A Data-Driven Approach for the Stability Analysis of Vortex-Induced Vibration

2018 ◽  
Author(s):  
Sandeep B. Reddy ◽  
Allan Ross Magee ◽  
Rajeev K. Jaiman
Keyword(s):  
Stability Analysis ◽  
Bluff Body ◽  
Data Driven ◽  
Bluff Bodies ◽  
Stability Boundaries ◽  
Data Driven Approach ◽  
Offshore Industry ◽  
The Stability ◽  
Lock In ◽  
And Control

In this paper, a general data-driven approach to construct a reduced-order model (ROM) for the coupled fluid-structure interaction (FSI) problem of a transversely vibrating bluff body in an incompressible flow is presented. The proposed data-driven approach relies on the Eigensystem Realization Algorithm (ERA) to design ROM models in a state-space format. The stability boundaries of the coupled FSI system are obtained by examining the eigenvalue trajectories of the ERA-based ROM. These stability boundaries provide us valuable quantitative insights into the lock-in phenomenon of the bluff-body vibration. We demonstrate the present ERA-based ROM technique for various configurations of bluff bodies such as an isolated single cylinder, the side-by-side and the tandem cylinder arrangements. A comparative study on the effect of different appendages to suppress the VIV of a cylinder is also presented using the ERA-based stability analysis. The validity of the proposed method for the FSI stability analysis on such variety of configurations has not been presented before and is the novel contribution of this paper. Overall, the proposed data-driven framework is found to be much more effective in terms of computational costs and the predicted lock-in regions are comparable to high-fidelity full-order simulations. This work has a potential for a profound impact on the design optimization and control of bluff body structures used in offshore industry.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

Model reduction and mechanism for the vortex-induced vibrations of bluff bodies

2017 ◽  
Vol 827 ◽  
pp. 357-393 ◽  
Author(s):  
W. Yao ◽  
R. K. Jaiman
Keyword(s):  
Reynolds Number ◽  
Bluff Body ◽  
Low Reynolds Number ◽  
Smooth Curve ◽  
Bluff Bodies ◽  
Sharp Corner ◽  
Mass Ratio ◽  
Square Cylinder ◽  
Low Reynolds ◽  
Lock In

We present an effective reduced-order model (ROM) technique to couple an incompressible flow with a transversely vibrating bluff body in a state-space format. The ROM of the unsteady wake flow is based on the Navier–Stokes equations and is constructed by means of an eigensystem realization algorithm (ERA). We investigate the underlying mechanism of vortex-induced vibration (VIV) of a circular cylinder at low Reynolds number via linear stability analysis. To understand the frequency lock-in mechanism and self-sustained VIV phenomenon, a systematic analysis is performed by examining the eigenvalue trajectories of the ERA-based ROM for a range of reduced oscillation frequency $(F_{s})$, while maintaining fixed values of the Reynolds number ($Re$) and mass ratio ($m^{\ast }$). The effects of the Reynolds number $Re$, the mass ratio $m^{\ast }$ and the rounding of a square cylinder are examined to generalize the proposed ERA-based ROM for the VIV lock-in analysis. The considered cylinder configurations are a basic square with sharp corners, a circle and three intermediate rounded squares, which are created by varying a single rounding parameter. The results show that the two frequency lock-in regimes, the so-called resonance and flutter, only exist when certain conditions are satisfied, and the regimes have a strong dependence on the shape of the bluff body, the Reynolds number and the mass ratio. In addition, the frequency lock-in during VIV of a square cylinder is found to be dominated by the resonance regime, without any coupled-mode flutter at low Reynolds number. To further discern the influence of geometry on the VIV lock-in mechanism, we consider the smooth curve geometry of an ellipse and two sharp corner geometries of forward triangle and diamond-shaped bluff bodies. While the ellipse and diamond geometries exhibit the flutter and mixed resonance–flutter regimes, the forward triangle undergoes only the flutter-induced lock-in for $30\leqslant Re\leqslant 100$ at $m^{\ast }=10$. In the case of the forward triangle configuration, the ERA-based ROM accurately predicts the low-frequency galloping instability. We observe a kink in the amplitude response associated with 1:3 synchronization, whereby the forward triangular body oscillates at a single dominant frequency but the lift force has a frequency component at three times the body oscillation frequency. Finally, we present a stability phase diagram to summarize the VIV lock-in regimes of the five smooth-curve- and sharp-corner-based bluff bodies. These findings attempt to generalize our understanding of the VIV lock-in mechanism for bluff bodies at low Reynolds number. The proposed ERA-based ROM is found to be accurate, efficient and easy to use for the linear stability analysis of VIV, and it can have a profound impact on the development of control strategies for nonlinear vortex shedding and VIV.

Stability Analysis and Control Measures of the Kim-Yun-Mine Collapse

2012 ◽  
Vol 594-597 ◽  
pp. 358-361
Author(s):  
Shan Shan Zhang ◽  
Yu Liang Wu
Keyword(s):  
Rock Mass ◽  
Stability Analysis ◽  
Stereographic Projection ◽  
Control Measures ◽  
Collapse Characteristics ◽  
Basic Characteristics ◽  
The Stability ◽  
Mine Collapse ◽  
And Control ◽  
Dangerous Rock Mass

Collapse is one of the major geological disasters all over the world and threats to life and property safety of people. To make a better understanding of the reason it occurs and how to deal with it, the Kim-Yun-Mine collapse is researched. There are one dangerous rock mass and two collapse accumulation body. The basic characteristics of the collapse is described clearly according to the geological exploration data, and the stability of the dangerous rock mass and the collapse accumulated body is analyzed in the way of engineering geology and stereographic projection. At last, we put forward comprehensive control measures based on the results of stability analysis and collapse characteristics.

scholarly journals Local stability analysis and eigenvalue sensitivity of reacting bluff-body wakes

2016 ◽  
Vol 788 ◽  
pp. 549-575 ◽  
Author(s):  
Benjamin Emerson ◽  
Tim Lieuwen ◽  
Matthew P. Juniper
Keyword(s):  
Stability Analysis ◽  
Acoustic Waves ◽  
Local Stability ◽  
Bluff Body ◽  
Density Ratio ◽  
Global Mode ◽  
Eigenvalue Sensitivity ◽  
Local Stability Analysis ◽  
Measured Time ◽  
The Stability

This paper presents an experimental and theoretical investigation of high-Reynolds-number low-density reacting wakes near a hydrodynamic Hopf bifurcation. This configuration is applicable to the wake flows that are commonly used to stabilize flames in high-velocity flows. First, an experimental study is conducted to measure the limit-cycle oscillation of this reacting bluff-body wake. The experiment is repeated while independently varying the bluff-body lip velocity and the density ratio across the flame. In all cases, the wake exhibits a sinuous oscillation. Linear stability analysis is performed on the measured time-averaged velocity and density fields. In the first stage of this analysis, a local spatiotemporal stability analysis is performed on the measured time-averaged velocity and density fields. The stability analysis results are compared to the experimental measurement and demonstrate that the local stability analysis correctly captures the influence of the lip-velocity and density-ratio parameters on the sinuous mode. In the second stage of the analysis, the linear direct and adjoint global modes are estimated by combining the local results. The sensitivity of the eigenvalue to changes in intrinsic feedback mechanisms is found by combining the direct and adjoint global modes. This is referred to as the eigenvalue sensitivity throughout the paper for reasons of brevity. The predicted global mode frequency is consistently within 10 % of the measured value, and the linear global mode shape closely resembles the measured nonlinear oscillations. The adjoint global mode reveals that the oscillation is strongly sensitive to open-loop forcing in the shear layers. The eigenvalue sensitivity identifies a wavemaker in the recirculation zone of the wake. A parametric study shows that these regions change little when the density ratio and lip velocity change. In the third stage of the analysis, the stability analysis is repeated for the varicose hydrodynamic mode. Although not physically observed in this unforced flow, the varicose mode can lock into longitudinal acoustic waves and cause thermoacoustic oscillations to occur. The paper shows that the local stability analysis successfully predicts the global hydrodynamic stability characteristics of this flow and shows that experimental data can be post-processed with this method in order to identify the wavemaker regions and the regions that are most sensitive to external forcing, for example from acoustic waves.

Automated Stability Analysis of a Vehicle in Combined Pitch and Roll

2002 ◽  
Author(s):  
James K. Sprague ◽  
Shyi-Ping Liu
Keyword(s):  
Stability Analysis ◽  
Rigid Body ◽  
Contact Forces ◽  
Design Parameters ◽  
Spherical Coordinates ◽  
Stability Boundaries ◽  
Overturning Stability ◽  
Heading Angle ◽  
The Stability ◽  
Six Degree Of Freedom

This paper presents a rigid body modeling approach using ADAMS™ for an overturning stability analysis of a vehicle stopped at an arbitrary heading angle on a steep grade. The vehicle is modeled as a six-degree-of-freedom rigid body with multiple contact forces acting on the ground. A gravity vector bounded by sets of spherical coordinates is applied to the vehicle to represent the physics of a vehicle stopped on a grade with any arbitrary combination of pitch and roll angles. A design of experiments study is performed to locate the overturning stability boundaries within given levels of design parameters. Results are output using two effective graphical means of depicting the stability regions and magnitude of contact forces.

Data-Driven Computing With Convolutional Neural Networks for Two-Phase Flows: Application to Wave-Structure Interaction

2018 ◽  
Author(s):  
X. Mao ◽  
V. Joshi ◽  
T. P. Miyanawala ◽  
Rajeev K. Jaiman
Keyword(s):  
Neural Network ◽  
Free Surface ◽  
Bluff Body ◽  
Data Driven ◽  
Model Parameters ◽  
Wave Forces ◽  
Approximation Procedure ◽  
High Fidelity ◽  
Bluff Bodies ◽  
Fluid Forces

Fluctuating wave force on a bluff body is of great significance in many offshore and marine engineering applications. We present a Convolutional Neural Network (CNN) based data-driven computing to predict the unsteady wave forces on bluff bodies due to the free-surface wave motion. For the full-order modeling and high-fidelity data generation, the air-water interface for such wave-body problems must be captured accurately for a broad range of physical and geometric parameters. Originated from the thermodynamically consistent theories, the physically motivated Allen-Cahn phase-field method has many advantages over other interface capturing techniques such as level-set and volume-of-fluid methods. The Allen-Cahn equation is solved in the mass-conservative form by imposing a Lagrange multiplier technique. While a tremendous amount of wave-body interaction data is generated in offshore engineering via both CFD simulations and experiments, the results are generally underutilized. Design space exploration and flow control of such practical scenarios are still time-consuming and expensive. An alternative to semi-analytical modeling, CNN is a class of deep neural network for solving inverse problems which is efficient in parametric data-driven computation and can use the domain knowledge. It establishes a model with arbitrarily generated model parameters, makes predictions using the model and existing input parametric settings, and adjusts the model parameters according to the error between the predictions and existing results. The computational cost of this prediction process, compared with high-fidelity CFD simulation, is significantly reduced, which makes CNN an accessible tool in design and optimization problems. In this study, CNN-based data-driven computing is utilized to predict the wave forces on bluff bodies with different geometries and distances to the free surface. The discrete convolution process with a non-linear rectification is employed to approximate the mapping between the bluff-body shape, the distance to the free-surface and the fluid forces. The wave-induced fluid forces on bluff bodies of different shapes and submergences are predicted by the trained CNN. Finally, a convergence study is performed to identify the effective hyper-parameters of the CNN such as the convolution kernel size, the number of kernels and the learning rate. Overall, the proposed CNN-based approximation procedure has a profound impact on the parametric design of bluff bodies experiencing wave loads.

A Data-Driven Methodology for Fault Detection in Electromechanical Actuators

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Anthony J. Chirico ◽  
Jason R. Kolodziej
Keyword(s):  
Feature Extraction ◽  
Failure Modes ◽  
Extraction Process ◽  
Data Driven ◽  
Ball Screw ◽  
Fault Classification ◽  
Electromechanical Actuators ◽  
Rotating Machine ◽  
Data Driven Approach ◽  
And Control

This research investigates a novel data-driven approach to condition monitoring of electromechanical actuators (EMAs) consisting of feature extraction and fault classification. The approach is able to accommodate time-varying loads and speeds since EMAs typically operate under nonsteady conditions. The feature extraction process exposes fault frequencies in signal data that are synchronous with motor position through a series of signal processing techniques. A resulting reduced dimension feature is then used to determine the condition with a trained Bayesian classifier. The approach is based on signal analysis in the frequency domain of inherent EMA signals and accelerometers. For this work, two common failure modes, bearing and ball screw faults, are seeded on a MOOG MaxForce EMA. The EMA is then loaded using active and passive load cells with measurements collected via a dSPACE data acquisition and control system. Typical position commands and loads are utilized to simulate “real-world” inputs and disturbances and laboratory results show that actuator condition can be determined over a range of inputs. Although the process is developed for EMAs, it can be used generically on other rotating machine applications as a Health and Usage Management System (HUMS) tool.

scholarly journals In-Line Monitoring and Control of Rheological Properties through Data-Driven Ultrasound Soft-Sensors

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5009 ◽  
Author(s):  
Stefania Tronci ◽  
Paul Van Neer ◽  
Erwin Giling ◽  
Uilke Stelwagen ◽  
Daniele Piras ◽  
...  
Keyword(s):  
Performance Indicators ◽  
Continuous Process ◽  
Principal Component ◽  
Feedforward Neural Networks ◽  
Data Driven ◽  
Monitoring And Control ◽  
Soft Sensors ◽  
Continuous Processes ◽  
Data Driven Approach ◽  
And Control

The use of continuous processing is replacing batch modes because of their capabilities to address issues of agility, flexibility, cost, and robustness. Continuous processes can be operated at more extreme conditions, resulting in higher speed and efficiency. The issue when using a continuous process is to maintain the satisfaction of quality indices even in the presence of perturbations. For this reason, it is important to evaluate in-line key performance indicators. Rheology is a critical parameter when dealing with the production of complex fluids obtained by mixing and filling. In this work, a tomographic ultrasonic velocity meter is applied to obtain the rheological curve of a non-Newtonian fluid. Raw ultrasound signals are processed using a data-driven approach based on principal component analysis (PCA) and feedforward neural networks (FNN). The obtained sensor has been associated with a data-driven decision support system for conducting the process.

scholarly journals Data-driven approach for monitoring, protection, and control of distribution system assets using micro-PMU technology

2017 ◽  
Vol 2017 (1) ◽  
pp. 1011-1014 ◽  
Author(s):  
Emma Stewart ◽  
Michael Stadler ◽  
Ciaran Roberts ◽  
Jim Reilly ◽  
Dan Arnold ◽  
...  
Keyword(s):  
Distribution System ◽  
Data Driven ◽  
Data Driven Approach ◽  
Protection And Control ◽  
And Control
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