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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Urban microclimate and its impact on building performance: A case study of San Francisco

Urban microclimate exerts an increasing influence on urban buildings, energy, and sustainability. This study uses 10-year measured hourly weather data at 27 sites in San Francisco, California, to (1) analyze and visualize the urban microclimate patterns and urban heat island effect; (2) simulate annual energy use and peak electricity demand of typical large office buildings and large hotels to investigate the influence of urban microclimate on building performance; (3) simulate indoor air temperature of a single-family house without air-conditioning during the record three-day heatwave of 2017, to quantify the divergence of climate resilience due to urban microclimate effect. Results show significant microclimate effects in San Francisco with up to 11 °C outdoor air temperature difference between the coastal and downtown areas on September 1, 2017, during the record three-day heatwave. The simulated energy results of the prototype large office and large hotel buildings using the 2017 weather data show over 100% difference in annual heating energy use and 65% difference in annual cooling energy use across different stations; as well as up to 30% difference in peak cooling electricity demand. The impacts on annual site or source energy use are minimal (less than 5%) as cooling and heating in a mild climate are a relatively small portion of overall building energy use in San Francisco. Results also show the microclimate effects influence indoor air temperature of unconditioned homes by up to 5 °C. Newer buildings and homes are much less affected by microclimate effects due to more stringent performance requirements of the building envelope and energy systems. Furthermore, these findings inform that San Francisco microclimate variations should be considered in urban energy planning, building energy codes and standards, as well as heat resilience policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Angle of Incidence Characterization of Six Laminated Solar Cells for 2020 DTU Fotonik Inter-Laboratory Comparison Study

Photovoltaic energy prediction models include functions or modifiers to account for sun angle reflection losses. These functions may be known interchangeably as Angle of Incidence (AOI) or Incident Angle Modifier (IAM). While standards exist, there is no universally accepted single best practice for developing these functions. They can be generated through characterization of representative modules or single cells, in natural sunlight or indoors using simulated light sources. Repeatability of measurements and the viability of cross-laboratory comparisons are critical to confidence in validation of both methods. To investigate the differences between methods and labs, The Technical University of Denmark (DTU) initiated an international round-robin test comparison between several key test labs with AOI measurement capability. A total of six minimodules were provided in three different cell/interconnect/backsheet combinations. Sandia characterized these minimodules using methods developed over two decades specifically for the outdoor characterization of full-size photovoltaic modules. This report documents the characterization results, summarizes key observations and tabulates the processed data for comparison to results provided by other characterization labs.

14 SOLAR ENERGY↗
DOE OSTI2025

Decision-Model Supported Algal Cultivation Process Enhancement (DMSACPE) (Final Technical Report)

This project was proposed in response to the FY19 Bioenergy Technologies Office Multi-Topic Funding Opportunity Announcement DE-FOA-0002029, Area of Interest Subtopic 1 (AOI1): Cultivation Intensification Processes for Algae. The main challenge identified by the FOA was “in translating results between laboratory research systems and larger-scale outdoor (or mass culture) systems. These difficulties limit reliable experimental durations, adequate and representative experimental volumes of material, and results that can be reproduced reliably. By overcoming the challenge in translating results between laboratory and mass cultures, the objective of AOI 1 is to increase the harvest yield, robustness, and quality of algae cultivation for biofuels and bioproducts”. This report marks the final technical deliverable of this project and reviews all the major findings of the research program.

09 BIOMASS FUELS↗
DOE OSTI2023

Frequency Emitter Geolocation Using Signal Strength Fingerprinting Informed By 3D Propagation Modeling

This work focuses on the problem of RF geolocation in complex multipath environments. Using 3D electromagnetic propagation modeling to characterize environments of interest will enable more accurate RF geolocation. Specifically, a path-loss radio map can be generated in simulation for use in received signal strength indicators (RSSI) fingerprinting, or pattern matching. RSSI fingerprinting is an example of data-based method that takes site specific information into account which should allow for better performance than other model-based methods that use a generalized model of electromagnetic propagation. This modeling capability will also be used to evaluate the relative performance of RSSI fingerprinting, pathloss model based RSSI methods such as differential received signal strength circles (DRSS), RSSI joint gaussian estimation, and time-difference of arrival (TDOA). New methods using this simulation derived electromagnetic characterization could improve the efficacy of currently deployed and future RF spectral monitoring solutions. Wireless InSite developed by Remcom is used as the simulation tool of choice in this work. An indoor location is simulated with a grid of fixed receivers and a grid of transmit locations. Using the output of the Wireless InSite simulation the response from a given transmit location to a given receive location can be generated. During the first year of the project various geolocation methods evaluated on purely synthetic, but realistic, data. The second year focused on testing and validating the efficacy of simulation informed RF geolocation using two physical testbeds. This work has shown that data-based approaches are more accurate than model-based ones at the expensive of requiring measured or simulated site-specific training data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Impact of refrigerant undercharge faults on building indoor conditions and HVAC system operation in residential Buildings: A simulation study

This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. Finally, this study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Simulations of short-term exposure to NO 2 and PM 2.5 to inform capture efficiency standards

The California building code requires all new or renovated residential dwelling units to have kitchen exhaust ventilation to manage air pollutants and moisture generated during cooking. Current performance requirements are specified as a minimum airflow and maximum sound level. This report presents an analysis to support consideration of adding a capture efficiency requirement to the code. The analysis uses a physics-based simulation model to calculate air pollutant concentrations in homes that result from cooking and the inflow of outdoor air, mediated by loss and removal process including deposition, dwelling unit ventilation, and the use of a range hood with varying levels of capture efficiency. Calculated pollutant concentrations are compared to relevant, health-based guidelines. The analysis considers the highest 1-hour concentration of nitrogen dioxide (NO 2 ), which is a product of natural gas combustion and the highest 24-hour concentrations of fine particulate matter (PM 2.5 ) which is emitted in substantial quantities from frying, broiling and grilling among other cooking activities. For NO 2 , the analysis considers cooking of a dinner for 3-4 persons consisting of pasta, meat sauce, a par-boiled vegetable, and baked garlic bread. For PM 2.5 , the analysis considers a day in which breakfast emitted particles at the 80th percentile and lunch and dinner entailed particle emissions at the 50th percentile of cooking emission events reported in the literature. Model simulations were performed to determine the level of range hood capture efficiency that will allow these cooking scenarios to occur in the vast majority (>99%) of new homes being built in California while maintaining pollutant concentrations below the health-based guidelines, if the range hood is used throughout cooking. All homes were assumed to have dwelling unit ventilation at the rate required in the building code. Simulation model input parameters were specified using a Monte Carlo approach to represent a range of housing characteristics, outdoor conditions, and indoor pollutant dynamics. Simulation results suggest that requiring a minimum capture efficiency of at least 70% is needed to avoid unacceptably high NO 2 (1-h average concentration of 100 ppb or higher) and at least 60% to avoid unacceptably high PM 2.5 (24-h average of 25 μg/m³ or higher). These results were driven by multi-family homes, which have a smaller volume of air to dilute any pollutants not captured and removed at the cooking area, resulting in higher concentrations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Secondhand Exposure to Simulated Cannabis Vaping Aerosols

Emissions from cannabis vaping degrade indoor air quality and expose non-users to secondhand pollutants. We investigated how the vaping mixture composition affects indoor aerosol characteristics and exposures. Simulated cannabis vaping aerosol was produced by flash evaporation in a 20 m3 chamber of mixtures containing terpenoids, cannabinoids, cannabis extract constituents, and the adulterant vitamin E acetate (VEA). Aerosol time- and size-resolved concentrations (8 nm-2.5 μm at 1 Hz) were measured, and a dosimetry model was used to evaluate the intake of secondhand aerosols. The results showed peak particle number (PN) concentrations between 0.7 × 106 and 13 × 106 cm-3 and peak mass concentration (PM1.0) between 65 and 1191 μg m-3 at t = 5 min after emission. Concentrations decreased to 21-57% of peak PN and 33-69% of peak PM1.0 at t = 60 min. The PM1.0 yield was 0.06 for a terpenoid-only mixture, 0.22-0.36 for tetrahydrocannabinol (THC)-terpenoid mixtures, and >1 for mixtures containing high concentrations of cannabidiol (CBD) or VEA. For intake deposition, the highest aerosol fraction was deposited in the pulmonary region, followed by the tracheobronchial and head regions. Deposition increased in the presence of THC, CBD, or VEA, with aerosols <100 nm contributing the majority of particles deposited in all regions.

Tang, Xiaochen↗
DOE OSTI2022

Simulation-Based Analysis of Impacts of Reduced Envelope and Duct Air Leakage on Indoor Air Pollutant Concentrations in Occupied Manufactured Homes

This report presents a simulation-based analysis that estimates the magnitude of indoor air pollutant concentration changes that could result from the proposed energy conservation standards. The analysis was conducted for a 1568 ft 2 , double-wide MH with variations in heating and cooling equipment (either furnace + air conditioner or heat pump) and various types of whole-house mechanical ventilation (continuous exhaust fan or central fan integrated supply), in three locations with varying climate conditions: Chicago IL, Fresno, CA; and Houston TX. The simulations tracked four air pollutants that can reach levels exceeding established safe target levels in homes: acrolein, formaldehyde, fine particulate matter (PM 2.5 ), and nitrogen dioxide (NO 2 ). The simulations considered acrolein and formaldehyde emitted from continuous indoor sources; acrolein, NO 2 and PM 2.5 from cooking; PM 2.5 from dispersed occupant activities; and NO 2 and PM 2.5 from outdoors, using historical data to identify typical levels. The impacts in homes operating or not operating whole-house mechanical ventilation equipment, kitchen and bath exhaust fans, and window opening as ventilation approaches were examined.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Load Shifting with Space Conditioning Heat Pumps During Heating Season in the Pacific Northwest

Ductless mini- and multi-split heat pumps are commonly deployed in Pacific Northwest residences, which historically have inefficient space heating and no space cooling. Heat pumps provide these residences with more energy-efficient space heating in the winter and space cooling for increasingly warm summer periods. For many of these applications, heat pumps add a new electrical load at the residence, compelling regional utilities to seek to understand their impacts on the electrical grid and opportunities for load management. In coordination with regional utilities, this study examined the winter load-shifting potential of residential multi-split heat pumps with variable capacity technology. The study included nine residential sites with a total of 20 ductless indoor units, which underwent simulated smart grid control. Grid control functionality was provided through the CTA-2045 communication protocol and included offsetting temperature setpoints for individual indoor units in the study. Field data collection included whole-house and heating, ventilation, and air conditioning electrical power consumption; indoor zone temperatures; occupant surveys; and CTA-2045 curtailment data for both baseline and simulated load-shifting events by site. This paper provides an overview of the load-shifting strategies and aggregated results from winter data collection.

ASHRAE, Heat pump, energy effciency, residential b↗
DOE OSTI2022

A Machine Learning-Assisted Framework to Control Thermally Anisotropic Building Envelopes in Residential Buildings

To curb the energy consumption of buildings and their related CO2 emissions, Oak Ridge National Laboratory (ORNL) has developed the thermally anisotropic building envelope (TABE) —a multi-layer design comprising insulation materials and metal foils connected to thermal loops. In this study, a machine learning-assisted framework was developed to control the TABE in residential buildings to reduce the computation load for future optimal rule-based control and application. First, a 2D finite element model was established in COMSOL to calculate the hourly heat flux through exterior walls installed with the TABE. Then, TABE wall heat fluxes were simulated for various given indoor and outdoor boundary conditions, as well as thermal loops fluid temperatures and flow rates. Since the finite element simulations are computationally expensive, an artificial neural network (ANN) was then trained to use as a proxy of the finite element (COMSOL) modeling. Finally, the trained ANN model was coupled with the EnergyPlus model to predict the energy consumption of a US Department of Energy prototype single-family house installed with the TABE. An optimal simple rule-based control was determined from predefined rules for a case study. The results demonstrate that the developed machine learning–assisted framework can reduce 99.9% of the computation time while efficiently managing residential building energy for installed TABE walls.

Shen, Zhenglai↗
DOE OSTI2023

Simulation-based assessment of ASHRAE Guideline 36, considering energy performance, indoor air quality, and control stability

This study assesses American Society of Heating, Refrigerating and Air-Conditioning Engineers Guideline 36 (G36) with a typical medium office building. Specifically, this study employed a Modelica model of a variable air volume (VAV) system that serves this building, which includes components for representing indoor virus transmission and filtration. It then implemented the G36 control sequences for both water-side and air-side equipment in Python. After that, this study conducted the assessment by co-simulating G36 and the Modelica model using the Building Operations Testing Framework. Unlike existing works, this work has three unique features: (1) It considers the interactions between control sequences for water-side and air-side equipment of the studied VAV system. (2) It assesses the performance of G36 from the perspective of IAQ. (3) It examines the short-term behaviors of the studied building under G36 to understand the control stability. This assessment confirms significant energy savings from G36, largely because of the interaction between supply air temperature and hot water controls. It also reveals a trade-off between the ability to slow the spread of virus and the energy performance via demand controlled ventilation. Lastly, it emphasizes the necessity of tuning local feedback control when implementing G36.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Indoor Wireless Localization of Uncooperative Sources Using a Ray Tracing Model

Indoor geolocation of radio frequency (RF) trans- mitters is challenging due to site-specific multipath effects, especially when the sources are uncooperative. A source being uncooperative means that a-priori the transmit time, location, and power are unknown. It is proposed to use a site-specific ray tracing model to generate realistic indoor RF responses, perform geolocation using multiple methods, and compare their performance. This work will look at two types of geolocation algorithms: (1) time-difference of arrival (TDOA) and (2) re- ceived signal strength Indicator (RSSI) fingerprinting or pattern- matching-based methods. An indoor space will be simulated with a grid of fixed receivers and a grid of transmit locations using Remcom’s Wireless InSite. Using the output of the Wireless InSite simulation the response from any given transmit location can be generated and used to evaluate geolocation performance.

indoor source localization, Wireless InSite, re- c↗
DOE OSTI2023

Classroom aerosol dispersion modeling: experimental assessment of a low-cost flow simulation tool

The purpose of this study was to assess the utility of a low-cost flow simulation tool for an indoor air modeling application by comparing its outputs with the results of a physical experiment, as well as those from a more advanced computational fluid dynamics (CFD) software package. In this study, five aerosol dispersion tests were performed in two different classrooms by releasing a CO 2 tracer gas from six student locations. Resultant steady-state concentrations were monitored at 13 locations around the periphery of the room. Subsequently, the experiments were modeled using both a low-cost tool (SolidWorks Flow Simulation) and a more sophisticated tool (STAR-CCM+). Models were evaluated based on their ability to predict the experimentally measured concentrations at the 13 monitoring locations by calculating four performance parameters commonly used in the evaluation of dispersion models: fractional mean bias (FB), normalized mean-square error (NMSE), fraction of predicted value within a factor of two (FAC2), and normalized absolute difference (NAD). The more sophisticated model performed better in 15 of the 20 possible cases (five tests at four parameters each), with parameters meeting acceptance criteria in 19 of 20 cases. However, the lower-cost tool was only slightly worse, with parameters meeting acceptance criteria in 18 of 20 cases, and it performed better than the other tool in 3 of 20 cases. Because it provides useful results at a fraction of the monetary and training cost and is already widely accessible to many institutions, such a tool may be worthwhile for many indoor aerosol dispersion applications, especially for students or researchers just beginning CFD modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Graphics Processing Unit (GPU) Approach to Large Eddy Simulation (LES) for Transport and Contaminant Dispersion

Recent advances in the development of large eddy simulation (LES) atmospheric models with corresponding atmospheric transport and dispersion (AT&D) modeling capabilities have made it possible to simulate short, time-averaged, single realizations of pollutant dispersion at the spatial and temporal resolution necessary for common atmospheric dispersion needs, such as designing air sampling networks, assessing pollutant sensor system performance, and characterizing the impact of airborne materials on human health. The high computational burden required to form an ensemble of single-realization dispersion solutions using an LES and coupled AT&D model has, until recently, limited its use to a few proof-of-concept studies. An example of an LES model that can meet the temporal and spatial resolution and computational requirements of these applications is the joint outdoor-indoor urban large eddy simulation (JOULES). A key enabling element within JOULES is the computationally efficient graphics processing unit (GPU)-based LES, which is on the order of 150 times faster than if the LES contaminant dispersion simulations were executed on a central processing unit (CPU) computing platform. JOULES is capable of resolving the turbulence components at a suitable scale for both open terrain and urban landscapes, e.g., owing to varying environmental conditions and a diverse building topology. In this paper, we describe the JOULES modeling system, prior efforts to validate the accuracy of its meteorological simulations, and current results from an evaluation that uses ensembles of dispersion solutions for unstable, neutral, and stable static stability conditions in an open terrain environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2026

Envelope-driven comfort risk in residential demand response

Residential demand response (DR) is a valuable resource for grid reliability, but remains challenging because the highly heterogeneous residential building stock leads to widely varying and hard-to-predict load and comfort responses during DR events. Although prior research has estimated the technical potential of DR-capable technologies for achieving energy demand savings, little is known about how they affect thermal comfort. In particular, it remains unclear how indoor thermal conditions due to DR depend on the thermal envelope characteristics of the housing stock. To address this gap, this study provides a systematic, location-specific assessment of indoor thermal performance during DR-events across the US housing stock using both typical DR weather data and detailed building metadata. We evaluate how envelope characteristics influence indoor temperatures during realistic simulated summer and winter DR events across 37 US locations, applying both temperature threshold and rate of temperature change criteria to estimate region-level probabilities of discomfort. Additionally, we show the impact of distinct weather patterns that intensify or abate thermal stress on comfort outcomes. Results show a near-universal overheating risk in summer DR events, where comfort outcomes are strongly influenced by rapid risk of comfort violations. In contrast, overall winter DR discomfort risk is lower, risk escalation is more gradual and shows greater sensitivity to event duration. These findings offer a data-driven quantification of comfort risk across diverse climates and building envelopes, demonstrating the need for region-specific DR scheduling and discomfort mitigation strategies tailored to local weather patterns and the performance of existing residential buildings.

Demand response↗
DOE OSTI2024

Defining weather scenarios for simulation-based assessment of thermal resilience of buildings under current and future climates: A case study in Brazil

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A building performance-based approach to determining energy resilience for grocery stores in the event of a power outage

Evaluating progress toward a built environment that is best equipped to serve communities during a regional power outage will require metrics that capture the energy resilience of the unique buildings and businesses most crucial to the well-being of those nearby. We focused on grocery stores as key buildings for which access, and thus energy resilience, is critical during a disaster when power is unavailable. We evaluated the energy resilience of these buildings by offering and testing building-scale metrics that assess business continuity potential during a power outage. Metrics proposed in this study are calculated based on the unique power loads characteristic to grocery stores, primarily refrigeration and maintaining safe indoor environmental conditions. Building simulations based on varying levels of backup power were carried out against occupant safety and comfort parameters to apply these metrics, with additional criteria imposed on grocery stores to capture the inventory and sales loss from food spoilage resulting from a lack of refrigeration power. Findings from this study demonstrate the feasibility of our proposed metrics and methodology to serve as a low-data burden means for stakeholders to evaluate the energy resilience of grocery stores, with greater implications in helping to understand the impact on community-scale energy resilience.

Siegel, Lino Sanchez↗