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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 OSTI2024

Network performance analysis for HPC datacenters (net_perf) v1.0

The software has two main features: (1) identify data movement trends in HPC data centers that use network flow monitoring (2) analyze the performance of individual data flows under the existing data movement management strategy and identify performance bottlenecks that impede timely data availability for science workflows. Its main advantage is that it is tailored for HPC network traffic by considering HPC data movement management intricacies.

Giannakou, Anna↗
DOE OSTI2026

A worldwide climatology of extreme air masses

Extreme temperature events are among the most damaging weather phenomena. In a warming world, more heat extremes and fewer cold extremes are expected in most regions in the future, a trade-off that warrants further understanding of such events. Here, we track and analyze large, persistent areas of hot and cold extreme temperatures in parallel, relative to the location and time of year, to quantify overall regional exposure to extreme temperatures. To accomplish this, we compare the frequencies, movements, trends, and sources/sinks in each type of extreme air mass, calling them extreme cold or extreme hot air masses (ECAMs/EHAMs). For most land regions, ECAMs occur more often than EHAMs, and ECAMs are more common in each hemisphere’s winter, when cold-air advection is strongest and most widespread. Average movement of ECAMs has a stronger equatorward component in winter than in summer, while movement of EHAMs is eastward all year, with less meridional movement than ECAMs. EHAMs have become more common almost everywhere on land, and the reverse is true for ECAMs, with the strongest trends in the northern hemisphere occurring in autumn, especially in the Arctic. The number of EHAMs around the world is increasing at a higher rate (+ 2.32 events/year) than ECAM numbers are decreasing (-1.27 events/year), causing a net increase in extremes, especially in some midlatitude regions. These results help to document the different processes driving hot and cold extremes, and thus the asymmetric and regionally varying trends in frequency for extreme air masses.

Ryan, James M↗
DOE OSTI2022

Shallow Aseismic Slip in the Delaware Basin Determined by Sentinel-1 InSAR

The Delaware Basin, Texas is currently a hot-spot of induced seismicity and ground deformation due to fluid extraction and injection associated with horizontal drilling techniques; however, the driving mechanism behind the seismicity and deformation remains under debate. Here, using vertical and east-west horizontal surface deformation measurements derived from Sentinel-1 interferometric synthetic aperture radar (InSAR), we show that the subsurface responds differently to oil and gas activity in the northern and southeastern portions of the basin. In the north, where there is little seismicity, deformation patterns display long-wavelengths and equidimensional patterns. In contrast, the southeast region hosts most of the seismicity and displays spatial deformation patterns with narrow linear features that strike parallel to the maximum principal horizontal stress and to trends in seismicity, suggesting movement along normal faults. We model a linear deformation feature using edge dislocations and show that the InSAR observations can be reproduced by slip on normal faults contained within the Delaware Mountain Group (DMG), the formation that hosts local wastewater injection and the majority of earthquakes. Our model consists of three parallel, high-angle normal faults, with two dipping toward one another in a graben structure. Slip magnitudes reach up to 25 cm and are spatially correlated with injection wells. Measured seismicity can only explain ~2% of the fault motion predicted by our fault model, suggesting that slip leading to the deformation is predominantly aseismic. We conclude that seismic and aseismic fault motion in the southeastern Delaware Basin is likely driven by wastewater injection near critically-stressed normal faults within the DMG.

58 GEOSCIENCES↗
DOE OSTI2020

SMART Mobility. Multi-Modal Freight Capstone Report

The U.S. Department of Energy’s Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium is a multiyear, multi-laboratory collaborative, managed by the Energy Efficient Mobility Systems Program of the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office, dedicated to further understanding the energy implications and opportunities of advanced mobility technologies and services. The first three-year research phase of SMART Mobility occurred from 2017 through 2019, and included five research pillars: Connected and Automated Vehicles, Mobility Decision Science, Multi-Modal Freight, Urban Science, and Advanced Fueling Infrastructure. A sixth research thrust integrated aspects of all five pillars to develop a SMART Mobility Modeling Workflow to evaluate new transportation technologies and services at scale. This report summarizes the work of the Multi-Modal Freight Pillar. The Multi Modal Freight Pillar’s objective is to assess the effectiveness of emerging freight movement technologies and understand the impacts of the growing trends in consumer spending and e-commerce on parcel movement considering mobility, energy, and productivity. For information about the other Pillars and about the SMART Mobility Modeling Workflow, please refer to the relevant pillar’s Capstone Report.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Effectively Using Remote I/O For Work Composition in Distributed Workflows

Distributed scientific workflows are becoming more important with the interest in incorporating AI into their loops. A critical programming and performance question is how to compose workflow tasks when data is produced on one system but must be consumed on another. Since the dominant technique is composition with remote I/O, this paper explores its performance expectations. We describe BigFlowSim, a workflow I/O simulator that captures key implementation choices for remote I/O, including intensity, reuse, locality, access pattern, and data movement.With BigFlowSim, we generate a synthetic benchmark. We quantify the effects of each parameter with a performance sensitivity study. We explain trends in terms of data movement reduction and show that, under certain conditions, it is possible to establish a total order among most parameters. We apply these insights to a high energy physics workflow, Belle II Monte Carlo and simulate several I/O optimizations. Speedups range from 5% to 2×, without changing compute time.

Friese, Ryan D.↗
DOE OSTI2026

A Critical Review of Heliostat Design for Concentrating Solar Thermal Technologies

Heliostat designs have undergone a widespread and eclectic development process, with many unique designs demonstrated. However, recent developments do not show the overall cohort converging toward a globally accepted universal design. Here, this study characterizes heliostats by breaking down and evaluating design traits based on emergent patterns from a comprehensive compilation of known heliostat designs spanning several decades. Four main categories for evaluation emerged: heliostat base, heliostat primary axis, heliostat drive, and facet support. Each of these four categories is further defined by four subtypes so that all heliostats fall into a single subtype within each category. The classified heliostats are ranked, yielding several view slices into the heliostat compilation or a breakdown of heliostats by type. An analysis of the breakdown shows several trends: a scale-up of established designs, new approaches at small to medium scales, and a movement toward greater adoption of linear drives. These trends reflect the most meaningful contributing factors to a proposed trajectory for a new era of heliostat designs striving to meet widely considered cost targets of $\$$50/m 2 or $\$$75/m 2 .

14 SOLAR ENERGY↗
DOE OSTI2020

The Effects of Annealing after Equal Channel Angular Extrusion (ECAE) on Mechanical and Magnetic Properties of 49Fe-49Co-2V Alloy

Equal channel angular extrusion (ECAE) of 49Fe-49Co-2V, also known as Hiperco® 50A or Permendur-2V, greatly improves the strength and ductility of this alloy, while sacrificing soft magnetic performance. In this work, ECAE Hiperco specimens were subjected to post-ECAE annealing in order to improve soft magnetic properties. The microstructure, mechanical properties, and magnetic performance are summarized in this study. Annealing begins above 650°C and a steep decline in yield strength is observed for heat treatments between 700 and 840°C due to grain growth and the Hall-Petch effect, although some strength benefit is still observed in fully annealed ECAE material compared to conventionally processed bar. Soft magnetic properties were assessed through B-H hysteresis curves from which coercivity (Hc) values were extracted. Hc decreases rapidly with annealing above 650°C as well, i.e. improved soft magnetic behavior. The observed trend is attributed to annealing and grain growth in this temperature regime, which facilitates magnetic domain wall movement. The coercivity vs. grain size results generally follow the trend predicted in the literature. The magnetic behavior of annealed ECAE material compares favorably to conventional bar, possibly due to mild crystallographic texturing which enhances properties in the post-ECAE annealed material. Overall, this study highlights a definitive tradeoff between mechanical and magnetic properties brought about by post-ECAE annealing and grain growth.

annealing↗
DOE OSTI2026

The Next Stage for Smart Cities: Metrics to Spur an Evolution from Technology for Efficiency to Technology for People's Needs

The Smart Cities movement is about a decade old, energized by the USDOT Smart Cities grant that sparked cities to dream big. Now, 10 years later, and multitudes of conferences and papers later, the biggest question regarding Smart Cities is whether any progress has been made? Smart Cities forums are typically led by the technology sector, either telecommunications, internet access, or most recently artificial intelligence (AI) concerns. Each of these areas have grown and found new applications in the context of Smart Cities, however, these technology applications are just tools toward ends. Do digital communications and assistance enable or foster improved access to real, tactile, non-virtual goods and services such as transportation, food, housing, trash removal, health care, education, social interaction? These and other topics are not 'big tech', but are each critical to quality of life outcomes and are day-to-day necessities, comprising many of the touch points through which citizens interact with municipal agencies. Regarding Smart Cities, relationships have not been fully refined as to how best to merge the emerging generation of technology and related trends with more mundane but essential requirements of tactile services. How should municipal managers and operators bring the range of new technology tools (e.g. on-demand transit, automated vehicles, digital connectivity) to bear to improve functions serving real- physical- non-virtual needs? How do we make cities more efficient in providing access to services and opportunities for improved quality of life? This paper examines the current Smart City movement, emerging trends, and the need to put metrics with respect to the delivery, access, and provision of real-life needs. Using a few case studies of past, present, and projected future scenarios, this paper attempts to bring Smart Cities into the real cities domain, perhaps pushing the moniker away from 'Smart' to 'Efficient' - something that can be measured, counted, and assessed. Through the consideration of the next stage of the evolution of cities from 'Smart' to 'Efficient', the combined efforts and experience of multiple researchers at the NREL and their network of collaborators highlights the need for updates metrics that can be used to start guiding efforts toward productive initiatives to improve quality of life.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Site-wide occupancy assessment using camera traps for seven mammalian species at Los Alamos National Laboratory

Los Alamos National Laboratory (LANL or Laboratory) is committed to solving national security challenges through scientific excellence and has been serving the nation and northern New Mexico for over 70 years. Being located on the Pajarito Plateau in the eastern flanks of the Jemez Mountains, the Laboratory is surrounded by a rich diversity of plants and animals. It is common to see many different species of wildlife on Laboratory property; however, sometimes interactions with wildlife can be negative. Vehicle accidents with wildlife have become a common occurrence. With the current and ongoing expansion of the Laboratory on the Pajarito Plateau, it has the potential to further impact wildlife movement including large game species. Local agencies and tribal Pueblos rely on large game species and do not want these species to be restricted from moving across property boundaries. Temporal and spatial aspects of where wildlife occur on the site is a phenomena that is either not well understood in uncommon species or needs periodic reevaluation for common species. Estimating the distribution of multiple species across the landscape provides wildlife biologists with crucial information for monitoring and conserving animal populations in a particular area. Utilizing motion activated wildlife cameras, also known as camera traps, to monitor wildlife populations has become an essential tool for biologists. Camera traps are non-invasive and cost-effective and can document multiple elusive or uncommon wildlife species, such as carnivores, simultaneously. Occupancy modeling provides a flexible framework for the analysis of the distribution for multiple wildlife species. It explicitly recognizes whether a species is spatially common or rare (occupancy = ψ) and if that species is easy or hard to detect (detection probability = p ). Multispecies and multi-season occupancy models can detect trends in species occupancy because individual species may vary in seasonal movements, detection probability, and transition rates between habitats. In this study, we assessed the site as a whole to ascertain when and where medium and large mammal species are present. Understanding wildlife patterns at the Laboratory will better inform future management decisions regarding land use and development strategies. We placed motion activated wildlife cameras in a random systematic sampling design and used these data to create occupancy models. We tested for differences in single-species occupancy and detection probability by season of mammal species captured on 20 camera traps placed across the Laboratory in a 40 mi² (103 km²) area. We focus the interpretation of our findings on seven mammal species found during this study. They are Rocky Mountain elk ( Cervus canadensis nelsoni ; hereafter “elk”), mule deer ( Odocoileus hemionus ; hereafter “deer”), mountain lion ( Puma concolor ; hereafter “lion”), American black bear ( Ursus americanus ; hereafter “bear”), coyote ( Canis latrans ), bobcat ( Lynx rufus ), and gray fox ( Urocyon cinereoargenteus ; hereafter “fox”).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multimodal Freight Energy Model for Emerging Freight Technology Analysis

While freight movement represents a small portion of the volume in the transportation sector, they are a critical contributor for energy consumed in that sector. To reduce logistics costs, energy consumption and negative environmental impact, emerging technologies, such as digitalization of logistics and alternative powertrain, have been developed and extended applications in freight. These trends are expected to grow and provide opportunities for greater efficiencies in freight movement and corresponding energy use. Still, the complexity of freight systems present challenges in evaluating these benefits, especially in the multimodal inter-city freight. Addressing the research need, this paper develops a multimodal freight energy modeling framework for the analysis of emerging freight technology scenarios. The framework is a bi-level optimization problem: network cost minimization problem (lower-level) and energy minimization problem (upper-level). The lower-level problem is a mode-path assignment problem in multimodal inter-city freight networks, where commodity-specific congestion effects on trans-shipment links are considered. For this model, an inverse modeling approach is applied to infer parameters of the lower-level model. The upper-level problem is designed to search for an optimal scenario that has the lowest energy consumption among different levels of technology applications. The proposed model is empirically tested to analyze truck load-pooling and multimodal load-pooling scenarios through stand-alone and mixed applications using freight shipments originating from or destined to the Chicago region. This framework can be used to explore the impact of emerging freight technologies on mode-path freight flow and energy consumption in the national multimodal freight network.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Emerging Trends in Freight [Slides]

Freight transportation accounts for only 5% of the vehicles on U.S. roads, but 10% of vehicle miles of travel and 27% of on-road energy use. With rail and water, freight accounts for 28% of U.S. transportation energy and emissions. Trucking is the primary mode for transporting goods, but rail is a significant mode for longer shipments. Economic, technological, and operational trends pose both challenges and opportunities to meet U.S. demand for goods movement as well as national decarbonization goals. This presentation provides a systems perspective for energy efficiency freight mobility and an overview of available NREL analytical tools to address these challenges.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

BEAM CORE: A Flexible Ecosystem for Freight, Demographics, and Vehicle Analysis

The Behavior, Energy, Autonomy, and Mobility Comprehensive Regional Evaluator (BEAM CORE) is an open-source, modular ecosystem of highly refined, agent-based modeling tools developed by Lawrence Berkeley National Laboratory and the National Laboratory of the Rockies. Organizations such as metropolitan planning organizations, agencies, and companies can use BEAM CORE to analyze freight movement and optimize logistics solutions, assess the impacts of emerging freight technologies or e-commerce trends, model dynamic population growth and evolution, and understand the drivers and impacts of electric vehicle adoption across households in a given region. Users can choose from a flexible suite of modeling modules according to their needs and priorities. The modules integrate with travel demand models to support enhanced analysis of diverse scenarios involving freight movement, vehicle technologies, and other key factors relevant to regional planning.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Robust Carbon Dioxide Plume Imaging Using Joint Tomographic Inversion of Seismic Onset Time and Distributed Pressure and Temperature Measurements (Final Report)

We develop and demonstrate rapid and cost-effective methodologies for spatiotemporal tracking of CO2 plumes during geologic sequestration using joint inversion of seismic data and distributed pressure and temperature measurements. Key elements of our methodology are: (a) a computationally efficient approach to pressure and temperature propagation, (b) analysis of time lapse seismic data using a novel ‘seismic onset time’ approach to detect fluid front propagation, and (c) data assimilation and uncertainty assessment via joint inversion of pressure, temperature and time lapse seismic data, and (d) validating the numerical tomographic inversion using a CO2 injection demonstration projects, specifically data collected from the from the Petra Nova Parish Holdings CCUS project in the West Ranch Field, Texas and the Chester-16 reef CO2 injection site in Northern Michigan which is part of the DOE Midwestern Carbon Sequestration Project. The research team is led by Texas A&M University and includes Battelle as a subcontractor with support from Shell, Anadarko, Chevron and JX Nippon. A carbon dioxide (CO2) water-alternating-gas (WAG) pilot was conducted to gain insights into tertiary oil recovery potential via CO2 flood in the West Ranch Field as part of the Petra Nova project, the world’s largest post-combustion CO2 capture and utilization initiative. With a fluvial formation geology and large contrasts in permeability, this is a challenging and novel application of CO2 enhanced oil recovery (EOR). We build a predictive dynamic model of the subsurface that incorporates the multiphase and compositional data acquired during the pilot operation. The calibrated model is used for the carbon dioxide plume imaging. The study began with an initialization of the pilot sector model extracted from a calibrated full-field model. The pilot model calibration follows a two-step hierarchical workflow. First, we performed a large-scale update of the permeability distribution by integrating available bottomhole pressure and multiphase production data. In the second step, local permeability field is fine-tuned using a streamline-based method to match CO2 breakthrough times at the producers. The predictive capability of the calibrated model was verified through two blind validation tests: (1) the model showed good agreement with saturation logs acquired at two observation wells; and (2) the model reproduced the CO2 recovery as a fraction of the injected CO2. The use of seismic onset times has shown great promise for integrating near-continuous seismic surveys for updating geologic models. In this study, we analyze the impact of seismic survey frequency on the onset time approach aiming to extend the application of onset time to infrequent seismic surveys. In addition, we quantitatively examine the nonlinearity of the onset time method and compare it to the commonly used amplitude inversion method. We carry out a sensitivity analysis of seismic survey frequency based on the complete seismic survey data (over 175 surveys) of steam injection in a heavy oil reservoir (Peace River Unit) in Canada. Our results show that an adequate onset time map can be obtained from the infrequent seismic surveys by interpolation between seismic surveys as long as there is no change in the dominant underlying physics between the successive surveys. The study also shows that nonlinearity of the onset time method can be -smaller than that of the amplitude inversion method by several orders of magnitude. Application to the Brugge benchmark case shows that the onset time method obtains comparable permeability update as the traditional seismic amplitude inversion method with faster computation and improved convergence characteristics. We extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warm-back analysis of the temperature data. In addition to employing simulation models and inverse methods for CO2 plume imaging, we also initialized a data-driven technology for detecting inter-well connectivity based on production and pressure data. Our machine-learning framework is built on the statistical recurrent unit (SRU) model and interprets well-based injection/production data into inter-well connectivity without relying on a geologic model. We test it on synthetic and field-scale CO2 EOR projects utilizing the water-alternating-gas (WAG) process. The validation of the proposed data-driven inter-well connectivity assessment is performed using synthetic data from simulation models where inter-well connectivity can be easily measured using the streamline-based flux allocation. The SRU model is shown to offer excellent prediction performance on the synthetic case. Despite significant measurement noise and frequent well shut-ins imposed in the field-scale case, the SRU model offers good prediction accuracy, the overall relative error of the phase production rates at most producers ranges from 10% to 30%. It is shown that the dominant connections identified by the data-driven method and streamline method are in close agreement. Texas A&M University, the lead organization in the project, was primarily responsible for the development of tomographic approaches for CO2 plume mapping in conjunction with distributed pressure, temperature and seismic onset time data. Battelle, as a subcontractor, was primarily responsible for the development of analytical and empirical methods for analyzing transient injection rate and pressure data from point/line sources such as injection and monitoring wells. An additional area of emphasis for Battelle was the use of machine learning for such tasks as inferring reservoir connectivity information from injection-production data, and identifying variable importance for machine learning-based proxy models developed from full-physics simulations. The two organizations also collaborated on the application of the tomographic inversion methodology for a field data set.

02 PETROLEUM↗
DOE OSTI2022

Moving small files in a networked environment

Globally distributed computing infrastructures, such as clouds and supercomputers, are currently used to manage data that is generated with an unprecedented speed from a variety of resources. Coping with this trend, the volume of data exchanged across distant sites increases substantially. To accelerate data transfer, high-speed networks are provided to connect remote sites. Most existing data movement solutions are optimized for moving large files. However, it is still challenging to transfer a large number of small files across networks. This disadvantage not only lowers data transfer performance, but also decreases overall system utilization. Here, we identify that moving small files is mainly constrained by degraded file system throughput, not just network performance as might be suspected. We have built a data transfer pipeline model to analyze the impact of small network I/O and storage I/O on data movement. Extending one of the widely used open source data movement solutions, GridFTP, we demonstrate several appropriate engineering approaches that mitigate the bottleneck and increase data transfer efficiency. We show optimizations that improve data transfer performance more than 5 times. In comparison to existing solutions, our approaches can save a significant amount of system resources for moving lots of small files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enabling hydrogen chemisorption on charged graphene

Two-dimensional (2D) materials, including graphitic derivatives, have long been of interest for hydrogen storage applications, due to their high theoretical storage capacity, low weight, and other useful properties. However, poor kinetics for hydrogen adsorption and surface diffusion as part of the proposed spillover process for hydrogenation have limited their technological potential. Here, we use first-principles calculations to study electronic doping as a means to improve hydrogen chemisorption on graphene, which we use here as a proxy for graphitic derivatives more broadly. We find that positively charged graphene sheets have vastly improved kinetics for hydrogen diffusion and adsorption, while they limit unwanted hydrogen desorption. This combination of effects should favor hydrogen chemisorption via spillover. We connect these trends to the C–H bond, which introduces states near the Fermi level. These states are depopulated as electrons are removed, thereby lowering the bond energy and permitting more facile movement of hydrogen. Our results suggest that spillover mechanisms for hydrogen chemisorption should be revisited if strategies to apply a large charge to graphitic systems can be realized. Moreover, switchable application of the charge may lead to the reversible chemisorption of hydrogen. While the large magnitude of charging required suggests that graphene itself may not be suitable for reversible hydrogen chemisorption, the factors we identify and discuss could significantly boost the prospects of graphitic derivatives and other 2D or layered materials for hydrogen storage applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗