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DOE OSTI2026

Oil-impregnated densified wood veneer with high electrical insulation enabled by nanosized oil channels

Growing energy demands and renewable integration are stressing the aging power grid infrastructure. Lignocellulosic oil-impregnated paper is widely used in power transformers but suffers from critical limitations, such as low dielectric strength, mechanical strength, and thermal conductivity, causing premature transformer failures. Here, we demonstrate a superior electrically insulating oil-impregnated paper design using the naturally anisotropic structure of densified wood veneer to achieve nanosized channels of oil that efficiently disrupt electrical breakdown pathways. The developed oil-impregnated densified wood (ODW) creates aligned cellulose fibers with 166 ± 87–nanometer oil nanochannels, achieving record dielectric strength of 105 kilovolts per millimeter. The structure also delivers a mechanical strength of up to 384 megapascals and a thermal conductivity of 0.33 watts per meter per kelvin, enabling enhanced longevity upon thermal aging tests. The ODW could replace conventional transformer insulation to enhance power transformer performance and improve lifetime. Moreover, its anisotropic oil-filled nanochannel design offers a general strategy for hybrid dielectrics in medium- and high-voltage applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Model files for estimating snow dynamics and stable water isotopes across the East River, CO.

A coupled hydrologic and snowpack stable water isotope model is used to assesses controls on isotopic inputs across the East River, Colorado, a large, mountainous basin. The hydrologic model uses the semi-empirical, spatially distributed and publicly available U.S. Geological Survey numerical code Precipitation-Modelling Runoff System (PRMS). Water and energy are tracked daily through the atmosphere, canopy and subsurface at a 100-m grid resolution. The isotope mass balance model follows previous work by Ala-aho et al. (2017) to track stable isotopes entering the soil system as snowmelt or rain. Water stores and fluxes needed for the isotope model use hydrologic model output for each timestep and model modeled grid location. This data package provides all files related to the hydrologic model (executable, input and output files), the isotopic model calibration and the historic isotopic model (water years 2015-2020). The isotope model source code and executable are provided. The file "readme.txt" includes information on the file structure, steps to run the model, and description of included folders.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

LDRD 2022 Annual Report: Laboratory Directed Research and Development Program Activities

Each year, Brookhaven National Laboratory (BNL) is required to provide a report of its completed Laboratory Directed Research and Development Program (LDRD) projects to the Department of Energy (DOE) Office of Scientific and Technical Information in accordance with DOE Order 413.2C Chg1 (MinChg) dated August 2, 2018. This report provides a detailed look at the scientific and technical activities for each of the LDRD projects funded by BNL in FY 2022, in fulfillment of that requirement. In FY 2022, the BNL LDRD Program funded 70 projects, 30 of which were new starts, at a total cost of $17.2M. The investments that BNL makes in its LDRD program support the Laboratory’s strategic goals. BNL has identified seven scientific initiatives that define the Laboratory’s scientific future and that will enable it to realize its overall vision. This requires simultaneous excellence in all aspects of BNL’s work – from science and operations, to external partnerships with the local, state, and national communities, and beyond. This is enabled by safe, efficient, and secure operations; by an unwavering commitment to a diverse, equitable, and inclusive environment, including workforce development, both with staff and reaching out to the community; and by a strong focus on renewed infrastructure. The seven scientific initiatives are: 1) Nuclear Physics: uncover the structure of visible matter by constructing and operating the Electron-Ion Collider at BNL to maintain international leadership in nuclear physics for decades; 2) Clean Energy and Climate: support a net-zero U.S. economy through fundamental research in basic energy and climate sciences to revolutionize grid-scale storage, renewable integration, and the study of atmospheric processes with a new facility to improve climate predictability; 3) Quantum Information Science and Technology: discover new quantum materials to enhance quantum computers and develop an entanglement sharing quantum network as a prototype for the first quantum internet; 4) Discovery Science Driven by the Human-AI Facility Integration: revolutionize the operation of experiments across the sciences at user facilities and in core programs; 5) High Energy Physics: understand the origin of space and time with the ATLAS high luminosity upgrade at CERN and the future Long Baseline Neutrino Facility/Deep Underground Neutrino Experiment; 6) Isotope Production: accelerate and expand isotope production to ensure the security of the Nation’s supply; 7) Accelerator Science and Technology: harness the cross-cutting accelerator science expertise at BNL to develop new facilities, improve and expand its user facilities, and promote the use of accelerators in industry. The funded projects support BNL’s seven scientific initiatives and priority programs as well as new areas of research and competencies at the Laboratory that are consistent with the Laboratory’s vision and mission. In total, these LDRD investments supported 43 postdoctoral researchers in whole or in part and resulted in 138 publications and 7 awards. This Program Activities Report represents the future of BNL science; it is an impressive body of exploratory work that investigates many scientific and technical directions in support of the DOE and BNL missions.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

UNH TDP - ADCP Raw Data and Processing Scripts - Fall 2021

This submission contains raw Acoustic Doppler Current Profiler (ADCP) data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons With the ARM Observations

The accurate representation of interactions between clouds and planetary boundary layer (PBL) is a persistent challenge in climate models, critical for simulating surface energy budget. The emergence of kilometer-grid-scale global storm resolving models (GSRMs) offers the potential for enhanced details of PBL processes in these complex interactions. This study evaluates the representation of PBL-coupled and decoupled clouds in nine GSRM simulations against extensive ground-based observations by the Department of Energy Atmospheric Radiation Measurement (ARM) program, across six sites encompassing diverse regimes such as marine and continental environments in tropics and midlatitude. By differentiating coupling based on the relative positions between cloud bases and PBL tops, our analysis focuses on the simulation of PBL height, cloud frequency, position and vertical extent. The GSRMs generally exhibit commendable agreements with observed cloud structures and PBL diurnal cycles across different ARM sites. In contrast to the relatively consistent representation of decoupled clouds, discrepancies exist between the simulated and the observed coupled clouds, particularly in areas of intense convection, for example, over tropical rainforests and mountainous regions. These biases are probably associated with the models' tendency to underestimate the boundary layer humidity and the frequency of coupled clouds within different ranges of PBL heights. This study underscores the importance for continuous improvements in the representation of boundary layer and convection within these global kilometer-grid-scale models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

LandScan mosaic enables high-resolution gridded population estimates with explicit uncertainty

Gridded population datasets represent high-resolution distributions of human occupancy, enabling informed decision-making across a broad range of fields. These data products are valuable for assessing environmental risk, urban development, disaster preparedness and resource allocation—areas where accurate population estimates directly enhance policy effectiveness and optimize resource distribution. Despite the importance of gridded population datasets, traditional population modeling approaches often overlook inherent uncertainties in the estimation process. This limitation can create a false sense of certainty in population estimates, potentially leading to flawed decisions by those who rely on the data. To address this methodological gap, we introduce a probabilistic machine learning modeling framework, LandScan Mosaic, that explicitly incorporates uncertainty into the population modeling process. Our approach systematically quantifies uncertainty in three key modeling parameters of the LandScan HD gridded population dataset: building use types, floor counts, and occupancy rates. By employing Monte Carlo simulations, we propagate these uncertainties through the modeling process, yielding probability distributions of population counts in place of deterministic point estimates. We demonstrate the practical application of this framework in Iloilo City, Philippines, using structured decision-making techniques and our probabilistic estimates to identify and prioritize areas most affected by projected flooding, supporting targeted interventions that address both economic and social risks. In doing so, we propose a population-specific approach for incorporating confidence into structured decision making processes. Through a comparative analysis with conventional deterministic approaches and point estimate approaches, including LandScan HD and WorldPop, we evaluate how the incorporation of machine learning and uncertainty influences decision rankings. This research advances population distribution modeling by offering a robust, quantitative approach that explicitly accounts for uncertainty in the underlying data, along with guidance for how users can apply uncertainty in their decision-making.

Environmental sciences↗

DOE OSTI2020

Seasonal representation of extreme precipitation indices over the United States in CMIP6 present-day simulations

Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States (US) with a comprehensive set of extreme precipitation indices, including an assessment of precipitation frequency, intensity and spatial structure. The observations exhibit significant differences in their estimates of area-average intensity distributions and spatial patterns of the mean and extremes of precipitation over the US. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly in comparison to gauge-based data. Also, the representation of the extreme precipitation indices by the CMIP6 models is better in the summer than winter. Although the ‘standard’ horizontal-resolution can vary significantly across CMIP6 models, from ~0.7° to ~2.8°, we find that resolution is not a good indicator of model performance. Overall, our results highlight common biases in CMIP6 models and demonstrate that no single model is consistently the most reliable across all indices.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

New Industry Partnerships: Solar Energy Technologies Office Support for Early Projects in the Energy Systems Integration Facility: An Agreement Closeout Summary Report

The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office (SETO)-funded New Industry Partnerships (NIPs) agreement established multiple new cooperative research projects with industry partners. Each project demonstrated the use of the National Renewable Energy Laboratory's Energy Systems Integration Facility (ESIF) as a national asset for research and development, testing, and validation of new technologies to support high penetrations of solar energy on the electric grid. The agreement was launched in 2013 and represented the SETO portion of the DOE EERE-wide Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) program. The focus of INTEGRATE was on multi-energy system testing and on testing the interactions of energy systems with information technology, communications, and telecommunications (ICT) systems. The NIPs agreement was a bit different from traditional research projects because it was specifically structured to enable partnerships for ESIF testing and as such required at least a 1:1 funds-in cost share from industry partners. In the end, the project engaged six different industry partnerships and resulted in testing a large number of innovative technologies. These included tiny inverters; power-to-gas technologies; and advanced simulation, analysis, and power-hardware-in-the-loop testing techniques. The agreement also provided significant impact for both research and industrial communities. This summary report provides a brief, high-level overview of these projects and their highlights along with lists of citations and other impacts. Readers are referred to the corresponding project reports for more in-depth information.

14 SOLAR ENERGY↗

DOE OSTI2023

Simulation of Reverse Osmosis Membrane Compaction Using Material Point Method (MPM)

Access to fresh drinking water in the future requires effective management of industrial wastewater and water purification from available resources. In this study, we present the simulation methodology and the analysis of a Reverse Osmosis (RO) membrane under various pressure conditions using the Material Point Method (MPM). In contrast to other numerical methods, MPM solves the continuum governing equations on material points in a Lagrangian framework. The method does not require a grid connecting the material points hence making it suitable to simulate large deformations during membrane compaction. The time integration is carried out using the explicit Euler method, while the spatial discretization is performed using linear or cubic-spline shape functions. A series of planar images containing detailed pore structures obtained from X-ray tomography experiments is converted to a three-dimensional collection of material points to simulate the membrane. Compressive loads are applied to the top layer of the membrane to simulate the experiments. The membrane deformation and pore size distribution before and after load application are reported and compared with the experimental measurements. The presentation discusses the numerical methods used, the performance of the solver on high-performance computing machines, and the results of membrane compaction in detail.

ENGINEERING,MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Electrifying Airport GSE: Monte Carlo Grid Impacts

Airports globally are shifting from ICE-powered to electric Ground Support Equipment (eGSE) to enhance efficiency, reduce operational costs, and improve operator health. Leveraging predictable routes, flat terrain, and low operational speeds, airports provide ideal conditions for electrification. This study evaluates freight GSE electrification at Dallas-Fort Worth International Airport (DFW), USA, using the Agile@ platform, which integrates three analytical methods: Freight Facility Model (FFM), Activity-Structure-Intensity-Fuel (ASIF), and Monte Carlo simulations. Results from 10,000 simulations indicate modest but critical increases in electricity demand and significant variability in GSE energy consumption. These insights emphasize the importance of data-driven scheduling, targeted maintenance, and strategic infrastructure planning. For high-uncertainty scenarios, airports are advised to deploy buffer energy storage systems (battery banks), implement demand-response charging strategies, schedule flexible workforce shifts, and prioritize proactive maintenance-particularly for equipment with higher operational uncertainty, such as tug tractors with trailers. Agile@ thus offers a robust, scalable, and data-driven framework to optimize long-term GSE planning and enhance reliability across diverse airport environments.

Bose, Ranjan [ORNL] (ORCID:0009000791026327)↗

DOE OSTI2021

A Nonstationary and Non-Gaussian Moving Average Model for Solar Irradiance

Historically, power has flowed from large power plants to customers. Increasing penetration of distributed energy resources such as solar power from rooftop photovoltaic has made the distribution network a two-way-street with power being generated at the customer level. The incorporation of renewables introduces additional uncertainty and variability into the power grid. Distribution network operation studies are being adapted to include renewables; however, such studies require high quality solar irradiance data that adequately reflect realistic meteorological variability. Data from satellite-based products are spatially complete, but temporally coarse, whereas solar irradiances exhibit high frequency variation at very fine timescales. We propose a new stochastic method for temporally downscaling global horizontal irradiance (GHI) to 1 min resolution, but we do not consider the spatial aspect due to limited availability of the in situ irradiance measurements. Solar irradiance's first and second-order structures vary diurnally and seasonally, and our model adapts to such nonstationarity. Empirical irradiance data exhibits highly non-Gaussian behavior; we develop a nonstationary and non-Gaussian moving average model that is shown to capture realistic solar variability at multiple timescales. We also propose a new estimation scheme based on Cholesky factors of empirical autocovariance matrices, bypassing difficult and inaccessible likelihood-based approaches. The model is demonstrated for a case study of three locations that are located in diverse climates through the United States. The model is compared against competitors from the literature and is shown to provide better uncertainty and variability quantification on testing data.

Cholesky factor↗

DOE OSTI2023

Grid Spacing Sensitivities of Simulated Mid-Latitude and Tropical Mesoscale Convective Systems in the Convective Gray Zone

The main objective of this study is to observationally constrain processes in tropical and midlatitude mesoscale convective systems (MCSs), and to use these constraints for model evaluation. To accomplish this, we leverage MCS observations collected at the U.S. DOE Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma and ARM's mobile GoAmazon2014/15 site in Manaus, Brazil (MAO). We simulate 13 and 11 of these observed MCSs at the SGP and MAO site, respectively, using the Weather Research and Forecasting model at 12-, 4-, 2-, and 1-km horizontal grid spacing. Observations from radiosondes, surface meteorology, and radar wind profilers are used to characterize MCS properties, such as MCS timing and location, cold pools, and convective drafts, and evaluate these simulations. SGP cases are found in better agreement with observations than MAO cases, and when simulated at 2 km, outperform simulations at 1 km regarding the timing of MCS overpass and the accuracy of surface variable trends. MAO simulations suggest a consistent improvement in model accuracy with increasing model resolution in depicting the downdraft structure, the timing of MCSs, and the surface variables changes, except for the latter two metrics at 2 km. Deficiencies are still evident at km-scales, suggesting the need for higher resolution to simulate tropical MCSs. Overall, location-dependent improvements in MCS representation are obtained with the increasing model resolution, prompting the evaluation of sub-km scale simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Evolution of solid electrolyte interphase and active material in the silicon wafer model system

Alloying anode materials for lithium-ion batteries, such as silicon (Si), are an important focus of materials research due to the demand for increased energy density for electric vehicles and stationary grid storage. However, progress towards next generation anode materials has been hindered by poor capacity retention due to the instability of the Si active material and the solid electrolyte interphase (SEI). Herein, the evolution of the active Si material and the SEI are simultaneously investigated from the perspective of chemical, structural, morphological, and electronic evolution in the Si wafer model system through its cycling life, using time-of-flight secondary ion mass spectrometry (TOF-SIMS), scanning transmission electron microscopy (STEM), atomic force microscopy (AFM), and scanning spreading resistance microscopy (SSRM). The results illustrate a dynamic evolution of the SEI and active Si material through cycling. Through an improved understanding of evolution of SEI and Si active material within the Si wafer model system, mitigation strategies may be designed to extend the lifetime of Si anodes.

25 ENERGY STORAGE↗

DOE OSTI2025

Space‐Time Causal Discovery in Earth System Science: A Local Stencil Learning Approach

Causal discovery tools enable scientists to infer meaningful relationships from observational data, spurring advances in fields as diverse as biology, economics, and climate science. Despite these successes, the application of causal discovery to space-time systems remains immensely challenging due to the high-dimensional nature of the data. For example, in climate sciences, modern observational temperature records over the past few decades regularly measure thousands of locations around the globe. To address these challenges, we introduce Causal Space-Time Stencil Learning (CaStLe), a novel meta-algorithm for discovering causal structures in complex space-time systems. CaStLe leverages regularities in local space-time dependencies to learn governing global dynamics. This local perspective eliminates spurious confounding and drastically reduces sample complexity, making space-time causal discovery practical and effective. For causal discovery, CaStLe flexibly accepts any appropriately adapted time series causal discovery algorithm to recover local causal structures. These advances enable causal discovery of geophysical phenomena that were previously unapproachable, including non-periodic, transient phenomena such as volcanic eruption plumes. Regularities in local space-time dependencies are transformed into informative spatial replicates, which actually improve CaStLe's performance when applied to ever-larger spatial grids. We successfully apply CaStLe to discover the atmospheric dynamics governing the climate response to the 1991 Mount Pinatubo volcanic eruption. We provide validation experiments to demonstrate the effectiveness of CaStLe over existing causal-discovery frameworks on a range of geophysics-inspired benchmarks while identifying the method's limitations and domains where its assumptions may not hold.

Nichol, J. Jake [Univ. of New Mexico, Albuquerque,↗

DOE OSTI2024

The vertical-velocity skewness in the atmospheric boundary layer without buoyancy and Coriolis effects

One of the main features of near-neutral atmospheric boundary layer (ABL) turbulence is the positive vertical velocity skewness $Sk_w$ above the roughness sublayer or the buffer region in smooth-walls. The $Sk_w$ variations are receiving renewed interest in many climate-related parameterizations of the ABL given their significance to cloud formation and to testing sub-grid schemes for Large Eddy Simulations (LES). The vertical variations of $Sk_w$ are explored here using wind tunnel and flume experiments collected above smooth, rough, and permeable-walls in the absence of buoyancy and Coriolis effects. These laboratory experiments form a necessary starting point to probe the canonical structure of $Sk_w$ as they deal with a key limiting case (i.e., near-neutral conditions). Diagnostic models based on cumulant expansions, realizability constraints, and constant mass flux approach routinely employed in the convective boundary layer as well as prognostic models based on third-order budgets are used to explain variations in $Sk_w$ for the idealized laboratory conditions. The failure of flux-gradient relations to model $Sk_w$ from the gradients of the vertical velocity variance σ$_w^2$ are explained and corrections based on models of energy transport offered. Novel links between the diagnostic and prognostic models are also featured, especially for the inertial term in the third-order budget of the vertical velocity fluctuation. The co-spectral properties of w′/σ w vs w′ 2 /σ$_w^2$ are also presented for the first time to assess the dominant scales governing $Sk_w$ in the inner and outer layers, where w′ is the fluctuating vertical velocity and σ w is the vertical velocity standard deviation.>

Boundary layer flow↗

DOE OSTI2022

Local nm-Scale Imaging of Electrical Contact for Series Resistance Degradation of Silicon Solar Cells

We report on an electrical conduction mechanism for series resistance (Rs) degradation observed in a utility scale solar farm by nm-scale imaging of the local resistance at the Ag/Si interface of c-Si front metallization. Scanning spreading resistance microscopy imaging revealed that the number of point or small area electrical contacts decreased in a degraded cell compared to an unaffected cell, demonstrating the direct root cause of the Rs degradation. The degraded cell shows both a morphological and chemical difference in the screen-printed finger contact compared to the unaffected cell, which likely caused the degradation during the long-term field service. The reduction in electrical contact is likely caused by a structural change: The Ag particles in contact with the Si cell aggregate into bulk Ag, and a highly resistive ceramic oxide is formed in a "belt" shape at the Ag/Si interface. This resistive belt with a thickness of ~1 um blocks the current conduction from cell emitter to the Ag grid. Our results demonstrate an example of the multi-scale characterization approach for understanding degradation mechanisms in photovoltaics.

c-Si solar cell↗

DOE OSTI2020

PETSc DMNetwork: A Library for Scalable Network PDE-Based Multiphysics Simulations

We present DMNetwork, a high-level package included in the PETSc library for the simulation of multiphysics phenomena over large-scale networked systems. The library aims at applications that have networked structures such as those in electrical, gas, and water distribution systems. DMNetwork provides data and topology management, parallelization for multiphysics systems over a network, and hierarchical and composable solvers to exploit the problem structure. DMNetwork eases the simulation development cycle by providing the necessary infrastructure through simple abstractions to define and query the network components. This article presents the design of DMNetwork, illustrates its user interface, and demonstrates its ability to solve multiphysics systems, such as an electric circuit, a network of power grid and water subnetworks, and transient hydraulic systems over large networks with more than 2 billion variables on extreme-scale computers using up to 30,000 processors.

extreme-scale computing↗

DOE OSTI2021

Electrical Systems of Pumped Storage Hydropower Plants: Electrical Generation, Machines, Power Electronics, and Power Systems

Adjustable-speed (AS) pumped storage hydropower (PSH) technology has the potential to become a large, consistent contributor to grid stability enabling higher penetrations of wind and solar energy on the future U.S. power system. AS-PSH has high-value characteristics, such as fast response to provide ancillary services to the grid, because it is a power converter interface with the grid (like battery storage), but at the same time it has the energy content large enough to supply both short-term (seconds-to-minutes) and long-term (minutes-to-hours) of energy needs, like more conventional power plants. However, designs must be optimized to lower the capital expenditure (CapEx) and to provide a high-quality grid interface capability (e.g. power quality, ancillary service provider, fault-tolerant or fault ride-through capability), which is a primary factor in the acceptance of AS-PSH into a utility’s generation mix. This CapEx will be greatly affected by the cost savings associated with the civil structure, turbine design, power electronics, control systems, or unique generator designs. A holistic design must be considered to get a full picture of the benefits of the technology proposed. The AS-PSH can be controlled to reduce the impact of transient disturbance on a power system and at the same time can be controlled to minimized subsequent component fatigue and potential oscillation modes within the plant, with the overall impact in reducing the operational expenditures (OpEx).Generating clean power to meet standards such as Institute of Electrical and Electronics Engineers (IEEE) 519 and International Electrotechnical Commission (IEC) 1000-3-2 will be a continuing challenge. For many technology developers, however, improved AS-PSH technologies will become a key component of generator-storage systems of the future given the prospects of increased performance and decreasing costs, and the ever-increasing penetration of renewables (e.g wind power and solar power).

13 HYDRO ENERGY↗