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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

EVSE Cybersecurity and Resilience

Consequence-driven Cybersecurity Analysis for Extreme Fast Charging Electric Vehicle Infrastructure Electric vehicle (EV) development and associated charging infrastructure are expected to advance rapidly. Thirty percent of all global vehicle sales may be EVs and hybrid EVs by 2025, and they will rely on increasingly sophisticated strategies for grid integration. Next-generation EV charging infrastructure is expected to include interconnected renewable resources, such as photovoltaic (PV) arrays and battery storage systems, along with grid-edge devices. Although distributed energy resources (DERs) are useful in several ways, such as peak shaving at high demand times and backup supply for added resilience, the integration of vehicle charging and DERs could create more avenues for cyberattack. Physical and/or remote access to EV charging station components, including charge ports, power electronics, controllers, and local generation (e.g., PV and energy storage) could be paths to cause power fluctuations, leading to altered operations at the charging station, escalated privileges to administrative systems, exfiltration of financial information (including personally identifiable information), and reduced grid stability. One compromised EV supply equipment component can open the door to a variety of exploitable vulnerabilities. Cloud computing and mobile application control have the potential to expand the threat surface to non-repudiation and firmware integrity challenges. Vendor clouds have access to hundreds of chargers, and if compromised, can scale the attack surface exponentially. The high power and voltage levels of xFC infrastructure (e.g., 400 kW at 1000- V DC) increase the hazards and ability to impact the grid and vehicles more than lower-power charging systems. Legacy communications systems and protocols could also put EV infrastructure at risk of cyberattacks requiring a robust patch management process. Communications networks link EVs and chargers to several stakeholders - including charging station operators, grid operators, vendors/manufacturers, and aggregators - who have both physical and network access to share information for control, monitoring, and analytics. Information in these networks that is vulnerable to compromise includes the state of charge, charging duration, payment information, electricity price, and load control. Analyzing and prioritizing these interconnections risks could help address cybersecurity related to data leakage and manipulation.

charging↗
DOE OSTI2022

Subcomponent Validation of Composite Joints for Marine Energy Structures

The Marine Energy Advanced Materials project addresses the barriers and uncertainties facing marine renewable energy developers in using composite materials for load-bearing structures. Sponsored by the U.S. Department of Energy's Water Power Technologies Office, the multiyear project comprises of collaborators from the National Renewable Energy Laboratory (NREL), Sandia National Laboratories, Pacific Northwest National Laboratory (PNNL), Montana State University (MSU), Florida Atlantic University (FAU), and industry stakeholders. As part of the Marine Energy Advanced Materials project, marine renewable energy industry surveys and assessments were conducted to identify key materials and knowledge gaps that hinder the adoption of composite materials in marine renewable energy structures. Specific knowledge gaps highlighted for composite materials were environmental effects, fatigue strengths, and bonded and bolted interconnects (composite/composite and composite/metal). It was concluded that many of these gaps could be addressed through subcomponent validation; consequently, a program was developed at NREL with the goals of developing subcomponent validation methods for appropriate marine energy materials, which would improve the understanding of design allowables for full-scale structural components and joints. Ultimately, the aim is to reduce timelines and costs associated with full-scale structural validation efforts while also providing near-net-scale static and fatigue data of composite/metal subcomponents for marine renewable energy systems. To approach these goals, a testing program was developed at NREL to investigate a variety of materials and structural design details at the subcomponent scale to understand (a) the effects of harsh and corrosive marine energy environments and (b) the static and fatigue strengths of the complex geometries. The recent study from this testing program is perhaps the largest that has ever been conducted with respect to specimen scale and geographic diversity of underwater environmental conditions that the specimens were subjected to. A variety of specimen geometries were designed by NREL to highlight key features of multimaterial (composite/composite or metal/composite) interconnects that may be used in marine renewable energy structural designs. The designs used several different composite matrices, adhesives, and marine-grade steels, which were highlighted in the surveys as being the most appropriate for harsh marine environments. Composite panels were then manufactured at MSU, and were subsequently manufactured into test specimens by NREL. The specimens were shipped to FAU and PNNL for conditioning in ocean water tanks at various temperatures for an extended period. The specimens were then returned to NREL for structural validation. This presentation will provide an overview of recent advances within the testing program in terms of specimen design and test methods, and will discuss results and key findings of the subcomponent testing program to date at NREL.

adhesives↗
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 OSTI2022

Magnetic undulator shim

A magnetic undulator shim having three interconnected sections arranged one after the other in a direction substantially parallel to the beam axis. The first section is adapted to magnetically engage a magnet having a horizontal surface and configured to extend partially onto the horizontal surface of the magnet. The magnet is adjacent to a pole and the magnet and the pole form a boundary. The second third sections are interconnected to form a shape. The shape corresponds to the boundary. The third section is adapted to magnetically engage a surface of the pole.

Xu, Joseph Z.↗
DOE OSTI2022

Ballistic reversible superconducting memory element

A reversible memory element is provided. The reversible memory element comprises a reversible memory cell comprising a Josephson junction and a passive inductor. A ballistic interconnect is connected to the reversible memory cell by a bidirectional input/output port. A polarized input fluxon propagating along the ballistic interconnect exchanges polarity with a stationary stored fluxon in the reversible memory cell in response to the input fluxon reflecting off the reversible memory cell.

Frank, Michael P.↗
DOE OSTI2023

Digital fabrication of a small diameter polymer optical waveguide

A novel polymer optical waveguide and method of manufacturing is presented herein. A digitally manufactured process is described which utilizes a micro-dispensed UV optical adhesive as the contour guiding cladding, a fused deposition modeling technology for creating a core, and a subtractive laser process to finish the two ends of the optical interconnect. The optical waveguide can be printed directly on a circuit board in some embodiments. Alternatively, using a slightly modified process including a step to bond the optical fiber to the substrate, the optical interconnect can be manufactured on a flexible substrate.

Bhethanabotla, Venkat Rama↗
DOE OSTI2023

Microfluidic systems, pumps, valves, fluidic chips thereof, and applications of same

Microfluidic systems, pumps, valves and applications of the same are provided. The microfluidic system may be a pump or a valve having a fluidic chip and an actuator controlling the opening and closing of the fluidic channel in the fluidic chip. The actuator may be disposed to tilt from the fluidic chip, forming a tilted-rotor peristaltic pump. Alternatively, the actuator may be a rolling ball actuator, and different fluidic chips may be used in different applications. For example, the fluidic chip may be a spiral pump chip having spiral channels, a rotary peristaltic pump chip having multiple output channels, or a multi-port valve chip having one port interconnected with multiple different ports. An analytical valve chip may switchably interconnect bioreactor and rinse/calibration input channels to sensor and waste output channels. The actuator of a random-access valve can move from one valve position to another without opening or closing intermediate ones.

Reiserer, Ronald S.↗
DOE OSTI2023

Optimizing connectivity in reconfigurable networks

A method may include determining whether the topology of a network includes a direct path between a first endpoint and a second endpoint in the network. A direct path may be used to send a first type of traffic from the first endpoint to the second endpoint whereas any currently available path may be used to send a second type of traffic from the first endpoint to the second endpoint. If the topology of the network does not include a direct path, the first type of traffic may be buffered at the first endpoint until the topology of the network is reconfigured to include the direct path. The topology of the network may be reconfigured when at least one switch in the network reconfigures, for example, by switching from one interconnection to another interconnection pattern. Related systems and articles of manufacture are also provided.

Mellette, William↗
DOE OSTI2023

Methods of making permeable aerogels

Methods of making permeable aerogels (100) can include providing a sol mixture (110) comprising an organic scaffold, an inorganic aerogel precursor, and a first solvent. The organic scaffold can be insoluble in the first solvent. The sol mixture can react to form a gel (120) such that an interconnected channel network is formed which is at least partially defined by the organic scaffold. The first solvent in the gel can be exchanged (130) with a second solvent. The second solvent can dissolve the organic scaffold to expose the interconnected channel network. The gel can be dried (140) to form the permeable aerogel.

Carlson, Krista↗
DOE OSTI2023

Microfluidic systems, pumps, valves, fluidic chips thereof, and applications of same

Microfluidic systems, pumps, valves and applications of the same are provided. The microfluidic system may be a pump or a valve having a fluidic chip and an actuator controlling the opening and closing of the fluidic channel in the fluidic chip. The actuator may be disposed to tilt from the fluidic chip, forming a tilted-rotor peristaltic pump. Alternatively, the actuator may be a rolling ball actuator, and different fluidic chips may be used in different applications. For example, the fluidic chip may be a spiral pump chip having spiral channels, a rotary peristaltic pump chip having multiple output channels, or a multi-port valve chip having one port interconnected with multiple different ports. An analytical valve chip may switchably interconnect bioreactor and rinse/calibration input channels to sensor and waste output channels. The actuator of a random-access valve can move from one valve position to another without opening or closing intermediate ones.

Reiserer, Ronald S.↗
DOE OSTI2025

DROP DURABILITY ASSESSMENT OF ELECTRONIC ASSEMBLIES UNDER OFF-AXIS LOADING WITH SKEWED FIXTURES

This thesis studies drop durability of electronic assemblies when the acceleration vector is oriented at 45° to the out-of-plane direction of the circuit card. The off-axis drop tests are accomplished with a skewed fixture and are conducted as a proxy for multiaxial drop testing. Advanced shock testing and vibration test methods have been developed over the last few decades to better represent real-world field environments during ground-based laboratory testing. However, many of these test methods require expensive and specialized equipment not available in most laboratories. An alternative approach for approximating simultaneous loading along multiple axes on conventional equipment utilizes skewed fixtures which have seen use in off-axis random vibration and drop impact testing. These methods generally rely on the conversion of a uniaxial input load from the test equipment (using a uniaxial drop tower or shaker) into a multiaxial load when resolved in the reference frame of the test article (mounted on a skewed fixture). Skewed fixture design is presented and recommendations for conducting skewed angle drop testing are introduced based on local measurements along the skewed face of the fixture to accurately monitor the impact event. Characterization tests were performed with a skewed fixture, at simultaneous acceleration loads from 500 to 3,000 g in two (in-plane and out-of-plane) directions, while meeting standard time domain tolerances. Upon experimental characterization, drop shock durability tests were conducted on a printed circuit assembly (PCA). Mean drops-to-failure were measured and quantified with Weibull statistics. Dominant solder joint failure modes were identified via failure analysis. Prior work on inclined angle impact testing is limited, and the majority of solder joint interconnect level fatigue studies are conducted considering perpendicular loading normal the circuit card. Low-cycle fatigue curves are generated based on plastic strain and plastic work density within the solder joint. A multiscale nonlinear finite element model is used to relate board-level flexure to solder joint interconnect level plastic strain. A high strain rate solder constitutive model allows for accurate modeling of solder plasticity resulting from high-impact drop shock. Fatigue parameters are computed from the Coffin-Manson relation and Palmgren-Miner damage accumulation. This work serves to apply established low-cycle fatigue methods for conventional drop shock loading (impact normal to circuit card) to non-perpendicular loading with a skewed fixture.

Hower, Jonathan [Kansas City National Security Cam↗
DOE OSTI2025

Overcoming Resilience Challenges on the Outer Banks of North Carolina

In 2021, the Town of Nags Head, located in the Outer Banks region of North Carolina, was among the first cohort of coastal and island communities to participate in the U.S. Department of Energy's Energy Transitions Initiative Partnership Project (ETIPP), which offers technical assistance and cash awards to competitively selected coastal, remote, and island communities seeking to transform their energy systems and increase their energy resilience. As a barrier island separated from mainland North Carolina, Nags Head experiences impacts from extreme weather events, including power outages, especially during hurricane season. To address challenges, the Nags Head ETIPP team sought to improve the town's disaster resilience by ensuring that critical facilities can remain powered during and after extreme events. With support from experts at Sandia National Laboratories, the National Renewable Energy Laboratory, and the Coastal Studies Institute, the community team identified the following goals for its participation in ETIPP: 1. Identify and prioritize critical facilities that, when powered during disruptions, support the town's resilience. 2. Analyze the potential for on-site solar photovoltaic (PV) and storage systems at 2-4 prioritized critical facilities owned and/or operated by the town. 3. Analyze the potential for interconnecting solar PV and storage systems at a cluster of public and privately owned facilities into a resilient microgrid. 4. Explore finance options and utility interconnection requirements and compensation mechanisms. 5. Summarize outcomes and next steps for town leadership.

25 ENERGY STORAGE↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2025

Data Centers and Digital Assurance Workshop 3 – Mitigations for Digital Assurance Risks

The third session of the TADA (Technical Assistance for Digital Assurance) Data Centers Cohort, held on November 18, 2025, focused on developing mitigation strategies for digital assurance risks identified in previous workshops. Hosted by Idaho National Laboratory (INL) and ScottMadden, the session emphasized the application of Cyber-Informed Engineering (CIE) to data center infrastructure, particularly at the utility–data center interface. Participants revisited and ranked key digital assurance risks, including architecture and interface weaknesses, governance gaps, and AI-enabled threats. The workshop introduced the 12 principles of CIE, advocating for consequence-focused design, engineered controls, and secure information architecture to proactively reduce cyber-physical vulnerabilities. These principles were applied to critical data center systems such as power distribution, UPS, cooling, SCADA/BMS, and grid-forming batteries. The session also addressed governance challenges at the interconnection boundary, highlighting the need for clear roles in telemetry sharing, firmware management, and trip settings. Special attention was given to emerging risks from behind-the-meter (BTM) generation, including reverse-power flow and the integration of small modular reactors (SMRs), which shift data centers from large loads to complex generation nodes. Participants explored how interconnection agreements can serve as enforceable instruments for digital assurance, and reviewed gaps in current standards such as NERC CIP, IEC 62443, and IEEE 1547. The workshop concluded with pathways to standardization, including model agreement language, state-level programs, and expanded NERC guidance. INL also presented tools and frameworks for secure procurement and supplier risk management, reinforcing the need for integrated engineering and policy solutions to secure the evolving data center–grid ecosystem. Session 3 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Integrating AI Data Centers with the Power Grid

The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Architectural scaling tradeoffs in modular 3D bosonic quantum processors

We propose a modular three-dimensional bosonic quantum processor built from repeatable coupled-cavity modules linked by configurable interconnect networks. Using hardware-motivated graph-theoretic measures, we compare nearest-neighbor, hub-based, and hybrid architectures in terms of interconnect count, communication distance, resource concentration, and implementation complexity. Rather than identifying a universally optimal topology, our analysis shows how these architectures redistribute the costs of scaling, including wiring and port requirements, nonlocal communication distance, exposure to shared resources, routing bottlenecks, and scheduling overhead. Case studies of a \(3\times3\) processor and a larger hierarchical architecture further distinguish finite-size performance from asymptotic scaling. The resulting framework provides a systematic basis for evaluating modular three-dimensional bosonic processors and for identifying the device-level parameters required for quantitative hardware design.

Zhu, Shaojiang [Fermilab] (ORCID:0000000293180092)↗
DOE OSTI2021

Utility-Scale Solar, 2021 Edition

Berkeley Labs "Utility-Scale Solar", 2021 Edition presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC. While focused on key developments in 2020, this report explores trends in deployment, technology, capital and operating costs, capacity factors, the levelized cost of solar energy (LCOE), power purchase agreement (PPA) prices, wholesale market value, and interconnection queue data.

2021↗
DOE OSTI2020

Benefit Analysis of Long-Duration Energy Storage in Power Systems with High Renewable Energy Shares

The integration of high shares of variable renewable energy raises challenges for the reliability and cost-effectiveness of power systems. The value of long-duration energy storage, which helps address variability in renewable energy supply across days and seasons, is poised to grow significantly as power systems shift to larger shares of variable generation such as wind and solar. This study explores the system-level services and associated benefits of long-duration energy storage on the 2050 Western Interconnection (WI). The operation of the future WI system with 85% renewable penetration is simulated using a two-stage production cost model. The impact of long duration energy storage on systemwide operations is examined for the 2050 WI system, using a range of round-trip efficiencies corresponding to four different energy storage technologies. The analysis projects the energy storage dispatch profile, system-wide production cost savings (from both diurnal and seasonal operation), and impacts on generation mix, and change in renewable generation curtailment.

25 ENERGY STORAGE↗