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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Empirical radius formulas for canonical neutron stars from bidirectionally selecting features of equations of state in extended Bayesian analyses of observational data

Significant advancement in Bayesian inference of nuclear equation of state (EOS) from gravitational wave and x-ray observations of neutron stars (NSs) has been made by the nuclear astrophysics community especially since GW170817. By extending the traditional Bayesian analysis which normally ends at presenting the marginalized posterior probability distribution functions (PDFs) of individual EOS parameters and their correlations (or sometimes only the Pearson correlation coefficients which are only reliably useful when the variables are linearly correlated while they are actually often not), we search for a data-driven and robust empirical formula for the radius 𝑅 1.4 of canonical NSs in terms of the characteristic EOS parameters (features). We also identify the single most important but currently poorly known EOS parameter for determining the 𝑅 1.4 . Using three regression-model-building methodologies: bidirectional stepwise feature selection, least absolute shrinkage selection operator (LASSO) regression, and neural network regression on a large set of posterior EOSs and the corresponding 𝑅 1.4 values inferred from earlier comprehensive Bayesian analyses of NS observational data, we systematically and rigorously develop the most probable 𝑅 1.4 formulas with varying statistical accuracy and technical complexity. Here, the most important EOS parameters for determining 𝑅 1.4 are found consistently in each of the feature selection processes to be (in order of decreasing importance): curvature 𝐾 sym , slope 𝐿, skewness 𝐽 sym of nuclear symmetry energy, skewness 𝐽 0 , incompressibility 𝐾 0 of symmetric nuclear matter, and the magnitude 𝐸 sym ⁡(𝜌 0 ) of symmetry energy at the saturation density 𝜌 0 of nuclear matter.

Bayesian methods↗
DOE OSTI2023

Denial of Service Attack Detection via Differential Analysis of Generalized Entropy Progressions

Denial-of-Service (DoS) attacks are one the most common and consequential cyber attacks in computer networks. While existing research offers a plethora of detection methods, the issue of achieving scalability, a low false positive rate, and high detection accuracy remains open. In this work, we address this problem by developing a differential method based on generalized entropy progression. In this method, named as DoDGE, we continuously fit the line of best fit to the entropy progression of destination addresses and check if the derivative, that is, the slope of this line is less than the negative of the dynamically computed standard deviation of the derivatives. Furthermore, to distinguish from flash events, we leverage the symmetry that when a flash event occurs, the derivative of the entropy progression of source addresses is positive. With this design, we omit the usage of the thresholds and the results with five real-world network traffic datasets confirm that DoDGE outperforms threshold-based DoS attack detection by two orders of magnitude in terms of false positives on average. When compared to ten machine learning (ML) models, DoDGE achieves a balanced accuracy of 99%, while the average balanced accuracy for the ML models is 52%. Moreover, the results show that DoDGE successfully differentiates between a flash event and a DoS attack. Furthermore, since the main computation cost of DoDGE is the entropy computation, which is linear in the volume of the unit-time network flow, uses integer only operations, and works on a small fraction of the total flow, it is lightweight and scalable.

Cybersecurity, wireless communication↗
DOE OSTI2026

Temporal Convolutional Network Using Empirical Mode Decomposition to Detect Faults in Grid Connected Systems

Grid-connected power electronic systems require timely and reliable fault detection to prevent equipment damage and reduce downtime. This paper presents a forecasting-based anomaly detection pipeline that decomposes voltage and current measurements into intrinsic mode functions (IMFs) using empirical mode decomposition (EMD), then trains a causal temporal convolutional network (TCN) on normal-operation IMF data to predict short-horizon future dynamics. Deviations between forecasts and observations are summarized as reliability-weighted residual scores and thresholded per sensor using robust statistics with temporal persistence constraints to suppress false positives. To reduce runtime, EMD is performed on downsampled signals for detection, while raw-rate EMD is applied only within a short region of interest for high-frequency interpretability near detected events. Results on a simulated grid-connected converter system demonstrate that IMF-domain forecasting improves anomaly separability relative to raw-signal forecasting and provides interpretable evidence of faults across decomposition channels.

Sutton, Elizabeth [ORNL] (ORCID:0009000078885935)↗
DOE OSTI2024

A Measurement-Based Adaptive Voltage Regulation Method Considering Topology Changes

This paper proposes an online adaptive data-driven distributed energy resource (DER) dispatch optimization method for voltage control considering topology changes. By using a local sensitivity factor (LSF)-enabled voltage control, traditional DER control can be reformulated into a linear programming (LP) problem, leading to faster computation speeds. Power injection alteration and topology changes are two common operational changes in the distribution network that can affect the LSF and voltage control performance. To address this issue, a robust estimation method is developed to adjust the sensitivity matrix at each time step for the time-varying power injection changes. When topology changes occur, only the allocated predominant LSF submatrices are updated based on measurement data, allowing for a fast adaptation to the system reconfiguration. Results obtained from a real distribution feeder in Southern California demonstrate its robustness as compared to traditional volt-var control and constant LSF matrix dispatch control methods.

DERs↗
DOE OSTI2020

Cyber Security Analysis for Nuclear Reactor Control Systems (Final Technical Report)

This project investigated the cyber-security impacts of moving from an all analog, point-to-point, instrumentation and control (I&C) system to a digital I&C system based on Modbus and a shared communication medium. A formalism called a hybrid attack graph was expanded to support the nuclear research reactor system. The hybrid attack graph allows one to check a system for vulnerabilities, in this case cyber-security vulnerabilities, and to document the attack vectors (scenarios) causing those vulnerabilities. In parallel, a simulation of the system was developed to model both the physical reactor parameters and operations, as well as the network interconnects and communications. This simulation platform was modeled on the nuclear research reactor located at Washington State University. The simulation platform provided a sandbox to evaluate and quantify the impact of identified and proposed vulnerabilities in the system and to determine the effectiveness of countermeasures at stopping these attacks. The simulation and hybrid attack graph tools were integrated to provide a streamlined process of generating attack scenarios, playing those scenarios out in the simulation, and then analyzing the results to correlate system state to states in the hybrid attack graph. This process was used to (1) quantify the impact of attack scenarios and (2) to determine if the system moved through the hybrid attack graph as anticipated. The hybrid attack graph tool was extended and customized to produce a tool to automatically identify critical assets (CAs) and critical digital assets (CDAs) as defined by NRC Regulatory Guide 5.71. This tool was verified using the nuclear research reactor at Washington State University. Finally, a series of educational modules covering the findings of the different aspects of this research have been created.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrating a Microgrid Controller with a Local OpenADR Server

When military microgrids isolate themselves from the main electrical grid, they must locally balance electricity supply and demand. Since local generation may be limited, the current strategy is to shed all but the most critical loads by tripping smart circuit breakers, which must then be reset manually (e.g., ESTCP project EW-201350). This strategy is typically applied at the building level, meaning that entire buildings housing mission critical activities must be excluded from any load management, while those considered non-critical may lose service entirely. The remotely controlled switchgear needed to manage load in this way is very expensive ($\$30,000$-$\$50,000$ per building). While effective at shedding load, this strategy disrupts installation operation and risks damaging equipment during both disconnection and re-energization. With the goals of lowering costs, protecting equipment, and enhancing the agility of DoD microgrids, this report demonstrates the use of cybersecure automated demand response (ADR) technology to manage microgrid loads during grid-independent (a.k.a. "islanded") operation. This automated approach achieves load shedding and shifting through communication signals sent to equipment controllers rather than by cutting off the flow of electricity within the microgrid itself. Because it operates only on the base network, with no connection to external entities, this strategy avoids the main cybersecurity concern raised by past applications of ADR on military bases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Description of the Gas Sampling and Circulation System for the LYNM PE1-A Experiment

A series of multiphysics experiments, referred to as Physics Experiment 1 (PE1) is underway at the U.S. Nevada National Security Site. The PE1 series includes detonations of three underground chemical explosions. As the name implies, there are a number of experiments investigating the signals generated by the explosion. The experiment series objectives are outlined in a report from Lawrence Livermore National Laboratory.1 One of the experiments is a gas migration experiment. Gas tracers were imbedded in the explosives and gas sampling boreholes were installed in the formation in the test bed. Connected to the monitoring points is a circulation system that moves gas to a central measurement location. This report does not describe the gas analysis or collection systems, but rather the circulation system that provides the gas for measurement. Here, we have combined four project documents into a single report that describes the design, build, installation, testing, and operation of the gas sampling network.

42 ENGINEERING↗
DOE OSTI2024

Quantum Random Walk Simulator Using Ultrafast Optical Switches

Quantum random walk processes have many intriguing applications in high energy physics including the simulation of parton shower evolution. We will present the design and initial results of a fiber loop time-bin quantum walk architecture using the hardware platform already in operation at the Fermilab Quantum Network in which the state of the photon is defined by its time-of-arrival. The fiber loop consists of an unbalanced Mach-Zehnder interferometer implemented using an ultrafast electro-optical switch. The input switch controls the photon path within the interferometer, while the output switch will direct the photon back into the interferometer or to single photon detectors to measure the probability distribution of arrival times. Depending on which path the photon takes each pass through the loop, its wave function will interfere on these optical switches similar to quantum interference on a beam splitter. This work is an important step towards utilizing real-world advantages of quantum information protocols to solve problems in high energy physics.

Cameron, Andrew [Fermilab]↗
DOE OSTI2026

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs With Physics‐Informed Neural Networks

Accurate representation of pumping wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) are emerging as a promising alternative to numerical models for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells when using a source/sink term, particularly during the early stages after pumping begins. We address this problem by introducing WellPINN, a workflow in which an initially trained PINN infers fluid pressure across the entire reservoir domain using a large equivalent well radius. This initial PINN solution is then locally refined around the well by a set of subdomain PINNs that are trained for smaller equivalent well radii. Continuity across these subdomain interfaces as well as at the initial condition is ensured by hard-constraining each PINN on its subdomain boundary. Our results demonstrate WellPINN as the first workflow of its kind to focus on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper are openly available at https://doi.org/10.20350/DIGITALCSIC/17260.

58 GEOSCIENCES↗
DOE OSTI2024

Motivation and Design of the OCPP Security Service

Pacific Northwest National Laboratory is conducting in-depth research aimed at exploring how zero trust security principles can be effectively applied to electric vehicle charging infrastructure. This investigation seeks to enhance the resilience and reliability of these systems against cyber threats, ensuring secure and uninterrupted access to charging services for electric vehicle users and electric supply. Zero trust is a security concept centered on the belief that system operators should not automatically trust users or systems based on their location, whether inside or outside the organization, but instead must verify everything trying to connect to their systems before granting access. A key aspect of the project is to demonstrate and validate zero trust approaches targeted to electric vehicle (EV) charging infrastructure. It has been observed that both open-source and commercial solutions often overlook the specific protocols employed in managing EV charging stations and proceeded with a general, protocol-agnostic approach. While these strategies effectively block non-authorized routes to the charging infrastructure, they do not tackle the situations where attackers may exploit legitimate access channels, such as the inattentive operator model posited by the Idaho National Laboratory. To address this gap, this paper proposes and discusses a new security service targeted to the Open Charge Point Protocol (OCPP), which is the de facto protocol for the management of charging stations and serves a critical role in the broader adoption of electric vehicles. The design and architecture of the proposed OCPP security service are discussed in detail, outlining how it aims to safeguard charging station management system (CSMS) functions. The service is particularly important in scenarios where the charging station operator (CSO), responsible for the maintenance and operation of charging stations, and the charging network provider (CNP), which manages the charging network's accessibility and billing, are separate entities. This distinction is crucial because CSOs and CNPs often have different priorities, objectives, and operational responsibilities, which may not always align perfectly. For instance, a CSO might prioritize uptime and customer satisfaction, while a CNP might focus on maximizing revenue and network utilization. Such misalignment can create security vulnerabilities, as each entity might implement different policies and standards, potentially leaving gaps in the overall security posture.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Distributed Small-Signal Stability Conditions for Inverter-Based Unbalanced Microgrids

The proliferation of inverter-based generation and advanced sensing, controls, and communication infrastructure have facilitated accelerated deployment of microgrids. A coordinated network of microgrids can maintain reliable power delivery to critical facilities during extreme events. Low-inertia offered by the power-electronics–interfaced energy resources, however, can present significant challenges to ensuring stable operation of the microgrids. In this work, distributed small-signal stability conditions for inverter-based microgrids are developed that involve the droop-controller parameters and the network parameters (e.g. line impedances, loads). The distributed closed-form parametric stability conditions derived in this paper can be verified in a computationally efficient manner, facilitating reliable design and operations of networks of microgrids. Dynamic phasor models have been used to capture the effects of electromagnetic transients. Furthermore, numerical results are presented, along with PSCAD simulations, to validate the analytical stability conditions. Effects of design choices, such as the conductor types, and inverter sizes, on the small-signal stability of inverter-based microgrids are investigated to derive useful engineering insights.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Autonomous semantic data discovery for distributed networked systems

Systems, methods, techniques and apparatuses for managing distributed applications of networked intelligent agents are disclosed. The agents are operably to autonomously discover semantic profiles and associated data of other agents in a networked system participating in a given application. The agents need not be in direct communication with or known to all the other agents in the networked system.

Brissette, Alexander↗
DOE OSTI2021

Hybrid Resources: Challenges, Implications, Opportunities, and Innovation

The electric power system has historically been designed to provide reliable energy to loads by using a relatively small number of well-understood generators. The distinction between load, generation, and transmission resources has been quite clear. Most of the responsibility for planning and operating a system - building a highly reliable network from less reliable parts - has been with the system manager, whether that be a utility, a regional market operator, or some similar entity. Given this historical context, many experts were initially perplexed by the rapidly growing popularity of hybrid resources, which combine multiple technologies into a single entity. Rather than depending on a system operator to provide instructions to individual technologies, hybrid resources intentionally take on more operational responsibility by optimizing and scheduling their combined functions. Interconnection queues in many regions reveal a large and growing interest in hybrids, suggesting that project developers and investors see them as providing advantages.

generators↗
DOE OSTI2024

Thermal Analysis of a Polypropylene Capacitor for Resonant Tuning Networks in WPT Applications

This study presents the thermal analysis of polypropylene capacitors operating at high frequencies and high currents used in the resonant tuning networks for electric vehicle (EV) wireless charging systems. The thermal analysis and performance results of the CELEM polypropylene capacitors are presented at different currents for a polyphase wireless power transfer (WPT) system while the study can be expanded to other capacitors by different manufacturers. Thermal equivalent circuit of the capacitor is derived and required cold plate size and design calculations are conducted by modeling the polypropene capacitor in MATLAB. The polypropene capacitor's finite element analysis (FEA) model is developed using COMSOL Multiphysics simulation software, and the thermal characteristics and analysis of the capacitor are presented in this paper.

Aktas, Ahmet↗
DOE OSTI2021

Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high–quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear–Test–Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. Here, we compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.

58 GEOSCIENCES↗
DOE OSTI2023

Modeling and Rapid Prototyping of Integrated Transmission-Distribution OPF Formulations with PowerModelsITD.jl

Conventional electric power systems are composed of different unidirectional power flow stages of generation, transmission, and distribution, managed independently by transmission system and distribution system operators. However, as distribution systems increase in complexity due to the integration of distributed energy resources, coordination between transmission and distribution networks will be imperative for the optimal operation of the power grid. However, coupling models and formulations between transmission and distribution is non-trivial, in particular due to the common practice of modeling transmission systems as single-phase, and distribution systems as multi-conductor phase-unbalanced. To enable the rapid prototyping of power flow formulations, in particular in the modeling of the boundary conditions between these two seemingly incompatible data models, we introduce PowerModelsITD.jl, a free, open-source toolkit written in Julia for integrated transmission-distribution (ITD) optimization that leverages mature optimization libraries from the InfrastructureModels.jl-ecosystem. The primary objective of the proposed framework is to provide baseline implementations of steady-state ITD optimization problems, while providing a common platform for the evaluation of emerging formulations and optimization problems. In this work, we introduce the nonlinear formulations currently supported in PowerModelsITD.jl, which include AC-polar, AC-rectangular, current-voltage, and a linear network transportation model. Results are validated using combinations of IEEE transmission and distribution networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

5G integrated edge computing platform for efficient component monitoring in coal-fired power plants

This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.

20 FOSSIL-FUELED POWER PLANTS↗