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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integration of a grey-box refrigerated case model in EnergyPlus via Python plugin

Commercial buildings, in particular grocery stores (due mainly to their large refrigeration load), provide opportunities for energy cost reductions. Grocery stores could offer substantial load flexibility to the power grid through participation in demand response programs because of their usage patterns and relatively high energy intensity. This load flexibility could come from modifying the control of heating, ventilation, and air conditioning (HVAC) systems, refrigeration systems, or both. Although estimation of the HVAC system’s load flexibility potential is relatively targeted in the literature, estimating load flexibility of refrigeration systems is nascent and has been a challenge, in part because of the lack of proper simulation tools that capture the dynamics in the refrigeration cases. The existing refrigerated case model within EnergyPlus, a whole building energy simulation program, assumes a constant case temperature throughout the simulation period and does not explicitly model the cycling of the compressor serving the refrigerated case. In addition, it does not encompass modeling of temperatures of the product inside the refrigerated case. This difference between modeled and actual operation can be a barrier to the development of demand control algorithm and accurate analysis of load flexibility potential. In this paper, we present a grey-box model for modeling refrigerated cases in grocery stores, which include medium temperature and low temperature. Four cases are modeled; two are low-temperature closed cases and two are medium-temperature cases with one closed and one open. Data from an experimental facility are used to train and test the models. Results demonstrate the efficacy of the grey-box models in predicting the temperatures. This model is integrated into EnergyPlus to capture the dynamic effects of case temperature on the environment and enhance the calculation of sensible and latent heat exchange with the environment (case credits). These enhancements can be leveraged more broadly to model advanced refrigeration controls such as defrost, develop and test unique algorithms that could affect refrigeration interactions with HVAC, and refine store design for any commercial building with refrigeration.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Species Transport Framework Development in SAM for System-Level Tritium Source Term Analysis

The SAM code is under development as a modern system-level modeling and simulation tool for advanced non–light water reactor safety analyses, with recent efforts to add capabilities to evaluate radiological source term risks in these novel reactor concepts. By leveraging the established system-level multiphysics thermal-hydraulic models in SAM, a framework for tightly coupled species transport modeling has been integrated into the code for engineering-scale source term evaluation. This species transport framework was first applied to the simulation of tritium, which is a well-known source term in conventional light water reactors. Tritium poses a unique risk in salt-cooled reactors, especially those with lithium-bearing salts such as the fluoride salt–cooled high-temperature reactor (FHR) concept, as tritium is generated in the salt coolant in significant quantities due to neutron interactions. A compounding factor is the increased mobility of tritium at high temperatures, which is able to permeate through metals while also potentially being retained in graphite pebbles and structures. Engineering-scale models for the tritium transport pathways in a FHR have been developed using the new species transport framework in SAM. The capabilities are assessed through analytical verification problems and validated with data from a graphite retention experiment. In conclusion, the system-level model is demonstrated by performing an initial estimate of baseline tritium generation and flows in a generic reference SAM FHR model, setting a foundation for future studies of source term transient analysis with the potential for further multiscale and multiphysics integration.

SAM↗
DOE OSTI2024

Jackson County Community Solar Modeling Results [Slides]

Jackson County, via the U.S. Department of Energy's Communities LEAP (Local Energy Action Program) requested support on the topic of Community Solar in their area. NREL completed a technical and financial modeling exercise to determine the potential for developing community solar in the region. The analysis performed determined that the current policy and economic environment, in large part due to Illinois's Solar For All program, is advantageous to the deployment and growth of community solar in Illinois. Across all 6 scenarios modeled, including current minimum and maximum financial incentives, the economics yielded positive net present values for both the project owner and its subscribers. The analysis shown in this presentation is only one of the first steps in deploying community solar and suggests Jackson County should pursue next steps. Such actions could include refining project economics, partnering with local utilities, policy makers, and developers to identify potential projects and outreach opportunities, and educating and engaging community members.

14 SOLAR ENERGY↗
DOE OSTI2021

Visual Modeling for Complex System Valuation: Implementation Guidance

Within the Transactive Systems Program (TSP) at Pacific Northwest National Laboratory (PNNL) the need to incorporate a valuation analysis design early within the research process of transactive energy systems led to development of a valuation methodology. This methodology allows the modeling of economic exchanges within a complex system and supports the evaluation of individual stakeholder economic outcomes in addition to systemwide costs and benefits. The use of visual modeling practices enables the research team to reach common understanding and agreement on the analysis design within the complex system. While this methodology was developed for the valuation of transactive energy systems, it can be applied to any complex system where a granular economic analysis is desired. It allows for the inclusion of equity analyses and ties individual activities and microeconomic outcomes with the systemwide macroeconomic impacts. This document serves as implementation guidance for analysts planning to deploy the methodology within a research study. The appendixes provide specific guidance on how this methodology is deployed within the TSP at PNNL for analysts seeking guidance for deployment within that context.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Numerical Methods to Evaluate Hyperelastic Transducers: Hexagonal Distributed Embedded Energy Converters

Hexagonal distributed embedded energy converters, also known as hexDEECs, are centimeter-scale energy transducers that leverage variable capacitance to generate electricity when their hyperelastic structure is dynamically deformed. To better understand, characterize, and optimize hexDEEC designs, a series of numerical methods and techniques were developed to model the hyperelastic mechanics of hexDEECs, electrostatic properties, and electricity generation characteristics. The numerical methods developed for the hyperelastic structural analysis were corroborated by empirical results from another study, and the models and equations for capacitance, electrostatic forces, and electrical potential energy were derived from fundamental electrostatic equations. These methods and techniques were implemented within the STAR-CCM+ multiphysics software Version 2020.3 (15.06.008) environment. Results from this analysis revealed methodologies and techniques necessary to model the energy converters, which will enable future exploration and optimization of more specific designs and corresponding applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Unwrapping Radiograph Target Disassembly Using Adaptive Machine Learning [Slides]

Advanced analysis methods were developed to determine the electron temperature and density evolution of targets disassembly by intense electron beams at DARHT based on shearing interferometer and shadowgraph measurements. New methods have been developed to spatially and temporally resolve electron velocity and temperature, expanding on recently published first results of Aluminum target disassembly. A surrogate model has been created for use in developing Adaptive Machine Learning analysis techniques. The results will also be used to guide the development of the system and experimental approach to future measurements on DARHT Axis-I and a new system to be deployed on DARHT Axis-II.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Compare Mechanistic Predictions for Doped UO 2 Mechanical Response and Other Properties with Empirical Models and Experimental Measurements

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation program develops predictive capabilities using computational methods for the analysis and design of advanced reactor and fuel cycle systems. This program has been supporting the development of BISON, a high-fidelity, high resolution fuel performance tool at the engineering scale. As part of its development, additional modeling capabilities and improvements have been developed for relevant fuel forms. In this work, a fuel creep deformation model for Cr-doped fuel has been implemented into BISON, along with improvements to the empirical UO 2 fuel creep model based on experimental data and improvements to the radial power factor calculation for doped fuels. This work allows for more accurate simulation analyses for both UO 2 and doped-UO 2 fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A Causal Inference Model Based on Random Forests to Identify the Effect of Soil Moisture on Precipitation

Soil moisture influences precipitation mainly through its impact on land–atmosphere interactions. Understanding and correctly modeling soil moisture–precipitation (SM–P) coupling is crucial for improving weather forecasting and subseasonal to seasonal climate predictions, especially when predicting the persistence and magnitude of drought. However, the sign and spatial structure of SM–P feedback are still being debated in the climate research community, mainly due to the difficulty in establishing causal relationships and the high degree of nonlinearity in land–atmosphere processes. To this end, we developed a causal inference model based on the Granger causality analysis and a nonlinear machine learning model. This model includes three steps: nonlinear anomaly decomposition, nonlinear Granger causality analysis, and evaluation of the quality of SM–P feedback, which eliminates the nonlinear response of interannual and seasonal variability and the memory effects of climatic factors and isolates the causal relationship of local SM–P feedback. We applied this model by using National Climate Assessment–Land Data Assimilation System (NCA-LDAS) datasets over the United States. Here, the results highlight the importance of nonlinear atmosphere responses in land–atmosphere interactions. In addition, the strong feedback over the southwestern United States and the Great Plains both highlight the impacts of topographic factors rather than only the sensitivity of evapotranspiration to soil moisture. Furthermore, the SM–P index defined by our framework is used to benchmark Earth system models (ESMs), which provides a new metric for efficiently identifying potential model biases in modeling local land–atmosphere interactions and may help the development of ESMs in improving simulations of water cycle variability and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Preliminary Sensitivity Analysis for Sensors Impacts on Building Control Performance

This report describes the preliminary sensitivity analysis for sensor impacts on building control performance through the US Department of Energy’s Oak Ridge National Laboratory’s Flexible Research Platform (FRP-2) building. The rooftop unit system provides cooling and heating to the building. The main heating coil is a gas heating coil. Each zone is served by a variable air volume box with an electricity reheat coil. The rooftop unit and variable air volume box controls adopted the practical control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. For sensors, the incipient (time-changing) sensor errors, including bias sensor error and precision sensor error, are the inputs of interest. The outputs are energy consumption and thermal comfort (e.g., the predicted percentage of dissatisfied occupants). The large-scale simulation (3,600 cases) was conducted on a cloud platform by integrating sensor errors and ASHRAE Guideline 36 control sequences into an emulator based on the EnergyPlus simulation program with Python energy management system feature. The surrogate models were developed based on cloud simulation results. The uncertainty analysis showed that the sensor errors substantially affect building energy consumption and thermal comfort. The sensitivity analysis shows a ranking of sensor error impacts for each interested output item (e.g., cooling energy, reheat coil heating energy, predicted percentage of dissatisfied occupants). In FY 2022, sensor locations, types, and costs will be evaluated. The field test in Oak Ridge National Laboratory’s Flexible Research Platform building regarding sensor impacts will also be performed. Finally, a comparative analysis will be conducted based on the field test results and emulator results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Machine Learning Based Software Package for Reproducible Data Analysis for Electron Microscopy

Advances in hardware and computation technologies are enabling large hyperspectral imaging during Electron Microscopy (EM), resulting in datasets being produced at rapidly increasing spatial and temporal resolutions. This presents exciting opportunities for microstructure analysis, but also poses great challenges in data analysis. Under this STTR program, QuesTek Innovations LLC, a leader in the field of integrated computational materials engineering (ICME), collaborated with Lawrence Livermore National Laboratory and Argonne National Laboratory. The team developed an open-source machine learning (ML) powered tool for EM data analysis of multiple systems and data types, and collected EM data for ML model development using the cutting-edge PicoProbe equipment at Argonne National Laboratory.

Lin, Yu↗
DOE OSTI2026

Integrated Reliability and Economic Modeling for Transmission Across Large Regions: A Space Odyssey

Power flow modeling and stability analysis are needed to more-comprehensively assess system reliability but the development of the system portfolios and conditions require use of economic models (e.g., production cost). What are the state of art methods for efficiently linking economic and reliability models to enable examination of multiple snapshots and perform detailed nodal analyses?

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Physics-Informed Learning Machines for Multiscale and Multiphysics Problems (PHILMS) (Technical Report)

The research work at University of California Santa Barbara (UCSB) resulted in several new developments in the areas of scientific machine learning, numerical analysis, and practical methods for data-driven modeling, prediction, reductions, and simulation. Many of the projects were carried out in collaboration with members of the national laboratories at Sandia National Laboratories (SNL), Pacific Northwestern National Laboratories (PNNL), and other institutions. Results included developing new scientific machine learning methods, related theory and mathematical frameworks for analysis and training, data-driven numerical solvers, and related tools and software for scientific computation. During the support period, over 16+ papers were submitted for publication, and 4 open-source software packages were developed and released (available at http://atzberger.org/). In addition, 7+ students and 2 post-docs were mentored in collaboration with the laboratory staff for future careers in academia, government labs, and industry.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Convective shells in the interior of Cepheid variable stars: Overshooting models based on hydrodynamic simulations

Context. Because Cepheid variable stars have long been used as a cosmic benchmark for scaling distances in our Galaxy and beyond, the accuracy of stellar evolution models for Cepheids have wide-reaching effects. However, our understanding of the dynamics in the interiors of these physically complex stars is limited. Aims. Our goal is to provide a detailed multi-dimensional picture of hydrodynamic convection and convective boundary mixing in the interior of Cepheids. Methods. Using the Modules for Experiments in Stellar Astrophysics (MESA), we studied the structure of intermediate-mass stars that cross the instability strip. Then, we performed two-dimensional hydrodynamic simulations of six stars with the fully compressible Multidimensional Stellar Implicit Code (MUSIC). Our simulations did not model the radial pulsations but focused on the interior structure of this family of stars. We developed and applied a new statistical analysis to examine convection and convective boundary mixing in the interior of these stellar simulations. Results. Based on a grid of MESA models, we demonstrated that a common structure for intermediate mass Cepheids includes an interior convective shell as well as a thin outer convective envelope. Using the extreme value theory approach to analyze our MUSIC simulation data, we found that overshooting above the convective shell fills the space between these convectively unstable layers. We developed a new statistical analysis that provides a clearer picture of how overshooting fills this layer; it also allowed us to formulate a detailed comparison between overshooting above and below the convective shell. Our analysis effectively decomposes the overshooting layer into two layers: a weak overshooting layer and a strong overshooting layer. Statistically, this is accomplished by decomposing the strongly non-Gaussian probability density function into a mixture of gamma distributions. Using our mixture model, we showed that the ratio of overshooting lengths above and below the convective shell depends directly on the radial extent of the convective shell as well as its depth in the star. We proposed a new form for the diffusion coefficient that addresses the need for overlapping overshooting layers between convective shells. We introduced the idea of a “super-mixing layer” where overshooting from both the convective shell and the convective envelope results in efficient mixing and could be viewed as merging the two adjacent convective zones.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Produced Water-Economic, Socio, Environmental Simulation Model (PW-ESEim) Model: Proof-of-Concept for Southeastern New Mexico

A proof-of-concept tool, the Produced Water-Economic, Socio, Environmental Simulation model (PW-ESESim), was developed to support ease of analysis. The tool was designed to facilitate head-to-head comparison of alternative produced water source, treatment, and reuse water management strategies. A graphical user interface (GUI) guides the user through the selection and design of alternative produced water treatment and reuse strategies and the associated health and safety risk and economic benefits. At the highest conceptual level, alternative water strategies include the selection of a source water (locally or regionally available produced water), treatment strategy (pre-treatment, physical, chemical, biological, desalination, and post-treatment processes) and product water purpose (e.g., irrigation, industrial processing, environmental). After selection of these details, the PW-ESESim output a number of key economic, societal, environmental, public/ecological health and safety metrics to support user decision-making; specific examples include, cost of treatment, improvements in freshwater availability, human and ecologic health impacts and growth in local jobs and the economy. Through the simulation of different produced water treatment and management strategies, tradeoffs are identified and used to inform fit-for-purpose produced water treatment and reuse management decisions. While the tool was initially designed using Southeastern New Mexico (Permian Basin) as a case study, the general design of the PW-ESESim model can be extended to support other oil and gas regions of the U.S.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

17 WIND ENERGY↗
DOE OSTI2024

OpenFAST Modeling of the T-Omega Wind Floating Offshore Wind Turbine System

To reduce the cost of offshore wind energy through a more efficient design of the floating support structure, T-Omega Wind developed a novel lightweight and shallow-draft platform concept that aims to achieve a wave-following behavior without resonance amplification. The rotor and generator are carried by four tower legs with each leg supported by a shallow-draft float at the base. In preparation for a more detailed analysis, a coupled aero-hydro-elastic model of the proposed concept is developed in OpenFAST. The detailed modeling approach is presented, including a novel application of the SubDyn substructure dynamics module of OpenFAST to approximate the loads on the axle tube at the tower top from the rotor hub bearings. Preliminary results obtained with the OpenFAST model by rigidifying the substructure and blades indicate that the original sizing of the design can lead to large hub acceleration in the axial/surge and pitch directions due to platform-pitch motion. The high axial acceleration also potentially leads to large bending moments in the four tower legs. To address these issues, an updated design is developed with, among other changes, increased float spacing at the tower base and improved shear-transmitting geometry of the tower legs. The new design suggests significantly reduced extreme hub accelerations under the same conditions even with structural flexibility and will be further analyzed in the future through full aero-hydro-servo-elastic simulations.

FOWT↗
DOE OSTI2024

OpenFAST Modeling of the T-Omega Wind Floating Offshore Wind Turbine System

To reduce the cost of offshore wind energy through a more efficient design of the floating support structure, T-Omega Wind developed a novel lightweight and shallow-draft platform concept that aims to achieve a wave-following behavior without resonance amplification. The rotor and generator are carried by four tower legs with each leg supported by a shallow-draft float at the base. In preparation for a more detailed analysis, a coupled aero-hydro-elastic model of the proposed concept is developed in OpenFAST. The detailed modeling approach is presented, including a novel application of the SubDyn substructure dynamics module of OpenFAST to approximate the loads on the axle tube at the tower top from the rotor hub bearings. Preliminary results obtained with the OpenFAST model by rigidifying the substructure and blades indicate that the original sizing of the design can lead to large hub acceleration in the axial/surge and pitch directions due to platform-pitch motion. The high axial acceleration also potentially leads to large bending moments in the four tower legs. To address these issues, an updated design is developed with, among other changes, increased float spacing at the tower base and improved shear-transmitting geometry of the tower legs. The new design suggests significantly reduced extreme hub accelerations under the same conditions even with structural flexibility and will be further analyzed in the future through full aero-hydro-servo-elastic simulations.

ENGINEERING,WIND ENERGY↗
DOE OSTI2020

Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic

In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.

30 DIRECT ENERGY CONVERSION↗