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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Review on Bioinspired Proton Exchange Membrane Fuel Cell: Design and Materials

With the climate crisis gathering recognition, there has been significant interest in clean energy. As a reliable and clean energy solution, markets transitioning toward sustainable energy have identified polymer electrolyte membrane fuel cells (PEMFCs) as a critical technology. The increasing interest in PEMFCs is primarily due to their high energy density, high efficiency and zero greenhouse gas emission. However, additional development is required to overcome the current barriers associated with fuel cell component design, high manufacturing cost, and insufficient stability. Bioinspired designs have the potential to provide new and valuable insights using novel techniques for extracting design inspirations from biological structures. Applying these design concepts to develop materials and structures for various fuel cell components can be transformative. The well–adapted structures and functional features of biological systems have been proven to greatly enhance the performance of current designs through novel and optimized pathways. Bioinspired designs have indeed lived up to performance expectations and even exceed them. Herein, the potential and value of using these bioinspired designs are discussed. Recent studies and advancements based on nature–inspired structures for components of PEMFCs and how these design considerations substantiate their claim as an attractive alternative to conventional designs are discussed.

25 ENERGY STORAGE↗
DOE OSTI2025

Pricing Strategy of Electric Vehicle Aggregators Based on Locational Marginal Price to Minimize Photovoltaic (PV) Curtailment

The global climate crisis demands urgent action to mitigate global warming. Using renewable energy sources, such as solar and wind power, for electricity generation is crucial. This shift from centralized to distributed power systems, however, brings challenges, including voltage fluctuations and renewable energy curtailment. The rapid growth of the electric vehicle (EV) industry adds complexity, increasing overall electricity demand and straining the power supply during peak charging times. This paper proposes a scheduling strategy for EV aggregators to reduce renewable energy curtailment and stabilize grid operation by strategically scheduling EV charging. Using Multi -Agent Transport Simulation (MATSim), a traffic simulation tool, EV driving data in Denver, Colorado, USA, were modeled. The EV aggregator adjusts charging fees based on locational marginal prices, encouraging EVs to charge at different stations according to pricing. Simulations on an IEEE 33-bus system with distributed energy resources and EV charging stations validate the proposed algorithm, demonstrating its effectiveness in reducing curtailment by 12.55% and stabilizing grid operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Dynamic Thermal Performance Analysis of PCM Products Used for Energy Efficiency and Internal Climate Control in Buildings

PCMs are attractive for the future generation of buildings, where energy efficiency targets and thermal comfort expectations are increasingly prioritized. Experimental analysis of local thermal processes in these dynamic components and whole-building energy consumption predictions are essential for the proper implementation of PCMs in buildings. This paper discusses the experimental analysis of the thermophysical characteristics of both a latent heat storage material (PCM) and a product containing this PCM. The prototype product under investigation is a panelized PCM technology containing inorganic, salt-hydrate-based PCM. The thermal analysis includes studies of melting and freezing temperatures, enthalpy changes during phase change processes, nucleation intensity, sub-cooling effects, and PCM stability. The PCM’s stability is also investigated, as is the ability of PCM products to control local temperatures and peak load transmission times. Two inorganic PCM formulations based on calcium chloride hexahydrate (CaCl 2 .6H 2 O) were prepared and tested in laboratory conditions. Material-scale testing results were compared with outcomes from the system-scale analysis, using both laboratory test methods as well as field exposure in test huts. This work demonstrates that PCM technologies used in buildings can effectively control both the magnitude of thermal storage capacity as well as the time of the peak thermal load. It was found that commonly used material-scale testing methods may not always be beneficial in assessing the dynamic thermal performance characteristics of building technologies containing PCMs.

42 ENGINEERING↗
DOE OSTI2024

The Realistic Potential of Soil Carbon Sequestration in U.S. Croplands for Climate Mitigation

Existing estimates of the climate mitigation potential from cropland carbon sequestration (C-sequestration) are limited because they tend to assume constant rates of soil organic carbon change over all available cropland area, use relatively coarse land delineations, and often fail to adequately consider the agronomic and socioeconomic dimensions of agricultural land use. This results in an inflated estimate of the C-sequestration potential. We address this gap by defining a more appropriate land base for cover cropping in the United States for C-sequestration purposes: stable croplands in annual production systems that can integrate cover cropping without irrigation. Our baseline estimate of this suitable stable cropland area is 32% of current U.S. cropland extent. Even an alternative, less restrictive definition of stability results in a large reduction in area (44% of current U.S. croplands). Focusing cover crop implementation to this constrained land base would increase durability of associated C-sequestration and limit soil carbon loss from land conversion to qualify for carbon-specific incentives. Applying spatially-variable C-sequestration rates from the literature to our baseline area yields a technical potential of 19.4 Tg CO 2 e yr –1 annually, about one-fifth of previous estimates. We also find the cost of realizing about half (10 Tg CO 2 e yr –1 ) of this potential could exceed 100 USD Mg CO 2 e –1 , an order of magnitude higher than previously thought. While our economic analyses suggest that financial incentives are necessary for large-scale adoption of cover cropping in the U.S., they also imply any C-sequestration realized under such incentives is likely to be additional.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

AICCA: AI-Driven Cloud Classification Atlas

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Observational evidence for groundwater influence on crop yields in the United States

As climate change shifts crop exposure to dry and wet extremes, a better understanding of factors governing crop response is needed. Recent studies identified shallow groundwater—groundwater within or near the crop rooting zone—as influential, yet existing evidence is largely based on theoretical crop model simulations, indirect or static groundwater data, or small-scale field studies. Here, we use observational satellite yield data and dynamic water table simulations from 1999 to 2018 to provide field-scale evidence for shallow groundwater effects on maize yields across the United States Corn Belt. We identify three lines of evidence supporting groundwater influence: 1) crop model simulations better match observed yields after improvements in groundwater representation; 2) machine learning analysis of observed yields and modeled groundwater levels reveals a subsidy zone between 1.1 and 2.5 m depths, with yield penalties at shallower depths and no effect at deeper depths; and 3) locations with groundwater typically in the subsidy zone display higher yield stability across time. We estimate an average 3.4% yield increase when groundwater levels are at optimum depth, and this effect roughly doubles in dry conditions. Groundwater yield subsidies occur ~35% of years on average across locations, with 75% of the region benefitting in at least 10% of years. Overall, we estimate that groundwater-yield interactions had a net monetary contribution of approximately $10 billion from 1999 to 2018. This study provides empirical evidence for region-wide groundwater yield impacts and further underlines the need for better quantification of groundwater levels and their dynamic responses to short- and long-term weather conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Identification of opportunities for integrating chemical processes for carbon (dioxide) utilization to nuclear power plants

The energy and industrial sectors are responsible for more than 75% of global carbon dioxide emissions, which are primary contributors to climate change. Decarbonization strategies to reduce overall carbon dioxide emissions are urgently needed. Among the numerous decarbonization strategies that are being defined and implemented, a particular strategy of interest for the present work is “the Re-X or zero waste” strategy, which involves recycling, reuse, repurposing, and remanufacturing of industrial emissions, by-products, and waste in general. A circular value chain or Circular Economy is embedded within this strategy. In a circular carbon economy, efficient reuse of emitted carbon dioxide is considered. However, the stability of carbon dioxide implies the requirement of significant amounts of energy for its transformation into value-added chemicals or products, which must be supplied by low carbon emitting energy sources. Nuclear power plants are low carbon energy sources that, additionally to electricity, could also supply heat and radiation for chemical transformations. This work identifies the opportunities and challenges for the development of integrated energy systems to upgrade and transform carbon dioxide, involving chemical and nuclear energy. The analysis encompasses possible use of the different forms of energy that can be obtain from nuclear reactors (i.e., radiation, electricity, and heat), by reviewing the published literature on potential routes for its conversion. Overall, our review indicates that an universal technology of the one-fits-all solution-type is an utopic dream. Instead, a suite of (contaminants tolerant) technologies for processing various concentrations CO 2 and producing a variety of products is currently needed.

08 HYDROGEN↗
DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗
DOE OSTI2024

CO2 Capture Using Amines Bound to Silica

One of the main culprits of global warming is the increased amount of carbon dioxide, or CO2, in the atmosphere. NASA's global climate reports a 13% increase in atmospheric CO2 from 2000 to present. Adsorptive CO2 capture by nitrogen groups of amine-containing solvents is one of the most mature technologies deployed in petrochemical and natural gas processing plants to purify industrial gases. However, key challenges in widespread application include solvent induced reactor corrosion, amine degradation, and high regeneration energy for repeated cycling. An alternative approach is to immobilize amines on solid supports. Key performance metrics of solid amine-based CO2 adsorbents include the CO2 adsorption capacity and stability to degradation over hundreds of thousands of regeneration cycles. Our research aims to develop descriptors for CO2 capture capacity and stability against oxygen-induced degradation for amines bound to porous silica supports using experimental and computational techniques. We experimentally measure the change in CO2-uptake using solid amine adsorbents with varying chemical compositions and exposure to varying gas streams and use high-performance computers to simulate the nature and strength of CO2-adsorption and oxidative degradation reaction mechanisms. Insights from our work can facilitate the development of stable solid amine adsorbents for large-scale CO2 capture processes.

amines↗
DOE OSTI2022

Connecting the Radiative Influences of Aerosol upon the Mass Flux Profiles of Shallow Cumuli across the Southeast Atlantic Ocean Basin and its Boundaries (Final Report)

The Atlantic Ocean covers approximately 25% of Earth’s surface and the atmosphere above it is home to a complex array of clouds and aerosols that have important influences on regional and global weather and climate. These influences must be accurately depicted in short range, medium range, seasonal, and climate forecast models. Conditions over the tropical Atlantic are particularly complex due to continental scale plumes of dust from the Sahara Desert and smoke from agricultural burning in Africa that drift across the Atlantic Ocean basin toward the Americas. These plumes are often found meandering in the lower atmosphere above shallow tropical clouds that form above the ocean surface, presumably mingling with these clouds on occasion due to convective mixing processes. Elevated dust and smoke particles absorb incoming sunlight and substantially warm the marine atmosphere in the layer in which they are present. This warming may alter the thermal stability of the marine atmosphere and may throttle or enhance the development of clouds, change their internal structure and the rate at which they precipitate. Alternatively, it may isolate the lower atmosphere from drier layers above enabling water vapor to accumulate near the ocean surface potentially leading to the development of deeper convection. Our study was organized around the principal concept of determining the impact of African smoke and dust plumes upon cloud development at ASI and understanding how a popular shallow convection parameterization used in models responds to the presence of this aerosol. We analyzed observations collected during the US Department of Energy (DOE) Layered Atlantic Smoke Interactions with Clouds (LASIC) campaign using the Atmospheric Radiation Measurement Program’s Mobile Facility #1 (AMF-1), which was deployed to Ascension Island (ASI) in Southeastern Atlantic for a one-year period. Ascension Island is immediately downwind from an African source of these plumes, but far enough removed to enable the lower atmosphere to have reacted to their presence. A main goal of our study was to compute radiative heating rate profiles over Ascension Island and along the trajectory from the biomass burning regions along coastal Africa to Ascension Island. To compute the radiative heating rate due to aerosols and clouds, we employed observed profiles of temperature, humidity, and clouds from LASIC alongside aerosol optical properties from the Modern-era Retrospective analysis for Research and Applications Version 2 (MERRA-2), as input for the Rapid Radiation Transfer Model (RRTM). Radiative heating was also assessed across the southeast Atlantic Ocean using an ensemble of back trajectories from the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We were successful in this effort and the resulting publication is already being cited despite its relatively short lifetime in the literature. The second part of our study involved the process-level representation of the clouds observed over the Southeastern Atlantic in models. Our initial task, upon which the balance of this portion of the study depended, was to evaluate the representativeness of the clouds observed by AMF-1. What orographic influence did Ascension Island have on the measured cloud structure? We set out to answer this question by trying to separate observations that were clearly indicative of orographic forcing from those that were representative of open ocean. The siting of AMF-1, which was 340-m above sea-level on the slope of a steep escarpment, proved demonstrably problematic for cloud process measurements. Despite considerable effort using multiple approaches including artificial intelligence (AI), we were unable to successfully compensate for the orographic forcing present at the AMF-1 site on ASI and, hence, produce process-level summaries of the convective mass flux representative of open ocean over the Southeastern Atlantic. Even though the project has officially ended, we are making a final attempt using two new types of clustering algorithm (i.e., AI) on the recommendation of a former student, who is an expert in this area. Since we have only recently begun to test these new AI schemes, the recommendations from the second part of our project outlined below are based on our experience at the time of this report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations

The El Niño Southern Oscillation (ENSO) has profound impacts on weather patterns across the globe, yet there is no consensus on its response to global warming. Several modelling studies find a stronger ENSO in global warming scenarios, while other studies suggest ENSO weakening. Using a broad range of models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and four types of warming experiments, here we show that the majority of the models predict a stronger ENSO by century-end in Shared Social Pathway (SSP) experiments, and in idealized 1pctCO 2 and abrupt 4xCO 2 experiments. Several models, however, do predict no change or ENSO weakening, especially in the idealized experiments. Critically, the strongest forcing (abrupt-4xCO 2 ) does not induce the strongest ENSO response, while differences between the models are much greater than those between warming scenarios. For the long-term response (over 1000 years) the models disagree even on the sign of change. Furthermore, changes in ENSO sea surface temperature (SST) variability are only modestly correlated with the tropical Pacific mean state change. The highest correlation for ENSO SST amplitude is found with the mean zonal SST gradient in the SSP5-8.5 experiment (R = –0.58). In contrast, changes in ENSO rainfall variability correlate well with changes in the mean state, as well as with changes in ENSO SST variability. When evaluating the Bjerknes Stability Index for a subset of models, we find that it is not a reliable predictor of ENSO strengthening, as this index tends to predict greater stability with warming. We argue that the enhanced ENSO stability is offset by increases in atmospheric noise or/and potential nonlinear effects. However, a robust inter-model mechanism that can explain a stronger ENSO simulated with global warming is still lacking. Therefore, caution should be exercised when considering ENSO changes based on a single model or warming scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Maritime Aerosol and CCN Profiles Derived From Ship₋Based Measurements Over Eastern North Pacific During MAGIC

Atmospheric aerosols are widely recognized to give rise to a substantial radiative forcing to the climate by scattering and absorbing radiation and by modifying the microphysical, lifetime, and radiative properties of clouds. During the Marine ARM GPCI Investigation of Clouds (MAGIC) over the Eastern North Pacific (ENP), the ship-based measured cloud condensation nuclei (CCN) concentration at 0.2% supersaturation (N CCN,0.2 ) and condensation nuclei concentration (N CN ) had mean values of 116.7 and 219.4 cm ⁻3 , with the highest concentrations found closest to LA due to an increase in aerosol sources. Moving westward, both N CCN,0.2 and N CN gradually decreased until stabilizing near 100 cm ⁻3 and 200 cm ⁻3 , respectively. Using the methods proposed by Ghan and Collins (2004), https://doi.org/10.1175/1520-0426(2004)021<0387:uoisdt>2.0.co;2 and Ghan et al. (2006), https://doi.org/10.1029/2004jd005752, N CCN,0.2 profiles are retrieved using the surface measured N CCN,0.2 as a constraint. For coupled conditions, correlations between the retrieved N CCN,0.2 profiles and cloud-droplet number concentration (N C ) increase from 0.26 at the surface to 0.38 near cloud base, particularly true for non-drizzling clouds. Although the correlations are lower than expected, the percentage increase (46.2%) is encouraging. Finally, the relationships between cloud breakup (CB) and the stratocumulus to cumulus transition (SCT) with environmental conditions and associated aerosols are also studied. The decreased N CN trend east of CB is mainly caused by precipitation scavenging, while the increased N CN trend west of CB is strongly associated with the increased surface wind speed and fewer drizzle events. A further study is needed using high-resolution models to simulate these events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Radiation-assisted electrochemical reduction of CO 2 to CO

Carbon monoxide (CO) is a versatile intermediate feedstock for many applications and can be produced through the electrochemical reduction of carbon dioxide (CO 2 ). However, current electrochemical CO production is hindered by high overall costs, primarily due to low conversion efficiencies and significant energy requirements. Herein, we report a unique way of enhancing the electrochemical reduction of CO 2 to CO using gamma (γ) photons. The γ-irradiation applied to the electrochemical cell setup induces the production of e˙ - , which results in an increased CO 2 ionization and production of excited CO 2 (CO$^{*}_{2}$) molecules via lower energy barrier. The ionized CO 2 ˙ - is quickly stabilized over a silver catalyst, providing an alternative low activation energy route for CO 2 reduction. In conclusion, a decrease in the overpotential barrier enhanced the electrochemical reduction of CO 2 to CO by 25%.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Sub-Ambient Performance of Potassium Sarcosinate for Direct Air Capture Applications: CO 2 Flux and Viscosity Measurements

Absorption based direct air capture (DAC) technologies have garnered significant interest in recent years due to their scalability, competitive regeneration energy requirements, and low susceptibility to degradation. One of the key advantages of DAC lies in its flexible siting options and the potential to utilize low-value land. However, most of the research in this field has been focused on ambient climate zones (T > 20 °C), overlooking sub-ambient (–30 °C < T < 20 °C) regions, which comprise approximately 70 % of the Earth’s surface. To fully realize the potential of DAC, it is essential to understand how DAC solvents perform in these sub-ambient conditions before any large-scale deployment can be considered. Among DAC solvents of interest, potassium sarcosinate (K-SAR) has emerged as a promising candidate due to its high CO 2 capacity, fast uptake kinetics, compatibility with contactor packing materials, low volatility, good thermal and oxidative stability, and competitive regeneration energy requirements compared to current industry standards. This paper characterizes the CO 2 flux and viscosity of K-SAR at sub-ambient conditions and explores the potential of using ethylene glycol and triethylene glycol as additives to prevent solvent freezing in DAC applications. For 1 M K-SAR, the CO 2 flux ranges between 1.3 × 10 -5 and 8.0 × 10 -5 mol m –2 s –1 across a temperature range of –5 °C to 45 °C. Ethylene glycol is shown to effectively suppress the freezing point of K-SAR below –30 °C with volumetric loadings of the additive as low as 0.1. Here, a reaction model was developed to predict the CO 2 flux for 1 M K-SAR at different temperatures, demonstrating good agreement between experimental and theoretical fluxes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Battery Storage Unlocked: Lessons Learned From Emerging Economies

The Clean Energy Ministerial (CEM) is a global forum that promotes policies and programs that advance clean energy technology. The CEM's mission is to bring together a community of global leaders to scale clean energy, amplifying the impact to all the sectors of the economy and applying a whole-of-society approach to meet collective climate and clean energy goals. At COP28 in Dubai, United Arab Emirates, the CEM announced the Supercharging Battery Storage Initiative as a vehicle to accelerate battery storage deployment around the world. The initiative supports countries around the world in co-creating strategies that enhance policy, regulation, supply chain, manufacturing, and financing solutions for battery energy storage deployment. Additionally, the initiative seeks to reduce the cost of the technology and promote diversified, sustainable, and secure supply chains (CEM n.d.). Through international collaboration, the initiative supports the integration of renewable energy globally, while securing the stability and reliability of the electricity grid.

batteries↗
DOE OSTI2021

Logarithmic profile of temperature in sheared and unstably stratified atmospheric boundary layers

The impact of buoyancy on the mean velocity, temperature, and scalar concentration profiles in the lower atmosphere is typically investigated within the framework of Monin-Obukhov similarity theory (MOST). MOST is the theoretical foundation for parametrizing surface-atmosphere exchanges in nearly all weather, climate, and hydrological models.According to MOST, the classic logarithmic profiles of mean velocity and temperature break down as the buoyancy effects become important. However, recent studies on turbulent Rayleigh-Bénard convection and natural convection along vertical walls suggest that the mean temperature in the near-surface region still follows a logarithmic profile.Motivated by these new results, we study the mean potential temperature profile in sheared and unstably stratified atmospheric boundary layers using direct numerical simulations and field observations. We find that the mean potential temperature profile remains logarithmic across a wide range of stability parameters, which characterizes the relative importance of buoyancy versus shear effects. Compared to MOST, our results suggest that the buoyancy force does not modify the logarithmic nature of the mean potential temperature profile, but instead modulates its slope, which is no longer universal and differs from 1/κ,whereκisthe von Kármán constant. Furthermore, this study provides another perspective on scalar turbulence in the atmospheric boundary layer

58 GEOSCIENCES↗
DOE OSTI2021

Analysis of Residential Time-of-Use Utility Rate Structures and Economic Implications for Thermal Energy Storage

Thermal energy storage (TES) is an increasingly popular tool to level out the daily electrical demand and add stability to the electrical grid as more intermittent renewable energy sources are installed. TES systems can locally decouple high thermal loads from the operation of a heat pump or reduce the electrical energy demand of the heat pump by providing a more favorable temperature gradient. In addition, many policy makers and utility providers have introduced time-of-use (TOU) rate schedules for residential customers to better reflect the price of electricity generation and demand for specific times. TOU rate schedules price grid-provided electricity differently throughout the day depending on the region’s climate, time of year, and electrical production. Large differences between on-peak and off-peak electrical prices may create an economic advantage for a residential customer to install a TES system. In this work, the economic and energy savings are calculated for a modeled 2,400 square foot residential building with water/ice-based TES using a TOU rate structure. The weather data is from Fresno County, CA, ASHRAE climate zone 3B, and a representative residential TOU utility rate structure from Pacific Gas and Electric (PG&E) was used. The results showed that total energy consumption could be reduced by 14.5% with an 87.5% reduction in on-peak energy usage when the TES is installed. The cost of operating this system for space cooling was reduced by nearly 20% using the sample utility rate plan.

Sultan, Sara↗
DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗