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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO

Abstract Streambed grain sizes control river hydro‐biogeochemical (HBGC) processes and functions. However, measuring their quantities, distributions, and uncertainties is challenging due to the diversity and heterogeneity of natural streams. This work presents a photo‐driven, artificial intelligence (AI)‐enabled, and theory‐based workflow for extracting the quantities, distributions, and uncertainties of streambed grain sizes from photos. Specifically, we first trained You Only Look Once, an object detection AI, using 11,977 grain labels from 36 photos collected from nine different stream environments. We demonstrated its accuracy with a coefficient of determination of 0.98, a Nash–Sutcliffe efficiency of 0.98, and a mean absolute relative error of 6.65% in predicting the median grain size of 20 ground‐truth photos representing nine typical stream environments. The AI is then used to extract the grain size distributions and determine their characteristic grain sizes, including the 10th, 50th, 60th, and 84th percentiles, for 1,999 photos taken at 66 sites within a watershed in the Northwest US. The results indicate that the 10th, median, 60th, and 84th percentiles of the grain sizes follow log‐normal distributions, with most likely values of 2.49, 6.62, 7.68, and 10.78 cm, respectively. The average uncertainties associated with these values are 9.70%, 7.33%, 9.27%, and 11.11%, respectively. These data allow for the computation of the quantities, distributions, and uncertainties of streambed HBGC parameters, including Manning's coefficient, Darcy‐Weisbach friction factor, top layer interstitial velocity magnitude, and nitrate uptake velocity. Additionally, major sources of uncertainty in grain sizes and their impact on HBGC parameters are examined.

58 GEOSCIENCES↗
DOE OSTI2023

Valuation of pollination services from habitat management: a case study of utility scale solar energy facilities in the United States

Creating and maintaining pollinator habitats following the ecological infrastructure concept in degraded or unutilized land, such as solar energy facilities, is a practical way to synergistically advance the food, energy, and ecology nexus. Given the large land-use requirements for solar farming—the fastest growing renewable energy technology–considerable attention has been focused on strategies to maximize multiple ecosystem services. In this study, we coupled the principles of agronomy and ecology with economics and integrated national-scale data on crops, pollinators, and solar facilities to identify locations for creating pollinator habitats and estimating the economic value of pollination from the habitats. We examined opportunities for pollination services from pollinator-friendly utility-scale solar facilities adjacent to 42 million hectares of pollination-dependent crops in the conterminous United States at high resolution of 1 ha. We used the net income method to estimate the potential economic value of creating habitat in the land adjacent to solar facilities in the eight states with the greatest number of solar installations. Creating pollinator habitats at the 217 utility-scale solar facilities in these states could support adjacent 80,000 hectares of high pollinator dependent crops, which could potentially generate a pollination value of $\$$120 to $\$$264 million USD. The location-specific information and high-resolution maps generated for the United States demonstrate integration of grey and green infrastructure to support the food, energy, and environment nexus.

14 SOLAR ENERGY↗
DOE OSTI2026

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are leveraged to accelerate scientific discovery. These workflows often require transmitting huge amounts of scientific data through wide-area networks. Although high-speed networks like ESnet and transfer services such as Globus have improved data mobility, several challenges remain. The sheer volume of data can overwhelm network bandwidth, widely used transport protocols such as TCP suffer from inefficiencies due to retransmissions triggered by packet loss, and existing fault-tolerance mechanisms like erasure coding introduce substantial overhead. In this paper, we propose Janus, a resilient and adaptable data transmission approach designed for cross-facility scientific workflows. Unlike traditional TCP-based methods, Janus leverages UDP, integrates erasure coding for fault tolerance, and combines it with error-bounded lossy compression to reduce overhead. This novel design allows users to balance data transmission time and accuracy, optimizing transfer performance based on specific scientific requirements. Additionally, Janus dynamically adjusts erasure coding parameters in response to real-time network conditions, ensuring efficient data transfers even in fluctuating environments. We develop optimization models for determining ideal configurations and implement adaptive data transfer protocols to enhance reliability. Through extensive simulations and real-network experiments, we demonstrate that Janus significantly improves transfer efficiency while maintaining data fidelity.

Esaulov, Vladislav [Georgia State University, Atla↗
DOE OSTI2022

In-Situ Spatial Mapping of Hydrogen in Yttrium Hydrides at LANSCE

This report summarizes the neutron imaging techniques and experimental activities performed at the Los Alamos Neutron Science Center (LANSCE) with the main goal of measuring temperature driven hydrogen diffusion within bulk-yttrium hydride (YH x ) materials. The experimental results and technique developments reported herein support the US Department of Energy Office of Nuclear Energy’s (DOE-NE) Microreactor Program under Technology Maturation. In particular, the critical need to experimentally validate and verify hydrogen-diffusion models of metal hydrides used in hightemperature microreactor designs is addressed by means of high-spatial-resolution neutron imaging coupled with custom-built and application-specific furnaces. These capabilities were designed to apply large temperature gradients across centimeter-sized YH x pellets to simulate conditions faced in the microreactor environment. Neutron imaging, combined with in-situ sample heating, enables near real-time tracking of hydrogen diffusion in YH x on the sub-millimeter scale. In this report, an overview of neutron imaging methodology and technologies are given in the context of recent spatial measures of hydrogen concentrations in similar metals hydrides. Additionally, the commissioning and operation of a custom-built compact dual-zone furnace is given along with details on the first two in-situ heating measurements of YH x performed over the 2020 and 2021 LANSCE operation cycles. In parallel with in-situ heating measurements, neutron computed tomography scans were performed to visualize hydrogen aggregates in post-heated YH x samples. Analysis and results from these neutron imaging measurements are given along with outlooks and plans for improving future hydrogen diffusion measurements at LANSCE.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model

This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.

biofuel sustainability↗
DOE OSTI2020

Non-LTE modelling of cyanoacetylene: evidence for isomer-specific excitation

ABSTRACT Cyanoacetylene molecules are widespread in the interstellar medium (ISM) and several of its isomers have been detected in cold molecular clouds and circumstellar gas. Accurate estimates of the abundance ratio of cyanoacetylene isomers may provide deep insight into their environment. Such knowledge requires rigorous modelling of the emission spectra based on non-local thermodynamic equilibrium (LTE) radiative transfer calculations. To this end, we computed excitation cross-sections of HC2NC and HNC3 induced by collision with para- and ortho-H2, using a quantum mechanical close-coupling method. Then, by thermally averaging these data, we derived rate coefficients for the first 31 low-lying rotational levels of each isomer for temperatures up to 80 K. For the para-H2 collider, the propensity rules are in favour of rotational transitions involving Δj1 = 2 for both isomers, while for the ortho-H2 collider, Δj1 = 2 and Δj1 = 1 rotational transitions are favoured for HC2NC and HNC3, respectively. A comparison of rate coefficients for the HC3N isomers shows differences up to an order of magnitude, especially at low temperatures. Finally, we performed non-LTE radiative transfer calculations to assess the impact of such variations in the analysis of observations. Our simulation suggests that the lack of collisional data specific to each isomer could lead to errors up to a factor of 2–3 in the excitation temperatures. We expect that these data could help in better understanding the cyanoacetylene chemistry and constraining the nitrogen chemistry in the ISM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

In Situ Detection of Amino Acids from Bacterial Biofilms and Plant Root Exudates by Liquid Microjunction Surface-Sampling Probe Mass Spectrometry

The plant rhizosphere is a complex and dynamic chemical environment where the exchange of molecular signals between plants, microbes, and fungi drives the development of the entire biological system. Exogenous compounds in the rhizosphere are known to affect plant-microbe organization, interactions between organisms, and ultimately, growth and survivability. The function of exogenous compounds in the rhizosphere is still under much investigation, specifically with respect to their roles in plant growth and development, the assembly of the associated microbial community, and the spatiotemporal distribution of molecular components. A major challenge for spatiotemporal measurements is developing a nondisruptive and nondestructive technique capable of analyzing the exogenous compounds contained within the environment. A methodology using liquid microjunction-surface sampling probe-mass spectrometry (LMJ-SSP-MS) and microfluidic devices with attached microporous membranes was developed for in situ, spatiotemporal measurement of amino acids (AAs) from bacterial biofilms and plant roots. Exuded arginine was measured from a living Pantoea YR 343 biofilm, which resulted in a chemical image indicative of biofilm growth within the device. Spot sampling along the roots of Populus trichocarpa with the LMJ-SSP-MS resulted in the detection of 15 AAs. Of note, variation in AA concentrations across the root system was observed, indicating that exudation is not homogeneous and may be linked to local rhizosphere architecture and different biological processes along the root.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Locality-Aware Scheduling for Scalable Heterogeneous Environments

Heterogeneous computing promise boost performance of scientific applications by allowing massively parallel execution of computational tasks. However, manually managing extremely heterogeneous, multi-device systems is complicated and may result in sub-optimal performance. Specifically, data management is an extremely challenging problem on multi-device systems. In this work, we introduce two locality-aware schedulers for the Minos Computing Library (MCL), an asynchronous, task-based programming model and runtime for extremely heterogeneous systems. The first scheduler implements a pure locality-aware algorithm to maximize data reuse, though it might incur in ”hot-spots” that limit system utilization. The second scheduler mitigates this drawback by dynamically targeting between locality-awareness and system utilization based on the current workload and available computing devices. Our results show that locality-awareness greatly benefit applications that exhibit data reuse, providing up to 6.9x and 7.9x over the original MCL scheduler and equivalent OpenCL implementations, respectively. Moreover, our schedulers introduce negligible overhead compared with the original MCL scheduler and achieve similar performance for applications that don’t benefit from data locality.

Architecture, co-design, Task-based programming mo↗
DOE OSTI2020

Ion irradiation and modification: The role of coupled electronic and nuclear energy dissipation and subsequent nonequilibrium processes in materials

Understanding material responses to energy deposition from energetic charged particles is important for defect engineering, ion-beam processing, ion-beam analysis and modification, geologic aging, space exploration, and nuclear applications. As an incident ion penetrates a solid, its energy is transferred to electrons and to atomic nuclei of the solid. Much of this electronic energy deposition is subsequently transferred to the atomic structure via electron–phonon (e–ph) coupling, leading to local inelastic thermal spikes in which energy dissipation is influenced by the local environment. In addition, intense ionization can lead to high densities of localized electronic excitations in wide-bandgap materials and ceramics that can affect defect dynamics and atomic mobility. Specifically, energy exchange between electrons and atomic nuclei, along with localized electronic excitations, can lead to substantial competitive (ionization-induced annealing), additive (both electronic and nuclear energy depositions contributing to damage production), and synergistic (more damage than the sums of separate damage processes) effects. Although nonmonotonic effects of the e–ph coupling strength and athermal processes are demonstrated for pre-existing defects and residual damage during ion–solid interactions, there is limited understanding of when such electronic effects must be considered in atomic-scale models of damage production and evolution in a broad variety of materials. Complex ceramics and chemically disordered solid solution alloys with different constituent elements allow a systematic evaluation of defect dynamics and irradiation performance with increasing complexity. Current knowledge regarding tuning of bonding characteristics and chemical disorder to control atomic-level dynamics is reviewed. Although a lack of fundamental understanding obstructs the advancement of reliable predictions for ion beam material modification, it highlights challenges and opens research opportunities. Insights into the complex electronic and atomic correlations with extreme energy deposition will strengthen our ability to design materials and predict ion-irradiation-induced damage in a radiation environment, and they may pave the way to better control fundamental processes and design new material functionalities for advanced technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

WM26 Paper Multi-Robot Collaboration for Hazardous Environments

Hazardous nuclear and industrial facilities are rarely designed for robots. Work in these domains demand precise manipulation and robust mobility in cluttered, constrained spaces where off-the-shelf platforms struggle and “one-size-fits-all” machines become costly and complex. Idaho National Laboratory (INL) is developing an autonomous, multi-robot inspection system that coordinates task-specific platforms rather than relying on a single omni-tool robot. An electric truck serves as a power and compute hub for a custom manipulator co-developed with Florida International University (FIU), a commercial mini crawler, a pan–tilt–zoom camera, and a Nexxis Argus LiDAR mapping system. Working in concert, these robots generate spatial, radiation, and temperature maps of the pit environments at the Hanford Waste Tank Farms. These systems will capture visual records and environmental telemetry to allow for analysis post inspection. The system architecture uses Robot Operating System 2 (ROS 2) for publish/subscribe integration, NVIDIA Isaac Sim and Unity for simulation and visualization, and algorithms such as NVBlox to fuse data into unified 3D overlays. This robot-agnostic approach reduces operator burden by enabling autonomy across heterogeneous platforms and lets each robot be used where it is strongest. Having autonomous functions means operators don’t have to fully control multiple different components. The ease of use could allow for more widespread adoption of advanced robotics at waste management sites that see continued use. By coordinating simpler, purpose-built mechanisms, the approach lowers design and manufacturing complexity, reduces capital risk in contaminated settings, and improves controllability for complex inspection and manipulation tasks. We present the architecture, early results, and lessons learned from building and deploying this coordinated multi-robot system, with the goal of accelerating safe, cost-effective adoption of advanced robotics at waste-management sites.

42 - ENGINEERING↗
DOE OSTI2026

EASY-SHIFT v Alpha

The software is a generic, price- and load-responsive control algorithm integrating heat pumps with thermal energy storage. The algorithm leverages simple models of the system and easily accessible data to schedule operation of heat pumps and thermal energy storage in ways that minimize the cost of operating the heating/cooling system. This tool is specifically designed to be easy to interact with, and something that industry partners are able to adopt. There are two current state of the art approaches. Industry tends to develop very simple algorithms, with predetermined schedules that are not capable of changing operation in response to changes in operating environment. For example, a control designed to avoid high-price electricity from 5-8 PM will not be able to adapt if the high-price period changes to 4-9 PM. Academia commonly develops algorithms called Model predictive control (MPC). MPC requires extensive data and highly trained staff to develop a specific type of simulation model of the building, connect the building to optimization algorithms, and leverage powerful computers. Industry, with limited time/finance budgets for any project, is resistant to adopting MPC due to the associated high complexity and cost.

Grant, Peter [Lawrence Berkeley National Laborator↗
DOE OSTI2024

Novel Microbial Routes to Synthesize Industrially Significant Precursor Compounds

Ethylene is the most widely employed organic precursor compound in industry. The potential to impact ethylene formation via recently discovered microbial processes is tenable using plentiful CO2 feedstocks. The overall long-term objective of this project was to develop an industrially compatible microbial process to synthesize ethylene in high yields. The key objective of this project was to fully define and initially characterized a recently discovered and genetically regulated anaerobic pathway to produce high levels of ethylene called the Dihydroxyacetone Phosphate - Ethylene Pathway in phototrophic bacteria. This was addressed through the following specific aims: 1. Fully probe the catalytic potential of all enzymes of the DHAP ethylene pathway and determine the regulatory mechanism of DHAP-ethylene pathway gene expression. 2. Discover effective and active ethylene enzymes encoded in cultured and uncultured organisms from anoxic environments. 3.Model the thermodynamics and kinetics of ethylene synthetic pathways to guide engineering efforts in integrating best performing DHAP-ethylene pathway enzymes into model bacteria chassis for enhance ethylene yields. Through this project we discovered the initially missing genetic and enzyme component of the DHAP-ethylene pathway that directly synthesized ethylene and other important industrial compounds like methane and ethane from specific substrates. We uncovered and partially characterized a nitrogenase-like reductase that functions in DHAP-ethylene pathway specifically and in methionine synthesis in general. This nitrogenase-like system is called the Methylthio-Alkane Reductase (MAR) for its ability to cleave volatile organic sulfur compounds into methanethiol (CH3-SH) for methionine synthesis and a hydrocarbon byproduct. Key to the DHAP-ethylene pathway, MAR is the essential enzyme that cleaves 2-methylthioethanol (CH3-S-CH2-CH2-OH) into ethylene. Coordinately, we uncovered that the MAR genes and genes associated with conversion of methanethiol (CH3-SH) to methionine are under genetic control of a LysR Type Transcriptional Regulator called SalR, whose activity is dependent upon the amount of sulfate available to the cell. When sulfate as the preferred sulfur source for cell growth drops below 200 micromolar, SalR become active for expressing the MAR and methionine biosynthesis genes to enable the cell to grow from volatile organic sulfur compounds and make ethylene. Metabolic thermos-kinetic modeling revealed that these MAR reactions for ethylene and other hydrocarbon production are highly thermodynamically favorable and are one of the largest driving forces for ethylene production by the DHAP-ethylene pathway for high ethylene yields. Modeling also indicated that a key aldolase and to a lesser extent an isomerase of the DHAP-ethylene pathway for production of the ethylene precursor, 2-methylthioethanol, also would increase ethylene yields. Through metagenomic mining and gene synthesis by the JGI DNA synthesis program, over 500 aldolase and isomerase homologs were synthesized and screened. From this, variants were uncovered with substantially higher activity that increased ethylene yields 5-fold via the aldolase reaction and 1.5-fold via the isomerase reaction. Each of these elements that increase ethylene production were integrated together via plasmid under appropriate gene promoter elements in the phototrophic bacterium, Rhodospirillum rubrum, resulting in at least 3 orders of magnitude increase in ethylene yield from carbon dioxide feedstock.

10 SYNTHETIC FUELS↗
DOE OSTI2024

DOE Cold Climate Heat Pump Challenge: Development, Metrics, and Early Field Observations

Space heating in residential buildings is a major contributor of Greenhouse Gas (GHG) emissions in the United States. New, advanced electric heat pumps are poised to provide a low carbon alternative to traditional fossil-based heating, especially in colder climates. Widespread deployment of cold climate heat pumps could help address the significant portion of building emissions and primary energy used in American households, but these gains will require broader acceptance from consumers and decision makers. As part of the “Energy, Emissions, and Equity” (E3) initiative, the U.S. Department of Energy launched the Cold Climate Heat Pump Challenge (CCHP) in 2021 to accelerate the deployment of the next generation of air source heat pumps. The Challenge is currently focused on residential, centrally ducted, electric heat pumps, with a nominal cooling capacity greater than or equal to 24,000 Btu/h (2 tons) and less than or equal to 65,000 Btu/h (5 tons). The Challenge specifications represent a best-in-class heat pump product that provides high-efficiency heating performance in cold climates, employs environmentally friendly low-Global Warming Potential (GWP) refrigerants, and is designed to be grid interactive. Spearheaded by DOE in partnership with the US Environmental Protection Agency (EPA) and Natural Resources Canada (NRCan), the CCHP Challenge brings together numerous major heat pump manufacturers and key stakeholders including utilities and state agencies across the country. In addition to performance testing in a laboratory environment, a key component of this research is evaluating the in-field performance of the prototype heat pumps developed as part of the Challenge. This paper will discuss the development of the CCHP Challenge, key performance specifications, and the energy and non-energy metrics that will be evaluated through the field study. The types of data that are being collected from the units installed in homes across North America as part of the field validation will be described, including details about the sensors and data acquisition system. Data cleaning and analysis methodologies will be discussed, along with early observations from the winter 2022-2023, spring and summer performance periods, and an assessment of non-energy metrics through pre- and post-installation homeowner surveys. Challenges uncovered and lessons learned throughout the process will also be discussed. Finally, the paper will discuss next steps for the second winter assessment period, areas needing additional research, and potential applications of the data collected and analyzed through this work.

Mendon, Vrushali V.↗
DOE OSTI2026

Three-Dimensional Grid Visualization for Planning Activities: A Dubai Case Study

National Laboratory of the Rockies (NLR), in collaboration with the Dubai Electricity and Water Authority (DEWA) and Infra-X, has undertaken the Energy Visualization Analysis Project. The aim of this project is to enhance analytical and 3D visualization capabilities for distribution network planning and renewable energy integration. As modern grid continues to evolve with large-scale solar PV deployment and emerging distributed energy resources (DERs), the ability to effectively analyze, visualize, and communicate complex grid behaviors has become increasingly critical. The project focuses on developing empirical use cases based on real distribution feeder data and engineering workflows, ensuring the outcomes are directly aligned with operational environment. Through time-series power flow simulations and nodal hosting capacity analysis, the study quantifies the impacts of high PV penetration on voltage and thermal limits within representative 11 kV feeders. These analyses identify specific nodes and conditions where DER integration challenges arise. Furthermore, a Battery Energy Storage System (BESS) optimization algorithm was applied to determine the optimal size and placement of storage systems that can mitigate network constraints and enhance hosting capacity. The comparative results between base-case and BESS-augmented scenarios clearly demonstrate improvements in network stability and load management efficiency. In parallel, the NLR team developed an immersive 3D visualization framework, enabling interactive exploration of grid simulations using commodity head-mounted display (HMD) systems. This framework transforms conventional 2D simulation data into spatially intuitive visual environments - allowing engineers to analyze feeder conditions, PV hosting potential, and BESS effects in real time. This report represents the first foundational phase in establishing a visualization-driven analytical ecosystem. It provides a methodological foundation for data integration, visualization architecture, and simulation-based decision support, paving the way for large-scale adoption of immersive visualization across DEWA's Smart Grid Initiative, R&D activities, and future network resilience studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Development of a Framework and Methodology for an Advanced Reactor Materials Environmental Effects Design Guide

Advanced non-light-water reactor components may operate at elevated temperature while experiencing cyclic loading, significant neutron irradiation, and exposure to reactor coolant. ASME Boiler and Pressure Vessel Code, Section III, Division 5, provides design rules for elevated-temperature service but does not include specific procedures to account for environmental effects on material properties. This report develops an initial framework and methodology for an Environmental Effects Design Guide (EEDG) focused on neutron irradiation; coolant-environment effects are reserved for future work. The proposed approach treats irradiation as a property-based overlay on the existing Division 5 design process, with two routes: a sparse-data route applying two reduction factors — FCR on creep-rupture strength and FF on fatigue life — for the creep-fatigue evaluations that typically control the design of advanced high-temperature reactor components, and a fuller framework developing the property-to-rule chain across the four Division 5 checks (primary load, strain limits and ratcheting, creep-fatigue, and buckling), together with swelling and weldments as scope items. Both routes are scoped by an in-pile qualification that restricts the use of post-irradiation-examination-derived properties in regimes where an in-pile mechanism could control the design outcome. Illustrative outputs derived on a compiled annealed Type 316 database — FCR ≈ 0.78–0.86 and FF ≈ 0.4 — demonstrate the calculation method within that specific dataset. The framework is an initial, testable design-rule concept; it identifies a practical path for preliminary design evaluations under sparse data and the material data and testing needed to develop the framework further.

Barua, Bipul (ORCID:0000000247184113)↗
DOE OSTI2024

Optimizing High-Throughput Inference on Graph Neural Networks at Shared Computing Facilities with the NVIDIA Triton Inference Server

Abstract With machine learning applications now spanning a variety of computational tasks, multi-user shared computing facilities are devoting a rapidly increasing proportion of their resources to such algorithms. Graph neural networks (GNNs), for example, have provided astounding improvements in extracting complex signatures from data and are now widely used in a variety of applications, such as particle jet classification in high energy physics (HEP). However, GNNs also come with an enormous computational penalty that requires the use of GPUs to maintain reasonable throughput. At shared computing facilities, such as those used by physicists at Fermi National Accelerator Laboratory (Fermilab), methodical resource allocation and high throughput at the many-user scale are key to ensuring that resources are being used as efficiently as possible. These facilities, however, primarily provide CPU-only nodes, which proves detrimental to time-to-insight and computational throughput for workflows that include machine learning inference. In this work, we describe how a shared computing facility can use the NVIDIA Triton Inference Server to optimize its resource allocation and computing structure, recovering high throughput while scaling out to multiple users by massively parallelizing their machine learning inference. To demonstrate the effectiveness of this system in a realistic multi-user environment, we use the Fermilab Elastic Analysis Facility augmented with the Triton Inference Server to provide scalable and high-throughput access to a HEP-specific GNN and report on the outcome.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Data-driven offshore CO 2 saline storage assessment methodology

The world produces approximately 50 billion tonnes of greenhouse gases annually. This is measured in CO 2 -equivalents, and geologic CO 2 storage has the potential to advance decarbonization and mitigate greenhouse gas emissions. New technologies to assess offshore carbon storage are needed to address resource, regulatory, and commercial needs. Although most efforts to assess storage resources focus on onshore criteria, offshore reservoirs offer significant storage potential and distinct development challenges. Potential advantages of offshore carbon storage include being further from human population centers and less potential to interact with groundwater. The U.S. Department of Energy's method for evaluating storage capacity in non-oil-bearing saline reservoirs has been enhanced to support assessments for offshore environments in the Offshore CO 2 Saline Storage methodology (OCSS). This methodology applies data-driven capabilities to estimate saline storage capacity while accounting for features specific to offshore reservoirs. Features include changes in CO 2 density and sedimentary differences that impact estimates of permanence and capacity. The Offshore CO 2 Saline Storage Calculator mechanizes OCSS to estimate storage capacity. This paper presents the methodology and estimates for 18 geologic domains in the Gulf of Mexico. Potential storage distributions, sensitivity analyses, and the incorporation of spatial data and tools to support safe site selection are also discussed.

58 GEOSCIENCES↗
DOE OSTI2023

Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs

Fully distributed, integrated surface–subsurface hydrological models (ISSHMs) have seen renewed interest due to availability of better software, high performance computing facilities, and high-resolution, spatially extensive data products. ISSHMs are valuable as tools for advancing system understanding as they can resolve multiple processes defined on the plot scale including three-dimensional interaction of surface water and groundwater. Here, we evaluated the performance of an ISSHM, the Advanced Terrestrial Simulator (ATS), on seven diverse catchments across the continental US using widely available data products to define model inputs without calibration. We compare the ATS-simulated streamflow and evapotranspiration with gauge observations and MODIS-derived evapotranspiration, respectively. Using the Kling-Gupta Efficiency (KGE) as metric, ATS with default data products performed reasonably well at 6 of 7 catchments for streamflow. However, in one of those 6 catchments ATS had poor performance on baseflow and ATS’s overall performance was thus judged to be inadequate despite the acceptable KGE. ATS performance for evapotranspiration was good in all 7 catchments using default data products. In the two catchments where ATS streamflow performance using default data products was not acceptable, the performance was significantly improved by using local information on subsurface properties below the soil. We also compare the model-simulated streamflow and evapotranspiration with the Sacramento soil moisture accounting (SAC-SMA) model, a semi-distributed model that was calibrated on a catchment-by-catchment basis. Uncalibrated ATS performance is comparable to the calibrated SAC-SMA model in terms of streamflow while ATS performance is similar to or better (much better in certain catchments) in reproducing MODIS-derived evapotranspiration. Reasonably good performance of ATS without catchment-specific calibration provides new confidence in the ISSHM class of models and community data products as tools for advancing understanding of watershed function in a changing environment.

54 ENVIRONMENTAL SCIENCES↗