy balance model

The sensitivity of climate which may have a local maximum as the ice cap passes through a midlatitude region where the atmosphere's transport efficiency varies strongly with latitude is examined. This behavior, found in a two level primitive equations climate model forced with annual mean insolation, was reproduced in an energy balance model (EBM) by making the diffusion coefficient a function of latitude. The two level seasonally varying EBM was applied and the global mean surface temperature vs. solar constant for this model are shown and two regions of enhanced sensitivity appear. The snowcover distributions around the year for three cases are shown.

Suarez, M. J.↗

Application of machine learning to evaluating and remediating models for energy and environmental engineering

Machine learning (ML) algorithms have been increasingly successful in their applications to solve energy and environmental engineering problems. ML algorithms have the advantage of being able to solve highly nonlinear issues effectively. Furthermore, considering the limited sample size of data collected in energy and environmental engineering, obtaining a ML model with reasonable accuracy is simple. Unfortunately, the vast majority of the current applications of ML algorithms lack effective screening of dominant factors and comprehensive model validation, which weakens the predictive ability of the models. The present study takes the minimum miscible pressure (MMP) of CO 2 - oil systems as an example. It establishes a systematic and robust predictive model to address this issue. Based on 147 sets of slim tube tests, the predictive models of the MMPs are investigated by application of eight ML algorithms. The paper concludes that most of the published ML models in the field of energy and environmental engineering prediction are not reliable. Furthermore, it addresses the main reasons for the poor performance of some predictive models built by ML and provides guidelines on how to make such models robust. Further, to the best of our knowledge, this is the first study to point out the defects of current ML modeling methods and propose countermeasures for their application in energy and environmental engineering problems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Renewable Thermal Energy Systems: Modeling Developments and Future Directions (Report 3)

Increasing global energy consumption, increasing population, expansion of all types of produced goods and food, and the climate crisis have necessitated increasing the share of renewable heat in industry, which is vital for both decarbonization. The aim of this work is to evaluate current and prospective renewable heat technologies in stand-alone and hybrid configurations, together with their technical capabilities, performance, and cost. Renewable thermal energy systems (RTES) for buildings and industrial applications have been investigated using techno-economic analysis to determine their potential economic and environmental impacts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Modeling the Energy Use of a Connected and Automated Transportation System (Poster)

Early research points to large potential impacts of connected and automated vehicles (CAVs) on transportation energy use - dramatic savings, increased use, or anything in between. Due to a lack of suitable data and integrated modeling tools to explore these complex future systems, analyses to date have relied on simple combinations of isolated effects. This poster proposes a framework for modeling the potential energy implications from increasing penetration of CAV technologies and for assessing technology and policy options to steer them toward favorable energy outcomes. Current CAV modeling challenges include estimating behavior change, understanding potential vehicle-to-vehicle interactions, and assessing traffic flow and vehicle use under different automation scenarios. To bridge these gaps and develop a picture of potential future automated systems, NREL is integrating existing modeling capabilities with additional tools and data inputs to create a more fully integrated CAV assessment toolkit.

30 DIRECT ENERGY CONVERSION↗

Comparison of different simulation programs in modeling the energy savings of cool roofs

The Roof Savings Calculator is a web-based application first developed in 2010 that uses detailed building energy simulations to calculate the energy savings of cool roofs for different building types in over 200 U.S. locations. Although modeling the heat flows of a roof may be straightforward at first glance, modeling the energy savings due to changes in the roof albedo is complicated because it involves modeling the interzone heat transfer between the attic and the conditioned space below. Since direct measurement of energy savings is also problematic, the authors have decided to compare what several independent detailed simulation programs show for the energy savings from cool roof strategies.

Huang, Yu (Joe)↗

Atomistic modeling of energy band alignment in CdSeTe surfaces

Atomistic modeling based on Desnity Functional Theory (DFT)- 1/2 method coupled with surface Green’s function has been employed to investigate the energy band alignment results in cadmium selenium telluride (CdSeTe) surfaces. The structural and electronic properties of the bulk ternary alloy were established before cleaving the CdSeTe low index surface facets. The dependency of atomic-scale electronic features on different plane orientations was explored by studying the energy band alignment diagrams in the unreconstructed and reconstructed surfaces. While the low index CdSeTe surface geometry reconstructions replicate the CdTe surface geometries, the energy band alignment features of the unreconstructed and reconstructed CdSeTe surfaces differ from those observed in CdTe low index surfaces. The structurally relaxed reconstructed CdSeTe surface results in purely flat bands as opposed to an internal cusp feature observed in CdTe surfaces. The presence of an internal energy cusp in CdTe(111) surfaces is expected to play a key role in enhancing the hole charge transport towards the back of CdTe-only solar cell device and the absence of such feature in CdSeTe surface may explain one of the reason for the lower performance of CdSeTe-only solar cell seen experimentally.

36 MATERIALS SCIENCE↗

High-energy radiation damage in zirconia: Modeling results

Zirconia is viewed as a material of exceptional resistance to amorphization by radiation damage, and consequently proposed as a candidate to immobilize nuclear waste and serve as an inert nuclear fuel matrix. Here, we perform molecular dynamics simulations of radiation damage in zirconia in the range of 0.1–0.5 MeV energies with account of electronic energy losses. We find that the lack of amorphizability co-exists with a large number of point defects and their clusters. These, importantly, are largely isolated from each other and therefore represent a dilute damage that does not result in the loss of long-range structural coherence and amorphization. We document the nature of these defects in detail, including their sizes, distribution, and morphology, and discuss practical implications of using zirconia in intense radiation environments.

36 MATERIALS SCIENCE↗