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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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A comparison of occupancy-sensing and energy-saving performance: CO2 sensors versus fisheye cameras
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Exploring the role of building codes in protecting occupants from overheating—A Canadian perspective
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Occupant-driven end use load models for demand response and flexibility service participation of residential grid-interactive buildings
As demand response becomes increasingly used as a tool to support improved grid flexibility, it is important to consider that there are many potential types of energy end uses that may be used to support such flexibility. Residential appliances, often accounting for 30 % or more of residential energy use, are a currently untapped source of demand flexibility, particularly when aggregated together across homes. To date there has been very limited analysis of residential appliances for use as grid-interactive loads. As such, this research uses disaggregated energy end use data for 564 households, to model the electricity demand flexibility potential of the use of residential dishwashers, clothes washers, clothes dryers, ovens, and ranges (oven + stovetop) on both weekdays and weekends. This includes both at the building level, as well as aggregated to the grid level, specifically the Midcontinent Independent System Operator (MISO) region. This study was divided into two parts. Part 1 focuses on determining appliance-level loads, and Part 2, which involves aggregation to the grid. Findings suggest that among the studied appliances, clothes dryers provide the greatest demand reduction potential for most times of the day, followed by dishwashers and clothes washers. The maximum potential reduction for clothes dryers is found to be approximately at 11:00 a.m. and this potential sustains throughout most of the daytime period. When considering the willingness of households to participate, based on a survey of households in the Midwest region, clothes dryers still have the most potential for demand reduction. The availability of appliances for load modulation on weekdays and weekends indicates similar load reduction potential for all appliances. Overall, the results of this study suggest that there is an opportunity for shifting appliance usage to optimize grid efficiency and enhance demand response strategies.
Toxicity reference values for force health protection: Provisional occupational exposure guidelines
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Cation Site Occupancy in Natural Ferroan Double Carbonates via Mössbauer and Fe K-Edge X-ray Absorption Spectroscopy
Double carbonates are minerals with a calcite type structure with alternating cation layers composed of Ca and Mg/Fe coordinated by carbonate groups. While the perfectly ordered AB-stacked crystal is the thermodynamically most stable configuration, natural mineral formation pathways can leave signatures through kinetically trapped disorder such as AB antisite cation substitutions. This study probes the degree of cation ordering in naturally occurring double carbonate samples.in particular, the distribution of Fe A and B crystallographic sites (A site is Ca layer and B site is Mg/Fe layer). Mössbauer, X-ray diffraction, X-ray absorption fine structure (both X-ray absorption near edge structure, XANES, and extended x-ray absorption fine structure, EXAFS), and energy dispersive X-ray spectroscopies were used to collect a comprehensive experimental data set, which we interpret using density functional theory to elucidate the structural effects of cation disorder. Our results show that the A (nominally Ca) site can host a relatively high Fe fraction. We discuss the implications in terms of mineral formation.
Vegetation canopy height shapes bats’ occupancy: a remote sensing approach
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Cosmological parameters from the joint analysis of density split and second order statistics: An emulator based on the halo occupation distribution
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Occupant-Centric Demand Response for Thermostatically-Controlled Home Loads
Efficiently managing energy usage to balance supply and demand on the electric grid is crucial, especially with the widespread deployment of distributed variable renewable electricity generation. This paper introduces two duty-cycle control methods for heating systems, adjusting thermostat setpoints to limit and shift electricity demand. The control approaches employ innovative techniques, such as adaptive duty cycling, to prioritize household thermal comfort while reducing peak demand. These control methods can respond to signals from the electric grid, including demand targets and time-of-use tariffs, and were tested physically on an electric furnace and heat pump in a test home during winter conditions in 2021 and 2022. The results are given as average demand reductions and energy use impacts with respect to the average indoor-outdoor temperature difference during the control period. For heat pumps, demand limiting control reduced power by 18.5% and 23.3% for indoor-outdoor temperature differences of 30°F and 40°F. Preheating-based demand shifting achieved reductions of 34.8% and 33.2% for the same temperature differences. Electric furnace tests showed demand reductions of 33.8% and 25.3% for demand limiting, and 56.1% and 45.7% for preheating-based demand shifting. These findings highlight the potential for innovative control methods to enhance grid efficiency and reduce energy consumption.
No Band Gap, No Problem: Atomic defects in narrow bandgap materials via a band-avoiding occupation-constrained density functional theory
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A space-time tracking algorithm for high occupancy events at future colliders
We propose to explore the potential advantages of a newclass of tracking algorithms loosely inspired by the Hough transformconcept and where we include the time of arrival of each hit as anadditional coordinate to be treated in the same way as a spatialcoordinate. A remarkable property of this algorithm is that theexecution time is proportional to the total number of hits to beprocessed, making it particularly attractive for high occupancysituations expected at future colliders. The particular structureof the algorithm also lends itself naturally to parallel hardwareimplementations which, combined to its intrinsic flexibility, shouldprovide a powerful tool for triggering at future colliders. To probethe effectiveness of the algorithm, we apply it to a quasi-realisticsimulated environment of a possible future muon collider experimentand report the performance.
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.
From roads to roofs: How urban and rural mobility influence building energy consumption
In this article, understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.
Atomistic Simulations of Thermal and Chemical Expansions of PrNi x Co 1‐x O 3‐δ Accelerated by Machine Learning Potentials
The electrodes and solid-state electrolytes in protonic ceramic electrochemical cells (PCECs) experience significant lattice expansions when exposed to high steam concentrations at elevated temperatures. In this paper, phonon calculations based on a new machine learning potential (MLP) are employed to elucidate the volume expansions of the proton-conducting PrNi x Co 1-x O 3-δ (PNC) lattices, manifested under a combined influence of oxygen vacancies (V$^{\cdot\cdot}_O$ ) and proton uptake (OH$^{\cdot}_O$ ) in the bulk at varying Ni/Co occupancies. It is revealed that the Ni/Co occupancy contributes to thermal and chemical expansions differently, where thermal expansions are related to Co occupancy. In contrast, chemical expansions are more closely associated with the Ni occupancy. Both V$^{\cdot\cdot}_O$ and OH$^{\cdot}_O$ lead to higher thermal expansions when compared to the pristine PNC. The temperature increase will negatively impact the hydration-induced chemical expansions. For combined thermal and chemical expansions, it is predicted that the strategies that boost the PCEC's electrochemical performance may harm the electrode–electrolyte interfacial stability, when the Ni occupancy is high, due to severe chemical expansions. Mitigating chemical expansions of the Ni-abundant PNC will benefit the interfacial stability. Finally, the presented computational methods for phonon calculations, based on emerging machine learning interatomic potential techniques are anticipated to have a lasting impact on future PCEC development.
Effective optimization of atomic decoration in giant and superstructurally ordered crystals with machine learning
Crystals with complicated geometry are often observed with mixed chemical occupancy among Wyckoff sites, presenting a unique challenge for accurate atomic modeling. Similar systems possessing exact occupancy on all the sites can exhibit superstructural ordering, dramatically inflating the unit cell size. In this work, a crystal graph convolutional neural network (CGCNN) is used to predict optimal atomic decorations on fixed crystalline geometries. This is achieved with a site permutation search (SPS) optimization algorithm based on Monte Carlo moves combined with simulated annealing and basin-hopping techniques. Our approach relies on the evidence that, for a given chemical composition, a CGCNN estimates the correct energetic ordering of different atomic decorations, as predicted by electronic structure calculations. This provides a suitable energy landscape that can be optimized according to site occupation, allowing the prediction of chemical decoration in crystals exhibiting mixed or disordered occupancy, or superstructural ordering. Verification of the procedure is carried out on several known compounds, including the superstructurally ordered clathrate compound Rb8Ga27Sb16 and vacancy-ordered perovskite Cs2SnI6, neither of which was previously seen during the neural network training. In addition, the critical temperature of an order–disorder phase transition in solid solution CuZn is probed with our SPS routines by sampling site configuration trajectories in the canonical ensemble. This strategy provides an accurate method for determining favorable decoration in complex crystals and analyzing site occupation at unprecedented speed and scale.
Comparison of the performance of TLD, OSL and RPL personal dosimetry systems to the American National Standard Institute (ANSI) test categories
The Laboratory Accreditation Program (LAP) tests the capability and performance of the United States Department of Energy's (USDOE) facilities to accurately measure and quantify the whole-body and extremity radiation equivalent doses to the occupational workers. The dosimetry methods used in personal dosimetry can be Thermoluminescence (TL), Optically Stimulated Luminescence (OSL) and Radiophotoluminescence (RPL), among other techniques available. Currently, the TL (LiF:Mg,Ti) and OSL (Al 2 O 3 :C) dosimetry systems are DOELAP-accredited for occupational dose measurements and regulatory reporting. In this study, the performance of a beryllium oxide (BeO) OSL dosimetry system and of a silver-doped phosphate glass RPL dosimetry system is compared against the performance of an accredited LiF:Mg,Ti TL dosimetry system. The bias and standard deviation in each of the performance test categories are compared between these three systems for exposures to photons, beta, and mixed radiation fields with the criteria established by the American National Standard Institute (ANSI) and Health Physics Society (HPS) N13.11-2022 standard. The practical implementation of the dosimetry program and its equivalency to the occupational personal equivalent dose H p (0.07) and H p (10) measurements were evaluated. The TL, OSL and RPL dosimetry systems passed the ANSI performance test criteria for H p (0.07) and H p (10) occupational dose measurements and met the DOELAP accreditation requirements.
Hardware-in-the-loop Laboratory Performance Verification of Flexible Building Equipment in a Typical Commercial Building
This project aims to develop high-resolution equipment performance and occupant data that quantifies demand flexibility in typical commercial buildings. The dataset documented in this report includes comprehensive time-series measurements from hardware-in-the-loop (HIL) experiments conducted across multiple testbeds designed to simulate realistic operational environments for HVAC systems. Specifically, it captures minute-by-minute high-resolution data on the performance of various typical HVAC systems, including a variable-air-volume (VAV) air handling unit (AHU) system with chillers and an ice tank in the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST), a two-stage air-source heat pump (ASHP) at the NIST, and a water-source heat pump (WSHP) at Texas A&M University (TAMU). The data were generated under a range of controlled conditions reflecting different grid scenarios and climatic influences, as well as various control strategies, building types, occupancy patterns, and occupant behaviors. This dataset provides DE-EE0009153 Final Report 5 detailed insights into the demand flexibility of these systems, including their response to grid signals, occupant behaviors, energy consumption patterns, and operational efficiency under different conditions.
Minimum Detectable Intakes and Doses for Uranium Bioassays—Comparison between Alpha Spectrometry and ICP-MS
Naturally occurring uranium complicates monitoring for occupational exposures. There are several retroactive methods that can be used to monitor for occupational exposures, with benefits and drawbacks to each. Analysis of uranium in urine by mass spectrometry and alpha spectrometry is compared, and methods of determining an occupational exposure are presented. Furthermore, the minimum detectable concentrations from each analysis and a method for intake determination based on the analytical results are compared for various solubility types and mixtures. Mass spectrometry with radiochemical separation was found to be the most sensitive analysis for detecting occupational exposures to anthropogenic mixtures based on minimum detectable doses calculated from the proposed method for intake determination.