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DOE OSTI2021

Unraveling the implications of finite specimen size on the interpretation of dynamic experiments for polycrystalline aluminum through direct numerical simulations

Normal and Pressure-shear plate impact (NPI and PSPI) experiments are popular experimental techniques for studying the mean-field macroscopic behavior of polycrystalline metals under high-rate dynamic loading. However, since both configurations rely upon geometry for subjecting the specimen to high strain rates, these experiments often involve a limited specimen size. Moreover, because of the inherent heterogeneities present within polycrystalline metals, it is difficult to ascertain if the size of the specimen and/or regions where measurements are made are sufficiently large for making representative inferences about the mean-field macroscopic properties from single-point velocity measurements. In the present study, we quantify the expected measurement variability on observable point measurements in NPI and PSPI experiments by carrying out direct numerical simulations (DNS) of statistically representative polycrystalline microstructures subjected to dynamic compression and compression-shear loading. In particular, we consider the role of specific material heterogeneities (e.g. the grain-to-grain difference in size, crystallographic orientation) on dispersion in the normal and transverse particle velocity records and on local fluctuations in key state variables (e.g. velocity, accumulated plastic strain) by incorporating these effects directly into a representative synthetic microstructure geometry and crystalline description of pure polycrystalline aluminum. The form of the present study is a large parametric investigation, consisting of ten ensembles of one hundred simulations. Each of the thousand simulations reflects a randomly realized synthetic microstructure in one of five cases of decreasing average grain size for the two loading configurations. Our analysis of the DNS results demonstrates that for both of these experimental configurations, the grain size directly correlates with the coefficient of variation (CV) in simulated point measurements, showing a convergent decrease in CV to zero (i.e. particle velocity record approaches the mean-field value) with decreasing grain size. Remarkably, the magnitude of variations in the particle velocity record is shown to be largest where the deviatoric stresses are most significant. In the case of NPI, this occurs at the elastic and plastic wavefront, whereas, in the case of PSPI, the magnitude of fluctuations are approximately constant throughout the experimental window time. The reasoning for the scatter in particle velocity due to the heterogeneous microstructure is demonstrated to be dependent on the mechanisms for accommodating deformation and on the interaction of reflection waves generated at sites of heterogeneities occurring at the scale of grains. Lastly, we develop a power-law description for the magnitude of scattering versus characteristic length, which provides a statistical framework for assessing the required number of grains per characteristic specimen dimension for minimizing scatter within these two experimental configurations (NPI, PSPI).

36 MATERIALS SCIENCE↗

DOE OSTI2025

Akiachak Energy Efficiency Retrofit Project

The goal of the project is to reduce the overall energy use of the Akiachak Native Community (ANC) by implementing energy efficiency measures in five high-use Tribal buildings. This project will have the following outcomes: Projected annual energy savings of $17,369; projected annual reduction in fuel oil #1 of 1,200 gallons and electricity of 17,751 kWh; annual reduction in carbon dioxide emissions of approximately 60,340 pounds/year. ANC will install energy efficiency measures in the Laundry, Tribal Indian Reorganization Act (IRA) Office, Clinic, Daycare, and Police Station. ANC obtained energy audits on these buildings in 2018, and this project will implement high-payback recommendations such as replacing lighting with LEDs, installing setback thermostats and occupancy sensors, replacing furnaces with more efficient models, replacing the circulation pumps with variable speed ones, air tightening, and adding insulation. Buildings will see energy cost reductions from 15% to 40%. These retrofits will help build ANC’s long-term vision for sustainable energy usage and address the first goal of the Tribal IRA Council’s Energy Efficiency and Conservation Strategy, to “create and maintain functionally appropriate, sustainable, accessible, high quality tribal infrastructure and facilities.” ANC intends to replicate this project by using the resulting energy savings to address audit recommendations in other buildings as well as to demonstrate the value of energy efficiency to community members. Other outcomes will include an increase in community resiliency, reduced dependence on outside shipments of fuel oil, training for maintenance staff, and no-touch control of building appliances to reduce transmission of diseases such as COVID-19.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2007

Reconstructed Historical Land Cover and Biophysical Parameters for Studies of Land-Atmosphere Interactions within the Eastern United States

The local environment where we live within the Earth's biosphere is often taken for granted. This environment can vary depending on whether the land cover is a forest, grassland, wetland, water body, bare soil, pastureland, agricultural field, village, residential suburb, or an urban complex with concrete, asphalt, and large buildings. In general, the type and characteristics of land cover influence surface temperatures, sunlight exposure and duration, relative humidity, wind speed and direction, soil moisture amount, plant life, birds, and other wildlife in our backyards. The physical and biological properties (biophysical characteristics) of land cover help to determine our surface environment because they directly affect surface radiation, heat, and soil moisture processes, and also feedback to regional weather and climate. Depending on the spatial scale and land use intensity, land cover changes can have profound impacts on our local and regional environment. Over the past 350 years, the eastern half of the United States, an area extending from the grassland prairies of the Great Plains to the Gulf and Atlantic coasts, has experienced extensive land cover and land use changes that began with land clearing in the 1600s, led to extensive deforestation and intensive land use practices by 1920, and then evolved to the present-day landscape. Determining the consequences of such land cover changes on regional and global climate is a major research issue. Such research requires detailed historical land cover data and modeling experiments simulating historical climates. Given the need to understand the effects of historical land cover changes in the eastern United States, some questions include: - What were the most important land cover transformations and how did they alter biophysical characteristics of the land cover at key points in time since the mid-1600s? - How have land cover and land use changes over the past 350 years affected the land surface environment including surface weather, hydrologic, and climatic variability? - How do the potential effects of regional human-induced land cover change on the environment compare to similar changes that are caused by the natural variations of the Earth's climate system? To help answer these questions, we reconstructed a fractional land cover and biophysical parameter dataset for the eastern United States at 1650, 1850, 1920, and 1992 time-slices. Each land cover fraction is associated with a biophysical parameter class, a suite of parameters defining the biophysical characteristics of that kind of land cover. This new dataset is designed for use in computer models of land-atmosphere interactions, to understand and quantify the effects of historical land cover changes on the water, energy, and carbon cycles

Steyaert, Louis T.↗

NASA NTRS2003

Meridional Propagation of the MJO/ISO and Asian Monsoon Variability

In this study we examine the links between tropical heating, the Madden Julian Oscillation (MJO)/Intraseasonal Oscillation (ISO), and the Asian monsoon. We are particularly interested in isolating the nature of the poleward propagation of the ISO/MJO in the monsoon region. We examine both observations and idealized "MJO heating" experiments employing the NASA Seasonal-Interannual Prediction Project (NSIPP) atmospheric general circulation model (AGCM). In the idealized 10-member ensemble simulations, the model is forced by climatological SST and an idealized eastward propagating heating profile that is meant to mimic the canonical heating associated with the MJO in the Indian Ocean and western Pacific. In order to understand the impact of SST on the off equatorial convection (or Rossby-wave response), a second set of 10-member ensemble simulations is carried out with the climatological SSTs shifted in time by 6-months. The observational analysis highlights the strong link between the Indian summer monsoon and the tropical ISO/MJO activity and heating. This includes the well-known meridional propagation that affects the summer monsoons of both hemispheres. The AGCM experiments with the idealized eastward propagating MJO-like heating reproduce the observed meridional propagation including the observed seasonal differences. The impact of the SSTs are to enhance the magnitude of the propagation into the summer hemispheres. The results suggest that the winter/summer differences associated with the MJO/ISO are auxiliary features that depend on the MJO's environment (basic state and boundary conditions) and are not the result of fundamental differences in the MJO itself.

Wu, Man Li C.↗

NASA NTRS2015

Implementation of an Open-Scenario, Long-Term Space Debris Simulation Approach

This paper provides a status update on the implementation of a flexible, long-term space debris simulation approach. The motivation is to build a tool that can assess the long-term impact of various options for debris-remediation, including the LightForce space debris collision avoidance concept that diverts objects using photon pressure [9]. State-of-the-art simulation approaches that assess the long-term development of the debris environment use either completely statistical approaches, or they rely on large time steps on the order of several days if they simulate the positions of single objects over time. They cannot be easily adapted to investigate the impact of specific collision avoidance schemes or de-orbit schemes, because the efficiency of a collision avoidance maneuver can depend on various input parameters, including ground station positions and orbital and physical parameters of the objects involved in close encounters (conjunctions). Furthermore, maneuvers take place on timescales much smaller than days. For example, LightForce only changes the orbit of a certain object (aiming to reduce the probability of collision), but it does not remove entire objects or groups of objects. In the same sense, it is also not straightforward to compare specific de-orbit methods in regard to potential collision risks during a de-orbit maneuver. To gain flexibility in assessing interactions with objects, we implement a simulation that includes every tracked space object in Low Earth Orbit (LEO) and propagates all objects with high precision and variable time-steps as small as one second. It allows the assessment of the (potential) impact of physical or orbital changes to any object. The final goal is to employ a Monte Carlo approach to assess the debris evolution during the simulation time-frame of 100 years and to compare a baseline scenario to debris remediation scenarios or other scenarios of interest. To populate the initial simulation, we use the entire space-track object catalog in LEO. We then use a high precision propagator to propagate all objects over the entire simulation duration. If collisions are detected, the appropriate number of debris objects are created and inserted into the simulation framework. Depending on the scenario, further objects, e.g. due to new launches, can be added. At the end of the simulation, the total number of objects above a cut-off size and the number of detected collisions provide benchmark parameters for the comparison between scenarios. The simulation approach is computationally intensive as it involves tens of thousands of objects; hence we use a highly parallel approach employing up to a thousand cores on the NASA Pleiades supercomputer for a single run. This paper describes our simulation approach, the status of its implementation, the approach to developing scenarios and examples of first test runs.

laser↗

NASA NTRS2023

Eastern Indigo Snake (Drymarchon couperi) Shelter Site Use in Peninsular Florida and Implications for Habitat Conservation

Shelters are critical for many species as protection from predators and extreme temperatures. Successful conservation of reptiles requires understanding both shelter site requirements and availability. The Eastern Indigo Snake (EIS) is endemic to the southeastern United States and is federally listed. Recovery has focused on maximizing unfragmented landscapes, with less attention on fine-scale features such as shelter sites. In the northern EIS range, Gopher Tortoise (Gopherus polyphemus) burrows are used extensively for shelter. Although EIS in peninsular Florida often shelter in tortoise burrows, they also use other shelters where tortoise burrows are scarce or absent. Solely focusing EIS survey and management efforts where Gopher Tortoises are present may overlook occupied habitats and misallocate resources. We investigated the importance of different shelter sites in central Florida using data from radio-tracked EIS. We modeled the use of shelter categories as a function of sex, season, and habitat using Bayesian multinomial generalized linear models. Results showed that EIS in peninsular Florida used Gopher Tortoise burrows across all seasons and habitats. Tortoise burrow use was highest in xeric habitats and lowest in mesic habitats where burrows are most and least abundant, respectively. There was less variability in shelter site use in disturbed habitats and flatwoods. Cool season tortoise burrow use across sexes and habitats in our study was much lower than in southern Georgia. Our results indicate that EIS are less dependent on Gopher Tortoise burrows in peninsular Florida and that suitable habitats with few or no tortoise burrows could still provide conservation value for EIS.

Bayesian hierarchical modeling↗

DOE OSTI2025

Decomposing sources of value for electricity and negative emissions technologies in net-zero power systems

Deep decarbonization of the US power system would require rapid deployment of variable renewable energy (VRE) resources, which are projected to provide a substantial share of electricity generation at the time of net-zero emissions. However, the exact share of generation met by VRE and the roles of other technologies in supplying key electricity services—energy and firm capacity—remain uncertain. This study employs a detailed model of the US power sector to decompose the provision and value of electricity services, including negative emissions, by technology across a range of deep decarbonization scenarios. Results indicate that while technology deployment and the share of services provided by each technology vary significantly depending on future technological and market conditions, the value composition and future roles of individual technologies remain consistent. These findings offer guidance for research and development priorities and provide insights to inform electricity policy and planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Co‐Evolution of Specific Stiffness and Permeability of Rock Fractures Offset in Shear

Abstract Fractures and faults represent planes of weakness and compliance in rock masses that serve as focal points for both microearthquakes and fluid transport, with seismicity and permeability evolution closely linked. Contact stiffness is highly stress‐sensitive and directly influences permeability. We explore the co‐evolution of specific stiffness and permeability of rough fractures under normal stress and shear offset using numerical simulations. Individual rough fractures are represented by variable amplitude (Root mean square) and wavelength ( λ ) using a granular mechanics model. Contacting rough surfaces are mated, offset in shear, and then compacted in displacement mode. The compacting fractures generate stress‐dependent changes in contact porosity, which govern both permeability and stiffness evolution. We establish a universal dimensionless relationship linking specific stiffness and permeability that inherently incorporates the effects of surface roughness, shear offset, and microcracking. The observed cracking effect—where local stress redistribution and pressure‐driven microcrack propagation dynamically alter the aperture field—introduces a nonlinear permeability response at high stress. Increased roughness amplitude and larger shear offsets reduce stiffness while dampening permeability sensitivity to stress, demonstrating a strong interplay between surface texture and hydro‐mechanical behavior. While the model captures this behavior effectively, deviations emerge at very low porosities due to extreme aperture sensitivity in this limit.

He, Xinxin [Energy and Mineral Engineering Pennsyl↗

DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗

DOE OSTI2020

Arctic Tipping Points Triggering Global Change (LDRD Final Report)

The Arctic is warming and feedbacks in the coupled Earth system may be driving the Arctic to tipping events that could have critical downstream impacts for the rest of the globe. In this project we have focused on analyzing sea ice variability and loss in the coupled Earth system Summer sea ice loss is happening rapidly and although the loss may be smooth and reversible, it has significant consequences for other Arctic systems as well as geopolitical and economic implications. Accurate seasonal predictions of sea ice minimum extent and long-term estimates of timing for a seasonally ice-free Arctic depend on a better understanding of the factors influencing sea ice dynamics and variation in this strongly coupled system. Under this project we have investigated the most influential factors in accurate predictions of September Arctic sea ice extent using machine learning models trained separately on observational data and on simulation data from five E3SM historical ensembles. Monthly averaged data from June, July, and August for a selection of ice, ocean, and atmosphere variables were used to train a random forest regression model. Gini importance measures were computed for each input feature with the testing data. We found that sea ice volume is most important earlier in the season (June) and sea ice extent became a more important predictor closer to September. Results from this study provide insight into how feature importance changes with forecast length and illustrates differences between observational data and simulated Earth system data. We have additionally performed a global sensitivity analysis (GSA) using a fully coupled ultra- low resolution configuration E3SM. To our knowledge, this is the first global sensitivity analysis involving the fully-coupled E3SM Earth system model. We have found that parameter variations show significant impact on the Arctic climate state and atmospheric parameters related to cloud parameterizations are the most significant. We also find significant interactions between parameters from different components of E3SM. The results of this study provide invaluable insight into the relative importance of various parameters from the sea ice, atmosphere and ocean components of the E3SM (including cross-component parameter interactions) on various Arctic-focused quantities of interest (QOIs).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Structural Design of Bismuth Telluride Nanoplates through Process Variables

Binary pnictogen chalcogen compounds, primarily bismuth tellurides and selenides, are of great interest due to their applications in emerging quantum devices, as well as thermoelectric generators. The performance of bismuth telluride in these roles depends on its structure at the nanoscale, particularly the size, shape, and crystallinity of its nanocrystalline forms. However, current methods for controlling these features are often slow, inconsistent, or difficult to scale. Here, we demonstrate that through a solvothermal synthesis and hot injection process, precise control over the morphology of bismuth telluride nanoplates is possible with independent tuning of process variables, such as temperature and reaction time. We find that the nanoplate shape and internal porosity vary systematically with synthesis temperature and that the same morphological outcomes can be rapidly achieved at a fixed temperature by adjusting reaction duration. These results reveal that both the temperature and time can independently direct bismuth telluride morphological features, allowing for rapid, tunable synthesis strategies. Our approach offers a scalable framework, not only for bismuth telluride but also for related layered chalcogenides used in energy harvesting and quantum technologies.

Ackley, Jordan [Boise State Univ., ID (United Stat↗

DOE OSTI2026

Quantum effects on the dynamics and properties of soft materials

The quantum effects of nuclear and electronic motion play an important role in the structure, dynamics, and function of soft materials, yet they are difficult to capture with conventional classical simulations or static electronic–structure methods. In this work several complementary approaches for treating quantum effects in polymeric and soft–matter systems are demonstrated, with a focus being on the hydrogen-bonded networks, ion and charge transport, and photoactive chromophores. The proton transfer, tunneling, and isotope effects are captured within the reduced-dimensionality models by implementing grid-based nuclear quantum dynamics in terms of the discrete variable and Fourier bases. The nuclear quantum dynamics is extended to larger systems by employing the quantum trajectories and quantum–thermal bath schemes combined with on-the-fly electronic structure, enabling the description of high-dimensional polymeric environments at feasible cost. The dynamics in the electronic degrees of freedom, simulating the optical response in large chromophores such as chlorophylls, is performed using the real-time time-dependent density functional theory implemented in the real-space multigrid (RMG) code. These approaches are demonstrated on case studies of the proton and hydroxide transport in hydrated polymer membranes, charge transfer in conjugated polymers, and the optical spectra of chlorophyll chromophores relevant to polymerized chlorophyll materials and chlorophyll–polymer hybrids. The reviewed methods and applications highlight practical routes of including quantum effects in simulations of soft functional materials.

Garashchuk, Sophya [Univ. of South Carolina, Colum↗

DOE OSTI2026

Effects of cleavable comonomer structure and crosslinker chemistry on the performance and deconstructability of frontally cured pDCPD composites

Reclaiming undamaged carbon fiber from thermoset composites requires chemical recycling methods that leverage complete deconstruction of the polymer matrix. Cleavable comonomers enable chemical recycling of poly(dicyclopentadiene) (pDCPD) composites, but current systems suffer from reduced glass transition temperature (T g ), limiting application. Here, we investigate the effect of cleavable comonomer loading and cleavable crosslinker chemistry on network formation, thermomechanical properties, and fiber recovery in frontally cured pDCPD composites. All formulations fully deconstructed in acid and recovered fibers ranged from pristine to variably contaminated depending on crosslinker chemistry. Reducing backbone-cleaving comonomer content and introducing cleavable crosslinkers raised composite T g to 126–138 °C compared to 105 °C previously reported in deconstructable pDCPD composites. Although the cleavable crosslinkers increased T g in unreinforced polymers to 150–160 °C, the fiber-reinforced composite T g remained lower due to under-curing and interactions between the crosslinkers and the fiber surface. Increasing polymerization initiator loading improved comonomer conversion and yielded a deconstructable composite with T g = 151 °C, approaching the performance of non-deconstructable pDCPD composites (T g = 161 °C). This work clarifies how cleavable comonomer and crosslinker chemistry governs curing, thermomechanical performance, and reclaimed reinforcement quality, and the results highlight the need for more robust cleavable comonomers to realize multigenerational composite materials without sacrificing thermomechanical performance.

Carbon fibers↗

DOE OSTI2026

Harnessing the Second-Order Metal−Insulator Transition for Neuromorphic Computing

Vanadium oxides are widely studied phase change materials for brain-inspired computing architectures. Systems like VO 2 and V 2 O 3 exhibit first-order metal−insulator transitions (MITs) with hysteresis and percolative switching, increasing stochasticity and device variability. Here, we focus on the less explored Magnéli phase V 4 O 7 , which undergoes a continuous, non-hysteretic, second-order MIT. This surprisingly enables highly reproducible volatile resistive switching in spiking-neuron-type devices. We synthesize V 4 O 7 films, characterize their structural and transport properties, and demonstrate voltage and current-driven threshold switching with electrothermal feedback. In a Pearson–Anson oscillator, V 4 O 7 devices produce stable, tunable spiking across 20–200 kHz, with consistent operation among multiple devices. We introduce a numerical analog leaky-integrate-and-fire (aLIF) model that captures waveform shapes and their dependence on load resistance, temperature, and voltage. Furthermore, these findings suggest that second-order MIT materials like V 4 O 7 are promising for deterministic, scalable spiking neuron arrays for neuromorphic computing.

V4O7↗

NASA NTRS1996

Two-Dimensional Dynamic Simulation of a Continuous Foil Bearing

In this paper, the two dimensional(radial and circumferential) transient Navier-Stokes equations are used to solve the hydrodynamic problem in conjunction with the time dependent motion of the journal, and the deformable, spring supported foil. The elastic deformation of the foil and its supports are simulated by a finite element model. The time-dependent Navier-Stokes formulation is used to solve for the interaction between the fluid lubricant, the motion of the journal and the deformable foil boundary. The steady state, the quasi-transient and the full transient dynamic simulation of the foil-fluid journal interaction are examined on a comparative basis. For the steady state simulation, the fluid lubricant pressures are evaluated for a particular journal position, by means of an iterative scheme until convergence is achieved in both the fluid pressures and the corresponding foil deformation. For the quasi-transient case, the transient motion of the journal is calculated using a numerical integration scheme for the velocity and displacement of the journal. The deformation of the foil is evaluated through numerical iteration in feedback mode with the fluid film pressure generated by the journal motion until convergence at every time step is achieved. For the full transient simulation, a parallel real-time integration scheme is used to evaluate simultaneously the new journal position and the new deformed shape of the foil at each time step. The pressure of the fluid lubricant is iterated jointly with the corresponding journal position and the deformed foil geometry until convergence is achieved. A variable time-stepping Newmark-Beta integration procedure is used to evaluate the transient dynamics at each time step of the bearing.

Braun, M. Jack↗

NASA NTRSDate not supplied

Isolating the Surface Type Influence on Arctic Cloud Properties

In response to anthropogenic climate change, substantial declines in sea ice have been observed. An urgent question is whether and how clouds respond to the changing Arctic surface. Due to the important radiative influence of clouds, the response of clouds to sea ice cover change constitutes a potentially important climate feedback within the Arctic. The sign of this feedback is strongly influenced by the seasonality of the cloud response to sea ice loss. Given the importance of this phenomenon, previous research focused on using observations of inter-annual variability to quantify the cloud-sea ice relationship. However, the reliance on inter-annual variability to assess the covariance between clouds and sea ice make it challenging to control for the influence of large-scale meteorology on the clouds. We have devised an approach that applies an in situ, airborne observational strategy to quantify the influence of surface type on Arctic clouds to satellite data. Our event-based method composites cloud property differences for ocean, marginal ice zone, and sea ice surface types for >5000 MIZ crossing events. We find that cloud properties in non-summer months below ~1.5 km are influenced by surface type such that greater cloud fraction and water content are found over ocean relative to sea ice regions. Results are statistically significantly at the 95% confidence level. During summer, the surface type does not influence the cloud properties. The primary cause of the surface type dependent cloud property differences is the thermodynamic profiles differences that occur between the two surface types: namely, ocean footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their sea ice counterparts. We find that the differences in thermodynamic profiles by surface type that correspond to the cloud property differences also explains a significant portion of the variability across events. Our analysis provides evidence that the influence of surface type on cloud properties fundamentally occurs due to the inherent surface temperature differences between the surface types. Significant surface type influences on cloud properties are only found in the presence of surface temperature differences between the surface types and only non-summer months meet this criterion. A conceptual model of this process is as follows, fundamental properties of the ocean and sea ice surface types (e.g., surface albedo, heat capacity/thermal inertia, surface roughness, and thermal conductance) lead to systematic differences in the surface temperature by surface type. In response to this surface temperature difference, atmosphere boundary layer processes (including turbulent mixing and radiation) communicate the surface temperature differences to the lower atmosphere driving the greater stability over sea ice than ocean. These differences in the background thermodynamic state profiles between the surface types yield atmospheric conditions that are more conducive to cloud development over ocean than sea ice. The details of these boundary layer processes are sensitive to the surface type but may not be fundamental to the resulting thermodynamic structure and cloud differences. Thus, our results indicate that to accurately model that cloud response to sea ice loss in climate models the surface type properties that yield the systematic differences in ocean and sea ice temperature (e.g., surface albedo, surface turbulent fluxes parameters, and thermal conductance) must be accurately represented.

Arctic radiation budget↗

DOE OSTI2026

Refractory-based thermal energy storage for industrial process heat: one-dimensional modeling, control, and optimization

The variable and weather-dependent output of wind and solar power plants present a substantial challenge for planning and operating electricity-systems, particularly in the absence of cost-effective and dispatchable energy storage technologies. This study investigates a high-temperature, electrically heated, refractory-based thermal energy storage (RTES) system that stores electrical energy as sensible heat in dense ceramic bricks over the 950–1800 °C range. The stored heat can be discharged as a controlled hot-gas stream for industrial heating, fuel substitution in high-temperature processes, or electricity generation. The main novelty is a comprehensive modelling, control, mapping, and optimization framework that integrates one-dimensional transient gas–solid heat transfer, fan-assisted discharge, bypass-flow regulation, reheating logic, fan-power evaluation, insulation-loss assessment, and genetic-algorithm-based design optimization. The model uses feedback from outlet temperature and delivered power to regulate discharge, while a two-stage genetic algorithm optimizes brick-channel geometry, gas-flow operation, and multilayer insulation thicknesses. Storage capacities below 50 MWh and discharge powers of 5–30 MW are analyzed to evaluate hold time, thermal delivery, fan-power penalty, heat loss, state-of-charge evolution, and indicative capital cost. Results demonstrate that optimized and well-insulated refractory-based thermal energy storage units can provide stable, efficient, and repeatable heat delivery over multiple discharge cycles. The generated performance and cost maps support modular refractory thermal energy storage as a practical option for large-scale integration of wind and solar generation and for high-temperature industrial process heat.

25 ENERGY STORAGE↗

NASA NTRS2009

Modular Battery Charge Controller

A new approach to masterless, distributed, digital-charge control for batteries requiring charge control has been developed and implemented. This approach is required in battery chemistries that need cell-level charge control for safety and is characterized by the use of one controller per cell, resulting in redundant sensors for critical components, such as voltage, temperature, and current. The charge controllers in a given battery interact in a masterless fashion for the purpose of cell balancing, charge control, and state-of-charge estimation. This makes the battery system invariably fault-tolerant. The solution to the single-fault failure, due to the use of a single charge controller (CC), was solved by implementing one CC per cell and linking them via an isolated communication bus [e.g., controller area network (CAN)] in a masterless fashion so that the failure of one or more CCs will not impact the remaining functional CCs. Each micro-controller-based CC digitizes the cell voltage (V(sub cell)), two cell temperatures, and the voltage across the switch (V); the latter variable is used in conjunction with V(sub cell) to estimate the bypass current for a given bypass resistor. Furthermore, CC1 digitizes the battery current (I1) and battery voltage (V(sub batt) and CC5 digitizes a second battery current (I2). As a result, redundant readings are taken for temperature, battery current, and battery voltage through the summation of the individual cell voltages given that each CC knows the voltage of the other cells. For the purpose of cell balancing, each CC periodically and independently transmits its cell voltage and stores the received cell voltage of the other cells in an array. The position in the array depends on the identifier (ID) of the transmitting CC. After eight cell voltage receptions, the array is checked to see if one or more cells did not transmit. If one or more transmissions are missing, the missing cell(s) is (are) eliminated from cell-balancing calculations. The cell-balancing algorithm is based on the error between the cell s voltage and the other cells and is categorized into four zones of operation. The algorithm is executed every second and, if cell balancing is activated, the error variable is set to a negative low value. The largest error between the cell and the other cells is found and the zone of operation determined. If the error is zero or negative, then the cell is at the lowest voltage and no balancing action is needed. If the error is less than a predetermined negative value, a Cell Bad Flag is set. If the error is positive, then cell balancing is needed, but a hysteretic zone is added to prevent the bypass circuit from triggering repeatedly near zero error. This approach keeps the cells within a predetermined voltage range.

Button, Robert↗