scholarly journals Substructure Model Updating Through Iterative Convex Optimization

2012 ◽  
Author(s):  
Dapeng Zhu ◽  
Yang Wang
Keyword(s):  
Convex Optimization ◽  
Structural Model ◽  
Model Updating ◽  
Dominant Mode ◽  
Mode Shapes ◽  
Fe Model ◽  
Mass Model ◽  
Actual Structure ◽  
Residual Structure ◽  
Fe Model Updating

In order to obtain a more accurate finite element (FE) model for a built structure, experimental data collected from the actual structure can be used to update the FE model. This process is known as FE model updating. Numerous FE model updating algorithms have been developed in the past few decades. However, most existing algorithms suffer computational challenges, particularly when applied to a large structure with dense measurements. The reason is these approaches usually operate on a relatively complicated model for the entire structure. To address this issue, a substructure updating approach is presented in this paper. The Craig-Bampton theory is adopted to condense the entire structural model into a substructure (currently being analyzed) and a residual structure. Dynamic response of the residual structure is approximated using only a limited number of dominant mode shapes. To improve the convergence of this substructure approach for model updating, an iterative convex optimization procedure is developed and validated through numerical simulation with a 200 degrees-of-freedom spring-mass model. The proposed substructure model updating is shown to successfully detect the locations and severities of simulated damage.

A Method for FE Model Updating of Coupled Vibro-Acoustic Systems Using Constrained Optimization

2013 ◽  
Author(s):  
D. V. Nehete ◽  
S. V. Modak ◽  
K. Gupta
Keyword(s):  
Finite Element ◽  
Constrained Optimization ◽  
Model Updating ◽  
Numerical Study ◽  
Element Model ◽  
Rectangular Cavity ◽  
Mode Shapes ◽  
Fe Model ◽  
Structural Dynamic ◽  
Fe Model Updating

Finite element (FE) model updating is now recognized as an effective approach to reduce modeling inaccuracies present in an FE model. FE model updating has been researched and studied well for updating FE models of purely structural dynamic systems. However there exists another class of systems known as vibro-acoustics in which acoustic response is generated in a medium due to the vibration of enclosing structure. Such systems are commonly found in aerospace, automotive and other transportation applications. Vibro-acoustic FE modeling is essential for sound acoustic design of these systems. Vibro-acoustic system, in contrast to purely structural system, has not received sufficient attention from FE model updating perspective and hence forms the topic of present paper. In the present paper, a method for finite element model updating of coupled structural acoustic model, constituted as a problem of constrained optimization, is proposed. An objective function quantifying error in the coupled natural frequencies and mode shapes is minimized to estimate the chosen uncertain parameters of the system. The effectiveness of the proposed method is validated through a numerical study on a 3D rectangular cavity attached to a flexible panel. The material property and the stiffness of joints between the panel and rectangular cavity are used as updating parameters. Robustness of the proposed method under presence of noise is investigated. It is seen that the method is not only able to obtain a close match between FE model and corresponding ‘measured’ vibro-acoustic characteristics but is also able to estimate the correction factors to the updating parameters with reasonable accuracy.

Dynamics Modeling and Analysis of Riveted Mainframe Computer Structure

2017 ◽  
Author(s):  
Budy Notohardjono ◽  
Richard Ecker ◽  
Shawn Canfield
Keyword(s):  
Test Data ◽  
Dominant Mode ◽  
Mode Shapes ◽  
Dynamic Mode ◽  
Fe Model ◽  
Dynamics Modeling ◽  
Actual Structure ◽  
Beam Elements ◽  
Mainframe Computer ◽  
Riveted Joints

A typical mainframe computer rack is narrow, tall and long. In certain installations, during its functional operation, the server can be subjected to earthquake events. The rack is a steel structure joined together with steel rivets. One of the rack’s functions is to protect the critical components such as the processor, input-output and storage drawers from excessive motion by minimizing the amount of deflection. The riveted joints pose a challenge in accurately representing more than three thousand joints in a finite element (FE) model. In the FE model, bonding together sheet metal regions around the rivet joints will lead to a significantly stiffer model than the actual structure. On the other hand, an accurate representation of the riveted joints will lead to a better representation of the dynamic response of the server rack under vertical and horizontal loadings. This paper presents a method of analyzing rivet joints. The rivet joints are represented by beam elements with cylindrical cross-sections in the FE model. This is accomplished by identifying two parallel or overlapping plates and inserting discrete beam elements at the riveted joint. This method will be used to predict the dynamics modes of the structure. To validate the FE model, a prototype server rack was subjected to side to side vibration tests. A sine sweep vibration test identifies dominant mode shapes and the transmissibility of the input vibration. The results of the tests on the prototype rack serve as input for FE model refinement. The test data show that representing the riveted joints with beams does provide results that closely match the actual test data. A validated FE model will be used to evaluate dominant vibration modes for several configurations of rack weight as well as configurations to stiffen the structure in the side to side direction. The dynamic mode shapes visualize the effect of stiffening brackets on dominant frequencies of the rack. The optimal stiffening design will be the one that results in the minimum deflection under the standard testing profile.

scholarly journals One-Step FE Model Updating Using Local Correspondence and Mode Shape Orthogonality

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Martin Ø. Ø. Jull ◽  
Sandro D. R. Amador ◽  
Anders Skafte ◽  
Jannick B. Hansen ◽  
Manuel L. Aenlle ◽  
...  
Keyword(s):  
Mode Shape ◽  
Model Updating ◽  
Low Frequency ◽  
Mode Shapes ◽  
Fe Model ◽  
Modal Assurance Criterion ◽  
Mass Scaling ◽  
The Matrix ◽  
One Step ◽  
Fe Model Updating

In this paper, it is described how the matrix mixing model updating technique can be combined with the local correspondence (LC) mode shape expansion algorithm, to give a new finite element (FE) model updating method. The matrix mixing method uses that the inverse mass and stiffness matrices can be expressed as a linear combination of outer products of FE mode shape vectors, where the low-frequency part of these sums are substituted with expanded test modes. The approach is meant to update FE models in one-step and is exact, except for the following two approximations: the mode shape smoothing and the mass scaling of the expanded experimental mode shapes. A simulation study illustrates the errors from the two approximations and shows the ability of the technique to improve the modal assurance criterion (MAC) values so that they get very close to unity. Finally, the performance of the proposed updating method is assessed by means of an application example in which the FE model is updated based on the test modes of a real structure.

scholarly journals Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3057 ◽  
Author(s):  
Parisa Asadollahi ◽  
Yong Huang ◽  
Jian Li
Keyword(s):  
Bayesian Inference ◽  
Prediction Error ◽  
Structural Model ◽  
Model Updating ◽  
Real Data ◽  
Model Parameters ◽  
Fe Model ◽  
Inference Method ◽  
Bayesian Inference Method ◽  
Fe Model Updating

We focus on a Bayesian inference framework for finite element (FE) model updating of a long-span cable-stayed bridge using long-term monitoring data collected from a wireless sensor network (WSN). A robust Bayesian inference method is proposed which marginalizes the prediction-error precisions and applies Transitional Markov Chain Monte Carlo (TMCMC) algorithm. The proposed marginalizing error precision is compared with other two treatments of prediction-error precisions, including the constant error precisions and updating error precisions through theoretical analysis and numerical investigation based on a bridge FE model. TMCMC is employed to draw samples from the posterior probability density function (PDF) of the structural model parameters and the uncertain prediction-error precision parameters if required. It is found that the proposed Bayesian inference method with prediction-error precisions marginalized as “nuisance” parameters produces an FE model with more accurate posterior uncertainty quantification and robust modal property prediction. When applying the identified modal parameters from acceleration data collected during a one-year period from the large-scale WSN on the bridge, we choose two candidate model classes using different parameter grouping based on the clustering results from a sensitivity analysis and apply Bayes’ Theorem at the model class level. By implementing the TMCMC sampler, both the posterior distributions of the structural model parameters and the plausibility of the two model classes are characterized given the real data. Computation of the posterior probabilities over the candidate model classes provides a procedure for Bayesian model class assessment, where the computation automatically implements Bayesian Ockham razor that trades off between data-fitting and model complexity, which penalizes model classes that “over-fit” the data. The results of FE model updating and assessment based on the real data using the proposed method show that the updated FE model can successfully predict modal properties of the structural system with high accuracy.

scholarly journals Vision-Based Structural FE Model Updating Using Genetic Algorithm

2021 ◽  
Vol 11 (4) ◽  
pp. 1622
Author(s):  
Gun Park ◽  
Ki-Nam Hong ◽  
Hyungchul Yoon
Keyword(s):  
Genetic Algorithm ◽  
Model Updating ◽  
Structural Parameters ◽  
Fe Model ◽  
Structural Members ◽  
Stiffness Reduction ◽  
Before And After ◽  
Scale Test ◽  
Fe Model Updating ◽  
Story Building

Structural members can be damaged from earthquakes or deterioration. The finite element (FE) model of a structure should be updated to reflect the damage conditions. If the stiffness reduction is ignored, the analysis results will be unreliable. Conventional FE model updating techniques measure the structure response with accelerometers to update the FE model. However, accelerometers can measure the response only where the sensor is installed. This paper introduces a new computer-vision based method for structural FE model updating using genetic algorithm. The system measures the displacement of the structure using seven different object tracking algorithms, and optimizes the structural parameters using genetic algorithm. To validate the performance, a lab-scale test with a three-story building was conducted. The displacement of each story of the building was measured before and after reducing the stiffness of one column. Genetic algorithm automatically optimized the non-damaged state of the FE model to the damaged state. The proposed method successfully updated the FE model to the damaged state. The proposed method is expected to reduce the time and cost of FE model updating.

scholarly journals Damage Diagnosis in 3D Structures Using a Novel Hybrid Multiobjective Optimization and FE Model Updating Framework

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Nizar Faisal Alkayem ◽  
Maosen Cao ◽  
Minvydas Ragulskis
Keyword(s):  
Structural Damage ◽  
Model Updating ◽  
Complex Problem ◽  
Dynamic Responses ◽  
Mode Shapes ◽  
Fe Model ◽  
Modal Strain Energy ◽  
3D Structures ◽  
Multiobjective Particle Swarm Optimization ◽  
Damage Tracking

Structural damage detection is a well-known engineering inverse problem in which the extracting of damage information from the dynamic responses of the structure is considered a complex problem. Within that area, the damage tracking in 3D structures is evaluated as a more complex and difficult task. Swarm intelligence and evolutionary algorithms (EAs) can be well adapted for solving the problem. For this purpose, a hybrid elitist-guided search combining a multiobjective particle swarm optimization (MOPSO), Lévy flights (LFs), and the technique for the order of preference by similarity to ideal solution (TOPSIS) is evolved in this work. Modal characteristics are employed to develop the objective function by considering two subobjectives, namely, modal strain energy (MSTE) and mode shape (MS) subobjectives. The proposed framework is tested using a well-known benchmark model. The overall strong performance of the suggested method is maintained even under noisy conditions and in the case of incomplete mode shapes.

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