Extensions of the Modal Assurance Criterion

1990 ◽  
Vol 112 (4) ◽  
pp. 468-472 ◽  
Author(s):  
W. Heylen ◽  
T. Janter
Keyword(s):  
Model Updating ◽  
Measurement Model ◽  
Vector Spaces ◽  
Modal Assurance Criterion ◽  
Dynamic Data ◽  
Approximate Eigenvalue ◽  
Improve Model ◽  
Element Matrix ◽  
Model Correlation ◽  
Eigenvalue Solvers

The modal assurance criterion (MAC) in general measures the degree of proportion between two (modal) vectors, in the form of a correlation coefficient of a least squares ratio estimate. The MAC principle can be extended in several ways, thus increasing its field of applications. The partial MAC (PMAC) correlates parts of (modal) vectors. The spatial MAC (SMAC) allows to compare different vector spaces. Furthermore this paper suggests a way of calculating the MAC sensitivities to model changes. All those extensions are illustrated by their possible uses in correlating measured dynamic data with (finite element) matrix models and in the area of model updating. Those applications might be helpful tools to indicate regions of poor measurement-model correlation, to complete measured vectors, to judge approximate eigenvalue solvers, or to improve model updating procedures.

Scheme of Model Updating and Implement for Structural Dynamics Analysis

2012 ◽  
Vol 252 ◽  
pp. 140-143
Author(s):  
Dong Sheng Yao ◽  
Li Bin Zhao
Keyword(s):  
Cylindrical Shell ◽  
Shell Structure ◽  
Structural Model ◽  
Model Updating ◽  
Modal Assurance Criterion ◽  
Mass System ◽  
Actual Structure ◽  
Modal Data ◽  
Global And Local ◽  
Good Agreement

Model updating techniques are used to modify structural model for more accurate predictions of dynamics behavior. A simple survey on the model updating methods and correlation criteria is presented. Based on the inverse eigensensitivity method (IESM) and modal assurance criterion (MAC), a scheme of model updating for structures is presented and realized by user defined subroutine combined with APDL in commercial software ANSYS®. A four-DOF spring-mass system is assumed and updated, from which the predicted frequencies and MAC values are satisfied compared to the actual dynamics characteristics. This gives evidence that the presented model updating scheme is feasible and efficient. Furthermore, a cylindrical shell structure containing global and local modal information is established to research the updating ability of the scheme on some focused local modal information. The results due to the updated model of cylindrical shell structure show that not only the global modal data but also the local modal data have a good agreement with that of the actual structure.

A Correlation Improvement Technique for Model Updating of Structures

2016 ◽  
Vol 16 (08) ◽  
pp. 1550049 ◽  
Author(s):  
Fatih Altunel ◽  
Mehmet Çelik ◽  
Mehmet Çalişkan
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Model Updating ◽  
Element Model ◽  
Modal Assurance Criterion ◽  
Element Analysis ◽  
New Correlation ◽  
The Finite Element Analysis ◽  
Node Removal ◽  
The Finite Element Model

This study proposes a new correlation improvement technique for the optimum node removal location to get improved modal assurance criterion (MAC) matrix. The technique is applied to updating of the finite element model (FEM) of a structure. The developed routine is tried on a utility helicopter. It is proven that it is capable of showing better performance than the coordinate MAC (coMAC), commonly used in such analyses. Commercial software is utilized for the finite element analysis of the helicopter fuselage and tail. Experimental modal analyses are also performed for updating the model for tail of the helicopter to demonstrate the effectiveness of the new technique.

scholarly journals The misleading certainty of uncertain data in biological network processes

2021 ◽  
Author(s):  
Michael Irvin ◽  
Arvind Ramanathan ◽  
Carlos F Lopez
Keyword(s):  
Machine Learning ◽  
Cell Biology ◽  
Quantitative Data ◽  
Ordinal Data ◽  
Mechanistic Model ◽  
Uncertain Data ◽  
Network Models ◽  
Measurement Model ◽  
Biochemical Network ◽  
Improve Model

Mathematical models are often used to study the structure and dynamics of network-driven cellular processes. In cell biology, models representing biochemical reaction networks have provided significant insights but are often plagued by a dearth of available quantitative data necessary for simulation and analysis. This has in turn led to questions about the usefulness of biochemical network models with unidentifiable parameters and high-degree of parameter sloppiness. In response, approaches to incorporate highly-available non-quantitative data and use this data to improve model certainty have been undertaken with various degrees of success. Here we employ a Bayesian inference and Machine Learning approach to first explore how quantitative and non-quantitative data can constrain a mechanistic model of apoptosis execution, in which all models can be identified. We find that two orders of magnitude more ordinal data measurements than those typically collected are necessary to achieve the same accuracy as that obtained from a quantitative dataset. We also find that ordinal and nominal non-quantitative data on their own can be combined to reduce model uncertainty and thus improve model accuracy. Further analysis demonstrates that the accuracy and certainty of model predictions strongly depends on accurate formulations of the measurement as well as the size and make-up of the nonquantitative datasets. Finally, we demonstrate the potential of a data-driven Machine Learning measurement model to identify informative mechanistic features that predict or define nonquantitative cellular phenotypes, from a systems perspective.

Structural model updating using dynamic data

2014 ◽  
Vol 4 (3) ◽  
pp. 177-194 ◽  
Author(s):  
Antonio Javier García-Palencia ◽  
Erin Santini-Bell
Keyword(s):  
Structural Model ◽  
Model Updating ◽  
Dynamic Data

scholarly journals A Sequential Framework for Improving Identifiability of FE Model Updating using Static and Dynamic Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5099 ◽  
Author(s):  
Kim ◽  
Kim ◽  
Park ◽  
Jin
Keyword(s):  
Experimental Study ◽  
Objective Function ◽  
Model Updating ◽  
Heterogeneous Data ◽  
Fe Model ◽  
Validation Data ◽  
Dynamic Data ◽  
Parameter Identifiability ◽  
Fe Model Updating ◽  
Parameter Compensation

By virtue of the advances in sensing techniques, finite element (FE) model updating (FEMU) using static and dynamic data has been recently employed to improve identification on updating parameters. Using heterogeneous data can provide useful information to improve parameter identifiability in FEMU. It is worth noting that the useful information from the heterogeneous data may be diluted in the conventional FEM framework. The conventional FEMU framework in previous studies have used heterogeneous data at once to compute residuals in the objective function, and they are condensed to be a scalar. In this implementation, it should be careful to formulate the objective function with proper weighting factors to consider the scale of measurement and relative significances. Otherwise, the information from heterogeneous data cannot be efficiently utilized. For FEMU of the bridge, parameter compensation may exist due to mutual dependence among updating parameters. This aggravates the parameter identifiability to make the results of the FEMU worse. To address the limitation of the conventional FEMU method, this study proposes a sequential framework for the FEMU of existing bridges. The proposed FEMU method uses two steps to utilize static and dynamic data in a sequential manner. By using them separately, the influence of the parameter compensation can be suppressed. The proposed FEMU method is verified through numerical and experimental study. Through these verifications, the limitation of the conventional FEMU method is investigated in terms of parameter identifiability and predictive performance. The proposed FEMU method shows much smaller variabilities in the updating parameters than the conventional one by providing the better predictions than those of the conventional one in calibration and validation data. Based on numerical and experimental study, the proposed FEMU method can improve the parameter identifiability using the heterogeneous data and it seems to be promising and efficient framework for FEMU of the existing bridge.

Structural model updating of an in-service bridge using dynamic data

2015 ◽  
Vol 22 (10) ◽  
pp. 1265-1281 ◽  
Author(s):  
Antonio J. Garcia-Palencia ◽  
Erin Santini-Bell ◽  
Jesse D. Sipple ◽  
Masoud Sanayei
Keyword(s):  
Structural Model ◽  
Model Updating ◽  
Dynamic Data
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