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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Exploring the effectiveness of a back-supporting exosuit: Trunk muscle activity and user experience in controlled and real-world shoveling scenarios

Introduction: This study evaluates the effectiveness of a passive wearable exosuit (HeroWear Apex) in reducing lumbar muscle effort while shoveling. Method: Two experiments were conducted, involving: (1) moving calibrated sandbags at a predefined pace in a laboratory, and (2) moving loose dirt in an in-field setting. Studies were designed to emulate real-world shoveling conditions at Department of Energy - Environmental Management sites. Muscle activity of the lumbar and oblique muscles was analyzed, along with user perceptions. Due to the asymmetric nature of shoveling, analysis of muscle activity was split between the weighted and unweighted sides, with the weighted side being defined as the side of the body closest to the head of the shovel in a neutral posture. Results: While donning the device, both experiments showed a significant decrease in muscle activity for at least one lumbar muscle on the weighted side. Participants rated the device with a high usability score, and perceived exertion ratings were significantly lower while wearing the exosuit. While opinions varied regarding the device’s helpfulness, participants felt the device was comfortable and did not hinder motion during the task. Practical applications: The reduction in back muscle activity associated with wearing the exosuit has the potential to reduce muscle fatigue resulting from repetitive motions.

Exoskeleton↗
DOE OSTI2026

PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling

PVDeg version 0.7.0, released in December 2025, introduced major enhancements to improve usability and performance. This update reorganized tutorials and tool notebooks to create a more intuitive experience, enabling users to easily follow and adapt workflows for their specific analyses. In addition to structural improvements, both the notebooks and core logic underwent significant optimization for efficiency, robustness, and style. These refinements were supported by new testing frameworks built on nbval and pytest, adherence to PEP8 standards, and extensive code refactoring, which collectively simplify onboarding for new developers. Looking ahead, version 0.8.0 will deliver advanced AI-driven capabilities. The primary focus is to further develop and automate the degradation workflow, designed to analyze PV module degradation across diverse locations and system configurations. By integrating large language models (LLMs) to scan literature and compile a comprehensive database of materials and degradation rates, this feature will enable modeling of multiple materials and mechanisms within a single, streamlined workflow. Users will be able to evaluate degradation impacts on different system architectures under varying environmental conditions, facilitating informed decisions on bill-of-materials optimization for specific deployment scenarios. These advancements position PVDeg as a powerful, user-friendly tool for accelerating PV reliability research and system design.

14 SOLAR ENERGY↗
DOE OSTI2022

Quantitative Biostratigraphic Analysis and Age Estimates of Middle Cretaceous Sequences in The Baltimore Canyon Trough, Offshore Mid-Atlantic U.S. Margin

ABSTRACT We applied quantitative methods to previously published biostratigraphic data from the Baltimore Canyon Trough (offshore of the Mid-Atlantic U.S.A.) to provide an improved chronostratigraphic framework for Cretaceous sequences. Here, we successfully used graphic correlation of 228 planktonic foraminifera, nannofossil, and palynological events spanning 22 wells to define assemblage and interval zones as well as major paleoenvironmental changes in the Dawson Canyon, Logan Canyon (three sequences), and Missisauga Formations (two sequences, undifferentiated here). Ranking and scaling techniques were not successful because of the of the limited number of usable biostratigraphic markers. The ages of the sequences previously identified using well logs and seismic profiles were temporally constrained based on chronostratigraphically significant biostratigraphic markers that we identified: the late Cenomanian to Turonian DCx sequence (Rotalipora cushmani and Thalmanninella greenhornensis); the early Cenomanian LC1 sequence; the middle and late Albian LC2 sequence (Braarudosphaera africana, Planomalina buxtorfi, and Spinidinium vestitum); the late Aptian LC3 sequence (Cyclonephelium tabulatum); and the early Aptian to Barremian Missisauga sequences (Aptea anaphrissa, Pseudoceratium pelliferum, and Muderongia simplex). These five biostratigraphic associations are correlated with six prominent seismic reflectors and sequence boundaries that can be traced across the basin. Duration of hiatuses associated with these sequence boundaries are uncertain, though our Monte Carlo analysis allows extraction of age estimates from broad and sometimes contradictory ranges and suggests correlation of hiatuses with global sea-level falls. Together, these seismic and biostratigraphic interpretations can be applied (1) to evaluate reservoir continuity and the viability of offshore carbon storage reservoirs in the Baltimore Canyon Trough, (2) to better define the tectonostratigraphic evolution of the basin, and (3) to contribute to the understanding of regional and global variations in Cretaceous sea level.

Paleontology↗
DOE OSTI2025

Navigating Uncertainty: Challenges in Visualizing Ensemble Data and Surrogate Models for Decision Systems

Uncertainty visualization plays a critical role in transforming ensemble simulation data into actionable insights by effectively communicating various dimensions of uncertainty within a system. The emergence of artificial intelligence-driven surrogate models trained on multirun ensemble data offers a transformative opportunity to replace computationally intensive simulations with fast estimates, enabling users to explore data spaces with unprecedented depth and interactivity. However, integrating ensemble data and surrogate models into decision-making workflows and tools introduces novel challenges for uncertainty visualization. These include reconciling and clearly communicating the unique uncertainties associated with ensembles and their surrogate model estimates, and leveraging these approximations to inform actionable decisions. This work explores these challenges in the context of high-dimensional data visualization, bridging discrete datasets with their continuous representations and addressing the complexities of systems that support iterative navigation between input and output spaces. We evaluate the role of uncertainty visualization in fostering intuitive, actionable interactions and identify critical hurdles in advancing this frontier of computational simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Demonstration and Evaluation of the Human-Technology Integration Function Allocation Methodology

There is an imminent need for the existing nuclear power plants to reduce their operating and maintenance (O&M) costs to remain economically viable. Digital technology, including automation, provides a significant opportunity for the existing nuclear power plant fleet to transform the way in which work is accomplished, reducing O&M costs, and allowing the fleet to remain economically competitive. One notable opportunity to significantly reduce O&M costs pertains to modifications to the plant equipment and main control room (MCR). Existing instrumentation and control (I&C) technologies in the MCR are highly analog, costly to operate and maintain, and demand a high cognitive and physical workload from plant staff (i.e., operators). Digitalizing the MCR has a range of broad economic benefits, including improved plant performance and reduced manual work. Further, digital I&C systems can fundamentally change the way in which plant staff operate the plant; this is the concept of operation. Human-technology integration is important to ensure that impacts to the concept of operation are done in a way that account for capabilities of people and technology. Human-technology integration employs human factors engineering (HFE) methods and principles to maximize the benefits of digital technology, reducing human error, improving overall decision-making and usability. The U.S. Department of Energy Light Water Reactor Sustainability Program is applying human-technology integration research to ensure digital technologies are safe, reliable, and efficient. This paper documents the demonstration of the human-technology guidance developed by the Light Water Reactor Sustainability Program from a first-of-a-kind digital I&C upgrade, specifically addressing function analysis and allocation for a new digital I&C system that included changes in automation levels. The program’s specific approach is included in this work, following lessons learned. This document serves as a resource for industry to follow in applying human-technology integration and HFE to digital modifications, specific to function analysis and allocation. The lessons learned should be considered in the planning and execution of HFE activities that support such digital modifications.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Port of New York and New Jersey Drayage Electrification Analysis

The National Renewable Energy Laboratory (NREL) evaluated the potential for drayage electrification in the Port of New York and New Jersey (PoNYNJ), with a focus on operators: Harbor Freight Transport (HF), Safeway Trucking (SWT), and International Motor Freight Inc (IMF). This report summarizes the data collection and electrification evaluation of all three drayage operators, includes detailed operational data, and identifies the performance requirements for battery electric tractors (BETs) and corresponding infrastructure operated within the context of PoNYNJ drayage operation. This report also details a methodology to evaluate opportunities, strategies, and challenges associated with future expansions of BETs in meeting PANYNJ emissions goals. The Port Authority has established a goal of achieving Net Zero carbon emissions by 2050 across all facilities, including from tenant and stakeholder sources such as drayage trucks. NREL used real-world performance data collected on the three PoNYNJ drayage operations, along with modeling and analysis tools to compare BET to diesel trucks. From March to July 2021, NREL collected 1Hz vehicle and engine data from 46 drayage trucks at the three operators totaling nearly 121,000 miles of operation, providing enough information to assess vehicle operations for electrification potential. A Future Automotive Systems Technology Simulator (FASTSim) electric truck powertrain model was validated using PoNYNJ data and scenarios were run to evaluate drayage truck electrification requirements over the real-world cycles. The first scenario examined BET viability with minimal changes to existing operations. This assumes the trucks charge when stopped for two hours or longer, have a functional battery size of 375 kWh, and can charge at 270 kilowatts (kW) average which are the specification of the commercially available Freightliner eCascadia. The second scenario looked at what operational, charging infrastructure, and BET technology changes would be needed to fully electrify. Finally, detailed analysis was run on charging rate structure to understand operational costs to the fleets. The studied drayage trucks averaged 5.1 MPG, spent roughly 9% of their energy at idle, and drove an average of 140 miles per day with a maximum daily distance of 573 miles. The FASTSim model results indicate a comparable BET would use 417 kWh of energy per day on average accounting for cargo weight, which is close to the full usable capacity of the eCascadia currently available on the market. Based on the daily average operating data, partial fleet electrification is possible with current technology. However, some specific days of operation would require over 1,600 kWh of energy due to longer distances traveled by the trucks and more intense operation. Trucks used for long distance and intense operation cannot be readily electrified with current technology without operational changes. Full adoption of BETs could reduce CO 2 emissions from these fleets by roughly 75% today, eliminating 76 metric tons of CO 2 (MTCO 2 ) per vehicle each year, which equates to 24,100 MTCO 2 per year for all three operators. Commercially available direct current fast chargers (DCFC) have charge rates up to 350 kW. Based on the average daily modeled energy use for each operator, current industrial rate structures, and the assumption of 350 kW peak charging, full drayage electrification would increase electricity consumption. In addition, peak demand usage would increase with unmanaged charging along with cost of electricity having a direct impact on cost per mile for electric vehicles. The resulting cost per mile for BETs along with comparable cost per mile for conventional diesel trucks are also examined at $\$$4.00 per gallon of diesel. It will be important for PANYNJ and the drayage operators within the PoNYNJ to consider these load impacts to their existing electrical infrastructure and devise operational strategies that avoid coincident charging of vehicles to mitigate demand charges. Despite these electricity cost increases, savings from reductions in diesel consumption will help offset the costs of this increased electricity consumption. However, prices of both electricity and diesel are subject to change based on various factors meaning the realized savings will vary over time. This shows BETs could be cost-competitive on an energy cost per mile basis for all scenarios while diesel is above $\$$3.00/gal. Further, if diesel prices dropped to the 15-year low of $2.33/gal, it would still be cost competitive to operate the EVs with electricity costs of 16.3 ¢/kWh or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Pavement condition and climatic data in southeast Texas: A dataset for evaluating flood impacts on pavement performance

Effective pavement maintenance is essential for economic stability, optimal network performance, and roadway safety. Achieving this requires thorough evaluation of pavement conditions, including structural integrity, surface roughness, and distress characteristics. Pavement performance indicators play a critical role in influencing vehicle safety and ride quality. Recent advances have emphasized the use of data-driven modeling to anticipate pavement behavior, with the goal of optimizing resource allocation and refining Maintenance and Rehabilitation (M&R) strategies through accurate condition assessment. A foundational requirement for these modeling efforts is the availability of standardized, high-quality datasets that can support robust and reproducible infrastructure analysis. This data article presents a comprehensive dataset assembled to facilitate pavement performance prediction, with a geographic focus on Southeast Texas, particularly the flood-vulnerable area of Beaumont. The dataset encompasses pavement and traffic attributes, meteorological records, flood simulation outputs, ground deformation measurements, and topographic indices, enabling detailed examination of both load-associated and non-load-associated degradation mechanisms. Data preprocessing was performed using ArcGIS Pro, Microsoft Excel, and Python to ensure consistency and usability in data-driven modeling applications, including machine learning workflows. Key contributions of this dataset include its utility in analyzing the climatic and environmental factors affecting pavement conditions, identifying critical predictive features, and enabling in-depth correlation analysis across diverse variables. By filling existing gaps in input variable selection resources, this dataset supports the development of predictive tools for estimating future maintenance demand and enhancing the resilience of pavement networks in flood-impacted areas. The resource highlights the importance of standardized datasets for advancing pavement management practices and provides a robust foundation for ongoing infrastructure performance modeling.

42 ENGINEERING↗
DOE OSTI2023

CROCUS Sodar Measurements of Lower Atmospheric Wind Profiles at Argonne Testbed for Multiscale Observational Science (ATMOS) Site

The Scintec MFAS Sodar (Multiple-Frequency Acoustic Sounder) is an autonomous, ground-based acoustic remote sensing system designed to measure vertical profiles of horizontal wind speed, wind direction, and vertical velocity in the lower atmosphere. The instrument transmits sequences of acoustic pulses and detects the Doppler-shifted sound waves backscattered by small-scale temperature and velocity fluctuations caused by atmospheric turbulence. From these Doppler shifts, the system derives three-dimensional wind vectors by combining radial velocities from multiple beam orientations.The MFAS Sodar operates with a first usable range gate beginning at approximately 30 m above ground level and a configurable vertical resolution of 10 m. Under favorable acoustic conditions, the system provides wind profiles extending up to 600 m above ground level. Measurements are processed into 15-minute averaged profiles containing wind speed, direction, vertical velocity, and diagnostic quantities such as signal-to-noise ratio and echo strength.This dataset was collected at the Argonne Testbed for Multiscale Observational Science (ATMOS) facility in Lemont, Illinois, as part of DOE's CROCUS Urban Integrated Field Laboratory (UIFL) initiative. The purpose of these observations is to characterize the vertical wind structure and boundary-layer evolution across the urban–suburban gradient of the greater Chicago region. In particular, these data are intended to improve understanding of how local meteorology, such as lake-breeze penetration, nocturnal jets, and daytime mixing, varies between the densely built urban core and the suburban periphery. The MFAS observations provide critical context for evaluating high-resolution model simulations and for integrating with complementary lidar, radar, and in-situ meteorological measurements within the broader CROCUS UIFL network.All data are archived in NetCDF (Network Common Data Form) format and include wind and diagnostic parameters. The files can be accessed and analyzed using standard software that supports NetCDF, such as Python (e.g., xarray, netCDF4), MATLAB, R (e.g., ncdf4, raster), or Panoply (NASA’s NetCDF visualization application).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting lake surface water phosphorus dynamics using process-guided machine learning

Phosphorus (P) loading to lakes is degrading the quality and usability of water globally. Accurate predictions of lake P dynamics are needed to understand whole-ecosystem P budgets, as well as the consequences of changing lake P concentrations for water quality. However, complex biophysical processes within lakes, along with limited observational data, challenge our capacity to reproduce short-term lake dynamics needed for water quality predictions, as well as long-term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling, process-guided machine learning (PGML), to produce a phosphorus budget for Lake Mendota (Wisconsin, USA) and to accurately predict epilimnetic phosphorus over a time range of days to decades. In our implementation of PGML, which we term a Process-Guided Recurrent Neural Network (PGRNN), we combine a process-based model for lake P with a recurrent neural network, and then constrain the predictions with ecological principles. We test independently the process-based model, the recurrent neural network, and the PGRNN to evaluate the overall approach. The process-based model accounted for most of the observed pattern in lake P; however it missed the long-term trend in lake P and had the worst performance in predicting winter and summer P in surface waters. The root mean square error (RMSE) for the process-based model, the recurrent neural network, and the PGRNN was 33.0 μg P L -1 , 22.7 μg P L -1 , and 20.7 μg P L -1 , respectively. All models performed better during summer, with RMSE values for the three models (same order) equal to 14.3 μg P L -1 , 10.9 μg P L -1 , and 10.7 μg P L -1 . Although the PGRNN had only marginally better RMSE during summer, it had lower bias and reproduced long-term decreases in lake P missed by the other two models. For all seasons and all years, the recurrent neural network had better predictions than process alone, with root mean square error (RMSE) of 23.8 μg P L -1 and 28.0 μg P L -1 , respectively. The output of PGRNN indicated that new processes related to water temperature, thermal stratification, and long term changes in external loads are needed to improve the process model. By using ecological knowledge, as well as the information content of complex data, PGML shows promise as a technique for accurate prediction in messy, real-world ecological dynamics, while providing valuable information that can improve our understanding of process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Geologic Characterization of the South Georgia Rift Basin for Source Proximal CO2 Storage

The project Geologic Characterization of the South Georgia Rift Basin for Source Proximal CO2 Storage is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The South Carolina Research Foundation and partners evaluated the feasibility of CCS in the Jurassic/ Triassic (J / TR) saline formations of the buried Mesozoic South Georgia Rift (SGR) Basin that extends from South Carolina into Georgia. The J / TR sequence, based on preliminary assessment of limited geologic and geophysical data, appears to have both the appropriate areal extent and multiple horizons to permanently and safely store CO2 The presence of several igneous rock layers within the sequence may potentially provide adequate seals to prevent upward CO2 migration into the Coastal Plain aquifer systems. Approximately 81 kilometers of 2-D seismic reflection data were collected by Bay Geophysical, Inc. to explore a portion of the SGR located in southern Georgia. The 81 kilometers were divided into two lines approximately 40.5 kilometers each, with Line 1 intersecting Georgia well GGS 3457. Line 2 intersects Line 1 at the southern portion of Line 1 to maximize the extent of coverage away from GGS-3457 (a deep well drilled in the 1980s for oil and gas exploration). This well had a set of usable logs, including gamma and neutron logs that provided promising results related to CO2 storage. Results showed sandstone with porosity values greater than 10 percent and a thickness of 120 meters. The design of the seismic shot was to extrapolate information away from the well and to better define the extent of the SGR and the necessary reservoir and caprock for a successful CO2 injection. A numerical simulation model of CO2 Injection and migration was developed based on the geology log for the GGS-3457 well. The simulation model was used to investigate the feasibility of injecting 30 million metric tons of CO2 into SGR J / TA sediments and integrity of the diabase layers as seals to prevent CO2 migration.

2-D seismic↗
DOE OSTI2021

Metadata Schemas and Ontologies for Building Energy Applications: A Critical Review and Use Case Analysis

With the increasing digitalization of processes throughout the lifecycle of buildings, data exchanged between stakeholders and between building systems has grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent, hindering the development of applications that can be reused across buildings and limiting the scalability of innovative solutions. Semantics refers to the description of the meaning of the data in a way that can be consistently understood by applications. Recently, several competing initiatives have been developing metadata schemas and ontologies to express this semantic information for different applications in the building domain. This paper systematically reviews these schemas and conducts an analysis of five of them to evaluate their applicability to three high-value use cases for building operations: energy audits, automated fault detection and diagnostics and optimal control. The survey finds 40 schemas published in the last 10 years but but their actual use in industry is difficult to estimate. Among the five selected ontologies, several gaps are highlighted in relation to the three use cases. Recommendations for the future include better harmonization of these initiatives, more centralized repositories and search engines for these schemas as well as better industry engagement to facilitate their adoption.

Smart Building, Sematic, Metadata, Ontology, Data ↗
DOE OSTI2025

Laboratory evaluation of cyclic underground hydrogen storage in the Temblor sandstone of the San Joaquin Basin, California

Underground Hydrogen Storage (UHS) in depleted oil and gas reservoirs could provide a cost-effective solution to balance seasonal fluctuations in renewable energy generation. However, data and knowledge on UHS at subsurface conditions are limited so it is difficult to estimate how effective this type of storage could be. In this study, we perform high pressure experiment to measure the effectiveness of cyclic hydrogen (H 2 ) storage in a specimen of Temblor sandstone retrieved from the San Joaquin Basin of California. Our experiment mimics reservoir pressure conditions to measure H 2 -brine relative permeability and fluid-rock interactions over the course of ten charging and discharging cycles. Initial gas breakthrough occurred at 15 % to 25 % H2 saturation in the specimen with 3 % NaCl brine as the resident fluid. Continuing injecting to 4 pore volumes (PV) of H 2 yielded an asymptotic H 2 saturation of 38 % to 41 %, a level often referred to as the irreducible gas saturation based on two-phase flow. The boundary condition in this study mimics the near wellbore region, which experiences bi-directional H 2 flow. This bi-directional flow led to evaporative drying of the specimen resulting in 94 % H 2 saturation at the end of 10th cycle. This indicates that cyclic flow and evaporative drying can lead to more efficient reservoir storage where a larger fraction of the reservoir porosity is usable to store H 2 . The produced gas stream consisted of H 2 mixed with 8 % to 22 % H 2 O, indicating formation dry-out by evaporation. Meanwhile, produced water chemistry indicated calcite and silicate dissolution, with calcite sourced from fossil fragments. This led to a loss of cementation and weakened the rock sample. Combined, our results indicate dry-out, compaction, increased H 2 saturation, rock weakening, and permeability loss during cyclic UHS. Overall, we anticipate that the combined effects should lead to higher than anticipated UHS storage efficiency per volume of sandstone reservoir rock.

08 HYDROGEN↗
DOE OSTI2022

AI-Enabled Robots for Automated Nondestructive Evaluation and Repair of Power Plant Boilers. Final Report

Boiler failure could cause loss of life and safety issues, cost hundreds of thousands of dollars in equipment repairs, property damage and production losses, and drive up the cost of electric power. Boiler maintenance is challenging and risky for inspectors working on scaffolding in confined hazardous spaces inside of a boiler and sometimes the space is hard to access. The operation is also time-consuming due to the large area of vertical structures for inspection and the tremendous effort needed for scaffolding. Recently, the use of robotics (e.g., drones and crawlers) in power plants for maintenance is growing rapidly. However, the existing robotics solutions show two notable technological gaps: no live repair capability, and no Artificial Intelligence (AI) for smart autonomy. The objective of this project is to develop an integrated autonomous robotic platform that is equipped with compact non-destructive evaluation (NDE) sensors to perform live inspection, operates onboard repair devices to perform live repair, and uses AI for intelligent data fusion and predictive analysis for automated and smart spatiotemporal inspection, analysis and repair of the furnace walls in coal-fired boilers. The approach to achieve the objective includes developing NDE sensors with signal processing techniques, designing and evaluating repair devices for robots based on fusion and solid-state technologies, and an autonomous robotic platform that can attach to and navigate on boiler furnace walls using magnetic drive tracks. The robot is also powered by AI to automate data gathering (e.g., 3D mapping and damage localization) and predictive analysis. This project has advanced the state-of-the-art by providing technological breakthroughs including compact NDE and repair tools for robots, AI capabilities for smart autonomy, and a robotic platform for automated boiler maintenance. This project has great potential to result in significant benefits including limiting or eliminating the need to send operators to assess difficult-to-access or hazardous areas, enabling automated live inspection and repair, avoiding time consuming scaffolding (especially for partial maintenance during unplanned outage), collecting comprehensive and well-organized data smartly, and avoiding or limiting the need for onsite or remote piloting technicians. The impacts can be tremendous in terms of the time and cost savings, reducing the risk for human operators, and increasing boiler reliability, usability, and efficiency. In addition, by developing the new technologies on the autonomous inspection and repair robot, by involving multiple undergraduate and graduate students working together with the faculty members on this project, and by generating knowledge and building up collaborations with industrial partners, this effort will significantly update the education capabilities, support long-term fundamental research, and maintain the leadership of Colorado School of Mines and Michigan State University in energy fields.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Isolation and conversion of electrolyte components into a value-added product

The overall objective of this work was to develop a process for effectively (i) recycling the electrolyte components from the end-of-life batteries and reduce the environmental hazard, (ii) understand the fundamental differences between pristine and recycled electrolyte components, (iii) electrochemically and analytically evaluate the recycled electrolyte components and (iv) establish deviations from the pristine electrolyte, and effectively separation of value-added product. We focused on determining scaling up the electrolyte recovery process by distillation of end-of-life batteries to get yield enough to use it for electrolyte formulation of reuse in the battery cell. The used cells from the ORNL battery manufacture facility (BMF)as well as received from the industry partner, Austin Elements Inc., was scaled to 10 used multilayered pouch cells in each batch of distillation and systematically separated and identified the electrolyte components of the used cells with FTIR and NMR studies. We were able to recovery 10g of solvent mass from the used pouch cells. NMR spectroscopy showed that the solvent was pure EMC,DMC and the sample was used to make a new battery cell. The end-of-life battery, it was noted that some cells were visually drier than the other cells and were more advanced in aged. In that case, even though the same distillation conditions were utilized, no usable mass of electrolyte solvent was recovered. It is expected that most of the solvent was decomposed during the battery charge-discharge cycling. The solid salt residue was separated by chemical and water treatment processes. Components of resulting solid product was analyzed by XRD and XPS measurements. The solid products are very much related to the salts used in the electrolyte solution.

25 ENERGY STORAGE↗
DOE OSTI2025

Isolation and Conversion of Electrolyte Components into a Value-Added Product

The overall objective of this work was to develop a process for effectively (i) recycling the electrolyte components from the end-of-life batteries and reduce the environmental hazard, (ii) understand the fundamental differences between pristine and recycled electrolyte components, (iii) electrochemically and analytically evaluate the recycled electrolyte components and (iv) establish deviations from the pristine electrolyte, and effectively separation of value-added product. We focused on determining scaling up the electrolyte recovery process by distillation of end-of-life batteries to get yield enough to use it for electrolyte formulation of reuse in the battery cell. The used cells from the ORNL battery manufacture facility (BMF) as well as received from the industry partner, Austin Elements Inc., was scaled to 10 used multilayered pouch cells in each batch of distillation and systematically separated and identified the electrolyte components of the used cells with FTIR and NMR studies. We were able to recovery 10g of solvent mass from the used pouch cells. NMR spectroscopy showed that the solvent was pure EMC, DMC and the sample was used to make a new battery cell. The end-of-life battery, it was noted that some cells were visually drier than the other cells and were more advanced in aged. In that case, even though the same distillation conditions were utilized, no usable mass of electrolyte solvent was recovered. It is expected that most of the solvent was decomposed during the battery charge-discharge cycling. The solid salt residue was separated by chemical and water treatment processes. Components of resulting solid product was analyzed by XRD and XPS measurements. The solid products are very much related to the salts used in the electrolyte solution.

25 ENERGY STORAGE↗
DOE OSTI2021

Isotopic and Fuel Lattice Parameter Trends in Extended Enrichment and Higher Burnup LWR Fuel Vol I: PWR fuel

Commercial light water reactor (LWR) operators and fuel vendors in the United States are pursuing changes to nuclear fuel that include extended enrichment (EE) and accident-tolerant fuel (ATF) designs. The term EE (8% > 235 U > 5%) is used in this report to refer to a subset of high assay low-enriched uranium (HALEU) that is considered usable in commercial US LWRs in the near term. ATF features are designed to improve fuel system performance under accident conditions. One goal of EE is to improve fuel cycle economy by enabling fuel to be depleted to higher burnup than the typical current maximum pin burnup limits (62 gigawatt-days per metric ton of uranium [GWd/MTU]). Adoption of EE, ATF, and high burnup (HBU) fuels in the US commercial fleet requires a clear understanding of the effects on core physics parameters and used fuel isotopic content, as well as confidence in the accuracy of computer code predictions over an expanded range of materials, enrichment, and burnup. A thorough understanding of the applicability and adequacy of benchmark data (e.g., criticality, decay heat, isotopic content) for computer code validation is necessary to ensure that appropriate safety margins are maintained. To prepare for and support these potential changes, the effects of EE, ATF, and HBU are being assessed for selected representative LWR fuel designs. The project is divided into phases: this report summarizes the findings of Phase 1, which focuses on the lattice physics parameter and used fuel isotopic changes for a conventional Westinghouse 17×17 pressurized water reactor (PWR) design. The primary investigation tool is the SCALE Polaris code using the SCALE 56-group Evaluated Nuclear Data File (ENDF)/B-VII.1 cross sections. The goal of the current work is to (1) identify and explain important effects of EE and HBU (reactivity, lattice physics, and isotopic effects) assuming that PWR fuel design and usage remain similar to those for current enrichment fuel, (2) provide limited code-to-code comparisons with higher order cross section libraries and/or codes, and (3) identify any apparent anomalous trends in the results for further investigation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Multiple Stream Low-Cost Recycling Method

The global composite industry generates large quantities of waste and which mostly end as landfill due to lack of meaningful end-use applications for the multiple waste streams. In a recent report by the Electric Power Research Institute (EPRI), waste generated by the wind industry could reach 370,000 tons a year of composite wind energy blades being decommissioned and scrapped. Wind energy is just one major industry utilizing composite materials. The waste generated by industry includes End-of-Life (EoL) materials and manufacturing process scrap. GreenTex Solutions has developed a unique and innovative technology to recycle the composite waste streams in a range of forms made from production waste and EoL materials. This includes manufacturing waste materials such as dry chopped fiber tow, loose fibers, shredded fibers from reinforcement fabrics, cured/semi-cured prepregs, and it also includes fully-cured composite structure waste (such as edge trims from cured parts) from manufacturing aircraft, automobiles, wind blades, boats, and composite cylinders (tanks). Current recycling methods involve recovering the structural fiber by removing the matrix resin through methods such as pyrolysis. The resulting fibers are used in injection molding or wet laid nonwoven mats and other usable forms. The GreenTex technology bypasses these intermediate steps to create the lowest possible recycling processing costs and the lowest embodied energy/CO2 emissions. The end-product from the GreenTex technology is a finished industrial composite part/application versus intermediate fibers or fabrics. The GreenTex manufacturing process enables cross-industry reuse of recycled feedstock by taking waste from multiple industries (wind energy, aerospace, marine, etc.) and recycles the waste into a product used in other industries. The initial target market application is structural flooring for intermodal shipping containers and truck bodies. One of the team’s key partners is Wabash National which produced 29,000 truck bodies in 2019. The current flooring system is comprised of solid oak “butcher board” laminated panels. Additionally, Mediterranean Shipping Containers (MSC) transports over 1.8 million twenty-foot equivalent units per year in intermodal shipping containers. The current container floor is laminated hardwood that is harvested from the rain forests of Central and South America. The project is to develop a flooring system made from recycled composites that can be qualified for both companies (Wabash and MSC). Initial prototypes validated that the recycled composites panels are lighter and thinner with much higher mechanical strength. These results suggest a typical truck trailer would have 20% lower tare weight. The GreenTex technology is not limited to flooring and is widely applicable to other transportation elements such as walls, roof elements, cab areas and related structural components. Under this project different composite waste streams were evaluated and then combined to develop a formulation that would meet the targeted performance criteria for a flooring system. Wet compression molding was used to fabricate plaques at different tonnage using various composite waste streams. The plaques were tested for flexure and impact.

36 MATERIALS SCIENCE↗
DOE OSTI2021

EXTRACTING HUMAN RELIABILITY FINDINGS FROM HUMAN FACTORS STUDIES IN THE HUMAN SYSTEMS SIMULATION LABORATORY

Modernization of U.S. nuclear power plants (NPPs) is widespread, with most plants currently replacing and transitioning equipment, control systems, and human system interfaces (HSI)s from analog to digital displays. This conversion remedies the obsolescence of analog parts along with needs for increased intuitiveness of design, safety, and capabilities. The Human Factors and Reliability team at Idaho National Laboratory (INL) carried out twelve control room modernization studies in the newly designed Human Systems Simulation Laboratory (HSSL) over nine years. The HSSL was constructed as a testbed for evaluating human factors techniques and performance measures, HSI frameworks, and cutting-edge operational concepts in NPPs. Installing a full-scope training simulator enabled direct design and evaluation work on the same instrumentation and control (I&C) and HSIs located at U.S. plants. The subsequent addition of glass top bays afforded crews opportunities to implement operations via the simulator using full-scale representations of their home NPP. Additionally, functional HSI prototypes were created, providing an environment for operator-in-the-loop benchmark studies. The HSSL has assisted in upgrades of six commercial NPP control rooms and served as an invaluable proving ground for new NPP operations technology. Human reliability analysis (HRA) was not originally the focus of the studies; however, data relating to HRA such as type and frequency of human errors can be extracted from the studies. INL is currently extracting data from the HSSL study reports to apprise how information gathered from simulation, HSI, and other related studies can create a broad look across different data sources to help inform HRA methods.

99 GENERAL AND MISCELLANEOUS↗