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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 OSTI2026

Enhancing Building Resilience: Maintaining Energy Efficiency and Thermal Comfort During Power Outages in Cold Climates

The increasing frequency and intensity of extreme weather events, such as heatwaves and cold snaps, present significant challenges to building energy performance and occupant comfort. Highly correlated with climate events are widespread long duration power interruptions that may affect thousands of buildings and millions of customers. This study evaluates the impact of building energy performance and occupant thermal comfort in medium-sized office buildings in a cold climate region. Using energy models representing pre-1980 and 2019 vintages, simulations were conducted to assess energy performance under typical weather conditions and occupant thermal comfort during power interrupted extreme cold snap and heatwave climate events under both current 2020s and future 2050s weather conditions. The results show a projected 33% increase in cooling energy demand and a 19% reduction in heating energy by 2050. Findings reveal that older buildings are more susceptible to cold discomfort during cold snaps, while modern airtight buildings are more vulnerable to overheating during heatwaves. Various passive energy efficiency measures, such as improved infiltration control, thermal windows, solar-controlled windows, and cool envelopes, were evaluated for their ability to mitigate thermal discomforts. Solar controlled windows and weatherstripping contribute to reducing cold thermal discomfort by 21% during a power-interrupted cold snap. Solar-controlled windows were found to reduce hot thermal discomfort by 34% during a future power-interrupted heatwave. The study highlights the importance of targeted retrofitting strategies to enhance thermal resilience, especially during power outages, to ensure occupant safety and comfort during extreme climate events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A new dynamic zOnal model with air-diffuser (DOMA) - Application to thermal comfort prediction

A new Dynamic zOnal Model with Air-diffuser (DOMA) was developed. Several case studies were investigated and tested to evaluate and validate this program using measurement data. This new model was integrated into a TRaNsient SYstems Simulation program library and coupled with the multi-zone thermal model. The DOMA/TRNSYS coupled model was then used to predict room temperature distribution over an entire day of a single-zone building. The results show that increasing the heating outputs of the electric floor system, for example, from 75 to 200 W/m 2 , would not effectively improve the indoor thermal comfort, since the thermostat will reach the set point first and then turn off the system before the room gets enough heat and reach a comfortable level. This indicates the importance of selecting an appropriate location and set point for the thermostat when using a floor heating system. This potential thermal comfort issue can only be identified through the two-node model with a dynamic zonal model rather than the conventional PMV model, which thus suggests that for optimizing indoor thermal comfort of a building equipped with a time-sensitive control strategy and/or HVAC system, the TSENS results obtained from the two-node model integrated with DOMA are more appropriate than PMVs.

Construction & Building Technology↗
DOE OSTI2018

Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers

Personal Comfort Systems (PCS) provide individual occupants local heating and cooling to meet their comfort needs without affecting others in the same space. It saves energy by relaxing ambient temperature requirements for the HVAC system. Aside from these benefits, PCS offers a wealth of data that can describe how individuals interact with heating/cooling devices in their own environment. Recently developed Internet-connected PCS chairs have unlocked this opportunity by generating continuous streams of heating and cooling usage data, along with occupancy status and environmental measurements via embedded sensors. The data allow individuals' comfort and behavior to be learned, and can inform centralized systems to provide ‘just the right’ amount of conditioning to meet occupant needs. In summer 2016, we carried out a study with PCS chairs involving 37 occupants in an office building in California. During the study period, we collected >5 million chair usage data-points and 4500 occupant survey responses, as well as continuous measurements of environmental and HVAC system conditions. The data analysis shows that (1) local temperatures experienced by individual occupants vary quite widely across different parts of the building, even within the same thermal zone; (2) occupants often have different thermal preferences even under the same thermal conditions; (3) PCS control behavior can dynamically describe individuals' thermal preferences; (4) PCS chairs produce much higher comfort satisfaction (96%) than typically achieved in buildings. We conclude that PCS not only provide personalized comfort solutions but also offer individualized feedback that can improve comfort analytics and control decisions in buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control

In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Comfort Model Simplification for Tight Integration with Grid Service Optimizations

Localized generation, storage, and intelligent power electronics are increasingly being integrated into building. The excess storage, thermal, and generation capacity can be used to develop new markets for ancillary services such as peak reduction, voltage and frequency support, or load flattening. Widespread participation in these markets will make the power grid more resilient. However, widespread participation in these markets will require a simple and intuitive control over the excess capacity by the building occupants. Most importantly for adopting these technologies, participation in these markets should not require sacrificing occupant comfort. Many of the proposed control and optimization technologies for supplying the ancillary services do not explicitly incorporate occupant comfort into their models and when they do, generally utilize a rigid constraint on the indoor air temperature set a priori. This reduces flexibility in using excess thermal capacity. It also cannot take into account uncertainties in local factors that affect thermal comfort such as clothing insulation or relative humidity. In this paper we present and justify some assumptions that simplify the standard predictors of thermal comfort. We also develop a regression model of the thermal comfort that is linear with respect to the indoor air temperature. Next we develop the concept of thermal comfort variation that eliminates the dependence of the thermal comfort on uncertain thermal comfort factors. Then, we present a quadratic program utilizing the thermal comfort variation in both the objective function and as a constraint. Finally, we show the results of a time-of-use cost optimization that utilizes the simplified thermal comfort model.

Melin, Alexander↗
DOE OSTI2019

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. Finally, the results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2001

Entropy generation method to quantify thermal comfort

The present paper presents a thermodynamic approach to assess the quality of human-thermal environment interaction and quantify thermal comfort. The approach involves development of entropy generation term by applying second law of thermodynamics to the combined human-environment system. The entropy generation term combines both human thermal physiological responses and thermal environmental variables to provide an objective measure of thermal comfort. The original concepts and definitions form the basis for establishing the mathematical relationship between thermal comfort and entropy generation term. As a result of logic and deterministic approach, an Objective Thermal Comfort Index (OTCI) is defined and established as a function of entropy generation. In order to verify the entropy-based thermal comfort model, human thermal physiological responses due to changes in ambient conditions are simulated using a well established and validated human thermal model developed at the Institute of Environmental Research of Kansas State University (KSU). The finite element based KSU human thermal computer model is being utilized as a "Computational Environmental Chamber" to conduct series of simulations to examine the human thermal responses to different environmental conditions. The output from the simulation, which include human thermal responses and input data consisting of environmental conditions are fed into the thermal comfort model. Continuous monitoring of thermal comfort in comfortable and extreme environmental conditions is demonstrated. The Objective Thermal Comfort values obtained from the entropy-based model are validated against regression based Predicted Mean Vote (PMV) values. Using the corresponding air temperatures and vapor pressures that were used in the computer simulation in the regression equation generates the PMV values. The preliminary results indicate that the OTCI and PMV values correlate well under ideal conditions. However, an experimental study is needed in the future to fully establish the validity of the OTCI formula and the model. One of the practical applications of this index is that could it be integrated in thermal control systems to develop human-centered environmental control systems for potential use in aircraft, mass transit vehicles, intelligent building systems, and space vehicles.

Models, Biological↗
DOE OSTI2015

Creating high performance buildings: Lower energy, better comfort

Buildings play a critical role in the challenge of mitigating and adapting to climate change. It is estimated that buildings contribute 39% of the total U.S. greenhouse gas (GHG) emissions [1] primarily due to their operational energy use, and about 80% of this building energy use is for heating, cooling, ventilating, and lighting. An important premise of this paper is about the connection between energy and comfort. They are inseparable when one talks about high performance buildings. Worldwide data suggests that we are significantly overcooling buildings in the summer, resulting in increased energy use and problems with thermal comfort. In contrast, in naturally ventilated buildings without mechanical cooling, people are comfortable in much warmer temperatures due to shifting expectations and preferences as a result of occupants having a greater degree of personal control over their thermal environment; they have also become more accustomed to variable conditions that closely reflect the natural rhythms of outdoor climate patterns. This has resulted in an adaptive comfort zone that offers significant potential for encouraging naturally ventilated buildings to improve both energy use and comfort. Research on other forms for providing individualized control through low-energy personal comfort systems (desktop fans, foot warmed, and heated and cooled chairs) have also demonstrated enormous potential for improving both energy and comfort performance. Studies have demonstrated high levels of comfort with these systems while ambient temperatures ranged from 64–84°F. Energy and indoor environmental quality are inextricably linked, and must both be important goals of a high performance building.

42 ENGINEERING↗
DOE OSTI2020

Comfort units and systems, methods, and devices for use thereof

Despite otherwise uncomfortable conditions in a surrounding environment, a customizable microenvironment can be created around a user to maintain a comfortable temperature and/or humidity level using a comfort unit. For example, the environment may be an office building where conditions are out of the comfortable range to save on energy or for other reasons, a factory/shop environment that is poorly conditioned, or an outdoor location with little to no conditioning. A sensing unit can monitor biometric and environmental data and can determine a comfort level of the user. The comfort unit can then dynamically respond to the determined comfort level and adjust the microenvironment to improve the user's comfort level. The comfort unit can follow the user as the user moves within the macro-environment, or can otherwise move within the macro-environment to achieve certain functions, such as recharging or spatial shifting of thermal load within the overall macro-environment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Energy-Efficient and Comfortable Buildings through Multivariate Integrated Control (ECoMIC)

This project aims to develop an integrated control solution for enhanced energy efficiency and user comfort in commercial buildings. The developed technology is a zone-based control framework that minimizes energy usage while maintaining occupants’ visual and thermal comfort through control of electric lights, motorized venetian blinds and thermostats. The control framework is designed following a modular, scalable and flexible architecture to facilitate easy integration with exiting building management systems. The control framework contains two key algorithms: 1) the lighting load balancing algorithm and 2) the thermostat control algorithm. The lighting load balancing algorithm adopts a model-based closed-loop control approach to determine the optimal electric light and venetian blind settings. It is formulated into an optimization problem with minimizing lighting-related energy consumptions as the objective and delivering adequate task light and preventing daylight glare as the constraints. The thermostat control algorithm is based on a well-established thermal comfort model and formulated as a root-finding problem to dynamically determine the optimal thermostat setpoint for both energy savings and improved thermal comfort. To address building-wide scalability, a system architecture was developed for the zone-based control technology. Three levels of services are defined in the architecture: external services, facility level services and zone level services. The zone-level service includes the control algorithms described above as well as the corresponding interfaces, profiles, sensors and actuators to realize the zone controller. The facility level services connect to the zones through a backbone network, handle supervisory level information and controls, and thus facilitate building-wide scalability. The external services provide communication capability to entities outside of the building for grid interaction and remote access. Various aspects of the developed control technology were evaluated and verified through both simulations and testbed implementations. Simulations coupling a DOE medium office reference building in EnergyPlus building simulation software and a prototype controller in Matlab were performed. During summer time in a mixed-humid climate zone, the simulations revealed reductions of 27% and 42% in electric lighting load and cooling load, respectively, when compared to an advanced base case with daylight dimming and blinds automatically tilted to block direct sun. Two single-room testbeds were established. The testbed at Philips Lighting business building (Rosemont, IL) was designed for quantifying energy performance of integrated controls. This particular implementation achieved 40% and 79% savings on lighting and HVAC energy, respectively, compared to a relatively simple base case operated on predefined schedules. While the resulting energy savings was very encouraging, it should be noted that there may be several caveats associated with it. 1) The test was run during late spring and early summer, and the savings numbers might not be directly used to extrapolate the annual energy savings. 2) Due to the needs for separate control and metering of the small-scale demonstrator within a large building, the HVAC system, hence the corresponding savings, did not represent a typical energy code-compliant design. 3) The light level in the control case was regulated at a particular setpoint, which was lower than then the full-on light level in the base case, and the savings resulted from tuning down the light level to the setpoint was not attributable to the contribution of the developed technology. The testbed at the Lawrence Berkeley National Laboratory (Berkeley, CA) specifically focused on glare control integration, and has demonstrated the feasibility and capability of the glare detection and prevention technique. While the short one-month test in this testbed provided a functional indication of the developed technology, and it would require at least a full solstice-to-solstice cycle to ruinously quantify the performance, which was not possible within the project timeframe. There are certain limitations inherited from the operational assumptions, which could potentially affect the effectiveness and applicability of the developed control technologies. The system takes a typical ceiling-mounting approach for the photosensor locations, and therefore, the control performance relies on proper commissioning or the built-in intelligence of the photosensor for pertinent task light level estimations. For spaces where daylight penetration diminishes significantly deeper into the zone, certain modification to the control algorithms is required to accommodate multiple lighting control subzones and the corresponding sensors for providing a more uniform light level across the entire zone. Integrated control of visual and thermal comfort requires the lighting control zone and thermal control zone to coincide with each other. In other words, the area illuminated by a lighting circuit needs to be the same area served by the thermostat. Thus, the original zoning will potentially constrain the applicability of this technology in retrofitting projects. This project demonstrated the technical feasibility of a zone-based integrated control technology. From the simulation results and testbed implementations, up to 60% lighting energy savings in daylit areas relative to a “no-controls” case can easily be achieved. A 20% reduction of whole building energy consumption is also attainable. In the aspect of occupant comfort, the testbed demonstrated the ability to maintain specified light level on the workplane while promptly mitigate daylight glare 90% of the time. The control system also managed to maintain the thermal environment at a comfortable level 90% of the time. The aspect of system scalability was guaranteed by the system architecture design, based on which the testbeds were instantiated. Analysis on the aspect of economic benefit has yielded an about 6-year payback time for a medium-sized building, including the installation of all hardware and software, such as motorized blinds and LED luminaires. The payback time can be significantly reduced if part of the hardware is already in place for retrofitting projects. It needs to be noted that since the payback analysis was partly based on the testbed performance results, it is constrained by the caveats associated with the testbed implementations. The main uncertainty lies in the contribution from the space conditioning energy savings as it was non-trivial to realistically configure a room-size HVAC system for directly extrapolating whole-building HVAC energy savings. It is recommended to further evaluate the developed technology at a larger scale, where the lighting and HVAC energy consumption can be realistically measured at the building level, to more rigorously quantify the performance potentials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Review of thermal comfort infused with the latest big data and modeling progresses in public health

Thermal environment is important for both occupants’ comfort and health. Previously, the impacts of thermal environment were explored in the areas of thermal comfort and public health separately. This paper aims to bridge both disciplines by examining the correlation between comfort temperature and Minimum Mortality Temperature (MMT), which is a key index quantifying the association of health and weather temperature, through literature review and data-driven approach. It was found that the MMT data obtained from the public health area are generally in good agreement with the thermal neutral temperatures from the comfort perspective. The MMT data range from 17.2 °C to 30 °C, which are similar to the thermal neutral temperatures ranging from 19.5 °C to 30 °C based on the global field tests. Moreover, the MMT data demonstrate the potential to capture some complex distribution patterns of the field comfort data. The introduction of the health-temperature data could assist the intensive field experiments and modeling efforts and complement the thermal comfort dataset, which suffers from the problems of limited sample size. Some discrepancies between the two datasets were identified as well. The contextual factors other than the climate factor which may cause such discrepancies, such as socio-economics, population densities, etc. should be analyzed to enable the potential application of the health-temperature data and modeling to thermal comfort and health studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Simulating Physiological Response with a Passive Sensor Manikin and an Adaptive Thermal Manikin to Predict Thermal Sensation and Comfort

Reliable assessment of occupant thermal comfort can be difficult to obtain within automotive environments, especially under transient and asymmetric heating and cooling scenarios. Evaluation of HVAC system performance in terms of comfort commonly requires human subject testing, which may involve multiple repetitions, as well as multiple test subjects. Instrumentation (typically comprised of an array of temperature sensors) is usually only sparsely applied across the human body, significantly reducing the spatial resolution of available test data. Further, since comfort is highly subjective in nature, a single test protocol can yield a wide variation in results which can only be overcome by increasing the number of test replications and subjects. In light of these difficulties, various types of manikins are finding use in automotive testing scenarios. These manikins can act as human surrogates from which local skin and core temperatures can be obtained, which are necessary for accurately predicting local and whole body thermal sensation and comfort using a physiology-based comfort model (e.g., the Berkeley Comfort Model). This paper evaluates two different types of manikins, i) an adaptive sweating thermal manikin, which is coupled with a human thermoregulation model, running in real-time, to obtain realistic skin temperatures; and, ii) a passive sensor manikin, which is used to measure boundary conditions as they would act on a human, from which skin and core temperatures can be predicted using a thermophysiological model. The simulated physiological responses and comfort obtained from both of these manikin-model coupling schemes are compared to those of a human subject within a vehicle cabin compartment transient heat-up scenario.

33 ADVANCED PROPULSION SYSTEMS↗
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 OSTI2019

OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information

Thermal comfort is a decisive factor for the well-being, productivity, and overall satisfaction of commercial building occupants. Many commercial building automation systems either use a fixed zone-wide temperature set-point for all occupants or they rely on extensive sensor deployments with frequent online interaction with occupants. This results in inadequate comfort levels or significant training effort from users, respectively. However, the increasing ubiquity of cheap, depth-based occupancy tracking systems has enabled an improvement in inferential capabilities. We propose the novel system OccuTherm to model thermal comfort of occupants. We conducted a laboratory study with 77 participants to collect data for the implementation of a thermal comfort model that derives thermal comfort using the human body shape. Based on the comparison with model baselines and ablations, we show that our approach infers thermal comfort of individuals with 60 % accuracy when body shape information is taken into account; 6 % more than state-of-the-art approaches. We make our code, mobile app, datasets, and models freely available.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Improving Comfort in Hot-Humid Climates with a Whole-House Dehumidifier, Windermere, Florida [Fact Sheet]

Maintaining comfort in a home can be challenging in hot-humid climates. At the common summer temperature set point of 75°F, the perceived air temperature can vary by 11°F because higher indoor humidity reduces comfort. Often the air conditioner (AC) thermostat set point is lower than the desirable cooling level to try to increase moisture removal so that the interior air is not humid or "muggy." However, this method is not always effective in maintaining indoor relative humidity (RH) or comfort. In order to quantify the performance of a combined whole-house dehumidifier (WHD) AC system, researchers from the U.S. Department of Energy's Building America team Consortium of Advanced Residential Buildings (CARB) monitored the operation of two Lennox AC systems coupled with a Honeywell DH150 TrueDRY whole-house dehumidifier for a six-month period. By using a WHD to control moisture levels (latent cooling) and optimizing a central AC to control temperature (sensible cooling), improvements in comfort can be achieved while reducing utility costs. Indoor comfort for this study was defined as maintaining indoor conditions at below 60% RH and a humidity ratio of 0.012 lbm/lbm while at common dry bulb set point temperatures of 74°-80°F. In addition to enhanced comfort, controlling moisture to these levels can reduce the risk of other potential issues such as mold growth, pests, and building component degradation. Because a standard AC must also reduce dry bulb air temperature in order to remove moisture, a WHD is typically needed to support these latent loads when sensible heat removal is not desired.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1975

Simulator studies and psychophysical ride comfort models

An elementary psychophysical model to predict ride comfort was developed using flight and simulator data where subjects were exposed to six degrees of freedom. The model presumes that the comfort response is proportional to the logarithm of the stimulus above some threshold stimulus. In order to verify this concept of comfort modeling, it was necessary to obtain ride comfort data for single degree of freedom random motions and for combinations of random motions. Accordingly, a simulator program was performed at the NASA Langley Research Center to measure subjective comfort response ratings using one degree of freedom, two degrees of freedom, three degrees of freedom, and six degrees of freedom. An analysis of the single degree of freedom and two degrees of freedom data is presented. Preliminary models of ride comfort response for single degree of freedom random motions and for certain combinations of two degrees of freedom random motions were developed.

Stone, R. W., Jr.↗