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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

RectifHydPlus: Forty Year Hydropower Generation Reanalysis for Conterminous United States, Version 1.1.

This dataset contains monthly hydropower net-generation totals for 590 plants (each >10 MW) across the conterminous United States (CONUS) from 1980 to 2019. RectifHydPlus v1.1 includes one harmonized table of historical monthly generation—backfilled with observed monthly values where available—and two companion tables: (i) an estimates-only version with no backfill and (ii) a hydrological-control version that removes the effects of capacity and operational change. Each table comprises 23,600 records (590 plants × 40 years). The dataset was developed to address temporal gaps and inconsistencies in publicly available hydropower generation data as available through EIA-923 survey reports. Each record includes a quality label denoting the underlying proxy—from best (direct reservoir releases) to weakest (pattern copied from similar years). By combining the agency-reported survey records with observed and simulated hydrologic releases, RectifHydPlus offers complete, quality-labeled monthly estimates suitable for trend analysis and generation of hydropower generation inputs for energy-water modeling.

Turner, Sean [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2026

Enabling probabilistic learning on manifolds through double diffusion maps

Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.

Double diffusion maps↗
DOE OSTI2025

Inference of phase field fracture models

The phase field approach to modeling fracture uses a diffuse damage field to represent cracks. This representation mollifies singularities that arise in computations with sharp interface models and some of the resultant difficulties in the mathematical and numerical treatment of fracture. Phase field fracture models have proven effective at representing crack propagation, branching, and merging. Specific formulations, beginning with brittle fracture, have also been shown to converge to classical solutions. Extensions to cover the range of material failure, including ductile and cohesive fracture, lead to an array of possible models. There exists a large body of literature focusing on this class of models and on the impact of model form on the predicted crack evolution. However, there have not been systematic studies into how optimal models may be chosen. Here, we take a first step in this direction by developing formal methods for identification of the best parsimonious model of phase field fracture given full-field data on the damage and deformation fields. We consider some of the main models that have been used for the degradation of elastic response due to damage and its propagation. Our approach builds upon Variational System Identification (VSI), a weak form variant of the Sparse Identification of Nonlinear Dynamics (SINDy). Furthermore, in this first communication we focus on synthetically generated data but we also consider central issues associated with the use of experimental full-field data, such as data sparsity and noise.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveraging modern time-of-flight (ToF) spectrometers with large detectors. However, efficiently and robustly extracting interatomic force constants (FCs) parameterizing phonon dynamics from experimental spectra remains a bottleneck due to the complexity and high dimensionality of ToF INS datasets. Here, we present a machine learning approach for the direct inversion of FCs from single-crystal INS measurements. The framework leverages synthetic training data generated using universal machine-learned force fields and an efficient physics-based forward model. We benchmark two neural architectures–one emphasizing structured latent representation learning and the other direct, supervised spectral regression–across simulated datasets for two materials under idealized and noisy conditions. The latent-representation model is subsequently applied to experimental single-crystal INS data on germanium. The model is shown to reproduce FCs derived from both first-principles simulations and from iterative optimization, and furthermore achieves reliable inference even from sparse, single-orientation measurements representing short data acquisitions. Analysis of the learned latent space reveals semantically continuous and physically interpretable encodings that support strong cross-domain generalization. By bridging theoretical and experimental domains, we establish a path toward rapid inversion of experimental spectra and data-driven interpretation of temperature-dependent lattice dynamics.

42 ENGINEERING↗
DOE OSTI2024

Deep Generative Models in Energy System Applications: Review, Challenges, and Future Directions

In recent years, with the advent of mature machine learning products like ChatGPT, Stable Diffusion, and Sora, the world has witnessed tremendous changes driven by the rapid development of generative artificial intelligence (GAI). Beyond applications in text, speech, image, and video creation, deep generative models (DGMs) underpinning these cutting-edge technologies have also been employed by domain researchers to address scientific and engineering challenges. This paper aims to fill a gap in the research community by providing a systematic review of how DGMs have been utilized in energy system applications. After introducing four most popular DGMs, we review and categorize 196 research articles into five focus areas: data generation, forecasting, situational awareness, modeling, and optimal decision-making. Through this classification, we uncover trends in how DGMs are employed for each type of problem, highlighting GAI techniques that contribute to breakthroughs over traditional methods. We discuss limitations in existing literature, engineering challenges, and propose future directions, all tailored to the unique nature of problems in energy system engineering. Our goal is to offer insights for energy system domain researchers, providing a comprehensive view of existing studies and potential future opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Ultra-low thermal resistance and pressure drop copper and copper-tungsten diamond-shaped pin fin cold plates for liquid cooling of electronics

Modern and future data centers face increasing cooling challenges due to increasing chip thermal design power and die size, along with the need to reduce energy consumption used for cooling. High performance cooling solutions that maintain a low chip junction temperature are needed to ensure electronics reliability. This work develops an ultra-low thermal resistance and low pressure drop 75 mm × 75 mm cold plate, intended for next-generation electronics cooling. The cold plate features an array of diamond-shaped pin fins and integrated copper tungsten heat spreader, selected for its low coefficient of thermal expansion which reduces thermomechanical deformation and allows for closer integration of the cold plate with silicon dies. Starting with 300 candidate designs, three-dimensional computational fluid dynamics simulations predict the thermal-hydraulic performance of cold plate subsections. The highest performing geometries are evaluated with high fidelity simulations. Four cold plates are manufactured for experiments: three with diamond-shaped pin fins and one with straights fins for comparison purposes. The cold plates are fabricated from copper-tungsten (CuW), copper (Cu), or aluminum-silicon-magnesium alloy (AlSi10Mg). The diamond-shaped pin fins achieve a roughly 15 % lower thermal resistance compared to the conventional straight fin microchannel. The highest performing design achieves a chip-to-coolant (including thermal interface material) thermal resistance of 9.0 K/kW in CuW and 6.9 K/kW in Cu under a 1 kW heat load with an inlet-to-outlet pressure drop of 9.0 kPa and water as the working fluid. This work demonstrates ultra-low thermal resistance and pressure drop cold plates for large die, high heat load applications, and shows that CuW is an attractive cold plate material for improved reliability in next generation data center cooling.

Coefficient of thermal expansion↗
DOE OSTI2025

FY 2025 Multidimensional Data Correlation Platform: Unified Software Architecture for Advanced Materials and Manufacturing Technologies Data Management and Processing

The Advanced Materials and Manufacturing Technologies (AMMT) program continues to advance a data-driven approach to demonstrate the utility of additive manufacturing for fabricating components for nuclear applications. A key scientific goal is to leverage data to better understand manufacturing outcomes and thereby improve the performance, reliability, and lifespan of nuclear components. Ultimately, this effort supports the development of standards for certification and qualification of additively manufactured components, enabling broader industry adoption. In support of this objective, the AMMT program is building and deploying a data management platform to record, index, analyze, and make available the manufacturing data generated across the AMMT program. In FY 2023, the team conceptualized the architecture of the platform and, in FY 2024, deployed the first functional version at the Oak Ridge National Laboratory (ORNL) Manufacturing Demonstration Facility (MDF). In FY 2025, the platform was officially opened to all AMMT members. To enable this expansion, core modifications and enhancements were developed, including improvements to the user interface and workflows for data entry and retrieval. Most notably, robust security and access control mechanisms were implemented to protect data and manage information sharing. This effort featured a logging system, protected views, and controlled access mechanisms. This report documents these enhancements and the transition of the platform into program-wide use.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A high-throughput experimentation platform for data-driven discovery in electrochemistry

Automating electrochemical analyses combined with artificial intelligence is poised to accelerate discoveries in renewable energy sciences and technologies. This study presents an automated high-throughput electrochemical characterization (AHTech) platform as a cost-effective and versatile tool for rapidly assessing liquid analytes. The Python-controlled platform combines a liquid handling robot, potentiostat, and customizable microelectrode bundles for diverse, reproducible electrochemical measurements in microtiter plates, minimizing chemical consumption and manual effort. To showcase the capability of AHTech, we screened a library of 180 small molecules as electrolyte additives for aqueous zinc metal batteries, generating data for training machine learning models to predict Coulombic efficiencies. Key molecular features governing additive performance were elucidated using Shapley Additive exPlanations and Spearman’s correlation, pinpointing high-performance candidates like cis-4-hydroxy-d-proline, which achieved an average Coulombic efficiency of 99.52% over 200 cycles. The workflow established herein is highly adaptable, offering a powerful framework for accelerating the exploration and optimization of extensive chemical spaces across diverse energy storage and conversion fields.

Lin, Dian-Zhao [Johns Hopkins University, Baltimor↗
DOE OSTI2025

FY25 status report on the addition of candidate materials in Class B Code Case

This report provides the time-dependent allowable stress calculation strategy leveraging the limited creep rupture tests data generated to support the allowable stress for 100,000 hours in American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section II, Part D. A variable confidence index procedure to extrapolate material properties to support 500,000 hours design life is discussed. Time-dependent allowable stresses for Class B component design and analysis are presented for Grade 1 and Grade 2 of Alloy 625. The presented data extrapolation and allowable stress calculation method will support new material addition using limited creep rupture data in the new ASME Boiler and BPVC, Section III, Division 5, Class B rules.

Part D↗
DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Self-driven ion deflectometry measurements using MeV fusion-driven protons and accelerated deuterons in the deuterated hybrid x-pinch on the MAIZE LTD generator

Abstract We report on the results of point-projection ion deflectometry measurements from a mid-size university z-pinch experiment. A 1 MA 8 kJ LTD generator at the University of Michigan (called MAIZE) drove a hybrid x-pinch (HXP) with a deuterated polyethylene fiber load to produce a point-like source of MeV ions for backlighting. In these experiments, 2.7 MeV protons were generated by DD beam-target fusion reactions. Due to the kinematics of beam-target fusion, the proton energies were down-shifted from the more standard 3.02 MeV proton energy that is released from the center-of-mass rest frame of a DD reaction. In addition to the 2.7 MeV protons, strongly anisotropic beams of 3 MeV accelerated deuterons were detected by ion diagnostics placed at a radial distance of 90 mm from the x-pinch. Numerical reconstruction of experimental data generated by deflected hydrogen ion trajectories evaluated the total current in the vacuum load region. Numerical ion-tracking simulations show that accelerated deuteron beams exited the ion source region at large angles with respect to the pinch current direction.

Physics↗
DOE OSTI2026

Summary of the Initial Post-Irradiation Characterization of HFIR-Irradiated Low-N and High-N HT-9 Steel

Reference cladding systems for sodium fast reactors are based on the historical steel, HT-9. HT-9 is a Fe12Cr ferritic/martensitic steel with additions of Mo, W, V, and other minor elements and demonstrates low irradiation swelling and adequate mechanical properties. Extensive irradiation literature exists on the use of HT-9 as cladding for metal fuel, primarily irradiation on the U-Zr/HT-9 system from the Experimental Breeder Reactor-II (EBR-II) and Fast Flux Test Facility (FFTF) sodium fast reactor, and as a structural material from experiments in the FFTF. The large amount of historical data makes the U-Zr/HT-9 system the reference fuel specification for many nuclear reactor vendors that seek to license modern sodium-cooled fast reactors in the United States. However, it is yet unclear how variations in impurity content within HT-9 fundamentally affect irradiation performance at various irradiation temperatures. Recent work suggests that impurity content may noticeably alter the production of helium through nuclear transmutation. For these reasons, High-Flux Isotope Reactor (HFIR) irradiation of HT-9 steels with known variations in the impurity content is particularly timely to generate data to enable more accurate refinement of the chemical specification for nuclear-grade HT-9 material. This report summarizes the initial transmission electron microscopy characterization of HFIR-irradiated HT-9 steels following mechanical property measurements by the Advanced Fuels Campaign (AFC). This report includes qualitative results of the cavity, dislocation loop and cluster/precipitate microstructures as well as radiation-induced segregation. Quantitative results are being shared with partner institutions and will be included in more detail in a future report in FY2026.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Nonparametric Multiparticle Set Methods for Interpreting Environmental Samples

Collection and analysis of environmental samples is commonly used by a range of stakeholders in nuclear safeguards and security contexts. While the ubiquity of samples and their transport in the environment allow regular collection, developing and demonstrating methods for analyzing these samples is difficult. In this work, an environmental sample consists of a set of one or more individual particles. Recent advances in reactor simulation have allowed us to generate data that are more representative of real-world environmental samples, enabling statistically defensible method development and testing. The most notable of these advances is a drastic increase in the number of material depletion regions, which allows our simulations to capture the variation in isotopic composition seen at length scales consistent with environmental samples. Traditional approaches for handling multiparticle samples treat each particle in the sample individually, estimating the quantity of interest (e.g., core-average burnup) resulting from measurement and analysis of signatures (e.g., nuclide assays) from each individual particle. Individual estimates are then averaged to generate a single estimate of the quantity of interest over the entire sample. In this presentation, we introduce two novel approaches for interpreting environmental samples that comprise of multiple particles: (1) the Quantile-Quantile Comparator, which uses a multivariate generalization of quantile-quantile plots for comparing unknown statistical distributions, and (2) the Set Transformer, an attention-based neural network module designed to model interactions among elements (particles) in the input set (sample). Statistically representative sampling cannot be guaranteed as samples are passively collected and are beholden to what particles are available in the environment. These new analysis methods for set-input problems are expected to be more robust than traditional approaches to issues of sampling bias where particles are not uniformly distributed throughout regions of interest, as well as generally outperform traditional approaches by jointly considering all elements in the set. We will present results comparing the performance of traditional single particle approaches and the novel Quantile-Quantile Comparator and Set Transformer for interpretation of simulated environmental samples.

Phathanapirom, Birdy↗
DOE OSTI2024

StOKeDMD: Streaming Occupation kernel dynamic mode decomposition

Dynamic mode decomposition (DMD) has become a common technique for constructing surrogate models for dynamical systems from observed system states. The Occupation Kernel DMD (OKDMD) method proposed in (Rosenfeld et al., 2022) and (Rosenfeld et al., 2024) is a Liouville operator based method that builds surrogate models from system state trajectories. Here, this paper proposes an extension of OKDMD to the case when the system states are observed in a streaming fashion, i.e., only a small fraction of the state trajectory is available at a given time. The developed method, Streaming Occupation Kernel DMD (StOKeDMD), accommodates the streaming data input by leveraging properties of specific choices of kernel functions and occupation kernels. We apply the StoKeDMD method as a compression method for streaming data, analyze the memory complexity, and demonstrate the performance of StoKeDMD in the compression of streaming data generated from a Lorenz system and a fluid flow simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Shape-shifting Elephants: Multi-modal Transport for Integrated Research Infrastructure

Data Acquisition (DAQ) workloads form an important class of scientific network traffic that by its nature (1) flows across different research infrastructure, including remote instruments and supercomputer clusters, (2) has ever-increasing throughput demands, and (3) has ever-increasing integration demands---for example, observations at one instrument could trigger a reconfiguration of another instrument. Today's DAQ transfers rely on UDP and (heavily tuned) TCP, but this is driven by convenience rather than suitability. The mismatch between Internet transport protocols and scientific workloads becomes more stark with the steady increase in link capacities, data generation, and integration across research infrastructure.This position paper argues the importance of developing specialized transport protocols for DAQ workloads. It proposes a new transport feature for this kind of elephant flow: multi-modality involves the network actively configuring the transport protocol to change how DAQ flows are processed across different underlying networks that connect scientific research infrastructure. Multi-modality is a layering violation that is proposed as a pragmatic technique for DAQ transport protocol design. It takes advantage of programmable network hardware that is increasingly being deployed in scientific research infrastructure. The paper presents an initial evaluation through a pilot study that includes a Tofino2 switch and Alveo FPGA cards, and using data from a particle detector.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Review of Edge Computing Technology and Its Applications in Power Systems

Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗