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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Thermal Cooling Water Datasets for Electric Sector Modeling

This spreadsheet contains data inputs associated with representations of water (i.e., use, supply, costs) for thermo-electric based production. Values in this spreadsheet have been used to support multiple transmission-related planning studies, using models such as Regional Energy Deployment System Model (ReEDS). More information about associated studies can be found in Miara et al., 2019 (DOI: 10.1021/acs.est.9b03037); Cohen et al., 2022 (DOI: 10.1016/j.apenergy.2022.119193); and Cohen et al., 2024 (DOI TBD). Within these transmission-related studies, the values present in this spreadsheet are assigned for simulation of both existing units as well as new capacity build-outs. A summary of worksheets' content as well as associated sources are captured below.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

The Distribution System Operator with Transactive (DSO+T) Study

The Distribution System Operator with Transactive (DSO+T) study investigates the engineering and economic performance of a transactive energy retail market coordinating a high penetration of customer-side flexible energy assets. The study seeks to answer whether such an implementation is cost effective for customers, recovers sufficient revenue for DSOs, and is equally applicable and beneficial to a range of flexible asset types, renewable generation scenarios, and market assumptions. Using a highly interdisciplinary co-simulation and valuation framework, this assessment encompasses the entire electrical delivery system from bulk system generation and transmission, through the distribution system, to the modeling of individual customer buildings and flexible assets (including heating, ventilation, and air conditioning [HVAC] units, water heaters, batteries, and electric vehicles). The study exercises a transactive energy retail market coordination scheme designed to integrate with an existing day-ahead and real-time competitive wholesale electricity market. Software decision-making agents are designed for the retail market operator as well as various price-responsive flexible assets. The engineering and economic performance of the transactive energy scheme is studied for two separate flexible asset deployments: flexible loads (HVAC units and residential water heaters) and behind-the-meter batteries. The results of each transactive case are compared to a business-as-usual case. These cases are subject to two different renewable generation scenarios, a moderate renewable generation scenario, representative of current levels of renewable generation deployment, and a future high renewables scenario, including the increased deployment of rooftop solar photovoltaic and electric vehicles. The transactive coordination scheme is shown to produce effective and stable control and decrease peak loads 9–15%. The resulting annual demand flexibility provides net economic savings of $3.3–5.0B per year for a region the size of Texas. Detailed analysis shows that net benefits were seen for a range of distribution system operator, customer, and flexible asset types. Both participating customer (with transactive flexible assets) and nonparticipating customers (with nonflexible assets) see reductions in annual utility bills and net annual energy expenses in the range of 10–16%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Distribution System Operator with Transactive (DSO+T) Study: Volume 1 (Main Report)

The Distribution System Operator with Transactive (DSO+T) study investigates the engineering and economic performance of a transactive energy retail market coordinating a high penetration of customer-side flexible energy assets. The study seeks to answer whether such an implementation is cost effective for customers, recovers sufficient revenue for DSOs, and is equally applicable and beneficial to a range of flexible asset types, renewable generation scenarios, and market assumptions. Using a highly interdisciplinary co-simulation and valuation framework, this assessment encompasses the entire electrical delivery system from bulk system generation and transmission, through the distribution system, to the modeling of individual customer buildings and flexible assets (including heating, ventilation, and air conditioning [HVAC] units, water heaters, batteries, and electric vehicles). The study exercises a transactive energy retail market coordination scheme designed to integrate with an existing day-ahead and real-time competitive wholesale electricity market. Software decision-making agents are designed for the retail market operator as well as various price-responsive flexible assets. The engineering and economic performance of the transactive energy scheme is studied for two separate flexible asset deployments: flexible loads (HVAC units and residential water heaters) and behind-the-meter batteries. The results of each transactive case are compared to a business-as-usual case. These cases are subject to two different renewable generation scenarios, a moderate renewable generation scenario, representative of current levels of renewable generation deployment, and a future high renewables scenario, including the increased deployment of rooftop solar photovoltaic and electric vehicles. The transactive coordination scheme is shown to produce effective and stable control and decrease peak loads 9–15%. The resulting annual demand flexibility provides net economic savings of $3.3–5.0B per year for a region the size of Texas. Detailed analysis shows that net benefits were seen for a range of distribution system operator, customer, and flexible asset types. Both participating customer (with transactive flexible assets) and nonparticipating customers (with nonflexible assets) see reductions in annual utility bills and net annual energy expenses in the range of 10–16%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2002

Software for Processing Flight and Simulated Data of the ATIC Experiment

ATIC (Advanced Thin Ionization Calorimeter) is a balloon borne experiment designed to measure the cosmic ray composition for elements from hydrogen to iron and their energy spectra from approx.50 GeV to near 100 TeV. It consists of a Si-matrix detector to determine the charge of a CR particle, a scintillator hodoscope for tracking, carbon interaction targets and a fully active BGO calorimeter. ATIC had its first flight from McMurdo, Antarctica from 28/12/2000 to 13/01/2001. The ATIC flight collected approximately 25 million events. A C++-class library for building different programs for processing flight and simulated data of the ATIC balloon experiment is described. This library is compatible with the ROOT-system and includes classes and methods for solving a number of problems as the following: Reading data files in different formats (raw-data format, ROOT-format, ASCII-format, different formats for simulated data); Transferring all these formats to the only inner format of the library; Reconstruction of trajectories of primary particles with BGO calorimeter only. The Monte-Carlo simulations with GEANT code were used to obtain the basic tables for computing error corridors and chi(sup 2)-values for the trajectories. Obtaining error corridors for searching for signal of primary particle in the Si-matrix; Searching for hit of primary particle in the Si-matrix with using of error corridor and other criteria (chi(sup 2)-values, agreement between signals in Si-matrix and in the upper layer of scintillator and others); Determination of charge of primary particle; Determination of energy deposit in BGO calorimeter.

Panov, A. D.↗
DOE OSTI2025

Convergence and Quantum Advantage of Trotterized MERA for Strongly-Correlated Systems

Strongly-correlated quantum many-body systems are difficult to study and simulate classically. We recently proposed a variational quantum eigensolver (VQE) based on the multiscale entanglement renormalization ansatz (MERA) with tensors constrained to certain Trotter circuits. Here, we determine the scaling of computation costs for various critical spin chains which substantiates a polynomial quantum advantage in comparison to classical MERA simulations based on exact energy gradients or variational Monte Carlo. Algorithmic phase diagrams suggest an even greater separation for higher-dimensional systems. Hence, the Trotterized MERA VQE is a promising route for the efficient investigation of strongly-correlated quantum many-body systems on quantum computers. Furthermore, we show how the convergence can be substantially improved by building up the MERA layer by layer in the initialization stage and by scanning through the phase diagram during optimization. For the Trotter circuits being composed of single-qubit and two-qubit rotations, it is experimentally advantageous to have small rotation angles. We find that the average angle amplitude can be reduced considerably with negligible effect on the energy accuracy. Benchmark simulations suggest that the structure of the Trotter circuits for the TMERA tensors is not decisive; in particular, brick-wall circuits and parallel random-pair circuits yield very similar energy accuracies.

Miao, Qiang [Duke Quantum Center, Duke University,↗
DOE OSTI2023

Reducing Grid Costs while Abating Emissions: Opportunities for Flexible Building Loads

This study evaluates the value of technology-agnostic, shiftable flexible building loads in modeled 2030 and 2040 U.S. grids for four types of customers under three potential aggregated distributed energy resources programs. The value examined includes monetary value from providing grid services (e.g., energy, capacity, flexibility reserve, regulation reserve, contingency reserve) and from reducing greenhouse gas emissions. By comparing more than 845,164,800 simulated shifting opportunities, the study finds that the timing of consumption is critical for profit-driven customers. A program that is activated for 30 critical hours of system operation can result in to $73 per year in revenue for 1 kWh of shiftable load. Emission reduction, on the other hand, is best accumulated through a year-round program: 1 kWh of shiftable load can lead to up to 487.9 kg CO2e carbon reduction per year. The report also provides detailed insights on the trade-off between revenue and emissions, regional variation, short- versus medium- term value, and impacts from various building flexibility parameters, such as shifting window and dissipation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Data Reliability in BIM and Performance Analytics: A Survey of Contemporary AECO Practice

As awareness around building energy consumption increases, practitioners are encouraged to consider performance aspects regarding the built environment more closely and find ways to improve its efficiency. Improvements in building information modeling (BIM) and building performance simulation (BPS) tools present opportunities to facilitate information communication with a wider range of stakeholders. The building sector can benefit from the integration of performance informatics; however, there has been limited success in utilizing available technologies that promote data integration and management in favor of enriching our knowledge and understanding of buildings as artifacts of information. This phenomenon was investigated by conducting a survey, together with a review of relevant literature, to depict the relevant challenges and opportunities for the architecture, engineering, construction, and owner-operated (AECO) industry, as it undergoes digital transformation, as well as the working practices that have formed around them. It is argued that the current tools available to practitioners do not support effective data serialization between design and analytics processes, affecting the collaboration between team members. Lastly, a series of functional goals are proposed to support a higher level of reliability in the ways information is mobilized, by rethinking the technologies and methods for organizing information systems.

42 ENGINEERING↗
DOE OSTI2021

Surrogate Model of Flexible Research Platform EnergyPlus Models to Enable Sensitivity Analysis

This letter report describes the surrogate models developed from the EnergyPlus model of Oak Ridge National Laboratory’s Flexible Research Platform. Two data-driven black-box models were developed, and the outputs of the surrogate models were compared with the EnergyPlus model. The two models developed are a multilayer perceptron deep learning model, and a long short-term memory (LSTM) neural network model. The three factors for selecting the black-box models are scalability, computation time, and accuracy. A total of 107 input variables were the dominant variables in determining the outputs of building energy consumptions and thermal comfort. A total of 54 output variables were identified as the prediction targets, including the system- and zone-level outputs. The large set of the simulation cases were generated by integrating sensor errors into an emulator based on EnergyPlus and Python EMS, which includes advanced control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. The surrogate models were developed based on a set of large-scale simulation runs (i.e., 4,000 runs) on a cloud platform. The comparison analysis shows that the two black-box models had good accuracy for predicting new outputs for sensitivity analysis using the root mean square error metric. As a next step, the developed surrogate models will be used to perform sensitivity analysis for different sensor impacts (e.g., sensor types, sensor locations).

42 ENGINEERING↗
DOE OSTI2024

Is a Generator the Only Solution When the Grid Fails? Optimizing Systems for Resiliency and Carbon Reduction: Preprint

Traditionally, buildings are dependent on utility infrastructure, and when a grid failure happens, end users rely on the closest source of energy storage to sustain operation until power is restored. For buildings, that typically means using an electric generator. This electric generator either uses on-site energy storage such as fossil fuels in a tank or a gas connection which is, in turn, tied to gas wells—also a form of energy storage. Generators are popular for their ease of implementation and low capital costs; however, they have limited value outside of disruptions, and they are a source of scope 1 emissions, or direct greenhouse gas emissions from sources controlled by the building owner. In contrast, some power generation and storage systems, such as photovoltaic (PV) panels and battery energy storage systems (BESS), can serve the same purpose during grid disruptions while presenting advantages outside of power failure. This paper explores methods for storing and converting energy on-site to increase building resiliency, focusing on solutions that minimize scope 1 emissions. We analyze the cost and carbon impacts of energy efficiency measures, PV arrays, and BESS, with and without generators, in a simulation test case. We find significant benefits can be achieved both during and outside of power failure events when designing systems that integrate the on-demand capability of generators, the low carbon energy supplied by PV, and the storage capabilities of BESS. Specifically, adding even minimal BESS and PV can result in downsizing the generator, increasing generator efficiency and requiring less fuel.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2022

Resilience Metrics for Building-Level Electrical Distribution Systems with Energy Storage

The energy system infrastructure that delivers power to a building's loads needs to be resilient to withstand and recover from extreme outages (e.g., grid faults that leave millions of people without power during severe weather events). Building-level electrical distribution systems (BEDSs) distribute power from a building's energy sources - including the grid, solar photovoltaic (PV) panels, and batteries - to its loads, including lighting, HVAC, and plug loads. BEDS with storage can provide resilience by distributing local electricity supply to critical loads during an outage. Quantitative metrics are needed to assess the resilience improvements associated with new BEDS and storage system technologies. In this paper, we apply an existing metric, the probability of outage survival curve (POSC), to BEDS with storage and propose novel metrics that improve upon POSC. Through a simulation-based case study, we demonstrate how these metrics are impacted by the BEDS design and how they can be used to design a resilient system.

buildings↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Transformative Building Envelope Retrofit Using Insulation-Inflatable Walls Assisted by Automation

This work highlights the effort to develop a low-cost envelope retrofit for homes built prior to the implementation of energy conservation measures established by the Department of Energy. Inflatable structures were constructed to conform to the exterior envelope (a corner wall construction with a fenestration was used as a prototype). The structures were then filled with spray polyurethane foam insulation. The flexibility in construction and the ability to fill with spray polyurethane foam insulation were demonstrated. In addition, hygrothermal simulations showed that the installation of these systems does not compromise the moisture durability of uninsulated and insulated building envelopes across eight climate zones from hot humid to subarctic. Preliminary cost estimates, based on retail pricing, of the inflatable retrofit including 3 inches of spray foam insulation (insulation value of approximately R 20) is $\$ 4.25$ per square foot uninstalled. The installed cost was calculated using two labor rates, the labor rate for the installation of a weather resistive barrier and the installation of spray foam insulation. Aggregating those values into one labor rate results in an installed cost between $\$ 11$ and $\$ 28$ per square foot. The low end includes the installation of vinyl siding as the exterior cladding. The higher value includes an exterior stucco finish.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Resilience Metrics for Building-Level Electrical Distribution Systems with Energy Storage: Preprint

The energy system infrastructure that delivers power to a building's loads needs to be resilient, such that it can withstand and recover from extreme outages (e.g., grid faults that leave millions of people without power during severe weather events). Building-level electrical distribution systems (BEDSs) distribute power from a building's energy sources - including the grid, solar photovoltaic (PV) panels, and batteries - to its loads, including lighting, HVAC, and plug loads. BEDS with storage can provide resilience by distributing local electricity supply to critical loads during an outage. Quantitative metrics are needed to assess the resilience improvements associated with new BEDS and storage system technologies. In this paper, we apply an existing metric, the probability of outage survival curve (POSC), to BEDS with storage and propose a set of novel metrics that improve upon POSC. Through a simulation-based case study, we demonstrate how these metrics are impacted by the BEDS design and how they can be used to design a resilient system.

buildings↗
DOE OSTI2024

Scalable Implicit Solvers with Dynamic Mesh Adaptation for a Relativistic Drift-Kinetic Fokker–Planck–Boltzmann Model

In this work we consider a relativistic drift-kinetic model for runaway electrons along with a Fokker–Planck operator for small-angle Coulomb collisions, a radiation damping operator, and a secondary knock-on (Boltzmann) collision source. Here, we develop a new scalable fully implicit solver utilizing finite volume and conservative finite difference schemes and dynamic mesh adaptivity. A new data management framework in the PETSc library based on the p4est library is developed to enable simulations with dynamic adaptive mesh refinement (AMR), distributed memory parallelization, and dynamic load balancing of computational work. This framework and the runaway electron solver building on the framework are able to dynamically capture both bulk Maxwellian at the low-energy region and a runaway tail at the high-energy region. To effectively capture features via the AMR algorithm, a new AMR indicator prediction strategy is proposed that is performed alongside the implicit time evolution of the solution. This strategy is complemented by the introduction of computationally cheap feature-based AMR indicators that are analyzed theoretically. Numerical results quantify the advantages of the prediction strategy in better capturing features compared with nonpredictive strategies; and we demonstrate trade-offs regarding computational costs. The robustness with respect to model parameters, algorithmic scalability, and parallel scalability are demonstrated through several benchmark problems including manufactured solutions and solutions of different physics models. We focus on demonstrating the advantages of using implicit time stepping and AMR for runaway electron simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Robotics for Systems Integration in Buildings: Pilot Study of Viable Approaches to Install Hygrothermal and Rigid Electrical Systems

The Industrialized Construction Innovation (ICI) team at the National Renewable Energy Laboratory (NREL) has been exploring the use of robotics to integrate hygrothermal, mechanical, electrical, and plumbing systems in prefabricated building assemblies (off-site construction) and 3D-printed buildings (on-site construction). Such multisystem integration tasks often require specialized robots and custom end effectors to handle a range of rigid and nonrigid building components. This paper begins with a brief overview of the current state of robotics in construction, followed by a pilot study exploring the use of robotics to integrate a simple prototype multitrade wall assembly composed of structural studs, hygrothermal layer, wall finishing, and electrical fixtures. The study was funded by the US Department of Energy's (DOE's) Advanced Materials and Manufacturing Technologies Office (AMMTO). Insights about the implementation of design for manufacturing and assembly (DfMA) principles in designing the prototype wall for robotic assembly, and selection of appropriate robotic end-effector hardware to handle these components are included. Detailed comparison of computational toolpath simulations of the robotic assembly process and real-life demonstration of the same are presented. Finally, limitations and lessons learned from this study are included, along with future research recommendations for robotic assembly of more complex multitrade assemblies, including potential scenarios such as robotic outfitting of facilities in extraterrestrial environments.

advanced manufacturing↗
DOE OSTI2025

Updating the Building Science Advisor (BSA): A Tool to Assist in the Design of Durable Building Envelopes

Predicting the moisture durability of building envelope components remains challenging due to multiple influencing factors, including material selection, assembly positioning, local climate conditions, air tightness, interior environment, and construction quality. Building codes increasingly emphasize energy efficiency through enhanced insulation and tighter envelopes but offer limited guidance on moisture durability considerations. Consequently, builders face uncertainty, particularly as new materials and assemblies enter the market.The Building Science Advisor (BSA) is a free, web-based expert system developed to address these challenges by providing actionable insights into the moisture durability and energy efficiency of both new and retrofit wall designs. Recently updated, we are now providing version 3.0 of the tool. BSA features significant user interface improvements, enhancing navigation and user interaction through a refreshed, intuitive design. Additionally, the tool incorporates a newly developed database containing pre-simulated wall assembly cases, significantly reducing response times and improving the accuracy of moisture durability assessments. Furthermore, the updated BSA includes moisture content as a performance criterion, providing users with a more comprehensive understanding of moisture-related durability risks. These enhancements enable rapid, reliable assessments tailored to specific climate zones and local building practices. BSA continues to offer targeted guidance on wall retrofit scenarios and delivers access to an expanded library of location-specific building science resources.This paper describes these key updates, highlighting the enhanced features, expanded capabilities, and overall improvements to user experience and educational content. The paper includes a demonstration that illustrates how the revised BSA effectively supports practitioners in designing durable, energy-efficient building envelope assemblies.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2022

Simulation of a PCM Integrated Heat Pump Using Time-of-Use Utility Structure based Control Strategy for Demand Response

To mitigate the variation in demand on the electric grid, thermal energy storage (TES) is an alternative to electric batteries and constructing new peaking power plants. The aggressive carbon reduction goals of the United States are accelerating the electrification of building equipment. If space heating and cooling in the US were electrified using conventional heat pump technologies, the winter electric grid peak would approximately double. Integrating TES with heat pump (HP) enables electrification of space cooling and heating devices without overtaxing the grid.This paper evaluates the benefits of a novel phase change material (PCM) integrated heat pump configuration via simulation. A one-dimensional PCM heat exchanger model which discretizes the PCM tank and refrigerant tubes into small control volumes is developed. Each control volume can have different PCM temperatures, PCM properties, and heat transfer coefficients. The PCM tank is charged by a wrapped tank condenser and discharged by an internal refrigerant coil. The PCM heat exchanger model is integrated into DOE/ORNL Heat Pump Design Model for heat pump system simulation.To demonstrate the performance of the PCM integrated heat pump, a case study in Chicago was performed. A Time-of-Use utility structure-based control strategy is implemented to schedule the PCM tank charging and discharging mode switching. Compared with a conventional electric heat pump, the PCM integrated heat pump shows superior performance on load shifting and utility cost reduction. As a result, the proposed system demonstrates 24.6% utility saving for cooling application and 25.8% utility saving for heating application.

Li, Zhenning↗