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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data-Enabled Predictive Control for Building HVAC Systems

Model predictive control is widely used as a control technology for the computation of optimal control inputs of building heating, ventilating, and air conditioning (HVAC) systems. However, both the benefits and widespread adoption of model predictive control (MPC) are hindered by the effort of model creation, calibration, and accuracy of the predictions. In this paper, we apply the data-enabled predictive control (DeePC) algorithm for designing controls for building HVAC systems. The algorithm solely depends on input/output data from the system to predict future state trajectories without the need for system identification. The algorithm relies on the idea that a vector space of all input–output trajectories of a discrete-time linear time-invariant (LTI) system is spanned by time-shifts of a single measured trajectory, given the input signal is persistently exciting. Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated building modeled in EnergyPlus is a modified commercial large office prototype building served by an air handling unit-variable air volume HVAC system. Temperature setpoints of zones are used as control variables to minimize the HVAC energy cost of the building considering a time-of-use electricity rate structure. Furthermore, sensitivity analysis is conducted to gain insights into the effect of parameter tuning on DeePC performance. Simulation results are used to illustrate the performance of the algorithm and compare the algorithm with model-based MPC and occupancy-based setpoint controller. Overall, DeePC achieves similar performance compared to MPC for lower engineering effort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Long-Term Assessment of Commercial Building Energy and Carbon Emissions in the Northwestern Region Under Future Weather Trend

The future climate significantly impacts building performance and increases uncertainties in energy simulations. A rising temperature trend is expected to heighten cooling loads during summer and result in more carbon emissions. Understanding the impact of future climate on building performance is significant for policymakers to make informed decisions. Building retrofit measures can improve building energy efficiency and reduce operational carbon emissions, yet their effects under future climate conditions have not been fully investigated so far. Thus, we proposed an assessment methodology for evaluating long-term energy consumption and operational carbon reduction potential using a building stock dataset. For this study, commercial buildings in the northwestern (NW) region were utilized to assess the impacts of future climate and building retrofit. In addition, we selected Montana with a cold and dry climate as an example to analyze and discuss the carbon emission reduction potential in buildings. The main findings are: (1) Under future climate trends, changes in energy use intensity (EUI) will fluctuate due to variations in heating and cooling degree-days (HDDs and CDDs) and increasing HDDs will lead to increasing EUI. (2) After applying annual building retrofitting, the long-term EUI reduction potential of buildings in the NW region will decrease with the increasing retrofitting degree, and the short-term EUI reduction potential will be impacted by the change of heating and cooling degree days. (3) In Montana, the long-term carbon intensity reduction potential of retrofitted buildings will decrease under future climate trends with the increasing renewable energy penetration.

building energy modeling↗
DOE OSTI2024

Performance assessment of near-fault buildings subjected to physics-based simulated earthquake ground motions with fling step

The effects of the co-seismic static offset (known as fling step) and associated velocity pulses on civil structures have been difficult to study because the static offset is typically removed during the processing of earthquake ground motion records. Simulated ground motions contain fling features and require no processing; therefore, they create new opportunities for representing fling features in seismic hazard analysis and assessing their influence on the seismic demands on near-fault structures. We use physics-based fault rupture simulations to study the characteristics of ground motions with fling step and the sensitivity of the near-fault structural demands to strong fling features. We uncover that simulated ground motions with a large fling step tend to have higher spectral intensity than those without a fling step at the same rupture distance, especially at periods longer than 2 s. As a result, the structural demands on flexible buildings tend to be the most sensitive to the fling features. Statistical analysis suggests that the ground motion spectral shape (represented by spectral accelerations at multiple periods) is—in most cases—a sufficient predictor of the structural demands on near-fault low-rise and mid-rise buildings at locations that are susceptible to strong fling effects. Finally, ground motion record selection experiments reveal that representing the spectral shape features at periods that are most relevant to a given structure may be an effective strategy to reduce the bias in the estimated demands on near-fault long-period structures when the available database of records is considered deficient in fling features.

Fling step↗
DOE OSTI2024

Commercial Building Prototypes Based on ANSI/ASHRAE/IES Standard 90.1-2019 Appendix G PRM: Technical Support Document

The two paths for documenting compliance with ANSI/ASHRAE/IES Standard 90.1 are the prescriptive path and the performance path. Beyond code programs and rating systems (for example, USGBC-LEED ) are primarily known to use a third path – the Appendix G Performance Rating Method. An update in the 2016 edition of Standard 90.1 approved the Appendix G Performance Rating System for code compliance, extending its application and allowing for greater consistency of modeling rules for code and beyond code building energy modeling. The Appendix G PRM provides rules for the development of whole building energy models of baseline and proposed models for calculating the “performance cost index target” value using the simulated energy results of the baseline and proposed models and the building performance factors published in Table 4.2.1.1 of the Standard. This report documents (1) the methodology used for development of the baseline and proposed energy models of the Pacific Northwest National Laboratory and U.S. Department of Energy commercial building prototypes using the Appendix G Performance Rating Method; and (2) the building performance factors that were calculated using those models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Development and Evaluation of Occupancy-Aware Model Predictive Control for Residential Building Energy Efficiency and Occupant Comfort

The residential sector accounts for 25% of global primary energy consumption. Two methods have previously been proposed to reduce residential energy use associated with the provision of occupant thermal comfort: 1. Occupancy-based HVAC control, operating systems only during confirmed occupancy, and 2. model predictive control (MPC), harnessing a mathematical model and forecasts to find optimal operating strategies. Previous studies estimate the average energy savings of the two methods individually in the range of 21% and 16%, respectively. The research presented herein was carried out to evaluate the energy savings potential in residential buildings by combining both approaches across different climates, house vintages, and occupancy patterns. Occupancy and eight different physical modalities (e.g. CO2 and VOC) data were collected from five homes for time periods of 4–9 weeks. Collected data sets were used to train occupancy prediction models suggested by an extensive literature survey of occupancy model types. The trained prediction models were combined with MPC and detailed EnergyPlus building simulation models to evaluate residential building performance in terms of annual energy savings and thermal comfort, along with discomfort exceedance metrics. Multiple home types and regions were analyzed to understand regional and climate-dependent potential. Based on actual field data, the occupancy models had a prediction inaccuracy between 8% and 35% across the investigated homes. Average occupancy for the collected data ranged from 56% to 86%, a typical range reported in the literature. Building simulations were conducted for three control scenarios: conventional thermostatic control, occupancy-based, and occupancy-based MPC. The results indicate that all advanced strategies improve upon the conventional control, with some scenarios cutting energy use in half with only occasional incurrence of discomfort. The findings indicate that occupancy-aware model predictive residential building control has the potential to drastically reduce energy use and associated emissions while maintaining occupant comfort for both new and existing buildings.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2021

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↗
DOE OSTI2021

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↗
DOE OSTI2023

Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season

This paper presents a critical advancement in Building Energy Modeling (BEM) through an empirical validation approach using a high-quality dataset from a multizone commercial office building in Oak Ridge, TN, USA. BEM is widely utilized in diverse construction applications, but its effectiveness relies on the accuracy of its predictions. The study focuses on empirical validation of input parameters in BEM, including building envelope data, infiltration modeling, and rooftop unit system performance curves. The validation of simulation input parameters leads to substantial improvements in the accuracy of simulation results. Notable both NMBE and cv (RMSE) values are reduced by 0.5 % for indoor air temperature and 17 % for indoor air relative humidity compared to the previous model. At the system level, both NMBE and cv (RMSE) values are reduced by 2 % for fan energy consumption and 4 % for cooling energy consumption, compared to the previous model. A literature review highlights a significant gap in empirical validation studies, which predominantly concentrate on either component-level or whole building validation. Furthermore, many studies employ simplified setups that may not faithfully represent the complexities of multizone commercial buildings. This paper distinguishes itself by emphasizing the critical importance of component-level input parameter validation. It underlines the need to validate data related to building envelope components and HVAC system performance curves, resulting in more accurate simulation outcomes. In conclusion, the utilization of actual multizone commercial building data enhances the study's practical relevance. In summary, this research underscores the pivotal role of input parameter validation in enhancing the accuracy and reliability of BEM.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Tailoring material microstructure and property in wire-laser directed energy deposition through a wiggle deposition strategy

Developing effective strategies to directly control material microstructure, property, and anisotropy is an active research area in metal additive manufacturing. This work develops a wiggle deposition pattern for wire-laser directed energy deposition (DED) of 316L stainless steel (SS) to modify the solidification texture, particularly in the building direction, in as-deposited samples. Through multi-physics simulation, operando near-infrared imaging, and synchrotron x-ray characterization, it is found that the wiggle deposition strategy induces highly dynamic melt flow and oscillating thermal gradient in the melt pool, which is responsible for the variation of preferable grain growth direction and crystallographic texture in the sample. The specific texture reduces the anisotropy in the tensile strength of as-printed 316L SS samples cut along different directions. Also, it largely increases the ductility along the build direction. Crystal plasticity simulation is performed to correlate the sample texture with mechanical property. In conclusion, this work offers a unique approach for tailoring local properties through the control of melt pool instability by applying different tool paths.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cluster analysis of occupancy schedules in residential buildings in the United States

The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.

42 ENGINEERING↗
DOE OSTI2024

Quantum simulations for strong-field QED

Quantum field theory in the presence of strong background fields contains interesting problems where quantum computers may someday provide a valuable computational resource. In the noisy intermediate-scale quantum era it is useful to consider simpler benchmark problems in order to develop feasible approaches, identify critical limitations of current hardware, and build new simulation tools. Here we perform quantum simulations of strong-field QED (SFQED) in 3 + 1 dimensions, using real-time nonlinear Breit-Wheeler pair production as a prototypical process. The strong-field QED Hamiltonian is derived and truncated in the Furry-Volkov mode expansion, and the interactions relevant for Breit-Wheeler are transformed into a quantum circuit. Quantum simulations of a “null double slit” experiment are found to agree well with classical simulations following the application of various error mitigation strategies, including an asymmetric depolarization algorithm which we develop and adapt to the case of Trotterization with a time-dependent Hamiltonian. We also discuss longer-term goals for the quantum simulation of SFQED. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2023

Multi-Neutrino Entanglement and Correlations in Dense Neutrino Systems

The time-evolution of multi-neutrino entanglement and correlations are studied in two-flavor collective neutrino oscillations, relevant for dense neutrino environments, building upon previous works. Specifically, simulations performed of systems with up to 12 neutrinos using Quantinuum’s H1-1 20 qubit trapped-ion quantum computer are used to compute n-tangles, and two- and three- body correlations, probing beyond mean-field descriptions. n-tangle re-scalings are found to converge for large system sizes, signaling the presence of genuine multi-neutrino entanglement.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Sensitivity Analysis of Occupant Preferences on Energy Usage in Residential Buildings: Preprint

Residential buildings, accounting for 37% of the total electricity consumption in the United States, are suitable for demand-side management (DSM) programs to support effective and economical operation of the power system. A home energy management system (HEMS) enables residential buildings to participate in such programs. It is important to account for occupant preferences in HEMS to ensure occupant satisfaction while participating in DSM programs. For example, people who prefer a higher thermal comfort level are likely to consume more energy. In this study, we used foresee™, a HEMS developed by the National Renewable Energy Lab (NREL), to perform a sensitivity analysis of occupant preferences with the following objectives: minimize utility cost, minimize carbon footprint, and maximize thermal comfort. To incorporate the preferences into the HEMS, the SMARTER method was used to derive a set of weighting factors for each objective. We performed week-long building energy simulations using a model of a home in Fort Collins, Colorado, where there is mandatory time-of-use electricity rate structure. The foreseeTM HEMS was used to control the home with six different sets of occupant preferences. The study shows that occupant preferences can have a significant impact and is important to consider when modeling residential buildings. Results show that the HEMS could achieve energy reduction ranging from 3% to 21%, cost savings ranging from 5% to 24%, and carbon emission reduction ranging from 3% to 21%, while maintaining a low thermal discomfort level ranging from 0.78 K-hour to 6.47 K-hour in a one-week period during winter. These outcomes quantify the impact of varying occupant preferences and will be useful for controlling the electrical grid and developing HEMS solutions.

carbon footprint↗
DOE OSTI2022

Integration of Thermoelectric Modules to Vapor Compression Systems

This paper describes the technology of a thermoelectric (TE) integrated heat pump. It uses a TE heat pump to subcool the refrigerant liquid before entering the evaporator to enlarge the evaporating capacity; and elevate the heat in the refrigerant flow to a supplemental heating coil and discharge the capacity to the indoor air flow. Using the DOE/ORNL Heat Pump Design Model, four system configurations were modelled on a baseline two-speed heat pump, the baseline heat pump with adding a supplemental coil, the TE integrated heat pump having one stage TE heating to control a 20R liquid refrigerant temperature drop; and having two stages TE heating to control up to 40R temperature drop. The system models were used to produce performance curves for EnergyPlus building energy simulations. We selected template residential buildings, poorly insulated: pre-1990 buildings, and well-insulated: IECC2021 buildings; in five cold climate zones: 5A, 5B, 6A, 6B, 7. The building energy simulations demonstrated energy saving potentials, utility cost reductions and estimated payback periods.

Shen, Bo↗
DOE OSTI2021

A Scoping Study to Develop a Computational Fluid Dynamics Based Model to Predict Radiological Materials Packaging Temperatures within a Generic Staging Building

The objective of this work is to perform computational fluid dynamics simulations of a ventilated radiological-material-package staging building to determine the effect of including or excluding a variety of physical effects and computational methods on both the predicted package temperatures and required computational resources. The generic building contains 640 drum-packages containing heat-generating, radiological material supported on four racks that are eight levels tall. Additionally, there is a forced ventilation system, lighting, and insulated walls. Computational models were constructed that included or excluded (a) shelving, (b) effects of unsteadiness, and (c) radiation heat transfer. Simulations with each drum modeled separately were compared to simpler simulations with sets of four drums represented by an equivalent box-package. These models employed between 106 to 107 elements. Steady state simulations predicted package temperatures that were within 0.1°C of simulations that included transient effects and required only one-eighth the computational resources. Calculations that excluded shelving predicted temperatures within 0.6°C of simulations that included shelving and required one-fourth the computational resources. Excluding radiation heat transfer systematically increased temperatures by around 1.5°C but reduced computational resources by a factor of four. The box-package model reduced the computational resources by a factor of 3, but systematically predicted higher temperatures by around 1.1°C. These results will be used to develop an efficient computational fluid dynamics model to assess the ability of different staging building designs to prevent the temperature of package components from exceeding specified limits.

42 ENGINEERING↗
DOE OSTI2022

Studsvik R2 materials test reactor Ad Hoc depletion strategy for the derivation of the fuel isotopic composition of the MPCMIV benchmark

The Ad Hoc Depletion Strategy elaborated by the NINE company, developed in support of the organization of the MPCMIV (Multi-physics Pellet Cladding Mechanical Interaction Validation) benchmark input and output specifications, is presented. This work aims at illustrating the strategy itself and then showing the results obtained with its application over the Studsvik R2 Testing Reactor, which is analyzed in the benchmark. The objective of the application of the strategy is to compute the fuel elements isotopic compositions at the beginning of some core loadings of interest for the benchmark. To this objective, it is necessary to implement first the simulation model of the three single assembly types and perform the infinite lattice depletions, then, to build the full core model and to perform the simulation of the core cycle. All the models and simulations were carried out with the use of the Monte Carlo particle transport code Serpent 2. Finally, the simulations results are assessed against Studsvik isotopic compositions of the fuel elements discharged from the R2 Reactor at the end of the core loading. Several assumptions were necessary during all the steps of the strategy, to overcome the lack of information regarding the core management. For this reason, the solution found at the end of the current analysis may not be completely optimized and further improvements regarding the model assumptions will be tested in a future work. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Deriving Simulation Parameters for Storage-Type Water Heaters Using Ratings Data Produced from the Uniform Energy Factor Test Procedure: Preprint

Building energy modeling (BEM) is commonly used to estimate the energy usage of residential buildings. Uses for BEM include calculating home energy ratings, demonstrating compliance with performance-based energy codes, and establishing whether designs meet voluntary program requirements, such as ENERGY STAR® qualified homes. BEM requires simulating subsystems within the building, including storage-type water heaters. To properly simulate the in-situ performance of residential storage water heaters, it is necessary to determine key water heater parameters, including the overall heat loss coefficient (UA) and conversion efficiency (?c) of the water heater, based on the rated efficiency of the water heater. The testing procedure and rating standard for residential water heaters have recently changed: the new rating standard provides a Uniform Energy Factor (UEF) rather than the Energy Factor (EF) used previously. This paper discusses how to derive the necessary model parameters from the ratings data produced from the latest test procedure.

30 DIRECT ENERGY CONVERSION↗