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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Best of Both Worlds: Combined Thermal and Battery Storage for Widespread Building Decarbonization

To meet 2050 decarbonization targets, widespread building electrification is a critical complement to clean power generation. Behind-the-meter storage (BTMS) (e.g., battery electric energy storage [EES] and thermal energy storage [TES]) integrated with buildings or building end uses to store and supply energy at optimal times can minimize burdens associated with operation, planning, and upgrades to the electrical grid sometimes triggered by building electrification. Such BTMS systems can serve the dual purpose of providing enhanced resilience at the building and grid level, and support the deployment of renewable generation needed for wide-scale decarbonization. While TES can cost-effectively shed and shift thermal loads, it cannot generally backup or shift non-thermal building end uses. EES, by contrast, is more expensive, but applicable to all end uses (i.e., thermal and electrical loads). Combined together, these storage systems can be traded off against one another to perform optimally in meeting demand flexibility, decarbonization goals, and energy resilience of the buildings at a lower total system cost. This paper proposes a framework to define BTMS benefits, provides four illustrative electrification scenarios using TES and EES, and discusses the combined TES/EES benefits with building energy modeling results. The paper also highlights potential barriers to adoption of BTMS and a path forward.

buildings↗
DOE OSTI2022

An analytical method for identifying synergies between behind-the-meter battery and thermal energy storage

Electric utilities build generation capacity to meet the highest demand period, and they often pass on the costs associated with these peaking generators to building owners through demand charges. Building owners can minimize these demand charges by shifting energy use away from peak periods with behind-the-meter storage. This storage can include batteries, which can directly shift the metered load, or thermal energy storage, which can shift thermal-driven electric loads like air conditioning. However, there is a lack of research on how best to combine battery and thermal energy storage. In this study, we develop an analytical sizing method to calculate the potential demand reduction and annualized cost savings for different combinations of thermal and battery energy storage sizes. We show that adding batteries to a thermal energy storage system can increase the total system's load shaving potential. This is particularly true when the building has onsite photovoltaic generation or electric vehicle charging, which add significant variability to the load shape. We also show that for a given total storage size, selecting a higher fraction of thermal energy storage can significantly lower the cycling of the battery, and therefore extend the battery life. This, combined with the expected lower first cost of thermal energy storage materials compared to batteries, shows that hybrid energy storage systems can outperform a standalone battery or standalone thermal storage system. Assuming the thermal storage has a capital cost 6x lower than the battery, our analysis shows that the optimal system is 71% thermal energy storage and 29% battery energy storage for a scenario with electric vehicle charging. The annualized cost savings for this system are $48.6 k/yr, whereas an equivalently sized standalone thermal energy storage system would provide annualized cost savings of $28.5 k/yr and a standalone battery would lead to savings of $8.72 k/yr. The hybrid system also reduces battery cycling by 52% compared to a standalone battery, extending battery lifetime.

25 ENERGY STORAGE↗
DOE OSTI2021

Performance Assessment of the Overall Building Envelope Thermal Performance—Building Envelope Performance (BEP) Metric

Today, to describe the thermal performance of the building envelope and its components we use a variation of metrics; such as, R-value, ACH (air exchange rate per hour), SHGC (solar heat gain coefficient) of windows, U-factor etc. None of these performance indicators is meant to represent the overall thermal performance. In this paper, such a metric is introduced, the BEP (building envelope performance) value. Unlike the thermal resistance, typically expressed as an R-value, the BEP-value considers additional elements of heat transfer that affect the energy demand of the building because of exterior and interior (solar) thermal loads: conductive and radiant heat transfer, and air infiltration. To demonstrate BEP’s utility, validation studies were carried out by comparing the BEP-value to theoretical results using whole building energy simulation tools such as EnergyPlus and WUFI Plus. Results show that BEP calculations are comparable to calculations made using these simulation tools and that unlike other similar metrics, the BEP-value accounts for all heat transfer mechanisms that are relevant for the overall energy performance of the building envelope. The BEP-value thus allows comparing envelopes of buildings with different use types in a fair and realistic manner.

42 ENGINEERING↗
NASA NTRSDate not supplied

Artemis I Prelaunch Flight Readiness Analysis

The Prelaunch loads analysis traditionally captures the vehicle and Mobile Launcher (ML) loads associated with stacking in the Vehicle Assembly Building (VAB), rollout to the pad, and high-speed winds while out on the pad prior to liftoff. The following paper details the methodology used in this analysis for loads for the Artemis Flight Readiness assessment. This paper focuses on the stacking and assembly loads, the loads induced by fueling, and the load seen during rollout to the pad. Methods for integrating multiple structural models in dynamic simulations using interface forces, constraint modes, and residual vectors is discussed. Both linear and nonlinear responses are addressed. Simulation results are compared to measured data from the Dynamic Rollout Test (DRT) and Wet Dress Rehearsal (WDR).

SLS↗
DOE OSTI2023

Techno-Economic Analysis of Dynamic Building Envelopes Comprising Phase Change Materials and Switchable Insulations: Preprint

Integrating phase change materials (PCMs) in building envelopes is a recognized technique to reduce the space heating/cooling loads and provide load shedding and shifting capacity. However, PCM benefits have been found to reduce dramatically with increase in the insulation requirements. Dynamic building envelopes that can alter their thermal resistance based on the indoor and outdoor conditions can effectively harness free ambient heating/cooling, thereby greatly enhancing the benefits of the PCM-integrated envelope in managing thermal loads. In this study, we examine various combinations of PCMs and switchable insulations and compare their combined impact on heat flow through the building envelope. Using numerical analysis, we investigate the PCM- and switchable insulation-integrated building walls, calculate the potential energy saving benefits compared to the conventional static walls, and finally perform technoeconomic analysis to estimate the acceptable cost of the technology under various payback period scenarios.

dynamic building envelopes↗
DOE OSTI2022

Techno-Economic Analysis of Dynamic Building Envelopes Comprising Phase Change Materials and Switchable Insulations

Integrating phase change materials (PCMs) in building envelopes is a recognized technique to reduce the space heating/cooling loads and provide load shedding and shifting capacity. However, PCM benefits have been found to reduce dramatically with increase in the insulation requirements. Dynamic building envelopes that can alter their thermal resistance based on the indoor and outdoor conditions can effectively harness free ambient heating/cooling, thereby greatly enhancing the benefits of the PCM-integrated envelope in managing thermal loads. In this study, we examine various combinations of PCMs and switchable insulations and compare their combined impact on heat flow through the building envelope. Using numerical analysis, we investigate the PCM- and switchable insulation-integrated building walls, calculate the potential energy saving benefits compared to the conventional static walls, and finally perform techno-economic analysis to estimate the acceptable cost of the technology under various payback period scenarios.

dynamic building envelopes↗
DOE OSTI2020

Energy Performance Validation of a Gaseous Air Cleaning Technology for Commercial Buildings

The U.S. Department of Energy (DOE) Building Technologies Office Commercial Building Integration group conducts evaluations of new energy efficiency technologies as part of the High Impact Technology Catalyst program. The subject of this field validation project is an air cleaning technology that removes gaseous contaminants from indoor air to reduce outdoor air ventilation requirements. Reducing the volume of outdoor air required reduces heating, ventilating, and air conditioning (HVAC) loads and can provide significant energy savings for some buildings in certain climates. The technology, called the HLR (HVAC Load Reduction), was developed by enVerid. The technology uses a solid regenerable sorbent material that was developed by DOE's National Energy Technology Laboratory and licensed to the vendor for this application. The sorbent material was originally developed to adsorb carbon dioxide (CO2); the vendor continued to develop the sorbent and the overall system so that it can also remove volatile organic compounds (including aldehydes), carbon monoxide, ozone, and particulate matter 2.5 micrometers in diameter or smaller (PM2.5) from the air. This material has low regeneration temperature requirements, and its sorption properties are unaffected by humidity levels, both of which are beneficial for building applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

A Simulation Framework for Analyzing the Impact of Stochastic Occupant Behaviors on Demand Flexibility in Typical Commercial Buildings

As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants’ comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment ofthe building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants’ thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVAC system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVAC systems in terms ofdemand flexibility potential under the impact of occupant behaviors.

Chen, Zhelun↗
NASA NTRS2016

Meso-Scale Digital Materials: Modular, Reconfigurable, Lattice-Based Structures

We present a modular, reconfigurable system for building large structures. This system uses discrete lattice elements, called digital materials, to reversibly assemble ultralight structures that are 99.7% air and yet maintain sufficient specific stiffness for a variety of structural applications and loading scenarios. Design, manufacturing, and characterization of modular building blocks are described, including struts, nodes, joints, and build strategies. Simple case studies are shown using the same building blocks in three different scenarios: a bridge, a boat, and a shelter. Field implementation and demonstration is supplemented by experimental data and numerical simulation. A simplified approach for analyzing these structures is presented which shows good agreement with experimental results.

Reconfigurable↗
DOE OSTI2020

Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data

Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Geothermal-integrated thermally anisotropic building envelope for energy and peak-demand reduction

Buildings consume large amounts of energy for heating and cooling, while peak electricity demand places significant stress on the power grid. This paper presents a reduced-order co-simulation framework and load-oriented supervisory control strategy for a geothermal-integrated thermally anisotropic building envelope with a ground loop (TABE+GL). In TABE+GL, a hydronic loop embedded in the building envelope is directly coupled with a geothermal ground loop, allowing for bidirectional heat exchange between the envelope, the ground, and the indoor environment. A hybrid co-simulation framework was established by coupling a reduced-order resistor–capacitor (RC) thermal network model with EnergyPlus augmented with GHEDesigner modules. The RC model generated feasible heat flux options under three operating modes, and EnergyPlus predicted sensible loads, energy use, and pump energy demand. At each simulation step, a supervisory control algorithm selected the optimal loop configuration and duty factor that maximizes useful TABE geothermal utilization without exceeding the predicted sensible load, thereby avoiding overheating or cooling. Case studies were conducted for Los Angeles, California, Charleston, South Carolina, and Denver, Colorado. Results showed that the proposed framework reduced HVAC electricity consumption by 43%–67%, natural gas use for space heating by 11%–38%, and peak electricity demand by 43%–88%. These results highlight the potential of combining reduced-order envelope modeling, direct geothermal coupling, and load-oriented supervisory control to improve whole building energy performance and reduce peak demand across diverse weather conditions.

Howard, Daniel [Southern Adventist University]↗
DOE OSTI2022

Efficient Loads Surrogates for Waked Turbines in an Array

Accurately and efficiently predicting wind turbine structural loading is a crucial step in wind farm design. Without considering structural loading, wind farm optimization could negatively impact turbine fatigue and ultimate loads, especially for waked and partially waked turbines, which could result in higher maintenance costs and reduced turbine lifetime. However, predicting turbine loads throughout an array is a costly step, as these quantities require time-accurate results across long time histories, which is often intractable for large array optimization. Therefore, surrogate models that link array spacing to load outputs are often used, but the surrogates are then unique to the inflow conditions and array configurations in the training library. This work develops surrogate models for many wind turbine load outputs based solely on rotor plane velocity measurements, with no required input about array configuration or freestream inflow parameters. Surrogate models were constructed for many turbine quantities of interest (QoI), considering mean, standard deviation, ultimate, and fatigue loads. In general, most QoI statistics were accurately captured, as measured by predicted vs. actual correlation coefficient, confirming the suitability of the approach. Temporal mean values of the QoI required only temporal mean measurements of the rotor plane inflow velocity. However, accurate prediction of temporal standard deviation, ultimate, and fatigue values of QoI also required temporal standard deviations of the rotor plane velocity field. Poor surrogate performance was observed when too many correlated inputs were used, such as multiple velocity components. If the fewest inflow parameters are used to construct the surrogates, the average correlation coefficient value for all output QoI statistics is 0.89. Surrogates for standard deviations and damage equivalent loads (DELs) of turbine QoIs generally had lower accuracy and tower-base and shaft load channels posed the most difficult to capture accurately. The results suggest that these surrogates could be easily paired with analytic wake models, which are frequently used for pre-construction wind farm array optimization, to account for turbine loading in addition to power production. By including the optimal inflow conditions, the surrogate accuracy can improve to an average correlation coefficient value for all output QoI statistics of 0.92. This work has established the ability to build accurate surrogates for mean, standard deviation, ultimate load, and DEL turbine QoI values based on the rotor plane inflow velocity, and identified which inflow conditions lead to greater surrogate accuracy.

17 WIND ENERGY↗
DOE OSTI2022

The Environmental Impact of Deep Energy Retrofits in Residential Construction

Almost two thirds of the residential homes in the U.S. were constructed before the Department of Energy implemented energy conservation measures that were later formalized in the building codes. To reduce energy consumption and carbon emissions from the built environment, improvement to the existing housing stock is a prerequisite. However, improvements that lower energy consumption address carbon emissions related to heating, cooling and plug loads but not the embodied carbon of the building materials that also contribute to global carbon emissions. As the enclosure is made more efficient a larger portion of total carbon emissions is embodied carbon from the building materials. To mitigate carbon emissions, a holistic approach is required so that any improvement to the home results in a net reduction in carbon emissions. To understand the overall environmental impact of building enclosure improvements, the embodied and operational carbon emissions are calculated in this investigation and compared to the overall energy savings associated with enclosure improvements. The energy savings together with a reduction in operational carbon emissions are calculated using a novel building envelope metric. The net carbon emissions are then determined before and after the energy improvement measures. This approach allows or enables architects and builders to select designs that result in maximizing the overall reduction in energy and carbon emissions.

Aldykiewicz Jr, Antonio↗
DOE OSTI2020

Experimental study on a novel three-phase absorption thermal battery with high energy density applied to buildings

Buildings contribute to 75% of total electricity consumption and 80% of peak electricity demand in the United States. A significant portion of building electricity consumption goes to space conditioning. Therefore, using thermal energy storage (TES) to decouple the building electricity consumption and the fluctuating space-conditioning load has significant potential to shift peak electricity demand and improve the stability of the grid. To provide effective TES within the spatial constraints in buildings, a three-phase absorption thermal battery (TATB) system with very high energy storage density was designed and studied. The TATB stores low-temperature heat in hydrate crystals of salts and supports space conditioning through absorption systems. In this study, a benchtop TATB using lithium chloride (LiCl) hydrate crystals as the storage material was tested under practical operating conditions for its energy storage density and discharge rate. We report the TATB successfully generated LiCl hydrate crystals during charging mode and then dissolved the crystals during discharging mode. A material-based energy storage density of 300 kWh/m 3 (specific energy of 903 kJ/kg) was achieved with a discharge rate of up to 1.3 kW thermal energy. The test results verified the high performance of the TATB and encouraged further development of this technology.

25 ENERGY STORAGE↗
DOE OSTI2020

Accuracy of HVAC Load Predictions: Validation of EnergyPlus and DOE-2 using FLEXLAB Measurements

The aim of the project reported here was to better understand the level of accuracy of three building energy simulation (BES) engines (‘engines’) — EnergyPlus™, DOE-2.1e, and DOE-2.2 — by identifying and investigating significant deviations between the performance predicted by these engines and actual performance as measured in the FLEXLAB® test facility at Lawrence Berkeley National Laboratory (LBNL). The specific test conditions included some of those prescribed in ANSI/ASHRAE Standard 140 - Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Detailed measurements of FLEXLAB performance, including indoor temperatures and heat fluxes and air-flow and water flow rates and temperatures in the Heating, Ventilating and Air Conditioning (HVAC) system, together with hourly weather data, were recorded and used in analyzing the simulation results from EnergyPlus v8.8, DOE-2.2 v3.65 and DOE-2.1e v127. These engines are commonly used in the United States for building energy code compliance, federal, state, and utility incentives programs, as well as energy efficient design of new buildings and energy retrofit of existing buildings. Seven conventional overhead mixing ventilation scenarios were tested and each engine was found to have a similar level of agreement with the measurements of space-level heating and sensible cooling loads. These results provide useful information regarding the accuracy of these engines in predicting the cooling and heating load elements of whole building energy performance. This information is intended for practitioners who are concerned about transitioning between simulation tools with different engines and for managers of utility programs leveraging these tools for evaluating and/or projecting measure savings to be incentivized under their programs. The results of the comparisons of simulated and measured performance indicate that the predictions from all three engines are not significantly different. The 24-hour average value of the absolute mean bias indicates the likely magnitude of the error in any particular case. The average mean bias is reduced by cancelation of overprediction in one case by underprediction in another. The daytime absolute mean biases, which may be more important for both energy performance and occupant comfort, are ~6%, presumably because of the greater complexity involved in simulating in the presence of solar radiation. EnergyPlus typically overpredicts the cooling load and/or underpredicts the heating load by ~1.5% and the DOE-2 engines typically underpredict the cooling load by approximately the same amount. The Root Mean Square Error is relatively more sensitive to shorter term variations in the difference between predicted and measured loads; the three engines have similar values, ~10%, suggesting that the uncertainties in their predictions of peak loads may also be similar in magnitude. The implication of these results is that users, both designers and program analysts, can use EnergyPlus, DOE-2.1e, or DOE-2.2 to model conventional commercial buildings equipped with overhead mixing ventilation with a similar level of confidence. Further work is required to better understand the variability in the level of agreement between the engine predictions and FLEXLAB measurements, where a particular engine will agree well with FLEXLAB in some cases and not so well in others and another engine will agree or disagree in different cases. As the sources of this variability are identified and eliminated or reduced significantly, it is recommended that the experimental capabilities and methods developed in the study reported here should be applied to validating heating and cooling load calculations for spaces with different types of furniture and miscellaneous loads. These methods should then be applied to low energy space conditioning systems in EnergyPlus including, in particular, radiant slab and radiant ceiling panel cooling and heating systems and ‘mixed mode’ systems that combine mechanical cooling and natural ventilation systems, focusing on controls, including control of thermal mass. The work reported here addresses the conventional method of heating and cooling occupied spaces; other methods, such as the use of radiant heating and cooling systems have the potential to provide equivalent occupant comfort, or better, with lower energy consumption. These systems are addressed more explicitly in EnergyPlus but there is a need for empirical validation to give users the same level of confidence in modeling these systems that they have, or should have, in modeling conventional systems, based on the results presented here.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Thermal Storage Performance of a Dual-Purpose Underground Thermal Battery for Shaping the Electric Demand of Buildings

A dual-purpose underground thermal battery (DPUTB) integrates a ground heat exchanger with underground thermal energy storage. It can be installed in shallow boreholes (less than 6 m deep) and thus is less expensive than the conventional ground heat exchangers. The thermal energy storage can be used to shave or shift the electric load for meeting the thermal demands of a building. The charging and discharging performance of a lab-scale DPUTB were tested. The test results show that the DPUTB can be fully charged within 4 h and can provide 34 W cooling continuously for 2.5 h with a supply water temperature below 14°C. A small amount of phase-change material significantly increased the thermal storage capacity.

Wang, Lingshi↗