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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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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Laser Diode Analysis and Verification (Professional Report)

The goal of this project at Lawrence Livermore National Laboratory (LLNL) was focused on analyzing and verifying laser diodes to ensure that the diodes meet requirements for LLNL mission applications. The different stages of this testing were documented in a process flow map which included location color coding so that each step was clearly defined in scope and location of appropriate testing facility. Data were collected and analyzed and compared to minimum acceptable values to see if requirements were met. The process map documents the initial receipt, inspection, and testing of the laser diodes. Initial inspections started with Keyence Microscope imaging and then moved on to High Potential, Ramp, and Burst Testing. Data from the diode testing were processed through MATLAB and Python codes to verify various metrics such as slope efficiency, threshold current, back irradiance, and beam divergence met requirements. These metrics were then recorded in Excel summary reports. Approximately 95% of the laser diodes passed all tests. Presentations were given to Lawrence Livermore’s internal leadership team, an external partner, and to a Lab-wide audience. The data released for this report was constrained by information protection considerations of LLNL’s national security missions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Open Source Evaluation Framework for Solar Forecasting

The Solar Forecast Arbiter is an open-source evaluation framework for solar forecasting. The framework enables evaluations of solar irradiance, solar power, and net-load forecasts that are impartial, repeatable and auditable. The Solar Forecast Arbiter addresses stakeholder-informed use cases including evaluation of forecast skill, comparisons to reference data sets, private forecast trials, and evaluation of probabilistic forecast skill. The framework includes a data validation toolkit, reference data sources, data privacy protocols, and benchmark forecast capabilities for intra-hour and day ahead forecast horizons. Reports and metrics communicate the relative merits of the test and benchmark forecasts. The reports are created from standardized templates and include graphics for qualitatively evaluating deterministic and probabilistic forecasts and standard metrics for quantitatively evaluating forecasts. The Solar Forecast Arbiter is designed to support all solar forecasting stakeholders, including Solar Forecasting 2 Topic Area 2 and Topic Area 3 teams.

14 SOLAR ENERGY↗
DOE OSTI2022

Method for Generating Expert Derived Confidence Scores

We executed a pilot demonstration of a methodology for developing a new confidence metric to help operators calibrate their trust in ML event classifiers. This confidence metric was derived from domain expert judgment and was accompanied with a qualitative description describing the reason for each confidence rating. After learning the boundaries of an ML’s performance by studying a subset of events an SME rated his confidence in the ML’s ability to classify similar events and provided an explanation for his ratings. To demonstrate this methodology, we developed our expert driven confidence scores for the ML event classifier within the ESAMS. Next, we assessed the accuracy of the human expert confidence scores relative to the ML’s uncertainty quantification scores. This report includes a description of our methodology, summary of our findings and future directions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Development of an All-Aqueous Thermally Regenerative Redox Flow Battery to Support Fossil Fuel Assets

Low-temperature thermal energy, a largely untapped resource, holds significant promise for large-scale electrical power generation globally. Various stationary sources, including industrial entities and thermal power plants, emit considerable low-temperature heat that currently remains unutilized. This energy is often overlooked because its low temperature renders it unsuitable for efficient power generation using conventional methods. However, current research is exploring diverse technologies capable of converting low-temperature heat into grid-scale power, aiming to enhance grid efficiency, further decarbonization initiatives, and facilitate a shift toward more decentralized power systems. One such innovative technology is the thermally regenerative battery (TRB), noted for its high power and energy densities compared to similar technologies, positioning it as a potential game-changer in power generation. TRBs integrate two scalable and well-established unit operations: a redox flow battery and a distillation column. This integration suggests that once an effective TRB chemistry is established, the pathway to commercialization could be expedited. The copper-based thermally regenerative ammonia battery (Cu aq -TRAB) stands out as the first TRB that circumvents electrodeposition/dissolution reactions, stabilizing Cu(I) and Cu(II) within the electrolyte and maintaining stability of all electroactive species in an aqueous phase. This stabilization has led to improvements in coulombic efficiency, open circuit potential, and copper solubility, thereby enhancing power density, energy density, and overall energy efficiency. Preliminary tests were conducted to determine the effects of various electrolyte species on the performance metrics of the battery, both theoretically and experimentally. These tests revealed that the solubility of copper in the Cu aq -TRAB electrolyte was constrained by the Cu(I)-NH 3 complex. Adjusting the background electrolyte to 5 M NH4Br and the ligand concentration to 4 M NH 3 enabled the copper concentration to reach a maximum of 0.6 M. This modification led to an estimated theoretical maximum energy density of 9.5 Wh L -1 for the Cu aq -TRAB. Additionally, full cell testing indicated a tradeoff between peak power and energy density with varying copper and ammonia concentrations. Increasing the applied current density during discharge linearly raised the average power output, with a minimal reduction in energy density due to a balance between higher ohmic overpotential and reduced time for undesirable ammonia crossover. Furthermore, a comprehensive numerical sensitivity analysis of the complete Cu aq -TRAB system was performed. This analysis aimed to assess how the battery and the distillation column responded to changes in system input parameters, providing insights into optimal research directions for enhancing system performance. The analysis revealed that at room temperature, battery power was significantly more sensitive to ohmic losses than to mass transfer, with reaction rates having minimal impact. This trend continued even at higher temperatures. Also, the thermal energy required for ammonia separation was studied, showing that increased temperatures generally reduced energy requirements, except in low-pressure scenarios above 65 °C. An investigation into membrane performance in the Cu aq -TRAB was undertaken, given the significant impact of ammonia transport control and ohmic losses on system performance. Various membranes were evaluated to identify key performance metrics. Among the tested membranes, Selemion CMVN exhibited the highest performance, with a peak power density of 84 mW cm -2 and average values of 26 ± 6.8 mW cm -2 for power density and 2.9 Wh L -1 for energy density at an applied current density of 50 mA cm -2 . An economic assessment indicated a levelized cost of storage at $410 per MWh under optimal conditions, highlighting the commercial potential of the Cu aq -TRAB when utilizing cost-effective, readily available materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Unlocking Autonomous Telescopes through Reinforcement Learning: An Offline Framework and Insights from a Case Study

Optimizing observational astronomy campaigns is becoming a complex and expensive task for next-generation telescopes, where manual planning of observations may tend to reach suboptimal results in terms of optimization.Reinforcement Learning (RL) has been well-demonstrated as a valuable approach for training autonomous systems, and it may provide the basis for self-driving telescopes capable of scanning the sky and optimizing the scheduling for astronomy campaigns.We have developed a framework for the optimization of telescope scheduling using RL techniques, based on a dataset containing data on a discrete set of sky locations that the telescope should visit, and a reward metric. We compared several RL algorithms applied to an offline simulation dataset based at the Stone Edge Observatory, considering a discrete set of sky locations to visit and using “t-effective” as a reward metric, a measure of the quality of the data.Deep Q-Networks (DQNs), belonging to the class of value-based methods, have shown remarkable success in the optimization of astronomical observations in our dataset. In the full environment, the average reward value in each state was found to be 92%±5% of the maximum possible reward, while on the test set it resulted in 87%±9% of the maximum possible reward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Identification and Evaluation of Thermal Transport Components for Integrated Energy Systems

A proper evaluation and selection of component technologies is a critical aspect in the development of Integrated Energy System (IES). This study aims to investigate the key components and associated technologies required to build high-temperature heat transport systems for IES. Of particular interest is the component technologies needed to design and construct IES that combines advanced nuclear reactors with high-temperature industrial processes. This study particularly delved into knowledge base, evaluation metrics, and state-of-the-art commercial technologies, aiming to facilitate the evaluation and selection process of various thermal transport components. In addition, an evaluation process was proposed in order to assist in the optimal selection of the thermal transport components based on the knowledge base and evaluation metrics investigated through this study.

42 ENGINEERING↗
DOE OSTI2024

Nova Analysis (Final Technical Report)

As the adoption of solar plus storage technology is rapidly increasing, there is a need for a more wholistic view of homes adopting them. In homes, both energy efficiency (EE) upgrades and DERs provide value not just to the homeowner, but to utilities and even society at large through emissions. Traditionally, EE and DERs are separate sectors, which makes it difficult to understand the co-benefits of their adoption. To address this, we created a novel workflow of tools that allows for a complete analysis of both efficiency and DERs. We also looked at a suite of metrics designed to capture the different benefits provided to different stakeholders, demonstrated with multiple sets of field data. A final report demonstrates the potential of the simulation workflow by simulating hundreds of buildings spread across the U.S. and demonstrating how a variety of factors affect the optimal sizing of DERs along with several other key metrics including energy, utility bills, emissions, and average resilience hours.

14 SOLAR ENERGY↗
DOE OSTI2024

Nova Analysis: Holistically Valuing the Contributions of Residential Efficiency, Solar and Storage

Policies to address climate change and grid modernization, in combination with cost reductions and technological advancements in energy efficiency (EE) and distributed energy resources (DER), are driving rapid deployment of building electrification and energy efficiency retrofits, rooftop solar photovoltaics (PV), smart thermostats, smart water heaters, and battery energy storage. In residential buildings, there are multiple stakeholders (occupant, utility, aggregator, society at large) that are each focused on different value streams. This project uses a suite of metrics intended to all of these value streams to try to holistically analyze the benefits that come from solar, storage, and energy efficiency. To demonstrate the value of these metrics, a semi annual study was performed simulating hundreds of homes across the U.S. under several different upgrade scenarios.

14 SOLAR ENERGY↗
DOE OSTI2024

CO2 Capture from Biofuels Production and Storage into the Mt Simon Sandstone

Advanced carbon capture and storage (CCS) technologies offer significant potential for reducing anthropogenic carbon dioxide (CO2) emissions, while minimizing the cost of employing these technologies. Under the Industrial Carbon Capture and Storage (ICCS) Program, the U.S. Department of Energy (DOE) collaborated with industry in cost-sharing arrangements to demonstrate technologies that captured CO2 emissions from industrial sources and either stored or beneficially re-use them. The technologies included in the ICCS program progressed beyond the research and development stage to a scale that can be deployed into commercial practice within the industry. The Illinois Industrial Carbon Capture and Storage (IL-ICCS) project sought to demonstrate the ability of the Mt. Simon Sandstone to accept and retain industrial-scale volumes of carbon dioxide (CO2) from an anthropogenic source for permanent geologic sequestration. The project was a collaboration of Archer Daniels Midland (ADM) Company, the Illinois State Geological Survey (ISGS), Schlumberger Carbon Services (SCS), and Richland Community College (RCC), and had average annual injection rate of between 1,500 and 2,400 metric tons per day (MTPD) or 0.5 to 0.7 million metric tons (MMT) annually. The project site is in Decatur, Illinois on the property of ADM and RCC (Fig 1) and is directly adjacent to the Illinois Basin – Decatur Project (IBDP), a large scale pilot project of the Midwest Geological Sequestration Consortium (MGSC), which collected and injected CO2 from the ADM fuel ethanol production unit, where high purity biogenic CO2 is produced during the anaerobic fermentation of sugars to alcohol. The IL-ICCS project had an operational period of approximately six (6) years, in which 3.5 MMT of CO2 was captured, compressed, injected, and permanently stored in the Mt Simon Sandstone.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Evaluation of Foster Dam Juvenile Fish Passage, 2018 (Final Report)

This report presents the results of a fish passage study conducted by the Pacific Northwest National Laboratory (PNNL) for the U.S. Army Corps of Engineers Portland District (USACE) at Foster Dam (herein, Foster) during the spring (March–June) and fall (October–December) of 2018. The goal of this study was to provide biologists, engineers, resource managers, and regional decision-makers with information about passage of juvenile Chinook salmon and juvenile steelhead (or appropriate surrogates) through specific routes at Foster. The results of this study are intended to help facilitate decision-making regarding long-term measures for enhancing salmonid passage at Foster. Radio telemetry (RT) methods were instituted at low winter reservoir forebay elevation (low pool, 613 ft mean sea level (msl); spring 2018 and fall 2018) and high summer reservoir forebay elevation (high pool, 635 ft msl; late spring and early summer 2018) to accomplish the following objectives: I. Estimate the following passage metrics under typical operations (i.e., baseline conditions): a. Overall dam-passage survival and survival specific to each route of dam passage, calculated using two different methods: i. Cormack-Jolly-Seber (CJS) estimate: Probability of survival from dam passage to detection at the Primary Array, 19 river kilometers (rkm) downstream of Foster. ii. Virtual Release/Dead Fish Correction (ViRDCt) estimate: Corrects the bias that occurs from misidentifying fish that did not survive dam-passage as alive at the tailrace Egress Array, allowing an estimate of survival from dam passage to this array, 2.5 rkm downstream of Foster. b. Route distribution (passage proportions by route; also known as “passage efficiencies”). c. Efficiency metrics i. Dam-passage efficiency (DPE), the proportion of total fish passing the dam relative to the number of total fish detected in the near forebay of the dam and therefore available to pass. ii. Fish passage efficiency (FPE), the proportion of fish passing via a non-turbine route, relative to the number of total fish in the near forebay and available to pass. iii. Fish weir efficiency (FWE) and spill bay efficiency (SBE), the proportion of fish passing the weir or spill bays, respectively, relative to the total number of fish passing the dam. d. Reservoir residency time (time elapsed between release into reservoir and dam passage) and travel times through the system. II. Estimate efficiency and effectiveness of the new fish weir, compared to the turbines and the spillway. III. Under alternative operations, compare passage through each route (turbines, weir, spillway) using a block/treatment (on/off) design similar to the 2015 and 2016 studies. IV. Estimate efficiency of the new fish weir operating at a low level of discharge (< 500 cfs) and a high level of discharge (= 500 cfs), as well as dam passage survival at each weir discharge level. V. Evaluate survival and behavior of hatchery summer steelhead to wild surrogate winter steelhead for potential use of summer steelhead as a surrogate species winter steelhead. VI. Evaluate juvenile passage rates in the auxiliary water supply (AWS), forebay water supply (FWS), and hatchery water supply (HWS).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Operator-theoretic Resilience Calculations (ORC)

This project developed operational metrics to characterize the resilience of a dynamic network in real-time from time-series data by proposing: a) Resilience ratio b) Recovery energy. Resilience analysis considers the controllability and observability and provides a real-time under- standing on resilience without creating/simulating them. The report focuses on the application of resilience metrics on the HYPERSIM data and insights obtained.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Performance Assessment for the Environmental Restoration Disposal Facility (Annual Summary Report - FY 23)

Beginning in 1996, the Environmental Restoration Disposal Facility (ERDF) started accepting low-level radioactive, hazardous, and mixed wastes that were generated during cleanup activities at the Hanford Site. ERDF is composed of a series of cells or disposal areas and can accommodate future design expansions as needed. Currently, there are eight cells and two supercells in ERDF. Each supercell is the equivalent of two cells. During this reporting period (fiscal year 2023, extending from October 1, 2022, through September 30, 2023), approximately 9.37E+04 metric tons (1.03E+05 U.S. tons) of waste was disposed at ERDF. From ERDF inception through September 30, 2023, approximately 17.1 million metric tons (18.8 million U.S. tons) of waste has been disposed at ERDF, which equates to consumption of approximately 89.1% of the currently constructed disposal volume. According to the design of ERDF, the facility has the ability to be expanded as needed.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Estimate of Gamma Dose Rates from Arrays of Fermi-1 Blanket Elements During the MEDE Process

The Enrico Fermi Atomic Power Plant Unit 1 (Fermi-1) was a sodium-cooled fast breeder reactor located in Monroe County, Michigan. The reactor was powered by a core of enriched uranium metal alloy driver fuel, which was enveloped by an axial and radial blanket material consisting of depleted uranium metal alloyed with 3 wt.% molybdenum. There are 406 axial and 559 radial irradiated sodium-bonded Fermi-1 blanket assemblies in storage at INL, totaling 34 metric tons of heavy metal. Disposal of the Fermi-1 blanket material directly into a geological repository is prohibited due to the reactive characteristic of its bond sodium. A melt drain evaporate (MEDE) process can effectively remove bond sodium from Fermi-1 blanket material. Consequently, planning is underway to apply a MEDE process to treat the 34 metric tons of heavy metal of irradiated Fermi-1 blanket material. Given the irradiated Fermi-1 blanket material’s relatively low power history and 50+ years of decay, it can be treated in a shielded glovebox. To assess the requisite shielding, the dose rates of the Fermi-1 blanket elements in various process configurations are needed. The method to perform the dose rate calculations is to first generate an average source term and associated photon source spectra for the Fermi-1 blanket material in SCALE and then use the associated photon spectra to calculate dose rates using MCNP6.2 in seven representative geometries. Dose rates in rem/h were calculated on contact (1 cm from outer geometry surface), 30 cm away from outer geometry surface, and 1 m from outer geometry surface at axial heights spanning the length of the blanket material within the geometry. The maximum average dose rate for the single Fermi-1 radial blanket element is ~0.60 rem/h, for the Fermi-1 radial blanket assembly ~1.10 rem/h, for the single Fermi-1 axial blanket element ~0.5 rem/h, for the Fermi-1 axial blanket assembly ~1.01 rem/h, for the MEDE can ~ 0.98 rem/h, for the Fermi Storage Canister ~ 0.83 rem/h, and for the MEDE cans in the DOE Standard Canister ~0.51 rem/h. Based on these maximum average values, the bounding dose rates are assumed to be 3× the average dose rates calculated for the axial blanket material and 7.6× the average dose rates calculated for the radial blanket material, based on the distribution of Fermi-1 blanket material assembly burnup.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Computationally-Guided ODS Refractory HEAs via Additive Manufacturing

This document is the final report for ARPA-E ULTIMATE project DE-AR0001422. To achieve higher efficiency turbine operation, the RTX Technology Research Center (RTRC) explored the use of additive manufacturing (AM) to produce test specimens of novel high entropy alloys (HEA) enhanced with oxide dispersion strengthening (ODS). HEAs, with multiple principal elements, offer a vast alloy design space rich with unique properties especially enhanced solid solution strengthening. ODS alloys historically are some of the best performing materials for high temperature applications. Leveraging AM enables near net shape part production and incorporation of ODS into the alloy. Density, melting temperature, and elevated temperature strength properties of several novel HEA compositions were found to meet or exceed program targets; however, obtaining sufficient room temperature ductility in these alloys proved challenging. In order to obtain an alloy with sufficient ductility, the team explored more conventional refractory alloy compositions, focusing on a niobium based alloy. This inherently ductile alloy also satisfied program metrics for cost, density, and fracture toughness. Results showed the oxide dispersions increased high temperature strength, and improved creep resistance compared to the conventional alloy, though the program creep metric was not met. Further tuning of the alloy chemistry, and optimization of the oxide dispersion, is likely required to improve creep resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Western Combined Heat and Power Technical Assistance Partnership (Final Technical Report)

Since 2018, the Western Combined Heat and Power Technical Assistance Partnership (CHP TAP) has been dedicated to advancing the adoption and implementation of Combined Heat and Power. The CHP TAP has diligently worked towards its mission of fostering end-user engagement, promoting technical solutions, and enhancing energy security within the region. As we reflect on our journey, we are proud to announce the completion of the required metrics set forth at the beginning of our endeavor. From the outset, our aim has been clear: to leverage CHP technology as a catalyst for bolstering American competitiveness, utilizing local fuels, and fortifying energy resilience. Through collaborative efforts with a diverse array of stakeholders, including manufacturing, commercial, institutional, and federal facilities, and campuses, we have strived to realize this vision. In this report, we outline the achievements, activities, and impact of the Western CHP TAP, highlighting the transformative journey undertaken since 2018. From providing technical assistance and guidance to conducting educational workshops and advocating for supportive policies, our initiatives have been guided by a commitment to excellence and innovation. As we celebrate the completion of our required metrics, we reaffirm our dedication to continuing our mission, building upon our accomplishments, and shaping a sustainable energy future for the Western region. Through ongoing collaboration and engagement, we are confident that the Western CHP TAP will continue to serve as a beacon of progress and a catalyst for positive change in the years to come.

03 NATURAL GAS↗
DOE OSTI2024

NuGraph2: A Graph Neural Network for Neutrino Event Reconstruction

Neutrino experiments are set to probe some of the most important open questions in physics, from CP violation and the nature of dark matter. The technology of choice for many of these experiments is the liquid argon time projection chamber (LArTPC). In current LArTPC experiments, reconstruction performance often represents a limiting factor for the sensitivity. New developments are therefore needed to unlock the full potential of LArTPC experiments. NuGraph2 is a state of the art Graph Neural Network for reconstruction of data in LArTPC experiments. NuGraph2 utilizes a heterogeneous graph structure, with separate subgraphs of 2D nodes (hits in each plane) connected across planes via 3D nodes (space points). The model provides a consistent description of the neutrino interaction across all planes. NuGraph2 is a multi-purpose network, with a common message-passing attention engine connected to multiple decoders with different classification or regression tasks. These include the classification of detector hits according to the particle type that produced them (semantic segmentation) and the separation of hits from the neutrino interaction from hits due to noise or cosmic-ray background. Additional decoders are being developed, performing tasks such as the regression of the neutrino interaction vertex position. Performance results will be presented based on publicly available samples from MicroBooNE. These include both physics performance metrics, achieving 95% accuracy for semantic segmentation and 98% classification of neutrino hits, as well as computational metrics for training and for inference on CPU or GPU. The status of the NuGraph integration in the LArSoft software framework will be presented, as well as initial studies about model interpretability and injection of domain knowledge.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Containers on Switches: A Cluster School Experience

Network switches, such as those from Arista and Mellanox, often have underutilized computational resources in the form of built-in processors and memory. By leveraging these untapped resources, we can optimize functionality and efficiency of computational cluster networks. Our research focuses on deploying containers directly onto these switches to execute various auxiliary tasks ranging from metric logging to system-wide management via post-boot configuration. By doing so, we can significantly enchance the capabilities of the cluster without the need for additional dedicated hardware. Our research involved five distinct scenarios where switch utilization could have a profound impact on HPC Clusters: run cloud-init services via link-local connection; configuring a Telegraf container to export metrics; deploying a caching proxy; creating a reconfigurable IPv6 DHCP/DNS provider for VLAN; and implementing a client detection with Magellan discovery. These scenarios were containerized with podman and docker, and tested both physically on the switch virtually on a QEMU VM both running SONiC OS. Testing and findings indicate that network switches can indeed be used for these scenarios. They offer a wide range of possibilities beyond these applications. They run as expected as containers on the switches, and although there were some minor issues, work-arounds were implemented. Overall, this is a positive result that can be further explored with more scenarios.

97 MATHEMATICS AND COMPUTING↗