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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Supporting Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA (Final Report)

As modern power grids tend towards greater levels of automation and communication, the challenges of identifying and mitigating vulnerabilities to cyber-attacks are ones that are increasingly demanding attention. Today’s power system has evolved to form the foundational bedrock of modern society, and an attack on this infrastructure could prove disastrous. In this project we were tasked to investigate the use of distribution synchrophasors as an independent isolated sensor network with which we can corroborate, or flag potentially spoofed,Supervisory Control And Data Acquisition (SCADA) data. We adapted an approach to marry the underlying physical properties of power systems with the network communications used by power systems in order to offer insights unattainable by either data stream isolation. While the concept of intrusion detection systems (IDS) is well understood for monitoring network traffic and traditional IT computing systems, the approach discussed in this report is motivated by several key notions: first, current SCADA communications alone presents an incomplete view of the grid. Second, the power grid, and the equipment controlling it, is grounded by laws of physics. Given this, we leverage high-frequency physical grid measurements to understand the physical condition of the grid, and combine this with SCADA. While high-frequency physical grid measurements and SCADA communication over Internet Protocol (IP) networks are fundamentally disparate information sources, when collectively examined through appropriate lenses, they offer a much more nuanced depiction of the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Milestone report for MRT 8479: High Yield Xray Imager Preliminary Design Review

The High Yield Xray Imager (HYXI) is a new NIF target diagnostic system currently under development. The goal of HYXI is to provide high-fidelity, high temporal resolution x-ray imaging capability on high yield NIF implosions at 10MJ and above. The HYXI instrument design concept is based on the combination of two technologies that have been successfully utilized at the NIF on previous instruments, electron pulse-dilation and hybrid-CMOS sensor imaging. The combination of these two techniques will give HYXI sufficient data quality to ascertain differences in hot spot formation dynamics between high and low yield implosions. This information will highlight the critical hot spot conditions needed for ignition and burn. The HYXI design leverages the successful operation of the PDIXI x-ray imager at the NIF on multi-MJ yield shots. Also, a new radiation tolerance CMOS imaging array is being developed to eliminate the significant background noise which limits the data quality of PDIXI. The HYXI Preliminary Design Review was completed at the end of Q4 FY23. The HYXI project is a multi-year effort with a phased approach to be bring up system functionality over time in parallel with the development and fabrication effort of the new CMOS imaging array. Initial time integrated NIF data will be collected in Q2 FY25, first time resolved imaging with Daedalus starting in Q2 FY26 and the final performance qualification of the complete HYXI system is scheduled for Q2 FY27.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A time-domain electromechanical co-simulation framework for power system transient analysis with retainment of user defined models

This paper proposes a time-domain electromechanical co-simulation framework to accomplish concurrent running of multiple simulators in order to preserve user-defined models and allow maximum simulation accuracy for power system transient analysis. Here, a middleware interface is developed to manage the interaction and synchronization of the multiple simulators via boundary model. Consequently, it eliminates the requirement of model conversion of each subsystem and thus ensure the user-defined models in each specific software are retained for entire system situational awareness. Preliminary testing results on 140-bus Northeastern Power Coordinating Council (NPCC) model validate the effectiveness and accuracy of the proposed framework for PSS/E and PSLF co-simulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Sensitivity Analysis of Effectiveness of FLEX Strategies Using a CDF-Based Importance Measure Under Accident Conditions

One of the lessons learned from the Fukushima Daiichi nuclear power plant (NPP) accident is strengthening the station blackout (SBO) mitigation capabilities by enhancing defense in depth for all existing and new NPPs. One of the possible remedies is diverse and flexible coping strategies (FLEX). The objective of this study is to address the benefits of FLEX in various accident scenarios in terms of both risk and cost. FLEX was originally devised against SBO accidents. In this research, we investigate the fundamental plant responses against accidents considering two fundamentally different cases: accidents that lead to high pressure on the primary side and accidents that lead to low pressure on the primary side. Several uncertainties are associated with the characteristics of the FLEX portable equipment. Specifically, the time for FLEX deployment may depend on several factors such as type of accident, point of injection, availability of safety systems, battery backup timings, and human actions. This study utilizes a dynamic risk assessment framework to analyze accident scenarios and suggests a novel importance measure, which is a cumulative distribution function– based importance metric that characterizes the influence of input distribution on complete output distribution. The importance of the existing and newly developed FLEX strategy based on risk significance is illustrated with examples. The suggested measure provides clear insight into how FLEX affects risk of the whole system and additional risk margins thanks to new safety systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

5G-TSN Architecture Capable of Providing Real-time Situational Awareness to Fossil-Energy (FE) Generation Systems (Final Technical Report)

This final report highlights the comprehensive achievements of the project focused on developing and validating a 5G-Time Sensitive Networking (TSN) architecture tailored for real-time operational awareness in fossil energy systems. The initiative successfully advanced through a series of technical milestones, including the integration of EMI-aware network models, deployment of advanced simulation frameworks, and real-world performance characterization at key sites such as UTEP and Fabens. Through the strategic use of NetSim® software, the team created and validated network configurations for wired and wireless environments, tested under varying congestion conditions, and verified network slicing implementations for URLLC-specific applications. Major accomplishments include the migration of simulation tools to the latest NetSim® version to support accurate modeling of TSN and network slicing, extensive EMI measurement campaigns, and the development of a robust simulation model for end-to-end SCADA system integration. Simulations compared both TDD and FDD duplexing modes, revealing insights into their performance under congested conditions. The wireless network was benchmarked for throughput, jitter, and delay metrics, aligning with 3GPP Release 15/16 and IEEE 802.1-TSN standards. A peer-reviewed conference paper was accepted and published, contributing to the broader academic and industrial discourse on 5G-TSN integration in energy systems, in addition to a journal article. Despite minor delays due to software limitations, the project achieved its objectives and delivered validated architecture ready for deployment in advanced energy network environments.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Data-Driven Operator Theoretic Methods for Phase Space Learning and Analysis

This paper uses data-driven operator theoretic approaches to explore the global phase space of a dynamical system. In this work, we defined conditions for discovering new invariant subspaces in the state space of a dynamical system starting from an invariant subspace based on the spectral properties of the Koopman operator. When the system evolution is known locally in several invariant subspaces in the state space of a dynamical system, a phase space stitching result is derived that yields the global Koopman operator. Additionally, in the case of equivariant systems, a phase space stitching result is developed to identify the global Koopman operator using the symmetry properties between the invariant subspaces of the dynamical system and time-series data from any one of the invariant subspaces. Finally, these results are extended to topologically conjugate dynamical systems; in particular, the relation between the Koopman tuple of topologically conjugate systems is established. The proposed results are demonstrated on several second-order nonlinear dynamical systems including a bistable toggle switch. Our method elucidates a strategy for designing discovery experiments: experiment execution can be done in many steps, and models from different invariant subspaces can be combined to approximate the global Koopman operator.

42 ENGINEERING↗
DOE OSTI2025

Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

The goal of this project is to develop advanced methods for day-ahead net-load forecasting, by leveraging the state-of-the-art machine learning techniques. The developed models produce both point and probabilistic forecasts for a variety of use cases, and are versatile to work with different types of data sets. The innovation lies in the novel design of the architectures, leveraging the most recent advances in machine learning that have not been explored in power systems, accompanied by techniques in the broader artificial intelligence fields such as fuzzy systems. This project has achieved the following accomplishments: (1) preprocessing of over 10 data sets covering varying geographical regions, time horizons, and system levels, which form a robust foundation for training and evaluating forecasting models across a wide range of realistic grid scenarios; (2) development of an interactive web app that enables exploratory analysis of load and generation data, and supports better understanding of data trends, anomalies, and correlations, facilitating model development and stakeholder engagement; (3) implementation of over 10 benchmark models for point and probabilistic forecasting, which include a mix of conventional machine learning methods and state-of-the-art deep learning approaches, providing a comprehensive baseline for performance comparison and validation of the proposed models; (4) development of a fuzzy system based gradient boosting model, tailored for small (less than 3 years) data sets, which achieves a mean absolute percentage error (MAPE) of 4% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (5) development of a Transformer (a state-of-the-art deep learning architecture) based neural network model, tailored for large (3 years or more) data sets, which achieves a MAPE of 2% for point forecasting and a 20% improvement in average pinball loss for probabilistic forecasting; (6) development of a methodology for quantifying DR potential, and extensions of the previous models for multi-target forecasting of net load and DR potential, which achieve a MAPE of 10% for DR potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Solving Unit Commitment Problems with Demand Responsive Loads: Preprint

This paper focuses on using variations of the Frank-Wolfe algorithm for solving unit commitment problems with high volumes of demand responsive loads on the power grid. We present a formulation of the unit commitment problem with demand responsive loads. We then show through reformulation and relaxations of the problem that variations of the Frank-Wolfe algorithm can be used to determine the time series decisions for the demand responsive loads. We show through computational experiments on the RTS-GMLC test system that the time series of demand responsive load decisions obtained through our approach are near optimal and provide details regarding how large scale parallel implementations of our approach can be highly computationally efficient.

demand response↗
DOE OSTI2025

Sparsified Time-dependent PDEs FNO (STFNO) v1.0.0

STFNO (Sparsified Time-dependent PDEs FNO code) is an extension of the popular Fourier Neural Operator (FNO) architecture to the solution of coupled systems of time-dependent partial differential equations. STFNO leverages the sparsified dependencies on the field quantities based on the semi-discretiezed form of the PDEs, enabling significant reduction in the number of model parameters. STFNO has been extensively tested on two fusion simulation codes, NIMROD and GTC, and can be easily tailored to other systems of PDEs.

Rahman, Mustafa [Lawrence Berkeley National Labora↗
DOE OSTI2025

An interregional optimization approach for time series aggregation in continent-scale electricity system models

Modeling electric power systems with high shares of weather-dependent resources requires tradeoffs between temporal, spatial, and operational resolution. Many studies perform time series aggregation using clustering algorithms to reduce the temporal dimension, but when modeling continent-scale electricity systems that are large enough to contain multiple independent weather systems, this approach requires large numbers of representative periods to minimize errors in regional wind and solar capacity factors. Here, a new optimization-based approach for representative period selection and weighting is introduced that minimizes regional errors in average renewable capacity factors and electricity demand. The method delivers higher regional fidelity with fewer representative periods than alternative clustering methods when applied to wind, solar, and demand profiles for the contiguous United States. When representative periods are selected from multiple weather years, the optimized method reproduces regional averages with lower error than a complete 365-day time series from any single weather year. The method identifies only representative (as opposed to outlying) periods but can be combined with an iterative "stress period" identification approach to guide efficient decision-making considering both average and high-risk weather conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Time Evolution in Quantum Cosmology

The quantum description of time evolution in non-linear gravitational systems such as cosmological space-times is not well understood. We show, in the simplified setting of mini-superspace, that time evolution of this system can be obtained using a gauge fixed path integral, as long as one does not integrate over proper time. Using this gauge fixed action we can construct a Hamiltonian in the coherent - or classical - state basis. We show that by construction the coherent states satisfy the classical dynamical equations of General Relativity. They do not satisfy the Hamiltonian constraint. A consequence of this is that the Wheeler-DeWitt equation should not be satisfied in quantum gravity. Classical states have a natural non-trivial time evolution since they are not eigenstates of the Hamiltonian. A general feature of the unconstrained quantum theory of gravity is the prediction of a pressureless dark matter component of either sign energy density in the classical universe which may lead to novel phenomenology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

SQuaD: Smart Quantum Detection for Photon Recognition and Dark Count Elimination

Quantum detectors of single photons are an essential component for quantum information processing across computing, communication and networking. Today's quantum detection system, which consists of single photon detectors, timing electronics, control and data processing software, is primarily used for counting the number of single photon detection events. However, it is largely incapable of extracting other rich physical characteristics of the detected photons, such as their wavelengths, polarization states, photon numbers, or temporal waveforms. This work, for the first time, demonstrates a smart quantum detection system, SQuaD, which integrates a field programmable gate array (FPGA) with a neural network model, and is designed to recognize the features of photons and to eliminate detector dark-count. The SQuaD is a fully integrated quantum system with high timing-resolution data acquisition, onboard multi-scale data analysis, intelligent feature recognition and extraction, and feedback-driven system control. Our \name experimentally demonstrates 1) reliable photon counting on par with the state-of-the art commercial systems; 2) high-throughput data processing for each individual detection events; 3) efficient dark count recognition and elimination; 4) up to 100% accurate feature recognition of photon wavelength and polarization. Additionally, we deploy the SQuaD to an atomic (erbium ion) photon emitter source to realize noise-free control and readout of a spin qubit in the telecom band, enabling critical advances in quantum networks and distributed quantum information processing.

Linne, Karl C. [U. Chicago (main)] (ORCID:00090009↗
DOE OSTI2024

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

AI-based foundation models like FourCastNet, GraphCast are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions.

Lu, Dan↗
DOE OSTI2024

A Time-Domain Protection Approach for AC Transmission Systems With Grid-Forming Resources

Ac transmission protection must reliably detect, classify, and locate short-circuit faults from voltage and current measurements. At present, these functionalities, which have been classically engineered using phasors approaches, are being challenged by the dynamic behavior and fault-current limits of converter-based generation. This paper tackles these challenges by engineering a time-domain protection approach that leverages the classical Bergeron model in a new manner. Low- and high-impedance faults are detected and classified by ascertaining how well line voltage and current measurements match the Bergeron equations. Faults are located by posing a novel one-variable optimization problem, whereas voltage and current waveforms at the fault location are estimated by unveiling rigorous relationships. The proposed elements are secure against external faults, measurement errors, and variation of line parameters and sampling time. Furthermore, these advances are tested via electromagnetic transient simulations and are significant to satisfy IEEE and North American Electric Reliability Corporation requirements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Expedited Real Time Processing for the NETL HYPER Cyber-Physical System (Final Report)

The primary objective of the proposed project was to provide the National Energy Technology Laboratory’s Hybrid Performance (Hyper) Facility the needed numerical methods algorithm(s), software development and implementation support to enact real time cyber-physical systems (CPSs) that simulate process dynamics on the order of five milliseconds or smaller. The proposed paths forward comprised three distinct approaches to hasten transient simulations. They fell under the numerical methods categorical options of: 1) optimizing key parameters within the facility’s pre-existing real time processing scheme; 2) introducing an “informed” processing approach wherein a priori computations expedite real time attempts; and 3) implementing alternatives to the employed explicit-implicit blended finite difference (spatio-temporal) approach. Each of these three classes were proposed independently as options for improvement, yet in some cases one class of approach could have complemented another.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗
DOE OSTI2021

Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems

For decades, uncertainty quantification techniques based on the spectral approach have been demonstrated to be computationally more efficient than the Monte Carlo method for a wide variety of problems, particularly when the dimensionality of the probability space is relatively low. The time-dependent generalized polynomial chaos (TD-gPC) is one such technique that uses an evolving orthogonal basis to better represent the stochastic part of the solution space in time. Here in this paper, we present a new numerical method that uses the concept of enriched stochastic flow maps to track the evolution of the stochastic part of the solution space in time. The computational cost of this proposed flow-driven stochastic chaos (FSC) method is an order of magnitude lower than TD-gPC for comparable solution accuracy. This gain in computational cost is realized because, unlike most existing methods, the number of basis vectors required to track the stochastic part of the solution space does not depend upon the dimensionality of the probability space. Four representative numerical examples are presented to demonstrate the performance of the FSC method for long-time integration of second-order stochastic dynamical systems in the context of stochastic dynamics of structures.

FSC↗
DOE OSTI2021

A probabilistic graphical model foundation for enabling predictive digital twins at scale

A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale. This work proposes a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the asset–twin system as a set of coupled dynamical systems, evolving over time through their respective state spaces and interacting via observed data and control inputs. The formal definition of this coupled system as a probabilistic graphical model enables us to draw upon well-established theory and methods from Bayesian statistics, dynamical systems and control theory. The declarative and general nature of the proposed digital twin model make it rigorous yet flexible, enabling its application at scale in a diverse range of application areas. Here, we demonstrate how the model is instantiated to enable a structural digital twin of an unmanned aerial vehicle (UAV). The digital twin is calibrated using experimental data from a physical UAV asset. Its use in dynamic decision-making is then illustrated in a synthetic example where the UAV undergoes an in-flight damage event and the digital twin is dynamically updated using sensor data. The graphical model foundation ensures that the digital twin calibration and updating process is principled, unified and able to scale to an entire fleet of digital twins.

42 ENGINEERING↗