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American Control Conference (ACC)

作者： Xiang, Weiming Augusta Univ Sch Comp & Cyber Sci Augusta GA 30912 USA

ISBN: (纸本)9781665441971

In this work, a data-driven modeling framework of switched dynamical systems under time-dependent switching is proposed. The learning technique utilized to model system dynamics is Extreme Learning Machine (ELM). First, a method is developed for the detection of the switching occurrence events in the training data extracted from system traces. The training data thus can be segmented by the detected switching instants. Then, ELM is used to learn the system dynamics of subsystems. The learning process includes segmented trace data merging and subsystem dynamics modeling. Due to the specific learning structure of ELM, the modeling process is formulated as an iterative Least-Squares (LS) optimization problem. Finally, the switching sequence can be reconstructed based on the switching detection and segmented trace merging results. An example of the data-driven modeling DC-DC converter is presented to show the effectiveness of the developed approach.

关键词： data-driven modeling System dynamics Extreme learning machines Merging Training data Switches DC-DC power converters

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data-driven modeling of multiaxial fatigue in frequency domain

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MARINE STRUCTURES 2022年第0期84卷 103201-103201页

作者： Ravi, Sandipp Krishnan Dong, Pingsha Wei, Zhigang Univ Michigan Dept Naval Architecture & Marine Engn Ann Arbor MI 48109 USA

A data-driven approach to modeling multiaxial fatigue in frequency domain is proposed. The paper presents a methodology through which models can be built to predict fatigue damage based on a multiaxial cycle counting method and the associated life assessment approach in frequency domain. Through apt parameterization of spectrum in conjunction with principal component analysis transformation, every biaxial stress state in frequency domain can be represented as a point on a common feature space. The proposed methodology consists of three phases: exploratory study, data generation and model development. Two structural components are used for the demonstration. The performance of the models are evaluated in comparison with time domain approaches, and results are compared. The proposed methodology is able to predict multiaxial fatigue damage in frequency domain within acceptable error limits, allowing for a fast and efficient method to tackle multiaxial fatigue analysis in frequency domain.

关键词： Multi-axial fatigue analysis Structural stress data-driven modeling Neural network Frequency domain

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data-driven modeling of the temporal evolution of breakers' states in the French electrical transmission grid?

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NONLINEAR ANALYSIS-HYBRID SYSTEMS 2022年第0期46卷

作者： Gonzalez, Mauricio Girard, Antoine EthiFinance EthiFinance Analyt 11 Ave Delcasse F-75008 Paris France Univ Paris Saclay CNRS CentraleSupelec L2S 3 rue Joliot Curie F-91190 Gif Sur Yvette France

In electrical transmission grids, it is common to observe the states of circuit breakers. While they are known at irregular times, system modeling and grid state estimation are of the highest importance to ensure secure operations. This paper proposes a richer method to estimate the grid state over its reference configurations based on the temporal evolution of its breakers' states. The first contribution consists in developing a general multi-observation continuous-time finite-state Hidden Markov Model with filter-based parameter estimation to infer the hidden state (e.g., the grid reference configuration) handling multiple observed processes with irregular ''jump"times (e.g., the breakers' states). As a second contribution, we build a numerical scheme with no discretization error adapted to all state jumps generated by the observed processes. Finally, we apply our model to simulated and real data to illustrate the approach's performance. The available data consists of historical records of breakers' states during the electrical transmission grid operated normally. For this real-data-driven application, we also present a clustering approach to identify the set of grid reference configurations. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： data-driven modeling Hidden Markov Models EM algorithms Electrical transmission grid

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data-driven modeling of transonic unsteady flows and efficient analysis of fluid-structure stability

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JOURNAL OF FLUIDS AND STRUCTURES 2022年 111卷

作者： Yao, Xiangjie Huang, Rui Hu, Haiyan State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing 210016 China

The reduced-order aerodynamic models constructed via the linear/nonlinear system identification methodologies cannot reveal the flow characteristics of the fluid-structure coupling system because the state variables of the reduced-order model do not explicitly represent fluidic properties. In this study, a data-driven modeling procedure is proposed to reconstruct a physics-based, reduced-order aerodynamic model. In the procedure, the transonic unsteady flows of concern are projected onto low-dimensional base vectors first via the proper orthogonal decomposition of pressure snapshots subject to a specific structural excitation. Then a state-space representation of the temporal coefficients of proper orthogonal decomposition modes subject to the structural excitation is established by using the dynamic mode decomposition with control. Finally, for the fluid-structure stability analysis, pressure snapshots are recovered from the coefficients of proper orthogonal decomposition, and aerodynamic forces are derived by integrating the pressure coefficients around the wing surface. The state vector in above-mentioned data-driven model has a clear sense in physics with regard to pressure distribution. To demonstrate the accuracy of the proposed procedure, a two-dimensional, transonic aeroelastic wing with an NACA0012 profile is studied. The unsteady aerodynamic forces, frequency responses of the reduced-order aerodynamic model, transonic flutter boundary, and flow characteristics at the flutter condition are predicted and compared with direct computational fluid dynamic simulations. The results show that the modeling procedure can accurately predict the transonic flutter boundary and flow characteristics. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： data-driven modeling Transonic unsteady flow Fluid-structure interaction Stability analysis

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data-driven modeling and Characterization of Carbon Nanotube Nanocomposite Strain Sensors for Human Health Monitoring Applications 9

Data-Driven Modeling and Characterization of Carbon Nanotube...

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9th RSI International Conference on Robotics and Mechatronics (ICRoM)

作者： Amindehghan, Mohammad Amin Nankali, Mohammad Nouri, Norouz Mohammad Iran Univ Sci & Technol Tehran Sch Mech Engn Tehran Iran

ISBN: (纸本)9781665420945

Developing electromechanical sensors with flexible and skin-mountable structures are highly demanded in human-related applications such as health care and sports fields. Conductive polymer nanocomposite with piezoresistive behavior is one solution to overcome stretchability and sensitivity limitations in conventional electromechanical sensors. In this study, we have developed a stretchable nanocomposite strain sensor based on multi-walled carbon nanotubes (MWCNTs) and Polydimethylsiloxane (PDMS) elastomer with improved behavior by investigating the effects of nanofillers concentration, curing temperature of the polymer, and the effect of mechanical pre-stretching on the response of the sensor. Optimizing these factors result in a significant improvement in the performance of the sensor by increasing the linearity (more than 20%), sensitivity (up to threefold), and hysteresis error decreased to less than 3%. However, the sensor still demonstrates relaxation errors during step strain and initial stretch/strain cycles test due to the viscoelastic nature of elastomers, which is common in every polymer-based nanocomposite sensor and, therefore, causes inaccuracy in practical applications. We developed a data-driven dynamic model using the System Identification technique in MATLAB to cover the time-dependent behavior of the sensor and predict the input strain more accurately. The model could reduce the strain prediction error up to 10% in comparison to the gauge factor equation. Finally, to demonstrate the potential applications of our sensor in human health applications, it was attached to the body for monitoring respiration and carotid artery signals.

关键词： Carbon nanotubes data-driven modeling Nanocomposite Piezoresistive behavior Strain sensors

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data-driven modeling of Power-Electronics-Based Power System Considering the Operating Point Variation 13

Data-Driven Modeling of Power-Electronics-Based Power System...

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13th IEEE Energy Conversion Congress and Exposition (IEEE ECCE)

作者： Zhang, Mengfan Wang, Xiongfei Xu, Qianwen Aalborg Univ Dept Energy Technol Aalborg Denmark KTH Rolyal Inst Technol Elect Power & Energy Syst Div Stockholm Sweden

ISBN: (纸本)9781728151359

Large-scale integrations of power-electronics devices have introduced the stability challenges to the conventional power system. The stability of the power-electronics-based power systems, which are modeled by a Multi-Input Multi-Output (MIMO) transfer function matrix, can be analyzed based on the Nyquist Criterion. However, since no or limited information about the internal control details, this matrix can only be obtained using the measured data. On the other hand, the elements of the matrix will change along with the operating point of each power-electronics converter, which introduces the challenge to guarantee the interaction stability of each inverter at different operating points. In this paper, a data-driven method is proposed to overcome this operating-point dependent challenge. An artificial neural network (ANN) is used to characterize the operating-point dependent model of power-electronics-based power systems. The comparison results confirm the accuracy of the impedance model obtained by this data-driven modeling method.

关键词： data-driven modeling Stability criteria Transfer functions Artificial neural networks Power system stability Systems modeling Large scale integration

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A universal drag model for liquid-solid fluidization: Experiment, data-driven modeling, CFD modeling and simulation

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POWDER TECHNOLOGY 2024年 448卷

作者： Zhou, Guangming Xie, Le Cent South Univ Coll Chem & Chem Engn Changsha 410083 Hunan Peoples R China

Various industrial applications are performed in liquid-solid fluidized beds where drag force plays a significant role in affecting the expansion characteristics of granular-bed, and then determines the mass/heat transfer performance. This study employs dimensionless learning data-driven modeling method, which is derived from the principle of dimensional invariance, to automatically discover the relationship between the drag coefficient and hydraulic dimensionless numbers from the liquid-solid fluidization data. It is found that the Fr number (_ u2l /(gds)) also plays important role in improving the prediction accuracy of drag model except for Re number (_ dsul rho l/mu l). The proposed data-driven modeling method has desired robustness, and the yielded drag model can be applicable to other liquid-solid systems, such as water-polystyrene spheres and water-coal particles, although it is derived from the fluidization of spherical glass beads in rising tap-water. The proposed drag model can also provide good CFD simulation results that agree very well with the experiment data with the relative error less than 5 %.

关键词： Drag relations Fluidized bed data-driven modeling CFD

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data-driven modeling of general damping systems by k-means clustering and two-stage regression

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年第PartB期167卷 108572-108572页

作者： Guo, Jia Wang, Li Fukuda, Iori Ikago, Kohju Tohoku Univ Int Res Inst Disaster Sci Sendai Miyagi Japan Sun Yat Sen Univ Dept Appl Mech & Engn Guangzhou Peoples R China

Damping is one of the most complicated phenomena in structural analysis and design. In principle, damping arises with energy dissipation in the vibration and therefore, friction, plasticity and viscosity etc are common sources of damping. Though various damping models have been proposed, they are only applicable to some certain damping phenomena and there is no unified way to model an arbitrary damping system. To the end, this paper presents a data driven framework for modeling of general damping systems. There are three key ingredients in establishing this framework. At first, pre-defined dictionaries of basis functions are built to describe the hysteretic and viscous behaviors of a general damping model. Secondly, the k means clustering technique is applied to separate the two datasets corresponding to respective hysteretic and viscous parts of the damping model from the measured data. Thirdly, a two-stage regression procedure is invoked where the viscous and hysteretic parts of the damping model are identified sequentially from the readily separated two datasets. Such identification proceeds by means of linear least-squares regression and sparse regularization. As a consequence, if a new damping system whose hysteretic and viscous behaviors are totally reflected in the dictionary, the underlying model equation can be directly and quickly recovered through the proposed data driven approach. Numerical examples as well as an experimental test are studied to demonstrate the effectiveness and efficiency of the proposed data-driven modeling approach for general damping systems.

关键词： data-driven modeling Hysteretic damping Viscous damping Sparse regression k-means clustering

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data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost

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ENERGY EXPLORATION & EXPLOITATION 2020年第6期38卷 2413-2435页

作者： Xiong, Xinwei Lee, Kyung Jae Univ Houston Dept Petr Engn 5000 Gulf Freeway Houston TX 77204 USA

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

关键词： data-driven modeling well placement optimization waterflooding heterogeneous reservoirs artificial neural network reducing observation cost

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Research on a data-driven modeling Method for Precast Concrete Balcony Components

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BUILDINGS 2024年第1期14卷 96页

作者： Cai, Jie Wang, Xin Shi, Junfeng Xie, Xingxing Feng, Yu Wu, Yingjun Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China Beijing Olive Hill Software Co Ltd Beijing 100095 Peoples R China

In this paper, a data-driven modeling method for precast concrete (PC) balcony components was proposed to solve the problems of low informatization and the difficult modeling of components at the design stage. Through the analysis of the characteristics of PC balcony components and the combination of modular design methods, the paper designed a data structure for the components and developed a data-driven modeling tool for PC balcony components that can realize the input of structural design data, automatically generating component models. First, this paper introduced the data-driven modeling concept and the modeling process. Second, the PC balcony components in common prefabricated residential projects were analyzed to identify their characteristics. By using a modular design approach, these components were divided and a module dataset was created based on the split modules. Consequently, a data structure for the prefabricated balcony component model was established, wherein both conventional parameters and adaptive parameters between modules were interrelated. Finally, the function of data-driven modeling was achieved by developing a modular design tool on the Revit platform using the C# programming language. The application conducted on a prefabricated building project demonstrated that the software tool and modeling method in this paper effectively improve the level of informatization and modeling efficiency of PC balcony components. The modular design approach was satisfied with the standardization and diversification requirements of balcony components, thereby offering insights for modeling other complex components.

关键词： PC balcony component data-driven modeling modularization Revit secondary development

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