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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Chapter 9: Impact of Variable Renewable Energy Sources on Bulk Power System Planning and Operations

Wind and solar photovoltaics (PV) have experienced remarkable growth in recent years, with many consequent benefits within and outside of power systems. At the same time, wind and solar PV have unique characteristics relative to the historically dominant dispatchable technologies like coal, gas, and nuclear power plants that have required and will continue to require changes in power system planning and operations. This chapter discusses planning and operational challenges of integrating wind and solar PV into bulk power systems. We first present the key characteristics of wind and solar PV that differentiate it from conventional technologies, such as variable and uncertain electricity generation, asynchronous interconnection to the power system, and near-zero marginal costs. We then link these characteristics to power system planning and operational challenges at low through high wind and solar penetrations. Finally, we discuss near- and long-term solutions to those challenges, such as diversifying the generation mix and wind and solar fleets, improving system flexibility, diversifying ancillary service products, and integrating generation and transmission planning.

bulk power system↗
DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗
DOE OSTI2024

Thermal simulation of the EIC HSR interconnect module - RF fingers

The Electron Ion Collider (EIC) Hadron Storage Ring (HSR) will reuse most of the existing superconducting magnets from the RHIC storage ring. However, the existing beam vacuum chamber and stripline BPMs will not be compatible with the planned EIC hadron bunches that will have a 3x higher intensity and be 10x shorter, and some operational scenarios with large radial offsets of the beam in the vacuum chamber. To address these challenges, a copper coated beam screen will be implemented, the existing RHIC stripline BPMs will be shielded and an interconnect module design, including new BPM will be installed adjacent to the existing BPMs. A thermal analysis of the new arc BPM interconnect housing has been conducted to assess the heating caused by beam induced resistive wall heating and electron cloud heating. An analysis of the BPM module has been made and reported separately. This report will focus on the other side of the interconnect module, containing the RF fingers.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Revisiting stellar properties of star-forming galaxies with stellar and nebular spectral modelling

Spectral synthesis is a powerful tool for interpreting the physical properties of galaxies by decomposing their spectral energy distributions (SEDs) into the main luminosity contributors (e.g. stellar populations of distinct age and metallicity or ionised gas). However, the impact nebular emission has on the inferred properties of star-forming (SF) galaxies has been largely overlooked over the years, with unknown ramifications to the current understanding of galaxy evolution. The objective of this work is to estimate the relations between stellar properties (e.g. total mass, mean age, and mean metallicity) of SF galaxies by simultaneously fitting the stellar and nebular continua and comparing them to the results derived through the more common purely stellar spectral synthesis approach. The main galaxy sample from SDSS DR7 was analysed with two distinct population synthesis codes: FADO, which estimates self-consistently both the stellar and nebular contributions to the SED, and the original version of STARLIGHT, as representative of purely stellar population synthesis codes. Differences between codes regarding average mass, mean age and mean metallicity values can go as high as ~0.06 dex for the overall population of galaxies and ~0.12 dex for SF galaxies (galaxies with EW(Hα) > 3 Å), with the most prominent difference between both codes in the two populations being in the light-weighted mean stellar age. FADO presents a broader range of mean stellar ages and metallicities for SF galaxies than STARLIGHT, with the latter code preferring metallicity solutions around the solar value (Z ⊙ = 0.02). A closer look into the average light- and mass-weighted star formation histories of intensively SF galaxies (EW(Hα) > 75 Å) reveals that the light contributions of simple stellar populations (SSPs) younger than ≤10 7 (10 9 ) years in STARLIGHT are higher by ~5.41% (9.11%) compared to FADO. Moreover, FADO presents higher light contributions from SSPs with metallicity ≤ Z ⊙ /200 (Z ⊙ /50) of around 8.05% (13.51%) when compared with STARLIGHT. This suggests that STARLIGHT is underestimating the average light-weighted age of intensively SF galaxies by up to ~0.17 dex and overestimating the light-weighted metallicity by up to ~0.13 dex compared to FADO (or vice versa). The comparison between the average stellar properties of passive, SF and intensively SF galaxy samples also reveals that differences between codes increase with increasing EW(Hα) and decreasing total stellar mass. Moreover, comparing SF results from FADO in a purely stellar mode with the previous results qualitatively suggests that differences between codes are primarily due to mathematical and statistical differences and secondarily due to the impact of the nebular continuum modelling approach (or lack thereof). However, it is challenging to adequately quantify the relative role of each factor since they are likely interconnected. This work finds indirect evidence that a purely stellar population synthesis approach negatively impacts the inferred stellar properties (e.g. mean age and mean metallicity) of galaxies with relatively high star formation rates (e.g. dwarf spirals, ‘green peas’, and starburst galaxies). In turn, this can bias interpretations of fundamental relations such as the mass-age or mass-metallicity, which are factors worth bearing in mind in light of future high-resolution spectroscopic surveys at higher redshifts (e.g. MOONS and 4MOST-4HS).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Asynchronous I/Ol↗
DOE OSTI2026

Digital Assurance for Grid Reliability in the Era of Large Load Growth

The rapid expansion of large electric loads is reshaping the operational and regulatory landscape of the U.S. electric grid. These facilities are reaching new scales of expansion, now exceeding a gigawatt per site, and their highly sensitive, digitally driven behaviors introduce new reliability risks. Recent grid events, including large load losses following routine transmission disturbances, highlight the consequences of limited ride-through capability, inconsistent protection settings, inadequate modeling, and lack of behind-the-meter visibility. Parallels to earlier integration challenges of new grid technologies suggest that the grid’s existing processes, standards, and interconnection frameworks are no longer adequate for emerging large loads. This brief synthesizes lessons from the evolution of inverter-based resource regulation and applies them to large-load integration. It identifies critical gaps in modeling accuracy, interconnection processes, performance standards, and compliance mechanisms. Technical recommendations emphasize advanced monitoring, improved modeling, coordinated communication protocols, modernized substations, and structured behind-the-meter control schemes. Collectively, these measures provide a roadmap to maintain bulk power system reliability while enabling the continued growth of large, electrified digital infrastructure.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Quantum computing hardware for HEP algorithms and sensing

Quantum information science harnesses the principles of quantum mechanics to realize computational algorithms with complexities vastly intractable by current computer platforms. Typical applications range from quantum chemistry to optimization problems and also include simulations for high energy physics. The recent maturing of quantum hardware has triggered preliminary explorations by several institutions (including Fermilab) of quantum hardware capable of demonstrating quantum advantage in multiple domains, from quantum computing to communications, to sensing. The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermilab, is dedicated to providing breakthroughs in quantum computing and sensing, mediating quantum engineering and HEP based material science. The main goal of the Center is to deploy quantum systems with superior performance tailored to the algorithms used in high energy physics. In this Snowmass paper, we discuss the two most promising superconducting quantum architectures for HEP algorithms, i.e. three-level systems (qutrits) supported by transmon devices coupled to planar devices and multi-level systems (qudits with arbitrary N energy levels) supported by superconducting 3D cavities. For each architecture, we demonstrate exemplary HEP algorithms and identify the current challenges, ongoing work and future opportunities. Furthermore, we discuss the prospects and complexities of interconnecting the different architectures and individual computational nodes. Finally, we review several different strategies of error protection and correction and discuss their potential to improve the performance of the two architectures. This whitepaper seeks to reach out to the HEP community and drive progress in both HEP research and QIS hardware.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
DOE OSTI2021

Interconnected cathode-electrolyte double-layer enabling continuous Li-ion conduction throughout solid-state Li-S battery

All-solid-state lithium (Li) batteries with high energy density are a promising solution for the next-generation energy storage systems in large-scale devices. To simultaneously overcome the challenges of poor ionic conduction of solid electrolytes and shuttling of active materials, here we introduce a functional electrolyte-cathode bilayer framework with interconnected LLAZO channels from the electrolyte into the cathode for advanced solid-state Li-S batteries. Differing from the traditional solid-state batteries with separated layer compositions, the introduced bilayer framework provides ultrafast and continuous ion/electron conduction. Instead of transferring Li+ across the polymer and garnet phases which involve huge interfacial resistance, Li+ is directly conducted through the LLAZO channels created continuously from the cathode layer to the solid electrolyte layer, significantly shortening the diffusion distance and facilitating the redox reaction of sulfur and sulfides. A stable cycle life is demonstrated in the prototype Li-S solid-state batteries assembled with the introduced LLAZO-LLAZO@CNF interconnected bilayer framework. High capacity is obtained at room temperature, indicating the superior electrochemical properties of the bilayer framework that result from the unique design of the interconnected LLAZO garnet phase.

25 ENERGY STORAGE↗
DOE OSTI2022

Leveraging SETS resilience capabilities for safe-to-fail infrastructure under climate change

As the rehabilitation of infrastructure is outpaced by changes in the profile, frequency, and intensity of extreme weather events, infrastructure’s service disruptions and failures become increasingly likely. Safe-to-fail approaches for infrastructure planning and design improve the capacity of cities to adapt for uncertain climate futures by identifying social, ecological, and technological systems (SETS) capabilities to prepare for potential failure scenarios. In this work, we argue for transforming infrastructure planning and design to effectively utilize safe-to-fail approaches by navigating the opportunities and trade-offs of SETS resilience capabilities. From a technological vantage point, traditional infrastructure planning approaches account for social and ecological domains as external design conditions rather than embedded system characteristics. Safe-to-fail approaches directly challenge the isolation of the technological domain by necessitating a recognition that SETS domains are interconnected and interdependent in infrastructure systems, as such risks and system capabilities for resilience must be managed cohesively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modeling and Automation Framework for High IBR Integration in Large-Scale Power Systems

The increasing prevalence of power electronics- interfaced renewable generation sources is leading to a gradual replacement of traditional thermal generation-based synchronous machines. In this context, the modeling of a large-scale power grid that incorporates a significant number of inverter-based resources is crucial for understanding the dynamics and effects of these resources on the power system. This study investigates the positive sequence model of grid-following and grid-forming inverters. Additionally, this work explores the integration of distributed energy resources using population as an indicator of their relative geographic locations. To address challenge to integrate these inverter based resources into a realistic grid of the US Western interconnection, automation scripts are developed to streamline the process of replacing conventional generators with grid-following and grid-forming inverters, as well as allocating distributed energy resources. Different penetration levels of these inverters are considered, and their frequency regulation support following a disturbance is compared through dynamic simulations.

Lyu, Xue↗
DOE OSTI2020

Comparison of Thermal Management Approaches for Integrated Traction Drives in Electric Vehicles

The continuous push to increase power densities of electric vehicle (EV) traction drive systems necessitates combining electric motor and power electronics into one unit. A single, compact traction drive unit with fewer interconnecting components also facilitates fast, automated assembly of electric vehicles, driving production costs down and enabling wider adoption of EVs. There are a number of challenges associated with the integration of power electronics with the electric machine, including thermal management of the combined traction drive system. However, one important benefit of integration from the thermal management system perspective is the potential for using a single fluid loop instead of two separate cooling systems for the electric machine and the power electronics/inverter. This paper reviews several integration approaches and, employing finite element analysis (FEA), compares thermal management solutions for the combined electric machine and power electronics systems. Namely, three different scenarios are modeled: (1) independent component (motor and power electronics) cooling, which is compared to the combined cooling system approach for (2) radially and (3) axially integrated power electronics modules into the motor enclosure. Temperature distributions for selected thermal loads and thermal resistances from the key heat-generating components to the cooling fluid are compared for each scenario.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Ten years of spasers and plasmonic nanolasers

Ten years ago, three teams experimentally demonstrated the first spasers, or plasmonic nanolasers, after the spaser concept was first proposed theoretically in 2003. An overview of the significant progress achieved over the last 10 years is presented here, together with the original context of and motivations for this research. After a general introduction, we first summarize the fundamental properties of spasers and discuss the major motivations that led to the first demonstrations of spasers and nanolasers. This is followed by an overview of crucial technological progress, including lasing threshold reduction, dynamic modulation, room-temperature operation, electrical injection, the control and improvement of spasers, the array operation of spasers, and selected applications of single-particle spasers. Research prospects are presented in relation to several directions of development, including further miniaturization, the relationship with Bose–Einstein condensation, novel spaser-based interconnects, and other features of spasers and plasmonic lasers that have yet to be realized or challenges that are still to be overcome.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Emerging Threats and Technology Investigation: Industrial Internet of Things - Risk and Mitigation for Nuclear Infrastructure

Industries supporting the global nuclear infrastructure striving for cost savings, expansions in efficiency, and convenience are likely to adopt components (e.g., hardware, software) that comprise the Internet of Things (IoT) and Industrial Internet of Things (IIoT). These devices offer potential improvements along with security challenges. Modern conveniences achieved through application of technology have propagated through society in the form of interconnected devices, from doorbells to microwave ovens, commonly referred to as IoT. IoT devices are often Internet-connected devices that are designed to send data back to a cloud-based server, where a smart phone application then presents device status and control options. Home-based IoT applications carry a different set of risks when compared to a business or security environment, where there is also a history of convenience and interconnection. Industrial settings have long relied on specifically designed Supervisory Control and Data Acquisition (SCADA) systems for process control where IIoT devices are intended to inform business decisions and augment traditional processes. A recent National Institute of Standards and Technology (NIST) report provides a distinction between process control and IIoT in that traditional process control is not replaced by IIoT, but rather IIoT devices are intended to enhance industrial processes through additional monitoring of various sensors and application of data analytics models using artificial intelligence (AI) and machine learning (ML) (Fagan, Marron, et al. 2021) (Ross, et al. 2021).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Experimental Validation of a 40kW, 480V Point-to-Point DC Interlinks for Controller-Agnostic, Interoperable Networked Microgrids

This paper presents the experimental validation of point-to-point dc-interlinks for interconnecting two solar-based, laboratory-scale AC microgrids. DC interlinks provide a solution to numerous technical and operational challenges encountered in networked microgrids, including precise power flow control, stable and fast synchronization, enhanced stability, and improved voltage and frequency regulation. By decoupling microgrids through power converters, dc-interlinks enable power exchange among microgrids that can be owned by different entities (such as communities, utilities, or universities) and managed by diverse microgrid controller vendors. This characteristic makes this dc-interlinks a promising solution for networking real-world microgrids. The presented point-to-point dc interlink utilizes two four-quadrant converters: a 40kW 3-phase ac/dc regulating the dc-link voltage to 800V, and a 3-phase dc/ac controlling power flow. These converters, connected via a 20-foot dc cable, interconnect two ac microgrids operating at 480V, each featuring energy storage, photovoltaic generation, and load emulation. Experimental validation employs commercially available off-the-shelf (COTS) converters and real-world data from solar-powered microgrids in Adjuntas, Puerto Rico. To the authors’ knowledge, this work provides the first at-scale experimental validation of dc-interlinks for networked ac microgrids using COTS inverters, demonstrating their practicality and effectiveness in addressing real-world operational challenges.

Ferrari Maglia, Max [ORNL]↗
DOE OSTI2026

Speed to Power: Solutions for Accelerating Large Load Connections

Rapid growth in demand from data centers and other large loads is creating a range of new challenges for electricity planners, investors, system operators, and regulators, leading to bottlenecks that have slowed connection of large loads to the electric grid. In response, innovative solutions for accelerating large load connections are beginning to emerge across the U.S. Drawing on an extensive document and literature review, this report identifies more than 40 potential solutions for accelerating large load connections, organized into five functional areas: load forecasting, interconnection, resource planning and procurement, markets and operations, and cost allocation and ratemaking. The five functional areas provide a framework for organizing challenges and solutions to large load connection bottlenecks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Impact of Quaternary Pumped Storage Hydropower on Frequency Response of U.S. Western Interconnection with High Renewable Penetrations

As renewable penetration increases in the United States, maintaining stability and reliability of low-inertia power grid by providing sufficient frequency control capability becomes a challenge. Advanced pumped storage hydro technologies (APSH) will be expected to play an important role for future grid as not only an energy supplier, but also as an ancillary services provider. This paper studies the impact of using quaternary pumped storage hydropower (Q-PSH), as one of the newly proposed APSH technology, to provide primary frequency response. To quantify the impact of Q-PSH on frequency response of the U.S. Western Interconnection, a user-defined dynamic model of Q-PSH is developed on the GE Positive Sequence Load Flow (PSLF) platform and is implemented in a set of detailed U.S. Western Electricity Coordination Council (WECC) planning cases in which renewable penetration levels are 20%, 40%, 60% and 80%. Simulation results show that Q-PSH can help improve frequency nadir and settling frequency comparing to the conventional PSH.

frequency response↗