Model-Form and Parameter Uncertainty Quantification in Structural Vibration Simulation Using Fractional Derivatives

2019 ◽  
Vol 14 (5) ◽  
Author(s):  
Baoqiang Zhang ◽  
Qintao Guo ◽  
Yan Wang ◽  
Ming Zhan
Keyword(s):  
Uncertainty Quantification ◽  
Parameter Uncertainty ◽  
Process Model ◽  
Degrees Of Freedom ◽  
Fractional Derivatives ◽  
Calibration Method ◽  
Structural Vibration ◽  
Model Form ◽  
Lack Of Information ◽  
Vibration Simulation

Extensive research has been devoted to engineering analysis in the presence of only parameter uncertainty. However, in modeling process, model-form uncertainty arises inevitably due to the lack of information and knowledge, as well as assumptions and simplifications made in the models. It is undoubted that model-form uncertainty cannot be ignored. To better quantify model-form uncertainty in vibration systems with multiple degrees-of-freedom, in this paper, fractional derivatives as model-form hyperparameters are introduced. A new general model calibration approach is proposed to separate and reduce model-form and parameter uncertainty based on multiple fractional frequency response functions (FFRFs). The new calibration method is verified through a simulated system with two degrees-of-freedom. The studies demonstrate that the new model-form and parameter uncertainty quantification method is robust.

Preposterior Analysis to Select Experimental Responses for Improving Identifiability in Model Uncertainty Quantification

2013 ◽  
Author(s):  
Zhen Jiang ◽  
Wei Chen ◽  
Daniel W. Apley
Keyword(s):  
Uncertainty Quantification ◽  
Parameter Uncertainty ◽  
Process Model ◽  
Monte Carlo Analysis ◽  
Simply Supported ◽  
Sources Of Uncertainty ◽  
Physical Experiments ◽  
Bayesian Monte Carlo ◽  
Engineering Modeling ◽  
Calibration Parameters

In physics-based engineering modeling and uncertainty quantification, distinguishing the effects of two main sources of uncertainty — calibration parameter uncertainty and model discrepancy — is challenging. Previous research has shown that identifiability can sometimes be improved by experimentally measuring multiple responses of the system that share a mutual dependence on a common set of calibration parameters. In this paper, we address the issue of how to select the most appropriate subset of responses to measure experimentally, to best enhance identifiability. We propose a preposterior analysis approach that, prior to conducting the physical experiments but after conducting computer simulations, can predict the degree of identifiability that will result using different subsets of responses to measure experimentally. We quantify identifiability via the posterior covariance of the calibration parameters, and predict it via the preposterior covariance from a modular Bayesian Monte Carlo analysis of a multi-response Gaussian process model. The proposed method is applied to a simply supported beam example to select two out of six responses to best improve identifiability. The estimated preposterior covariance is compared to the actual posterior covariance to demonstrate the effectiveness of the method.

scholarly journals Approach to Assess Basic Deterministic Data and Model Form Uncertaint in Passive and Active Vibration Isolation

2021 ◽  
pp. 208-223
Author(s):  
Roland Platz
Keyword(s):  
Uncertainty Quantification ◽  
Vibration Isolation ◽  
Structural Vibration ◽  
Design Stage ◽  
Structural Dynamic ◽  
Early Design Stage ◽  
Model Form ◽  
Stiffness Properties ◽  
Active Vibration ◽  
Active Vibration Isolation

AbstractThis contribution continues ongoing own research on uncertainty quantification in structural vibration isolation in early design stage by various deterministic and non-deterministic approaches. It takes into account one simple structural dynamic system example throughout the investigation: a one mass oscillator subject to passive and active vibration isolation. In this context, passive means that the vibration isolation only depends on preset inertia, damping, and stiffness properties. Active means that additional controlled forces enhance vibration isolation. The simple system allows a holistic, consistent and transparent look into mathematical modeling, numerical simulation, experimental test and uncertainty quantification for verification and validation. The oscillator represents fundamental structural dynamic behavior of machines, trusses, suspension legs etc. under variable mechanical loading. This contribution assesses basic experimental data and mathematical model form uncertainty in predicting the passive and enhanced vibration isolation after model calibration as the basis for further deterministic and non-deterministic uncertainty quantification measures. The prediction covers six different damping cases, three for passive and three for active configuration. A least squares minimization (LSM) enables calibrating multiple model parameters using different outcomes in time and in frequency domain from experimental observations. Its adequacy strongly depends on varied damping properties, especially in passive configuration.

A self-calibration method for rotary tables’ five degrees-of-freedom error motions

Measurement ◽  
2021 ◽  
Vol 174 ◽  
pp. 109067
Author(s):  
Zhi-Feng Lou ◽  
Li Liu ◽  
Ji-Yun Zhang ◽  
Kuang-chao Fan ◽  
Xiao-Dong Wang
Keyword(s):  
Degrees Of Freedom ◽  
Calibration Method ◽  
Self Calibration

scholarly journals Cyber-Physical Industrial Control System Testbed: A Cyber-Security Solution

2020 ◽  
Author(s):  
Mohammad Nourizadeh ◽  
Mohammad Shakerpour ◽  
Nader Meskin ◽  
Devrim Unal
Keyword(s):  
Cyber Security ◽  
Process Model ◽  
Degrees Of Freedom ◽  
Industrial Process ◽  
Environmental Safety ◽  
Open Loop ◽  
Industrial Control ◽  
Systems Research ◽  
Process Plant ◽  
Real Risk

In this project, the hybrid testbed architecture is selected for the development of ICS testbed where the Tennessee Eastman (TE) plant is simulated inside PC and the remaining components are implemented using real industrial hardware. TE plant is selected as the industrial process for the developed cybersecur ity testbed due to the following reasons. First, the TE modTheel is a wellknown chemical process plant used in control systems research and it dynamics is well understood. Second, it should be properly cont rolled otherwise small disturbance will drive the system toward an unsafe and unstable operat ion. The inherent unstable open-loop property of the TE process model presents a real-world scenario in which a cyberattack could represent a real risk to human safety, environmental safety, and economic viability. Third, the process is complex, coupled and nonlinear, and has many degrees of freedom by which to control and perturb the dynamics of the process.

scholarly journals Effect of model-form definition on uncertainty quantification in coupled models of mid-frequency range simulations

2017 ◽  
Vol 93 ◽  
pp. 351-367 ◽  
Author(s):  
Kendra L. Van Buren ◽  
Morvan Ouisse ◽  
Scott Cogan ◽  
Emeline Sadoulet-Reboul ◽  
Laurent Maxit
Keyword(s):  
Uncertainty Quantification ◽  
Frequency Range ◽  
Coupled Models ◽  
Model Form

A Calibration Method for a Three-Degrees-of-Freedom Parallel Manipulator with a Redundant Passive Chain

2012 ◽  
Vol 162 ◽  
pp. 171-178 ◽  
Author(s):  
Takaaki Oiwa ◽  
Harunaho Daido ◽  
Junichi Asama
Keyword(s):  
Parallel Manipulator ◽  
Degrees Of Freedom ◽  
Least Squares Method ◽  
Coordinate Measuring Machine ◽  
Calibration Method ◽  
Displacement Sensor ◽  
Measurement Experiment ◽  
Measuring Machine ◽  
Measured Length ◽  
Three Degrees Of Freedom

This paper deals with parameter identification for a three-degrees-of-freedom (3-DOF) parallel manipulator, based on measurement redundancy. A redundant passive chain with a displacement sensor connects the moving stage to the machine frame. The passive chain is sequentially placed in three directions at approximately right angles to one another to reliably detect the motion of the stage. Linear encoders measure changes in lengths of the passive chain and the three actuated chains of the manipulator during traveling of the moving stage. Comparison between the measured length and the length calculated from forward kinematics of the 3-DOF manipulator reveals a length error of the passive chain. The least-squares method using a Jacobian matrix corrects 27 kinematic parameters so that the length errors of the passive chain are minimized. The above calculations were accomplished in both numerical simulations and experiments employing a coordinate measuring machine based on the parallel manipulator. Moreover, a length measurement simulation of gauge block measurement and a measurement experiment using the measuring machine were performed to verify the identified parameters.

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