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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Bistatic Synthetic Aperture Radar - Issues Analysis and Design

The physical separation of the transmitter from the receiver into perhaps separate flight vehicles (with separate flight paths) in a bistatic Synthetic Aperture radar system adds considerable complexity to an already complex system. Synchronization of waveform parameters and timing attributes become problematic, and notions of even the synthetic aperture itself take on a new level of abstractness. Consequently, a high-performance, fine-resolution, and reliable bistatic SAR system really needs to be engineered from the ground up, with tighter specifications on a number of parameters, and entirely new functionality in other areas. Nevertheless, such a bistatic SAR system appears viable.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Impact of Wildfires on Solar Resource Availability in California in a Changing Climate

Wildfires can emit large amounts of atmospheric particulate matters and influence not only air quality but also availability of photovoltaic (PV) generation due to scattering and absorption of solar radiation. Under anthropogenic changing climate, wildfire activity is projected to increase over western North America due to drier and warmer climate, implying increasing impact on solar resource and larger uncertainty in solar generation especially in regions with faster PV penetration. This study focuses on quantifying the impact of wildfires on aerosol optical depth (AOD) and thus solar resource over California using National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL). This assessment includes historical analysis and estimation of solar resource under wildfire scenarios (2020 wildfire and an enhanced wildfire scenario based on 2020 wildfires). Historical analysis for the period of July-October 2019/2020 (low/high fire activity period) shows that the averaged global horizontal irradiance (GHI) and direct normal irradiance (DNI) are reduced by about 30 and 90 W m-2 (5.6 and 15.3%), respectively, during high fire activity period. To create AOD dataset for enhanced fire scenario, 165% increase in burn area (in around 2050) is selected based on comprehensive literature review, which is further applied to the Fire INventory from NCAR (FINN) fire emission. WRF-Chem model with the enhanced FINN emissions is used to simulate and represent a preserving spatial distribution of burned areas and wildfire-emitted aerosols. Our initial analysis suggests that the"enhanced 2020 wildfire" AOD can increase by a factor of 1.3 - 2, which can significantly reduce solar irradiance and increase uncertainty in generation and reliability of power system in extreme wildfire events under a high solar penetration scenario. Overall, this study provides an estimate of the impacts of wildfires on solar resource to make informed decisions on reserve planning, generation scheduling, and reliability investments.

aerosol optical depth↗
DOE OSTI2025

Accelerating Control Systems with GitOps: A Path to Automation and Reliability

GitOps is a foundational approach for modernizing infrastructure by leveraging Git as the single source of truth for declarative configurations. The poster explores how GitOps transforms traditional control system infrastructure, services and applications by enabling fully automated, auditable, and version-controlled infrastructure management. Cloud-native and containerized environments are shifting the ecosystem not only in the IT industry but also within the computational science field, as is the case of CERN and Diamond Light Source among other Accelerator/Science facilities which are slowly shifting towards modern software and infrastructure paradigms. The ACORN project, which aims to modernize Fermilab’s control system infrastructure and software is implementing proven best-practices and cutting-edge technology standards including GitOps, containerization, infrastructure as code and modern data pipelines for control system data acquisition and the inclusion of AI/ML in our accelerator complex.

Gonzalez, M. [Fermilab]↗
DOE OSTI2021

Analyzing the Effects of Cyberattacks on Distribution System State Estimation

Key components of power systems—such as energy management systems, automatic generation control, and state estimation—are under serious vulnerability from cyberattacks. Cyber threats in electric grids have increased significantly because of the increased interconnectivity of supervisory control and data acquisition systems and public network infrastructure. As the penetration level of distributed energy resources increases, it is imperative to employ system-monitoring techniques such as state estimation for the reliable operation of distribution systems. Recently, multiple methods have been developed that exploit the low rank property of distribution system state matrix and are robust to bad data, such as matrix completion. This paper analyzes the impact of various realistic cyberattack scenarios on matrix completion. Realistic cyberattack scenarios are converted into data corruption models that are used in an extensive simulation of a custom IEEE 123-bus system.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

—Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today’s synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems.

Zhao, Junbo↗
DOE OSTI2023

Integration of Hybrids into Wholesale Power Markets

With growing commercial activity around utility-scale hybrid storage projects in the Unites States, independent system operators (ISOs) and regional transmission organizations (RTOs) are faced with making decisions on how to represent such emerging technology resources in the market clearing software through the definition of hybrid resource participation models. System operators are facing increasing uncertainties around efficient and reliable ways to operate these resources given the ambiguity around their impacts, particularly when high levels of hybrid resources are present. This study aimed to evaluate and compare the performance implications of different hybrid resource participation models in a realistic wholesale electricity market setting with significant deployment of hybrid resources, and to provide the industry with metrics that quantify the advantages and disadvantages of different participation options using realistic electricity market simulations. The different participation models are compared in terms of the impact to reliability of the overall system, the ability of the market to access the full capabilities of hybrid resources as measured by the economic efficiency, and asset profitability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Analyzing the Effects of Cyberattacks on Distribution System State Estimation: Preprint

Key components of power systems—such as energy management systems, automatic generation control, and state estimation—are under serious vulnerability from cyber attacks. Cyber threats in electric grids have increased significantly because of the increased interconnectivity of supervisory control and data acquisition systems and public network infrastructure. As the penetration level of distributed energy resources increases, it is imperative to employ system-monitoring techniques such as state estimation for the reliable operation of distribution systems. Recently, multiple methods have been developed that exploit the low rank property of distribution system state matrix and are robust to bad data, such as matrix completion. This paper analyzes the impact of various realistic cyber attack scenarios on matrix completion. Realistic cyber attack scenarios are converted into data corruption models that are used in an extensive simulation of a custom IEEE 123-bus system.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Stochastic multi-period optimal dispatch of energy storage in unbalanced distribution feeders

This report presents a convex, multi-period, AC-feasible Optimal Power Flow (OPF) framework that robustly dispatches flexible demand-side resources in unbalanced distribution feeders against uncertainty in very-short timescale solar Photo-Voltaic (PV) forecasts. This is valuable for power systems with significant behind-the-meter solar PV generation as their operation is affected by uncertainty from forecasts of demand and solar PV generation. The aim of this work is then to ensure the feasibility and reliability of distribution system operation under high solar PV penetration. We develop and present a novel, robust OPF formulation that accounts for both the nonlinear power flow constraints and the uncertainty in forecasts. This is achieved by linearizing an optimal trajectory and using first-order methods to systematically tighten voltage bounds. Case studies on a realistic distribution feeder shows the effectiveness of a receding-horizon implementation.

25 ENERGY STORAGE↗
DOE OSTI2022

Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

How can probabilistic solar forecasts lower costs and improve reliability for independent system operator (ISO) markets? We tackle this question in three steps. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty in those forecasts to error distributions for net load ramps for the California ISO (CAISO) using statistical and machine learning methods. Projected net load errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Finally, a multi-period look-ahead production cost model quantifies how conditional ramp requirements can a) decrease operating costs by lowering requirements compared to often conservative unconditional methods, and b) reduce generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. In addition to the products just described (quantification of solar uncertainty, its translation into requirements for ramp capability product, and quantification of the benefits of more accurate ramp requirements), this project also developed a visualization system that alerts system operators of ramp and uncertainty conditions within the network based on solar forecasts. The system is called Resource Forecast and Ramp Visualization for Situational Awareness (RaVIS). These four products represent significant advances in the state-of-the-art of probabilistic solar forecasting, development of weather-informed reserve requirements, production costing methods for estimating the benefits of more accurate reserve requirements, and visualization of system status, respectively. Yet the products are also practical and can be immediately implemented, potentially enabling system operators to save millions of dollars in ramp product procurement costs per year.

14 SOLAR ENERGY↗
DOE OSTI2024

Instrumentation for the Investigation of Pitch Bearing Design and Reliability

Recently, there has been an increasing level of industry interest in pitch system and pitch bearing reliability. Pitch bearings are used in wind turbines to connect the blade root to the hub. Some populations of pitch bearings have demonstrated a 12% failure rate in 20 years. As rotor diameters continue to increase for tall land-based and offshore wind turbines, pitch bearings are becoming even larger in diameter, which can make them vulnerable to deflections and consequent stress concentrations. There is an increased need to more accurately study pitch bearing deformations, misalignment, load distributions, and contact stresses. A significant body of work has investigated fatigue lives and wear characteristics of pitch bearings on ground-based test rigs. NREL has also recently begun a research program related to pitch bearing reliability, recognizing its growing importance for wind turbines. The purpose of this paper is to describe a set of instrumentation that was recently installed on a 1.5 MW wind turbine at the NREL Flatirons Campus and provide an example data set. To the authors' knowledge, this will be the first publicly available pitch bearing data collection campaign on an operational wind turbine.

17 WIND ENERGY↗
DOE OSTI2024

CMS Tier-0 data processing during the detector commissioning in Run 3

The CMS Tier-0 system is responsible for the prompt processing and distribution of the data collected by the CMS Experiment. A number of upgrades were implemented during the long shutdown 2 of the Large Hadron Collider, which improved the performance and reliability of the system. In this report, these upgrades are discussed and a more detailed description of the Tier-0 system is given. The experience of the data taking during Run 3 detector commissioning as well as the performance of the system are highlighted.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Pseudonymization at Scale: OLCF’s Summit Usage Data Case Study

The analysis of vast amounts of data and the processing of complex computational jobs have traditionally relied upon high performance computing (HPC) systems, which offer reliable and efficient management of large-scale computational and data resources. Understanding these analyses’ needs is paramount for designing solutions that can lead to better science, and similarly, understanding the characteristics of the user behavior on those systems is important for improving user experiences on HPC systems. A common approach to gathering data about user behavior is to extract workload characteristics from system log data available only to system administrators. Recently at Oak Ridge Leadership Computing Facility (OLCF), however, we unveiled user behavior about the Summit supercomputer by collecting data from a user’s point of view with ordinary Unix commands.In this paper, we discuss the process, challenges, and lessons learned while preparing this dataset for publication and submission to an open data challenge. The original dataset contains personal identifiable information (PII) about the users of OLCF which needed be masked prior to publication, and we determined that anonymization, which scrubs PII completely, destroyed too much of the structure of the data to be interesting for the data challenge. We instead chose to pseudonymize the dataset, which reduced the linkability of the dataset to the users’ identities. Pseudonymization is significantly more computationally expensive than anonymization, and the size of our dataset, which is approximately 175 million lines of raw text, necessitated the development of a parallelized workflow that could be reused on different HPC machines. We demonstrate the scaling behavior of the workflow on two leadership class HPC systems at OLCF, and we show that we were able to bring the overall makespan time from an impractical 20+ hours on a single node down to around 2 hours. As a result of this work, we release the entire pseudonymized dataset and make the workflows and source code publicly available.

Maheshwari, Ketan↗
DOE OSTI2022

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. Here this work presents how anticipatory control strategies can be implemented via model predictive control (MPC) of a single heat pipe’s temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that nonparametric models such as the ANN and ARX result in more fluctuated control actions compared to the MPC applied to the linear response surface model. However, when the cyber incidents are of a large magnitude, the linear response surface model produces smaller feasible regions than the nonparametric models under identical constraints.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗
DOE OSTI2026

The Heterogeneous Integration of Electronic Components

Heterogeneous integration (HI) of electronics components is broadly recognized as a powerful and crucial enabler for the continued growth of computing and communication. From 2010 onwards, the value of HI is increasingly visible in the advanced packaging used in artificial intelligence, high-performance computing, smartphones and communications product implementations. In this Perspective, we argue that HI is crucial to semiconductors and more broadly to the continued evolution of computing and communications. We use leading-edge advanced packaging examples to represent the value, advancements and opportunities for HI. To succeed, it is critical to develop comprehensive HI roadmaps that inform collaborations across the design, manufacturing and reliability spectrum between systems architects, packaging and semiconductor technologists to common goals. Although this article does not provide a full roadmap, we instead detail additional parameters for artificial intelligence, smartphone and other cellular communication devices, and their constituent building blocks including interconnects, power electronics, photonics, thermal management, reliability, modelling and co-design, to foster greater collaboration opportunities among academia, research laboratories and industry.

42 ENGINEERING↗
DOE OSTI2021

A Statistics-Based Threshold for the RMS-Energy Oscillation Detector

System operators and reliability coordinators currently rely on extensive baselining studies to set thresholds for their oscillation detectors. The resulting thresholds are based largely on engineering judgment and may vary significantly between organizations. In this paper, statistical distribution theory is used to derive a detection threshold for the widely deployed root-mean-square (RMS)-energy detector. This expression provides a theoretical basis for the detector's configuration, simplifies the process of selecting the threshold, and enables improved consistency among organizations that need to coordinate during system-wide events. Three methods for calculating the threshold using synchrophasor measurements are also proposed. These methods ensure that the threshold can be calculated reliably for various applications. Tests with simulated and field-measured data demonstrate that the statistics-based threshold provides consistent detection of grid disturbances while maintaining a low probability of false alarm.

Follum, James D.↗
DOE OSTI2024

Frontier system-on-chip (SoC) technology for microwave diagnostics (invited)

The next generation of fusion reactors, exemplified by projects such as the Demonstration Power Plant following the International Thermonuclear Experimental Reactor, faces the monumental challenge of proving the viability of generating electricity through thermonuclear fusion. This pursuit introduces heightened complexities in diagnostic methodologies, particularly in microwave-based diagnostics. The increased neutron fluence necessitates significant reductions in vessel penetrations and the elimination of internal diagnostics, posing substantial challenges. SoC technology offers a promising solution by enabling the miniaturization, modularization, integration, and enhancing the reliability of microwave systems. After seven years of research, our team successfully pioneered the V- and W-band system-on-chip approach, leading to the development of active transmitters and passive receiver modules applied in practical settings, notably within the DIII-D tokamak project. Arrays of these modules have supported microwave imaging diagnostics. New physics measurement results from the Electron Cyclotron Emission Imaging system on DIII-D provide compelling evidence of improved diagnostics following the adoption of SoC technology. Furthermore, we achieved a breakthrough in developing an F-band SoC, advancing higher frequency capabilities for fusion devices. These achievements represent a significant leap forward in fusion diagnostic technology, marking substantial progress toward establishing reliable and efficient plasma diagnostics for future fusion reactors.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗