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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Numerical Experimentation with Maximum Likelihood Identification in Static Distributed Systems

Many important issues in the control of large space structures are intimately related to the fundamental problem of parameter identification. One might also ask how well this identification process can be carried out in the presence of noisy data since no sensor system is perfect. With these considerations in mind the algorithms herein are designed to treat both the case of uncertainties in the modeling and uncertainties in the data. The analytical aspects of maximum likelihood identification are considered in some detail in another paper. The questions relevant to the implementation of these schemes are dealt with, particularly as they apply to models of large space structures. The emphasis is on the influence of the infinite dimensional character of the problem on finite dimensional implementations of the algorithms. Those areas of current and future analysis are highlighted which indicate the interplay between error analysis and possible truncations of the state and parameter spaces.

Scheid, R. E., Jr.↗
DOE OSTI2012

Pulsed Photofission Delayed Gamma Ray Detection for Nuclear Material Identification

Innovative systems with increased sensitivity and resolution are in great demand to detect diversion and to prevent misuse in support of nuclear materials management for the U.S. fuel cycle. Nuclear fission is the most important multiplicative process involved in non-destructive active interrogation. This process produces the most easily recognizable signature for nuclear materials. High-energy gamma rays can also excite a nucleus and cause fission through a process known as photofission. After photofission reactions, delayed signals are easily distinguishable from the interrogating radiation. Linac-based, advanced inspection techniques utilizing the fission signals after photofission have been extensively studied for homeland security applications. Previous research also showed that a unique delayed gamma ray energy spectrum exists for each fissionable isotope. Isotopic composition measurement methods based on delayed gamma ray spectroscopy will be the primary focus of this work.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements

This study presents an ensemble learning approach for fault classification and location identification in a smart distribution network containing photovoltaics (PV)-based microgrid. Lack of available data points and the unbalanced nature of the distribution system make fault handling a challenging task for utilities. The proposed method uses event-driven voltage data from smart meters to classify and locate faults. The ensemble voting classifier is composed of three base learners; random forest, k-nearest neighbours, and artificial neural network. The fault location (FL) task has been formulated as a classification problem where the fault type is classified in the first step and based on the fault type, the faulty bus is identified. The method is tested on IEEE-123 bus system modified with added PV-based microgrid along with dynamic loading conditions and varying fault resistances from 0 to 20 Ω for both unbalanced and balanced fault types. A further sensitivity analysis has been done to test the robustness of the proposed method under various noise levels and data loss errors in the smart meter measurements. The ensemble method shows improved performance and robustness compared to some previously proposed FL methods. Finally, the proposed method has been experimentally validated on a real-time simulation-based testbed using a state-of-the-art digital real-time simulator, industry standard DNP3 communication protocol and a cpu-based control centre running the FL algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Measurement-based Coherency Identification and Aggregation for Power Systems

In power system model reduction, a high reduction ratio is often desired to handle much more complex power systems. The bottleneck of traditional methods lies in: Coherency identification methods are conservative. Some coherency generators are not detected when system topology or operating points change, because coherency identification depends on system topology or operating points. There are some solitary generators in external systems. These generators do not belong to any coherency group. However, sometimes these solitary generators have little impact on tie-line power flow, and it might be possible to ignore their dynamics in model reduction. But because they do not belong to any coherency group, existing reduction methods cannot handle them well. In order to overcome the first problem, a measurement-based online coherency identification method is presented in this paper. By analyzing post-fault trajectories measured by Phasor Measurement Units (PMUs), coherency generators are identified through principal component analysis. The method can track time-varying system topology and operating points. In order to address the second problem, this paper introduces sensitivity analysis into traditional reduction methods. The sensitivity of tie-line power flow against injected active power of external system generators is derived. Those generators having loose connection with tie-line power are identified through the sensitivity analysis, and their dynamics are ignored by replacing them with negative impedances. We test if the sensitivity, based on static power flow, provides good guidance to reduce the dynamic model. Case studies show that the proposed method can handle well these solitary generators and the reduction ratio can be enhanced through this method. Future work will include generalization of the sensitivity method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Identification of open quantum systems from observable time traces

Estimating the parameters that dictate the dynamics of a quantum system is an important task for quantum information processing and quantum metrology, as well as fundamental physics. In our paper we develop a method for parameter estimation for Markovian open quantum systems using a temporal record of measurements on the system. Furthermore, the method is based on system realization theory and is a generalization of our previous work on identification of Hamiltonian parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2006

Closed-Loop Evaluation of an Integrated Failure Identification and Fault Tolerant Control System for a Transport Aircraft

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems developed for failure detection, identification, and reconfiguration, as well as upset recovery, need to be evaluated over broad regions of the flight envelope or under extreme flight conditions, and should include various sources of uncertainty. To apply formal robustness analysis, formulation of linear fractional transformation (LFT) models of complex parameter-dependent systems is required, which represent system uncertainty due to parameter uncertainty and actuator faults. This paper describes a detailed LFT model formulation procedure from the nonlinear model of a transport aircraft by using a preliminary LFT modeling software tool developed at the NASA Langley Research Center, which utilizes a matrix-based computational approach. The closed-loop system is evaluated over the entire flight envelope based on the generated LFT model which can cover nonlinear dynamics. The robustness analysis results of the closed-loop fault tolerant control system of a transport aircraft are presented. A reliable flight envelope (safe flight regime) is also calculated from the robust performance analysis results, over which the closed-loop system can achieve the desired performance of command tracking and failure detection.

Shin, Jong-Yeob↗
DOE OSTI2022

IMoFi - Intelligent Model Fidelity: Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration (Final Report)

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO) to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
DOE OSTI2022

IMoFi (Intelligent Model Fidelity): Physics-Based Data-Driven Grid Modeling to Accelerate Accurate PV Integration Updated Accomplishments

This report summarizes the work performed under a project funded by U.S. DOE Solar Energy Technologies Office (SETO), including some updates from the previous report SAND2022-0215, to use grid edge measurements to calibrate distribution system models for improved planning and grid integration of solar PV. Several physics-based data-driven algorithms are developed to identify inaccuracies in models and to bring increased visibility into distribution system planning. This includes phase identification, secondary system topology and parameter estimation, meter-to-transformer pairing, medium-voltage reconfiguration detection, determination of regulator and capacitor settings, PV system detection, PV parameter and setting estimation, PV dynamic models, and improved load modeling. Each of the algorithms is tested using simulation data and demonstrated on real feeders with our utility partners. The final algorithms demonstrate the potential for future planning and operations of the electric power grid to be more automated and data-driven, with more granularity, higher accuracy, and more comprehensive visibility into the system.

14 SOLAR ENERGY↗
NASA NTRS1976

Advanced space program studies, overall executive summary

Multidisciplined advanced planning studies were conducted that involve space operations and the associated system elements, identification of potential low cost system techniques, vehicle design, cost synthesis techniques, DoD technology forecasting, and the development of near and far term space initiatives with emphasis on domestic and military use commonality. Specific areas studied include: (1) manned systems utilization; (2) STS users; (3) vehicle cost/performance; (4) space vehicle applications to future national needs; (5) STS spin stabilized upper stage; and (6) technology assessment and forecast.

Source record↗
NASA NTRS2019

Model Based Engineering for Software Assurance

NASA's successful development of next generation space vehicles, habitats, and robotic systems will require reliable hardware and software systems. The aim of this initiative is to develop modeling methodology and tools to support Model-Based Systems Engineering (MBSE) for software assurance and reliability analysis. This effort expands the Unified Modeling Language (UML) software design models to include fault data for the extraction of Failure Modes and Effects Criticality Analysis (FMECA) and Fault Tree Analysis (FTA) for software. We explored different modeling approaches to integrate the UML software design models with the Systems Modeling Language (SysML) system models to generate an integrated model and reliability tools that take into account software and hardware interfaces.The benefits of this concept directly affect the safety community with quick turnarounds to produce software assurance and reliability analysis artifacts and the ability to visualize failure effects, both hardware and software. The result is enhanced system design integrity and early identification of system risks. This initiative will enable software assurance activities early in the system design lifecycle, facilitating the discovery of design weaknesses and enhancing the capability to produce safe, hazard-free systems

Wang, Lui↗
DOE OSTI2012

Consolidation and Centralization of Waste Operations Business Systems - 12319

This abstract provides a comprehensive plan supporting the continued development and integration of all waste operations and waste management business systems. These include existing systems such as ATMS (Automated Transportation Management System), RadCalc, RFITS (Radio Frequency Identification Transportation System) Programs as well as incorporating key components of existing government developed waste management systems and COTS (Computer Off The Shelf) applications in order to deliver a truly integrated waste tracking and management business system. Some of these existing systems to be integrated include IWTS at Idaho National Lab, WIMS at Sandia National Lab and others. The aggregation of data and consolidation into a single comprehensive business system delivers best practices in lifecycle waste management processes to be delivered across the Department of Energy facilities. This concept exists to reduce operational costs to the federal government by combining key business systems into a centralized enterprise application following the methodology that as contractors change, the tools they use to manage DOE's assets do not. IWITS is one efficient representation of a sound architecture currently supporting multiple DOE sites from a waste management solution. The integration of ATMS, RadCalc and RFITS and the concept like IWITS into a single solution for DOE contractors will result in significant savings and increased efficiencies for DOE. Building continuity and solving collective problems can only be achieved through mass collaboration, resulting in an online community that DOE contractors and subcontractors access common applications, allowing for the collection of business intelligence at an unprecedented level. This is a fundamental shift from a solely 'for profit' business model to a 'for purpose' business model. To the conventional-minded, putting values before profit is an unfamiliar and unnatural way for a contractor to operate - unless however; your objective is to build a strong, strategic alliance across the enterprise in order to execute an unprecedented change in waste management, transportation and logistical operations. The success of such an initiative can be achieved by creating a responsible framework by enabling key individuals to 'own' the sustainability of the program. This includes the strategic collaboration of responsible revolutionaries covering application developers, information owners and federal stakeholders to ensure compliance, security and risk management are 'baked' into the process and sustainability is fostered through continued innovation by both technology and application functionality. This ensures that working software can adapt to changing circumstances and is the principle measure of the success of the program. The consolidation of waste management business systems must be achieved in order to realize efficiencies in information technology portfolio management, data integrity, business intelligence and the lifecycle management of hazardous materials within the DOE enterprise architecture. By identifying best practices across the enterprise and aggregating computational and application development resources, you can provide a unified, holistic solution serviceable from a single location while being accessed from anywhere. The business impact of integrating and delivering a unified solution would reduce costs to the Department of Energy within the first year of deployment with increased savings annually. (author)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators

In this paper, we propose linear operator theoretic framework involving Koopman operator for the data-driven identification of power system dynamics. We explicitly account for noise in the time series measurement data and propose robust approach for data-driven approximation of Koopman operator for the identification of nonlinear power system dynamics. The identified model is used for the prediction of state trajectories in the power system. The application of the framework is illustrated using an IEEE nine bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1989

Numerical studies of identification in nonlinear distributed parameter systems

An abstract approximation framework and convergence theory for the identification of first and second order nonlinear distributed parameter systems developed previously by the authors and reported on in detail elsewhere are summarized and discussed. The theory is based upon results for systems whose dynamics can be described by monotone operators in Hilbert space and an abstract approximation theorem for the resulting nonlinear evolution system. The application of the theory together with numerical evidence demonstrating the feasibility of the general approach are discussed in the context of the identification of a first order quasi-linear parabolic model for one dimensional heat conduction/mass transport and the identification of a nonlinear dissipation mechanism (i.e., damping) in a second order one dimensional wave equation. Computational and implementational considerations, in particular, with regard to supercomputing, are addressed.

Banks, H. T.↗
NASA NTRS2012

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗
NASA NTRS1992

Comparison of candidate methods to distinguish noise modes from system modes in structural identification

In modal identification, nonphysical noise or computation modes always appear to help match the input-output data. This paper studies the ability of four criteria to distinguish which modes in a model are noise modes: (1) modal amplitude coherency, (2) the relative contribution of each mode to the pulse response indicated by the mode singular value, (3) the variances of the mode frequencies and damping factors produced by a chosen measurement noise level, and (4) identification of the backward-time in order to let the shift from positive to negative damping of the true system modes distinguish these modes from noise modes. Both simulated and experimental data are used to study the four criteria.

Longman, Richard W.↗