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An application of data-driven modeling for hydroelasticity of an elastically supported semi-circular pipe conveying fluid

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ENGINEERING COMPUTATIONS 2023年第5期40卷 1195-1227页

作者： Xu, Jianhang Li, Peng Yang, Yiren Southwest Jiaotong Univ Sch Mech & Aerosp Engn Chengdu Peoples R China

PurposeThe paper aims to develop an efficient data-driven modeling approach for the hydroelastic analysis of a semi-circular pipe conveying fluid with elastic end supports. Besides the structural displacement-dependent unsteady fluid force, the steady one related to structural initial configuration and the variable structural parameters (i.e. the variable support stiffness) are considered in the ***/methodology/approachThe steady fluid force is treated as a pipe preload, and the elastically supported pipe-fluid model is dealt with as a prestressed hydroelastic system with variable parameters. To avoid repeated numerical simulations caused by parameter variation, structural and hydrodynamic reduced-order models (ROMs) instead of conventional computational structural dynamics (CSD) and computational fluid dynamics (CFD) solvers are utilized to produce data for the update of the structural, hydrodynamic and hydroelastic state-space equations. Radial basis function neural network (RBFNN), autoregressive with exogenous input (ARX) model as well as proper orthogonal decomposition (POD) algorithm are applied to modeling these two ROMs, and a hybrid framework is proposed to incorporate *** proposed approach is validated by comparing its predictions with theoretical solutions. When the steady fluid force is absent, the predictions agree well with the "inextensible theory". The pipe always loses its stability via out-of-plane divergence first, regardless of the support stiffness. However, when steady fluid force is considered, the pipe remains stable throughout as flow speed increases, consistent with the "extensible theory". These results not only verify the accuracy of the present modeling method but also indicate that the steady fluid force, rather than the extensibility of the pipe, is the leading factor for the differences between the in- and extensible ***/valueThe steady fluid force and the variable structural parameters are co

关键词： data-driven modeling Reduced-order methods Semi-circular pipe conveying fluid Hydroelasticity Steady fluid force Variable parameters

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Experimental demonstration of SnO_(2) nanofiber-based memristors and their data-driven modeling for nanoelectronic applications

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Chip 2023年第4期2卷 142-153页

作者： Soumi Saha Madadi Chetan Kodand Reddy Tati Sai Nikhil Kaushik Burugupally Sanghamitra DebRoy Akshay Salimath Venkat Mattela Surya Shankar Dan Parikshit Sahatiya Department of Electrical&Electronics Engineering Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Department of Computer Science and Information Systems Birla Institute of Technology and Science PilaniHyderabad CampusHyderabad 500078India Nanomagnetic Division Ceremorphic India Pvt LtdIndia Ceremorphic Inc.San JoseCA 95131USA

This paper demonstrated the fabrication,characterization,datadriven modeling,and practical application of a 1D SnO_(2)nanofiber-based memristor,in which a 1D SnO_(2)active layer wassandwiched between silver(Ag)and aluminum(Al)*** yielded a very high ROFF:RON of~104(ION:IOFF of~105)with an excellent activation slope of 10 mV/dec,low set voltage ofVSET~1.14 V and good *** paper physically explained the conduction mechanism in the layered SnO_(2)*** conductive network was composed of nanofibersthat play a vital role in the memristive action,since more conductive paths could facilitate the hopping of electron *** structures experimentally extracted with the adoption of ultraviolet photoelectron spectroscopy strongly support the claimsreported in this *** machine learning(ML)–assisted,datadriven model of the fabricated memristor was also developedemploying different popular algorithms such as polynomialregression,support vector regression,k nearest neighbors,andartificial neural network(ANN)to model the data of the *** have proposed two types of ANN models(type I andtype II)algorithms,illustrated with a detailed flowchart,to modelthe fabricated *** with standard ML techniques shows that the type II ANN algorithm provides the bestmean absolute percentage error of 0.0175 with a 98%R^(2)*** proposed data-driven model was further validated with the characterization results of similar new memristors fabricated adoptingthe same fabrication recipe,which gave satisfactory ***,the ANN type II model was applied to design and implementsimple AND&OR logic functionalities adopting the fabricatedmemristors with expected,near-ideal characteristics.

关键词： Nanofiber-based memristors data-driven modeling Artificial neural network(ANN) SnO_(2)

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Maximum Likelihood Estimation in data-driven modeling and Control

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第1期68卷 317-328页

作者： Yin, Mingzhou Iannelli, Andrea Smith, Roy S. S. Swiss Fed Inst Technol Zurich Automatic Control Lab CH-8092 Zurich Switzerland

Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these methods lead to ill-conditioned data-driven model structures. In this article, we present a maximum likelihood framework to obtain an optimal data-driven model, the signal matrix model, in the presence of output noise. data compression and noise-level estimation schemes are also proposed to apply the algorithm efficiently to large datasets and unknown noise-level scenarios. Two approaches in system identification and receding horizon control are developed based on the derived optimal estimator. The first one identifies a finite impulse response model. This approach improves the least-squares estimator with less restrictive assumptions. The second one applies the signal matrix model as the predictor in predictive control. The control performance is shown to be better than existing data-driven predictive control algorithms, especially under high noise levels. Both approaches demonstrate that the derived estimator provides a promising framework to apply data-driven algorithms to noisy data.

关键词： data-driven modeling maximum likeli-hood estimation (MLE) model-predictive control (MPC) system identification

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Global data-driven modeling of wind turbines in the presence of turbulence

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CONTROL ENGINEERING PRACTICE 2013年第4期21卷 441-454页

作者： van der Veen, G. J. van Wingerden, J. W. Fleming, P. A. Scholbrock, A. K. Verhaegen, M. Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Natl Renewable Energy Lab Golden CO 80401 USA

This paper presents a practical approach to identify a global model of a wind turbine from operational data, while it operates in a turbulent wind field with a varying mean wind speed and under closed-loop control. The approach is based on the realization that the nonlinearities are dominated by the aerodynamics of the rotor, which change with the operating condition. The dynamics of a wind turbine can be decomposed into a nonlinear static part, governed by the torque and thrust characteristics of the rotor, and a linear time-invariant dynamic part The multi-input-multi-output linear dynamics are estimated using a recent closed-loop subspace identification method. The practical applicability of the algorithm is demonstrated by applying it to data obtained from the NREL CART 3 research turbine. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Closed-loop system identification Wind turbines data-driven modeling Hammerstein systems Turbulence

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Anomaly detection in streaming environmental sensor data: A data-driven modeling approach

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ENVIRONMENTAL MODELLING & SOFTWARE 2010年第9期25卷 1014-1022页

作者： Hill, David J. Minsker, Barbara S. Rutgers State Univ Dept Civil & Environm Engn Piscataway NJ 08854 USA Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA

The deployment of environmental sensors has generated an interest in real-time applications of the data they collect. This research develops a real-time anomaly detection method for environmental data streams that can be used to identify data that deviate from historical patterns. The method is based on an autoregressive data-driven model of the data stream and its corresponding prediction interval. It performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no pre-classification of anomalies. Furthermore, this method can be easily deployed on a large heterogeneous sensor network. Sixteen instantiations of this method are compared based on their ability to identify measurement errors in a windspeed data stream from Corpus Christi, Texas. The results indicate that a multilayer perceptron model of the data stream, coupled with replacement of anomalous data points, performs well at identifying erroneous data in this data stream. (C) 2009 Published by Elsevier Ltd.

关键词： Coastal environment data-driven modeling Anomaly detection Machine learning Real-time data Sensor networks data quality control Artificial intelligence

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A data-driven modeling Method of Virtual Synchronous Generator Based on LSTM Neural Network

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第4期20卷 5428-5439页

作者： Tian, Jiangbin Zeng, Guohui Zhao, Jinbin Zhu, Xiangchen Zhang, Zhenhua Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Shanghai Univ Elect Power Offshore Wind Power Res Inst Shanghai 201399 Peoples R China Aalborg Univ Dept Energy Technol DK-9220 Aalborg Denmark

The virtual synchronous generator (VSG) exhibits high-dimensional complexity, necessitating a tradeoff between accuracy and complexity when constructing small-signal models. To address this issue, this article proposes a data-driven modeling approach based on long short-term memory (LSTM) neural networks. The focus is on mapping relationships between electrical quantities, considering the influence of irrational factors on model accuracy. A detailed data-driven modeling approach for VSG is proposed and verified in this article. Due to the time-series correlation in the electrical data generated during VSG operation, the LSTM algorithm, known for its excellent time-series prediction capabilities, is chosen to construct the VSG data-driven model. Several complex VSG application scenarios are used to validate the effectiveness and accuracy of the proposed modeling approach. In conclusion, the LSTM-based VSG data-driven modeling outperforms small-signal models and recurrent neural network data-driven models in terms of accuracy and stability.

关键词： data-driven modeling long short-term memory (LSTM) virtual synchronous generator (VSG)

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A new data-driven modeling method for fermentation processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2016年 152卷 88-96页

作者： Yang, Qiangda Gao, Hongbo Zhang, Weijun Chi, Zhongyuan Yi, Zhi Northeastern Univ Sch Met Shenyang 110819 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

An accurate model is the premise for successfully implementing fermentation process optimization. Most data-driven models that are widely applied to fermentation processes are unfit for optimization or provide low precision. This paper presents a new data-driven modeling method for directly developing an ANN-based differential model that is fit for optimization. Moreover, this model can provide high precision because it can be discretized using the sampling period of the control variables as the step length. The lack of data pairs is addressed by transforming the model-training problem into a dynamic system parameter identification problem. Further, a particle swarm optimization algorithm with a time-varying escape mechanism (PSOE) is constructed to determine the model parameters. Finally, the uniform design method is used to select the model structure. The results of experiments conducted using practical data for a lab-scale nosiheptide batch fermentation process confirm the effectiveness of the proposed modeling method and PSOE algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： data-driven modeling Artificial neural networks Particle swarm optimization Fermentation process Nosiheptide

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A Neural-Network-Based Sensitivity Analysis Approach for data-driven modeling of Ship Motion

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IEEE JOURNAL OF OCEANIC ENGINEERING 2020年第2期45卷 451-461页

作者： Cheng, Xu Li, Guoyuan Skulstad, Robert Chen, Shengyong Hildre, Hans Petter Zhang, Houxiang Tianjin Univ Technol Sch Comp Sci & Technol Tianjin 300384 Peoples R China Norwegian Univ Sci & Technol Dept Ocean Operat & Civil Engn N-6009 Alesund Norway

Researchers have been investigating data-driven modeling as a key way to achieve ship intelligence for years. This paper presents a novel data analysis approach to data-driven modeling of ship motion. We propose a global sensitivity analysis (GSA) approach combining artificial neural network (ANN) and sparse polynomial chaos expansion (SPCE) techniques to accommodate high-dimensional sensor data collected from ship motion. An ANN is constructed as a surrogate model to associate ship sensor data with a certain type of ship motion. To account for the computational efficiency of GSA, an SPCE is integrated into the GSA to decrease the need for Monte Carlo (MC) samples generated by the ANN. A probe variable is designed to couple with the MC samples, which plays a role in determining the degree of convergence of variable importance. A test on benchmark function demonstrates the efficiency and accuracy of the proposed approach. A case study of ship heading with and without environment effects is conducted. The experimental results show that the proposed approach can identify and rank the most sensitive factors of ship motion. The proposed approach highlights the application of GSA in data-driven modeling for ship intelligence.

关键词： Marine vehicles data models Analytical models Computational modeling Probes Sensitivity analysis data-driven modeling environment effect global sensitivity analysis (GSA) ship intelligence ship motion

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data-driven modeling and Control of Cardiac System

Data-Driven Modeling and Control of Cardiac System

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作者： Branen, Andrew L. University of Idaho

学位级别：M.S., Master of Science/Master of Surgery

Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where the electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. This electroceutical therapy has been approved for treatment resistant depression, and is currently under investigation for heart failure, heart arrhythmia, hypertension, and gastric motility disorders. Recent studies have shown the ability to selectively activate different fibers in the vagus nerve, thus allowing for a highly specific control of physiological behavior through vagal nerve stimulation. One of the major challenges with the application of this therapy involves a closed loop controller to autonomously control the behavioral responses. This problem becomes additionally challenging when multiple locations and multiple stimulation parameters are considered for optimization. Using a physiological model of a rat heart, this thesis investigates a data-driven control scheme for closed-loop control of the rat cardiac system. In the first section of this thesis, a data-driven modeling approach is used to develop a model that maps vagus nerve stimulation parameter selection to the effect on the physiological variables of heart rate and blood pressure. The second part of this thesis develops a controller that uses the data-driven model by utilizing a model predictive control framework to control the heart rate and the blood pressure in closed-loop simulations of a rat model.

关键词： data-driven modeling Cardiac system Vagus nerve stimulation Electroceutical therapy

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Beyond Persistent Excitation: Online Experiment Design for data-driven modeling and Control

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 319-324页

作者： van Waarde, Henk J. Univ Cambridge Dept Engn Control Grp Cambridge CB2 1PZ England

This letter presents a new experiment design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online approach requires less data samples and is even shown to be completely sample efficient.

关键词： Trajectory Design methodology Linear systems data-driven modeling Noise measurement Adaptive systems Tuning Identification linear systems

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