Model Updating of Model Parameters and Model Form Error in a Uniform Framework

2019 ◽  
Author(s):  
Sifeng Bi ◽  
Nils Wagner ◽  
Michael Beer ◽  
Morvan Ouisse
Keyword(s):  
Model Updating ◽  
Model Parameters ◽  
Form Error ◽  
Model Form

Representation of bolted joints in a structure using finite element modelling and model updating

2020 ◽  
Vol 14 (3) ◽  
pp. 7141-7151 ◽  
Author(s):  
R. Omar ◽  
M. N. Abdul Rani ◽  
M. A. Yunus
Keyword(s):  
Finite Element ◽  
Dynamic Behaviour ◽  
Model Updating ◽  
Complex Structure ◽  
Bolted Joints ◽  
Model Parameters ◽  
Fe Model ◽  
Bolted Joint ◽  
Fe Modelling ◽  
Joint Structure

Efficient and accurate finite element (FE) modelling of bolted joints is essential for increasing confidence in the investigation of structural vibrations. However, modelling of bolted joints for the investigation is often found to be very challenging. This paper proposes an appropriate FE representation of bolted joints for the prediction of the dynamic behaviour of a bolted joint structure. Two different FE models of the bolted joint structure with two different FE element connectors, which are CBEAM and CBUSH, representing the bolted joints are developed. Modal updating is used to correlate the two FE models with the experimental model. The dynamic behaviour of the two FE models is compared with experimental modal analysis to evaluate and determine the most appropriate FE model of the bolted joint structure. The comparison reveals that the CBUSH element connectors based FE model has a greater capability in representing the bolted joints with 86 percent accuracy and greater efficiency in updating the model parameters. The proposed modelling technique will be useful in the modelling of a complex structure with a large number of bolted joints.

Application of a FRF Based Model Updating Technique for the Validation of Finite Element Models of Components of the Automotive Industry

1995 ◽  
Author(s):  
Stefan Lammens ◽  
Marc Brughmans ◽  
Jan Leuridan ◽  
Ward Heylen ◽  
Paul Sas
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Automotive Industry ◽  
Stiffness Matrix ◽  
Model Updating ◽  
Dynamic Stiffness ◽  
Element Model ◽  
Model Parameters ◽  
Finite Element Models ◽  
Analytical Dynamic

Abstract This paper presents two applications of the RADSER model updating technique (Lammens et al. (1995) and Larsson (1992)). The RADSER technique updates finite element model parameters by solution of a linearised set of equations that optimise the Reduced Analytical Dynamic Stiffness matrix based on Experimental Receptances. The first application deals with the identification of the dynamic characteristics of rubber mounts. The second application validates a coarse finite element model of a subframe of a Volvo 480.

Experimental Non-Linear Model Updating Applied in Cylindrical Journal Bearings

2001 ◽  
Author(s):  
Katia Lucchesi Cavalca ◽  
Sérgio Junichi Idehara ◽  
Franco Giuseppe Dedini ◽  
Robson Pederiva
Keyword(s):  
Linear Model ◽  
Model Updating ◽  
Optimization Method ◽  
Model Parameters ◽  
Numeric Model ◽  
Rotating Systems ◽  
Non Linear ◽  
Symmetric Rotor ◽  
Set Up ◽  
Experimental Values

Abstract The present paper proposes the use of non linear model updating applying unrestricted optimization method, in order to obtain a methodology, which allows the calibration of mathematical models in rotating systems. An experimental set up for this purpose consists of a symmetric rotor, on a rigid foundation supported by two hidrodynamic cylindrical bearings and with a central disk of considerable mass, working as na unbalancing excitation force. Once the numeric and experimental values are obtained, error vectors are defined, which are the minimization parameters, through the variation of the numeric model parameters. The method presented satisfactory results, as it was able to calibrate the mathematical model, and then to obtain reliable responses for the physical system studied. The research also presents a contribution for the rotating machine desing area as it presents a relatively simple methodology on the updating and revalidation of computacional models for machines and structures.

scholarly journals Probabilistic Updating of Structural Models for Damage Assessment Using Approximate Bayesian Computation

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3197 ◽  
Author(s):  
Zhouquan Feng ◽  
Yang Lin ◽  
Wenzan Wang ◽  
Xugang Hua ◽  
Zhengqing Chen
Keyword(s):  
Damage Assessment ◽  
Approximate Bayesian Computation ◽  
Model Updating ◽  
Likelihood Function ◽  
Probabilistic Approach ◽  
Model Parameters ◽  
Bayesian Computation ◽  
The Novel ◽  
Subset Simulation ◽  
Approximate Bayesian

A novel probabilistic approach for model updating based on approximate Bayesian computation with subset simulation (ABC-SubSim) is proposed for damage assessment of structures using modal data. The ABC-SubSim is a likelihood-free Bayesian approach in which the explicit expression of likelihood function is avoided and the posterior samples of model parameters are obtained using the technique of subset simulation. The novel contributions of this paper are on three fronts: one is the introduction of some new stopping criteria to find an appropriate tolerance level for the metric used in the ABC-SubSim; the second one is the employment of a hybrid optimization scheme to find finer optimal values for the model parameters; and the last one is the adoption of an iterative approach to determine the optimal weighting factors related to the residuals of modal frequency and mode shape in the metric. The effectiveness of this approach is demonstrated using three illustrative examples.

Updating Predictive Models: Calibration, Bias Correction and Identifiability

2010 ◽  
Author(s):  
Paul D. Arendt ◽  
Wei Chen ◽  
Daniel W. Apley
Keyword(s):  
Computer Model ◽  
Bayesian Approach ◽  
Bias Correction ◽  
Process Model ◽  
Model Updating ◽  
Future Research ◽  
Model Parameters ◽  
Sources Of Uncertainty ◽  
Uncertain Model ◽  
Many Sources

Model updating, which utilizes mathematical means to combine model simulations with physical observations for improving model predictions, has been viewed as an integral part of a model validation process. While calibration is often used to “tune” uncertain model parameters, bias-correction has been used to capture model inadequacy due to a lack of knowledge of the physics of a problem. While both sources of uncertainty co-exist, these two techniques are often implemented separately in model updating. This paper examines existing approaches to model updating and presents a modular Bayesian approach as a comprehensive framework that accounts for many sources of uncertainty in a typical model updating process and provides stochastic predictions for the purpose of design. In addition to the uncertainty in the computer model parameters and the computer model itself, this framework accounts for the experimental uncertainty and the uncertainty due to the lack of data in both computer simulations and physical experiments using the Gaussian process model. Several challenges are apparent in the implementation of the modular Bayesian approach. We argue that distinguishing between uncertain model parameters (calibration) and systematic inadequacies (bias correction) is often quite challenging due to an identifiability issue. We present several explanations and examples of this issue and bring up the needs of future research in distinguishing between the two sources of uncertainty.

Macro-modeling of spot weld strength and failure

2017 ◽  
Vol 34 (3) ◽  
pp. 941-959 ◽  
Author(s):  
Rim Chtourou ◽  
Nicolas Leconte ◽  
Bassem Zouari ◽  
Fahmi Chaari ◽  
Eric Markiewicz ◽  
...  
Keyword(s):  
Model Updating ◽  
Spot Weld ◽  
Sequential Optimization ◽  
Weld Strength ◽  
Model Parameters ◽  
Content Type ◽  
Pull Out ◽  
Macro Modeling ◽  
Lap Shear ◽  
Optimization Methodology

Purpose This paper aims to propose a macro modeling approach to simulate the mechanical behavior and the failure of spot welded joints in structural crashworthiness computations. Design/methodology/approach A connector element is proposed to simulate the behavior and failure of spot weld joints. An elastic-plastic damageable model is used to describe the non-linear response and rupture. The connector model involves several parameters that have to be defined. Some are directly identified based on mechanical interpretations and experimental tests characteristics. The remaining parameters are identified through a finite element model updating approach using Arcan tests. Resulting from a sensitivity analysis, an original two steps optimization methodology, using the Modes I and II Arcan tests results sequentially, has been implemented to identify the remaining model parameters. Findings The numerical results for Arcan tests in mixed Modes I/II are in a good agreement with the experimental ones. The model is also validated on tensile pull-out, single lap shear and coach-peel tests. Originality/value By comparison with previous published results, the proposed model brings a significant improvement. The main innovative aspects of this work are as follows: the proposed formulation, a reduced number of parameters to optimize, an original sequential optimization methodology based on physical and mechanical analyses and a mesh size independent connector element.

scholarly journals Rotor Model Updating and Validation for an Active Magnetic Bearing Based High-Speed Machining Spindle

2012 ◽  
Vol 134 (12) ◽  
Author(s):  
Adam C. Wroblewski ◽  
Jerzy T. Sawicki ◽  
Alexander H. Pesch
Keyword(s):  
Transfer Functions ◽  
Model Updating ◽  
Dynamic Properties ◽  
Magnetic Bearing ◽  
Mode Shapes ◽  
Model Parameters ◽  
Successful Implementation ◽  
Fe Model ◽  
Rotor Model ◽  
Machining Spindle

This paper presents an experimentally driven model updating approach to address the dynamic inaccuracy of the nominal finite element (FE) rotor model of a machining spindle supported on active magnetic bearings. Modeling error is minimized through the application of a numerical optimization algorithm to adjust appropriately selected FE model parameters. Minimizing the error of both resonance and antiresonance frequencies simultaneously accounts for rotor natural frequencies as well as for their mode shapes. Antiresonance frequencies, which are shown to heavily influence the model’s dynamic properties, are commonly disregarded in structural modeling. Evaluation of the updated rotor model is performed through comparison of transfer functions measured at the cutting tool plane, which are independent of the experimental transfer function data used in model updating procedures. Final model validation is carried out with successful implementation of robust controller, which substantiates the effectiveness of the model updating methodology for model correction.

scholarly journals Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Zheng ◽  
Yi Yu
Keyword(s):  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Truss Structures ◽  
Future Research ◽  
Model Parameters ◽  
Probabilistic Framework ◽  
Parameter Uncertainties ◽  
Truss Bridges ◽  
Uncertain Model

The vibration-based structural health monitoring has been traditionally implemented through the deterministic approach that relies on a single model to identify model parameters that represent damages. When such approach is applied for truss bridges, truss joints are usually modeled as either simple hinges or rigid connections. The former could lead to model uncertainties due to the discrepancy between physical configurations and their mathematical models, while the latter could induce model parameter uncertainties due to difficulty in obtaining accurate model parameters of complex joint details. This paper is to present a new perspective for addressing uncertainties associated with truss joint configurations in damage identification based on Bayesian probabilistic model updating and model class selection. A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. This method can not only draw samples which approximate the updated probability distributions of uncertain model parameters but also provide model evidence that quantify probabilities of uncertain model classes. The proposed probabilistic framework and its applicability for addressing joint uncertainties are illustrated and examined with an application example. Future research directions in this field are discussed.

Inclusion of Model Errors in Reliability-Based Optimization

2006 ◽  
Vol 128 (4) ◽  
pp. 936-944 ◽  
Author(s):  
Sankaran Mahadevan ◽  
Ramesh Rebba
Keyword(s):  
Computational Models ◽  
Approximation Error ◽  
Richardson Extrapolation ◽  
Model Errors ◽  
Form Error ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Model Form ◽  
Reliability Based Optimization ◽  
Richardson Extrapolation Method

This paper proposes a methodology to estimate errors in computational models and to include them in reliability-based design optimization (RBDO). Various sources of uncertainties, errors, and approximations in model form selection and numerical solution are considered. The solution approximation error is quantified based on the model itself, using the Richardson extrapolation method. The model form error is quantified based on the comparison of model prediction with physical observations using an interpolated resampling approach. The error in reliability analysis is also quantified and included in the RBDO formulation. The proposed methods are illustrated through numerical examples.

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