Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “System Identification”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Implementation of a Model Predictive Control Strategy to Regulate Temperature Inside Plug-Flow Solar Reactor With Countercurrent Flow

Abstract Solar-driven thermochemical energy storage systems are proven to be promising energy carriers (solar fuels) to utilize solar energy by using reactive solid-state pellets. However, the production of solar fuel requires a quasi-steady-state process temperature, which represents the main challenge due to the transient nature of solar power. In this work, an adaptive model predictive controller (MPC) is presented to regulate the temperature inside a tubular solar reactor to produce solid-state solar fuel for long-term thermal storage systems. The solar reactor system consists of a vertical tube heated circumferentially over a segment of its length by concentrated solar power, and the reactive pellets (MgMn2O4) are fed from the top end and flow downwards through the heated tube. A countercurrent flowing gas supplied from the lower end interacts with flowing pellets to reduce it thermochemically at a temperature range of 1000—1500 °C. A low-order physical model was developed to simulate the dynamics of the solar reactor including the reaction kinetics, and the proposed model was validated numerically by using a 7-kW electric furnace. The numerical model then was utilized to design the MPC controller, where the control system consists of an MPC code linked to an adaptive system identification code that updates system parameters online to ensure system robustness against external disturbances (sudden change in the flow inside the reactor), model mismatches, and uncertainty. The MPC controller parameters are tuned to enhance the system performance with minimum steady-state error and overshoot. The controller is tested to track different temperature ranges between 500 °C and 1400 °C with different particles/gas mass flowrates and ramping temperature profiles. Results show that the MPC controller successfully regulated the reactor temperature within ± 1 °C of its setpoint and maintained robust performance with minimum input effort when subjected to sudden changes in the amount of flowing media and the presence of chemical reaction.

Engineering↗
DOE OSTI2024

Use of Legacy Maritime Protocols Increases Exploitability of Virtual Aids to Navigation

With increased reliance on Virtual Aid(s) to Navigation (VAtoN) - also known as electronic Aid(s) to Navigation (eAtoN), or virtual buoys - a cyber event is likely to cause disruption to international maritime shipping. VAtoN has no physical hardware for visual reference and displays only on a vessel’s Electronic Chart Display Information System (ECDIS) and Automatic Radar Plotting Aid (ARPA); therefore, mariners must rely on the accuracy of the information provided. As VAtoN uses the National Maritime Electronics Association (NMEA) 0183 protocol for both Global Navigation Satellite System (GNSS) and Automatic Identification System (AIS), an insecure protocol that has been proven susceptible to spoofing, denial, and manipulation, the likelihood of a cyber-related event increases substantially.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

Method of resource-limited device and device class identification using system and function call tracing techniques, performance, and statistical analysis

Methods for cyber physical systems device classification are provided. A method can include receiving system and function calls and parameters and a device performance index from an unknown CPS device and a device performance index of similar class of CPS devices, calculating an autocorrelation value between different realizations of the system and function calls and parameters of the known CPS device, determining whether the autocorrelation value is greater than a threshold amount, and storing the system and function calls and parameters and the device performance characteristics of the known CPS device in the database. A method can also include calculating a correlation between system and function calls and parameters of an unknown CPS device and known CPS devices classes included in the database, as well as determining whether the maximum correlation is also greater than a threshold amount.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2015

Overview of the ultrasonic instrumentation research in the MYRRHA project

The Belgian Nuclear Research Centre SCK.CEN is in the process of developing MYRRHA, a new generation IV fast flux research reactor to replace the aging BR2. MYRRHA is conceptualized as an accelerator driven system cooled with lead bismuth eutectic mixture (LBE). As LBE is opaque to visual light, ultrasonic measurement techniques are employed as the main technology to provide feedback where needed. This paper we will give an overview of the R and D at SCK.CEN with respect to ultrasonic instrumentation in heavy liquid metals. High temperature ultrasonic transducers are deployed into the reactor to generate and receive the required ultrasonic signals. The ultrasonic waves are generated and sensed by means of a piezo-electric disc at the heart of the transducer. The acoustic properties of commonly used piezo-electric materials match rather well with the acoustic properties of heavy liquid metals, simplifying the design and construction of high bandwidth ultrasonic transducers for use in heavy liquid metals. The ultrasonic transducers will operate in a liquid metal environment, where radiation and high temperature limit the choice of materials for construction. Moreover, the high surface tension of the liquid metal hinders proper wetting of the transducer, required for optimal transmission and reception of the ultrasonic waves. In a first part of the paper, we will discuss the effect of these parameters on the performance of the overall ultrasonic system. In the second part of the paper, past, present and future ultrasonic experiments in LBE will be reviewed. We will show the results of an experiment where a transducer is scanned near the free surface of an LBE pool to render ultrasonic images of objects submerged in the heavy liquid metal. Additionally, the preliminary results of an ongoing experiment that measures the evolution of LBE wetting on different types of metals and various surface conditions will be reported. The evolution of wetting is an important parameter to assess the performance of ultrasonic systems that are submerged in LBE over longer time periods. In the last part we will briefly present the overall design of the two main ultrasonic systems that will be deployed in MYRRHA: the ultrasonic fuel identification system and the ultrasonic lost fuel localization system. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Comparative Study of Data-Driven Area Inertia Estimation Approaches on WECC Power Systems

With the increasing integration of inverter-based resources into the power grid, there has been a notable reduction in system inertia, potentially compromising frequency stability. To assess the suitability of existing area inertia estimation techniques for real-world power systems, this paper presents a rigorous comparative analysis of system identification, measurement reconstruction, and electromechanical oscillation-based area inertia estimation methodologies, specifically applied to the large-scale and multi-area WECC 240-bus power system. Comprehensive results show that the system identification-based approach exhibits superior robustness and accuracy relative to its counterparts.

area inertia estimation↗
DOE OSTI2021

Automating Discovery of Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system's physical characteristics, determining the optimal structure and optimization strategy for these models can be difficult. In this work, we explore methods for discovering neural state space dynamics models for system identification. Starting with a design space of block-oriented state space models and structured linear maps with strong physical priors, we encode these components into a model genome alongside network structure, penalty constraints, and optimization hyperparameters. Demonstrating the overall utility of the design space, we employ an asynchronous genetic search algorithm that alternates between model selection and optimization and obtains accurate physically consistent models of three physical systems: an aerodynamics body, a continuous stirred tank reactor, and a two tank interacting system.

genetic algorithms, neural architecture search, ne↗
DOE OSTI2012

Attributes from NMIS Time Coincidence, Fast-Neutron Imaging, Fission Mapping, And Gamma-Ray Spectrometry Data

This work tests a systematic procedure for analyzing data acquired by the Nuclear Materials Identification System (NMIS) at Oak Ridge National Laboratory with fast-neutron imaging and high-purity germanium (HPGe) gamma spectrometry capabilities. NMIS has been under development by the US Department of Energy Office of Nuclear Verification since the mid-1990s, and prior to that by the National Nuclear Security Administration Y-12 National Security Complex, with NMIS having been used at Y-12 for template matching to confirm inventory and receipts. In this present work, a complete set of NMIS time coincidence, fast-neutron imaging, fission mapping, and HPGe gamma-ray spectrometry data was obtained from Monte Carlo simulations for a configuration of fissile and nonfissile materials. The data were then presented for analysis to someone who had no prior knowledge of the unknown object to accurately determine the description of the object by applying the previously mentioned procedure to the simulated data. The best approximation indicated that the unknown object was composed of concentric cylinders: a void inside highly enriched uranium (HEU) (84.7 {+-} 1.9 wt % 235 U), surrounded by depleted uranium, surrounded by polyethylene. The final estimation of the unknown object had the correct materials and geometry, with error in the radius estimates of material regions varying from 1.58% at best and 4.25% at worst; error in the height estimates varied from 2% to 12%. The error in the HEU enrichment estimate was 5.9 wt % (within 2.5Σ of the true value). The accuracies of the determinations could be adequate for arms control applications. Future work will apply this iterative reconstructive procedure to other unknown objects to further test and refine it.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Constrained Block Nonlinear Neural Dynamical Models

Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical models by embedding local model structure and constraints. The proposed method consists of neural network blocks that represent input, state, and output dynamics with constraints placed on the network weights and system variables. For handling partially observable dynamical systems, we utilize a state observer neural network to estimate the states of the system's latent dynamics. We evaluate the performance of the proposed architecture and training methods on system identification tasks for three nonlinear systems: a continuous stirred tank reactor, a two tank interacting system, and an aerodynamics body. Models optimized with a few thousand system state observations accurately represent system dynamics in open loop simulation over thousands of time steps from a single set of initial conditions. Experimental results demonstrate an order of magnitude reduction in open-loop simulation mean squared error for our constrained, block-structured neural models when compared to traditional unstructured and unconstrained neural network models.

Skomski, Elliott↗
DOE OSTI2024

Convergence of Weak-SINDy Surrogate Models

In this paper, we give an in-depth error analysis for surrogate models generated by a variant of the Sparse Identification of Nonlinear Dynamics (SINDy) method. We start with an overview of a variety of nonlinear system identification techniques, namely SINDy, weak-SINDy, and the occupation kernel method. Under the assumption that the dynamics are a finite linear combination of a set of basis functions, these methods establish a linear system to recover coefficients. We illuminate the structural similarities between these techniques and establish a projection property for the weak-SINDy technique. Following the overview, we analyze the error of surrogate models generated by a simplified version of weak-SINDy. In particular, under the assumption of boundedness of a composition operator given by the solution, we show that (i) the surrogate dynamics converges towards the true dynamics and (ii) the solution of the surrogate model is reasonably close to the true solution. Finally, as an application, we discuss the use of a combination of weak-SINDy surrogate modeling and proper orthogonal decomposition (POD) to build a surrogate model for partial differential equations (PDEs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Next Generation Safeguards Initiative: Overview and Policy Context of UF6 Cylinder Tracking Program

Thousands of cylinders containing uranium hexafluoride (UF{sub 6}) move around the world from conversion plants to enrichment plants to fuel fabrication plants, and their contents could be very useful to a country intent on diverting uranium for clandestine use. Each of these large cylinders can contain close to a significant quantity of natural uranium (48Y cylinder) or low-enriched uranium (LEU) (30B cylinder) defined as 75 kg {sup 235}U which can be further clandestinely enriched to produce 1.5 to 2 significant quantities of high enriched uranium (HEU) within weeks or months depending on the scale of the clandestine facility. The National Nuclear Security Administration (NNSA) Next Generation Safeguards Initiative (NGSI) kicked off a 5-year plan in April 2011 to investigate the concept of a unique identification system for UF{sub 6} cylinders and potentially to develop a cylinder tracking system that could be used by facility operators and the International Atomic Energy Agency (IAEA). The goal is to design an integrated solution beneficial to both industry and inspectorates that would improve cylinder operations at the facilities and provide enhanced capabilities to deter and detect both diversion of low-enriched uranium and undeclared enriched uranium production. The 5-year plan consists of six separate incremental tasks: (1) define the problem and establish the requirements for a unique identification (UID) and monitoring system; (2) develop a concept of operations for the identification and monitoring system; (3) determine cylinder monitoring devices and technology; (4) develop a registry database to support proof-of-concept demonstration; (5) integrate that system for the demonstration; and (6) demonstrate proof-of-concept. Throughout NNSA's performance of the tasks outlined in this program, the multi-laboratory team emphasizes that extensive engagement with industry stakeholders, regulatory authorities and inspectorates is essential to its success.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

SympGNNs: Symplectic Graph Neural Networks for identifying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combine symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: (i) G-SympGNN and (ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. Finally, we also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

Deep learning↗
DOE OSTI2012

Wind turbine blade fatigue tests: lessons learned and application to SHM system development

This paper presents experimental results of several structural health monitoring (SHM) methods applied to a 9-meter CX-100 wind turbine blade that underwent fatigue loading. The blade was instrumented with piezoelectric transducers, accelerometers, acoustic emission sensors, and foil strain gauges. It underwent harmonic excitation at its first natural frequency using a hydraulically actuated resonant excitation system. The blade was initially excited at 25% of its design load, and then with steadily increasing loads until it failed. Various data were collected between and during fatigue loading sessions. The data were measured over multiple frequency ranges using a variety of acquisition equipment, including off-the-shelf systems and specially designed hardware developed by the authors. Modal response, diffuse wave-field transfer functions, and ultrasonic guided wave methods were applied to assess the condition of the wind turbine blade. The piezoelectric sensors themselves were also monitored using a sensor diagnostics procedure. This paper summarizes experimental procedures and results, focusing particularly on fatigue crack detection, and concludes with considerations for implementing such damage identification systems, which will be used as a guideline for future SHM system development for operating wind turbine blades.

17 WIND ENERGY↗
DOE OSTI2015

ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. our paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. Our results demonstrate that the measurement-based model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control.

24 POWER TRANSMISSION AND DISTRIBUTION↗