Sensitivity Analysis of the Extended Bouc-Wen Model of Hysteresis

2005 ◽  
Author(s):  
F. Ma ◽  
A. Imam
Keyword(s):  
Sensitivity Analysis ◽  
Random Vibration ◽  
Relative Sensitivity ◽  
Nonlinear Damping ◽  
Sensitivity Analyses ◽  
Simplified Model ◽  
Hysteretic Model ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in random vibration. It is routinely used in the characterization of nonlinear damping and in system identification. In this paper, the differential model of hysteresis is carefully re-examined and two significant issues are uncovered. First, it is found that the unspecified parameters of the model are functionally redundant. One of the parameters can be eliminated through suitable transformations in the parameter space. Second, local and global sensitivity analyses are conducted to assess the relative sensitivity of each model parameter. Through extensive Monte Carlo simulations, it is found that some parameters of the hysteretic model are rather insensitive. If the values of these insensitive parameters are fixed, a greatly simplified model is obtained.

On Parametric Analysis of Differential Hysteresis

2003 ◽  
Author(s):  
F. Ma ◽  
C. H. Ng ◽  
H. Zhang ◽  
A. Bockstedte ◽  
G. C. Foliente ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Parametric Analysis ◽  
Relative Sensitivity ◽  
Nonlinear Damping ◽  
Sensitivity Analyses ◽  
Simplified Model ◽  
Hysteretic Model ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in mechanics. It is routinely used in the characterization of nonlinear damping and in system identification. In this paper, the differential model of hysteresis is carefully re-examined and two significant issues are uncovered. First, it is found that the unspecified parameters of the model are functionally redundant. One of the parameters can be eliminated through suitable transformations in the parameter space. Second, local and global sensitivity analyses are conducted to assess the relative sensitivity of each model parameter. Through extensive Monte Carlo simulations, it is found that some parameters of the hysteretic model are rather insensitive. If the values of these insensitive parameters are fixed, a greatly simplified model is obtained.

Parameter Analysis of the Differential Model of Hysteresis

2004 ◽  
Vol 71 (3) ◽  
pp. 342-349 ◽  
Author(s):  
F. Ma ◽  
H. Zhang ◽  
A. Bockstedte ◽  
G. C. Foliente ◽  
P. Paevere
Keyword(s):  
Monte Carlo ◽  
Relative Sensitivity ◽  
Nonlinear Damping ◽  
Parameter Analysis ◽  
Sensitivity Analyses ◽  
Simplified Model ◽  
Hysteretic Model ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in mechanics. It is routinely used in the characterization of nonlinear damping and in system identification. In this paper, the differential model of hysteresis is carefully reexamined and two significant issues are uncovered. First, it is shown that the unspecified parameters of the model are functionally redundant. One of the parameters can be eliminated through suitable transformations in the parameter space. Second, local and global sensitivity analyses are conducted to assess the relative sensitivity of each model parameter. Through extensive Monte Carlo simulations, it is found that some parameters of the hysteretic model are rather insensitive. If the values of these insensitive parameters are fixed, a greatly simplified model is obtained.

Parameter Sensitivity Analysis of the Extended Bouc-Wen Model of Hysteresis

2002 ◽  
Author(s):  
Haochuan Zhang ◽  
Fai Ma
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Simplified Model ◽  
Model Parameters ◽  
Hysteretic Model ◽  
Structural Damping ◽  
Parameter Sensitivity Analysis ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in computational mechanics. It is routinely used in the characterization of structural damping and in system identification. In this paper, the differential model of hysteresis is carefully re-examined and two significant issues are uncovered. First, it is found that the unspecified parameters of the model are not independent. One of the model parameters can be eliminated through suitable transformations in the parameter space. Second, through local and global sensitivity analysis, it is found that some parameters of the hysteretic model are rather insensitive. If these insensitive parameters are set to constant values, a greatly simplified model is obtained.

scholarly journals Global sensitivity analysis of parameter uncertainty in landscape evolution models

2018 ◽  
Vol 11 (12) ◽  
pp. 4873-4888 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Landscape Evolution ◽  
Relative Sensitivity ◽  
Function Approach ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Model Function ◽  
Sediment Yields ◽  
Statistical Measures

Abstract. The evaluation and verification of landscape evolution models (LEMs) has long been limited by a lack of suitable observational data and statistical measures which can fully capture the complexity of landscape changes. This lack of data limits the use of objective function based evaluation prolific in other modelling fields, and restricts the application of sensitivity analyses in the models and the consequent assessment of model uncertainties. To overcome this deficiency, a novel model function approach has been developed, with each model function representing an aspect of model behaviour, which allows for the application of sensitivity analyses. The model function approach is used to assess the relative sensitivity of the CAESAR-Lisflood LEM to a set of model parameters by applying the Morris method sensitivity analysis for two contrasting catchments. The test revealed that the model was most sensitive to the choice of the sediment transport formula for both catchments, and that each parameter influenced model behaviours differently, with model functions relating to internal geomorphic changes responding in a different way to those relating to the sediment yields from the catchment outlet. The model functions proved useful for providing a way of evaluating the sensitivity of LEMs in the absence of data and methods for an objective function approach.

scholarly journals Global Sensitivity Analysis of Parameter Uncertainty in Landscape Evolution Models

2017 ◽  
Author(s):  
Christopher J. Skinner ◽  
Tom J. Coulthard ◽  
Wolfgang Schwanghart ◽  
Marco J. Van De Wiel ◽  
Greg Hancock
Keyword(s):  
Sensitivity Analysis ◽  
Objective Function ◽  
Global Sensitivity Analysis ◽  
Landscape Evolution ◽  
Sensitivity Analyses ◽  
Large Set ◽  
Sediment Yields ◽  
Global Sensitivity ◽  
Environmental Models ◽  
Morris Method

Abstract. Landscape Evolution Models have a long history of use as exploratory models, providing greater understanding of the role large scale processes have on the long-term development of the Earth’s surface. As computational power has advanced so has the development and sophistication of these models. This has seen them applied at increasingly smaller scale and shorter-term simulations at greater detail. However, this has not gone hand-in-hand with more rigorous verifications that are commonplace in the applications of other types of environmental models- for example Sensitivity Analyses. This can be attributed to a paucity of data and methods available in order to calibrate, validate and verify the models, and also to the extra complexity Landscape Evolution Models represent – without these it is not possible to produce a reliable Objective Function against which model performance can be judged. To overcome this deficiency, we present a set of Model Functions – each representing an aspect of model behaviour – and use these to assess the relative sensitivity of a Landscape Evolution Model (CAESAR-Lisflood) to a large set of parameters via a global Sensitivity Analysis using the Morris Method. This novel combination of behavioural Model Functions and the Morris Method provides insight into which parameters are the greatest source of uncertainty in the model, and which have the greatest influence over different model behaviours. The method was repeated over two different catchments, showing that across both catchments and across most model behaviours the choice of Sediment Transport formula was the dominate source of uncertainty in the CAESAR-Lisflood model, although there were some differences between the two catchments. Crucially, different parameters influenced the model behaviours in different ways, with Model Functions related to internal geomorphic changes responding in different ways to those related to sediment yields from the catchment outlet. This method of behavioural sensitivity analysis provides a useful method of assessing the performance of Landscape Evolution Models in the absence of data and methods for an Objective Function approach.

A simple sensitivity analysis of the turbulence-induced flocculation model of cohesive sediment

2019 ◽  
Vol 79 (6) ◽  
pp. 1144-1151 ◽  
Author(s):  
Zhongfan Zhu
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Fractal Dimension ◽  
Cohesive Sediment ◽  
Sensitivity Analyses ◽  
Model Output ◽  
Global Sensitivity ◽  
Two Parameters ◽  
Breakup Parameter

Abstract In this study, the local and global sensitivity analyses of the Winterwerp model to the input parameters have been carried out using the Garson algorithm and the PaD2 method by virtue of an artificial neural network. The main results of the sensitivity analyses are that the model is most sensitive to the breakup parameter and that only two parameters (the floc aggregation and breakup parameters) are significant. The result that the model output is less sensitive to the choice of fractal dimension seems to imply that the modification work on the fractal dimension might be unnecessary.

scholarly journals Global Sensitivity Analysis of Attenuation Zones of 2D Periodic Foundations

2021 ◽  
Author(s):  
Xinnan Liu ◽  
Yuan Tian ◽  
Yihe Wang ◽  
Yiqiang Ren ◽  
Xiaoruan Song
Keyword(s):  
Sensitivity Analysis ◽  
Lower Bound ◽  
Initial Stress ◽  
Upper Bound ◽  
Filling Ratio ◽  
Sensitivity Analyses ◽  
Relative Importance ◽  
Global Sensitivity ◽  
Input Parameters ◽  
Periodic Constant

In this paper, global sensitivity analyses of attenuation zones of 2D periodic foundations are conducted. Global sensitivity analyses of upper bound frequency and lower bound frequency of the 1st attenuation zone of 2D periodic foundation are conducted considering four input parameters, i.e., initial stress ratio, filling ratio of core, filling ratio of resonator and periodic constant. Interactions and relative importance of input parameters are calculated.

Global Sensitivity Analysis of Optimal Climate Policies

2020 ◽  
Author(s):  
Alena Miftakhova
Keyword(s):  
Sensitivity Analysis ◽  
High Efficiency ◽  
Global Sensitivity Analysis ◽  
Economic Modeling ◽  
Sensitivity Analyses ◽  
Global Sensitivity ◽  
Computational Costs ◽  
Climate Policies ◽  
Careful Treatment ◽  
Polynomial Chaos Expansions

<p><span>A major tool that supports climate policy decisions, integrated assessment models are highly vulnerable to their initial assumptions and calibrations. Despite the broad literature rich in both single-model and multi-model sensitivity analyses, universal, well-established practices are still missing in this field. This paper endorses structured global sensitivity analysis (GSA) as an indispensable routine in climate–economic modeling. An application of a high-efficiency GSA method based on polynomial chaos expansions to DICE provides two insights. First, only global and comprehensive—as opposed to local or selective—sensitivity analysis delivers a trustworthy picture of the uncertainty propagated through the model. Second, careful treatment of the model’s structure throughout the analysis reconciles the results with established analytical insights—enhancing these insights with more details. The efficient GSA method provides a comprehensive decomposition of the uncertainty in a model’s output while minimizing computational costs, and is hence potentially applicable to models of higher complexity.</span></p>

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