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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
NASA NTRS2012

Anomaly Detection in Test Equipment via Sliding Mode Observers

Nonlinear observers were originally developed based on the ideas of variable structure control, and for the purpose of detecting disturbances in complex systems. In this anomaly detection application, these observers were designed for estimating the distributed state of fluid flow in a pipe described by a class of advection equations. The observer algorithm uses collected data in a piping system to estimate the distributed system state (pressure and velocity along a pipe containing liquid gas propellant flow) using only boundary measurements. These estimates are then used to further estimate and localize possible anomalies such as leaks or foreign objects, and instrumentation metering problems such as incorrect flow meter orifice plate size. The observer algorithm has the following parts: a mathematical model of the fluid flow, observer control algorithm, and an anomaly identification algorithm. The main functional operation of the algorithm is in creating the sliding mode in the observer system implemented as software. Once the sliding mode starts in the system, the equivalent value of the discontinuous function in sliding mode can be obtained by filtering out the high-frequency chattering component. In control theory, "observers" are dynamic algorithms for the online estimation of the current state of a dynamic system by measurements of an output of the system. Classical linear observers can provide optimal estimates of a system state in case of uncertainty modeled by white noise. For nonlinear cases, the theory of nonlinear observers has been developed and its success is mainly due to the sliding mode approach. Using the mathematical theory of variable structure systems with sliding modes, the observer algorithm is designed in such a way that it steers the output of the model to the output of the system obtained via a variety of sensors, in spite of possible mismatches between the assumed model and actual system. The unique properties of sliding mode control allow not only control of the model internal states to the states of the real-life system, but also identification of the disturbance or anomaly that may occur.

Solano, Wanda M.↗
DOE OSTI2020

Adaptive model tuning studies for non-invasive diagnostics and feedback control of plasma wakefield acceleration at FACET-II

Beam-driven plasma wakefield acceleration (PWFA) achieves the same energy gain in a single meter, for which conventional accelerators require several kilometers, however much work is still required to match the beam quality of conventional accelerators. The PWFA processes to be studied at the FACET-II facility will utilize extremely short (down to a few fs) high energy (10 GeV), high charge (few nC) electron and eventually positron bunches. The PWFA process is extremely sensitive to the detailed longitudinal current profiles of these bunches and it would be of great benefit to have precise measurement and control of these profiles. We present an adaptive model tuning technique for FACET-II to adaptively tune online models based on real time accelerator and beam data to continuously provide a non-invasive diagnostic which predicts the longitudinal phase space (LPS) of extremely short and highly compressed electron beams, which otherwise require destructive X-band transverse deflecting cavity (XTCAV)-based measurements. Based on simulation studies, our method has the potential for: (1). The development of a non-invasive longitudinal phase space diagnostic by adaptively tuning models based on non-invasive measurements such as energy spread spectra which could be recorded at all of the bunch compressors in the facility. (2). Utilize these diagnostic to perform model-independent feedback-based to achieve desired longitudinal phase space distributions. (3). Utilize the model-independent feedback approach to maximize energy gain while minimizing emittance growth and energy gain variance of the PWFA process by tuning accelerator parameters, while monitoring what longitudinal phase space the algorithm has found, information that will be useful for further analytical and simulation studies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Performance Evaluation of Vertical Federated Machine Learning Against Adversarial Threats on Wide-Area Control System: Preprint

Federated machine learning (FL) is gaining significant popularity to develop cybersecurity solutions in power grids because of its advanced capability to support decentralized data handing at local devices, its privacy preservation, and its low-bandwidth requirement. However, the evolving adversarial machine learning (AML) threats raise significant concerns for the cybersecurity of FL architectures. The FL-based split neural network (SplitNN) achieves high performance through the decentralized training of local neural network models while preserving data privacy across multiple entities. In this paper, we propose a methodology for evaluating the performance of a vertical FLbased anomaly detector against different types of AML attacks, including denial-of-service attacks, adversarial data injection attacks, and replay attacks on the trained local models deployed in the grid network. For a case study, we consider the modified IEEE 13-bus system, and we develop SplitNN-based binary and multiclass classification models to detect, locate, and identify different types of data integrity attacks on the volt-watt control with two pooling layers: maximum pooling and AvgPool. Our experimental results, computed through performance metrics, reveal that the severity of these AML attacks varies with the integrated pooling mechanism, the type of classification model, and the nature of the cyberattack. Further, the AML attacks negatively impacted the prediction time per sample for the pretrained SplitNN during the online testing.

adversarial threats↗
DOE OSTI2021

Chapter 18: Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

energy distribution↗
DOE OSTI2022

The CMS Barrel Calorimeter Processor demonstrator (BCPv1) board evaluation

For the CERN LHC phase 2, the barrel region of the CMS electromagnetic (ECAL EB) and hadronic calorimeters (HCAL HB) require new back-end electronics for their readout. To this purpose, a first version of the ATCA-based blade, the Barrel Calorimeter Processor (BCPv1), has been developed. The performance of the optical links as well as clock distribution are also presented here. Here, the BCPv1 has been tested together with front-end and trigger boards, as well as with the new DAQ and TCDS Hub (DTH) [1], to demonstrate that the BCPv1 meets the required specifications.

47 OTHER INSTRUMENTATION↗
NASA NTRS2012

Analysis of Vegetation Index Variations and the Asian Monsoon Climate

Vegetation growth depends on local climate. Significant anthropogenic land cover and land use change activities over Asia have changed vegetation distribution as well. On the other hand, vegetation is one of the important land surface variables that influence the Asian Monsoon variability through controlling atmospheric energy and water vapor conditions. In this presentation, the mean and variations of vegetation index of last decade at regional scale resolution (5km and higher) from MODIS have been analyzed. Results indicate that the vegetation index has been reduced significantly during last decade over fast urbanization areas in east China, such as Yangtze River Delta, where local surface temperatures were increased significantly in term of urban heat Island. The relationship between vegetation Index and climate (surface temperature, precipitation) over a grassland in northern Asia and over a woody savannas in southeast Asia are studied. In supporting Monsoon Asian Integrated Regional Study (MAIRS) program, the data in this study have been integrated into Giovanni, the online visualization and analysis system at NASA GES DISC. Most images in this presentation are generated from Giovanni system.

Shen, Sunhung↗
DOE OSTI2021

Model-free control for frequency response support in microgrids utilizing wind turbines

Islanded microgrids integrated with renewable energy resources bring additional challenges to the primary frequency control due to their power-electronic interfaced generations. Developing an efficient control strategy for inertia emulation and frequency response support of microgrids is becoming an important issue. Wind turbine dynamics, however, will degrade the emulated inertial and primary responses with most traditional control strategies. On the other hand, compensation methods often require the knowledge of system models at the design stage and state estimation for feedback once deployed online. To overcome these challenges, this paper explores a model-free control (MFC) strategy to emulate the desired inertia and support the frequency response of a diesel-wind microgrid system. Additionally, the proposed MFC strategy utilizes a model reference concept to obtain guaranteed inertial response and improve the frequency response of microgrids. This work considers different control strategies with different operating points. The proposed controller is implemented and verified using the modified 33-bus full nonlinear model of the three-phase diesel-wind system in Simulink. Simulation results demonstrate the improved frequency response by precisely emulating the desired inertia using the MFC strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2001

GCM Simulations of the Aerosol Indirect Effect: Sensitivity to Cloud Parameterization and Aerosol Burden

We describe the coupling of the Goddard Institute for Space Studies (GISS) general circulation model (GCM) to an online sulfur chemistry model and source models for organic matter and sea-salt that is used to estimate the aerosol indirect effect. The cloud droplet number concentration is diagnosed empirically from field experiment datasets over land and ocean that observe droplet number and all three aerosol types simultaneously; corrections are made for implied variations in cloud turbulence levels. The resulting cloud droplet number is used to calculate variations in droplet effective radius, which in turn allows us to predict aerosol effects on cloud optical thickness and microphysical process rates. We calculate the aerosol indirect effect by differencing the top-of-the-atmosphere net cloud radiative forcing for simulations with present-day vs. pre-industrial emissions. Both the first (radiative) and second (microphysical) indirect effects are explored. We test the sensitivity of our results to cloud parameterization assumptions that control the vertical distribution of cloud occurrence, the autoconversion rate, and the aerosol scavenging rate, each of which feeds back significantly on the model aerosol burden. The global mean aerosol indirect effect for all three aerosol types ranges from -1.55 to -4.36 W/sq m in our simulations. The results are quite sensitive to the pre-industrial background aerosol burden, with low pre-industrial burdens giving strong indirect effects, and to a lesser extent to the anthropogenic aerosol burden, with large burdens giving somewhat larger indirect effects. Because of this dependence on the background aerosol, model diagnostics such as albedo-particle size correlations and column cloud susceptibility, for which satellite validation products are available, are not good predictors of the resulting indirect effect.

Menon, Surabi↗
NASA NTRS2001

GCM Simulations of the Aerosol Indirect Effect: Sensitivity to Cloud Parameterization and Aerosol Burden

We describe the coupling of the Goddard Institute for Space Studies (GISS) general circulation model (GCM) to an online sulfur chemistry model and source models for organic matter and sea-salt that is used to estimate the aerosol indirect effect. The cloud droplet number concentration is diagnosed empirically from field experiment datasets over land and ocean that observe droplet number and all three aerosol types simultaneously; corrections are made for implied variations in cloud turbulence levels. The resulting cloud droplet number is used to calculate variations in droplet effective radius, which in turn allows us to predict aerosol effects on cloud optical thickness and microphysical process rates. We calculate the aerosol indirect effect by differencing the top-of-the-atmosphere net cloud radiative forcing for simulations with present-day vs. pre-industrial emissions. Both the first (radiative) and second (microphysical) indirect effects are explored. We test the sensitivity of our results to cloud parameterization assumptions that control the vertical distribution of cloud occurrence, the autoconversion rate, and the aerosol scavenging rate, each of which feeds back significantly on the model aerosol burden. The global mean aerosol indirect effect for all three aerosol types ranges from -1.55 to -4.36 W m(exp -2) in our simulations. The results are quite sensitive to the pre-industrial background aerosol burden, with low pre-industrial burdens giving strong indirect effects, and to a lesser extent to the anthropogenic aerosol burden, with large burdens giving somewhat larger indirect effects. Because of this dependence on the background aerosol, model diagnostics such as albedo-particle size correlations and column cloud susceptibility, for which satellite validation products are available, are not good predictors of the resulting indirect effect.

Menon, Surabi↗
DOE OSTI2024

Multi-Fidelity Modeling and Control for Building Temperature Control

The ability to control energy loads such as a building's heating, ventilation, and air conditioning (HVAC) system can help facilitate increased penetration of variable renewable energy sources into the electric grid. To be able to control these HVAC systems more effectively, detailed simulations of the corresponding building physics is becoming increasingly important. These detailed simulations can be complex, nonlinear, and can require immense computational power when used in an advanced control method such as model predictive control (MPC), prompting the need to explore less computationally intensive strategies. In this work, a multi-fidelity approach is proposed to combine samples from a complex, high-fidelity model with a simple, low-fidelity model within the MPC control loop. More specifically, the parameters of a reduced-order, linear building model are periodically updated with knowledge from its high-fidelity counterpart - an EnergyPlus model - in an online fashion using a Gaussian Process surrogate model. Hence, highly accurate predictions of current and future conditions in a building are maintained with a substantially reduced computational burden compared to using the high-fidelity models alone. In other words, this linear parameter varying model preserves the low computational requirements of a low-order linear model while accurately modeling a building's dynamics. This allows a building controller to take highly informed actions without requiring a large computational budget.

building modeling↗
DOE OSTI2023

Automating the Solar Interconnection Technical Evaluation Process: PREconfiguring and Controlling Inverter SEt-Points (PRECISE)

Utilities are receiving an increasing number of interconnection requests for distributed solar photovoltaic (PV) systems from their customers, requiring a solution to quickly and intelligently perform technical assessments of these requests and to manage these new renewable energy assets. In this paper, we present a stand-alone interconnection tool, PREconfiguring and Controlling Inverter SEt-points (PRECISETM), and how it overcomes the challenges of accelerating technical evaluations and leverages smart inverter functionality to provide local voltage support on an as-needed basis. The paper details the purpose and architecture of PRECISE, including tools, data source requirements, the model-based interconnection evaluation process, and technical results. PRECISE is an integrated software tool that customizes PV inverter settings for utilities by modeling and assessing impacts on local voltages and the need for advanced inverter functions (AIFs) (e.g., volt-VAR and volt-watt). PRECISE maximizes the use of readily available utility data sets to make fast online assessments of incoming PV interconnection requests and makes accept/reject recommendations along with custom settings for AIFs.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
NASA NTRS2007

NASA Tech Briefs, December 2007

Topics include: Ka-Band TWT High-Efficiency Power Combiner for High-Rate Data Transmission; Reusable, Extensible High-Level Data-Distribution Concept; Processing Satellite Imagery To Detect Waste Tire Piles; Monitoring by Use of Clusters of Sensor-Data Vectors; Circuit and Method for Communication Over DC Power Line; Switched Band-Pass Filters for Adaptive Transceivers; Noncoherent DTTLs for Symbol Synchronization; High-Voltage Power Supply With Fast Rise and Fall Times; Waveguide Calibrator for Multi-Element Probe Calibration; Four-Way Ka-Band Power Combiner; Loss-of-Control-Inhibitor Systems for Aircraft; Improved Underwater Excitation-Emission Matrix Fluorometer; Metrology Camera System Using Two-Color Interferometry; Design and Fabrication of High-Efficiency CMOS/CCD Imagers; Foam Core Shielding for Spacecraft CHEM-Based Self-Deploying Planetary Storage Tanks Sequestration of Single-Walled Carbon Nanotubes in a Polymer PPC750 Performance Monitor Application-Program-Installer Builder Using Visual Odometry to Estimate Position and Attitude Design and Data Management System Simple, Script-Based Science Processing Archive Automated Rocket Propulsion Test Management Online Remote Sensing Interface Fusing Image Data for Calculating Position of an Object Implementation of a Point Algorithm for Real-Time Convex Optimization Handling Input and Output for COAMPS Modeling and Grid Generation of Iced Airfoils Automated Identification of Nucleotide Sequences Balloon Design Software Rocket Science 101 Interactive Educational Program Creep Forming of Carbon-Reinforced Ceramic-Matrix Composites Dog-Bone Horns for Piezoelectric Ultrasonic/Sonic Actuators Benchtop Detection of Proteins Recombinant Collagenlike Proteins Remote Sensing of Parasitic Nematodes in Plants Direct Coupling From WGM Resonator Disks to Photodetectors Using Digital Radiography To Image Liquid Nitrogen in Voids Multiple-Parameter, Low-False-Alarm Fire-Detection Systems Mosaic-Detector-Based Fluorescence Spectral Imager Plasmoid Thruster for High Specific-Impulse Propulsion Analysis Method for Quantifying Vehicle Design Goals Improved Tracking of Targets by Cameras on a Mars Rover Sample Caching Subsystem Multistage Passive Cooler for Spaceborne Instruments GVIPS Models and Software Stowable Energy-Absorbing Rocker-Bogie Suspensions

Source record↗
NASA NTRS2012

Social Networking Adapted for Distributed Scientific Collaboration

Share is a social networking site with novel, specially designed feature sets to enable simultaneous remote collaboration and sharing of large data sets among scientists. The site will include not only the standard features found on popular consumer-oriented social networking sites such as Facebook and Myspace, but also a number of powerful tools to extend its functionality to a science collaboration site. A Virtual Observatory is a promising technology for making data accessible from various missions and instruments through a Web browser. Sci-Share augments services provided by Virtual Observatories by enabling distributed collaboration and sharing of downloaded and/or processed data among scientists. This will, in turn, increase science returns from NASA missions. Sci-Share also enables better utilization of NASA s high-performance computing resources by providing an easy and central mechanism to access and share large files on users space or those saved on mass storage. The most common means of remote scientific collaboration today remains the trio of e-mail for electronic communication, FTP for file sharing, and personalized Web sites for dissemination of papers and research results. Each of these tools has well-known limitations. Sci-Share transforms the social networking paradigm into a scientific collaboration environment by offering powerful tools for cooperative discourse and digital content sharing. Sci-Share differentiates itself by serving as an online repository for users digital content with the following unique features: a) Sharing of any file type, any size, from anywhere; b) Creation of projects and groups for controlled sharing; c) Module for sharing files on HPC (High Performance Computing) sites; d) Universal accessibility of staged files as embedded links on other sites (e.g. Facebook) and tools (e.g. e-mail); e) Drag-and-drop transfer of large files, replacing awkward e-mail attachments (and file size limitations); f) Enterprise-level data and messaging encryption; and g) Easy-to-use intuitive workflow.

Karimabadi, Homa↗
NASA NTRS2005

Decentralized Formation Flying Control in a Multiple-Team Hierarchy

This paper presents the prototype of a system that addresses these objectives-a decentralized guidance and control system that is distributed across spacecraft using a multiple-team framework. The objective is to divide large clusters into teams of manageable size, so that the communication and computational demands driven by N decentralized units are related to the number of satellites in a team rather than the entire cluster. The system is designed to provide a high-level of autonomy, to support clusters with large numbers of satellites, to enable the number of spacecraft in the cluster to change post-launch, and to provide for on-orbit software modification. The distributed guidance and control system will be implemented in an object-oriented style using MANTA (Messaging Architecture for Networking and Threaded Applications). In this architecture, tasks may be remotely added, removed or replaced post-launch to increase mission flexibility and robustness. This built-in adaptability will allow software modifications to be made on-orbit in a robust manner. The prototype system, which is implemented in MATLAB, emulates the object-oriented and message-passing features of the MANTA software. In this paper, the multiple-team organization of the cluster is described, and the modular software architecture is presented. The relative dynamics in eccentric reference orbits is reviewed, and families of periodic, relative trajectories are identified, expressed as sets of static geometric parameters. The guidance law design is presented, and an example reconfiguration scenario is used to illustrate the distributed process of assigning geometric goals to the cluster. Next, a decentralized maneuver planning approach is presented that utilizes linear-programming methods to enact reconfiguration and coarse formation keeping maneuvers. Finally, a method for performing online collision avoidance is discussed, and an example is provided to gauge its performance.

Mueller, Joseph .↗
DOE OSTI2022

A Nonlinear Direct Power Controller for a Three-Phase Grid-Connected Inverter with Online Parameter Update for PV Application

This work proposes a nonlinear direct power controller for a three-phase grid-connected inverter for photovoltaic application. Grid-connected converters for solar applications require advanced control architectures to ensure optimal operation and extraction of maximum power from the photovoltaic arrays. Therefore, to ensure this, a nonlinear power controller architecture has been proposed to extract the maximum power from the solar arrays and to achieve the most optimal operation. To further improve the performance, a parameter update scheme has been utilized which is based on model reference adaptive system. The latter helps to accomplish not only good dynamic response but also excellent tracking performance. The overall control come estimation architecture is verified based on computer simulations via MATLAB/Simulink and PLECS domain and various important case study results are presented.

direct power control↗
DOE OSTI2023

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The role of geography in the complex diffusion of innovations

The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System

Frequency control from photovoltaic (PV) power plants has great potential to address the frequency response challenge of the power system with high penetrations of renewable generation. Using model-based approaches to determine the optimal PV headroom reserve, however, requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic 1-day operation profile of the WECC system and demonstrates a savings of more than 40% PV headroom compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

frequency control↗