Finite element model updating of an experimental vehicle model using measured modal characteristics

Methods for modal identification and structural model updating are employed to develop high fidelity finite element models of an experimental vehicle model using acceleration measurements. The identification of modal characteristics of the vehicle is based on ac-celeration time histories obtained from impulse hammer tests. An available modal identification software is used to obtain the modal characteristics from the analysis of the various sets of vibration measurements. A high modal density modal model is obtained. The modal characteristics are then used to update an increasingly complex set of finite element models of the vehicle. A multi-objective structural identification method is used for estimating the parameters of the finite element structural models based on minimizing the modal residu-als. The method results in multiple Pareto optimal structural models that are consistent with the measured modal data and the modal residuals used to measure the discrepancies between the measured modal values and the modal values predicted by the model. Single objective structural identification methods are also evaluated as special cases of the proposed multi-objective identification method. The multi-objective framework and the corresponding compu-tational tools provide the whole spectrum of optimal models and can thus be viewed as a gen-eralization of the available conventional methods. The results indicate that there is wide variety of Pareto optimal structural models that trade off the fit in various measured quanti-ties. These Pareto optimal models are due to uncertainties arising from model and measure-ment errors. The size of the observed variations depends on the information contained in the measured data, as well as the size of model and measurement errors. The effectiveness of the updated models and the predictive capabilities of the Pareto vehicle models are assessed

Integrated Reverse Engineering Strategy for Large-Scale Mechanical Systems: Application to a Steam Turbine Rotor

An integrated reverse engineering methodology is proposed for a large-scale fully operational steam turbine rotor, considering issues that include developing the CAD and FE model of the structure, as well as the applicability of model updating techniques based on experimental modal analysis procedures. First, using an integrated reverse engineering strategy, the digital shape of the three sections of a steam turbine rotor was designed and the final parametric CAD model was developed. The finite element model of the turbine was developed using tetrahedral solid elements resulting in fifty-five million DOFs. Imposing impulsive loading in a free-free state, measured acceleration time histories were used to obtain the dynamic responses and identify the modal characteristics of each section of the complete steam turbine. Experimentally identified modal modes and modal frequencies compared to the FE model predicted ones constitute the actual measure of fit. CMA-ES optimization algorithm is then implemented in order to finely tune material parameters, such as modulus of elasticity and density, in order to best match experimental and numerical data. Comparing numerical and experimental results verified the reliability and accuracy of the applied methodology. The identified finite element model is representative of the initial structural condition of the turbine and is used to develop a simplified finite element model, which then used for the turbine rotordynamic analysis. Accumulated knowledge of the dynamic behavior of the specific steam turbine system, could be implemented in order to evaluate stability or instability states, fatigue growth in the turbine blades, changes in the damping of the bearing system and perform necessary scheduled optimal and cost-effective maintenance strategies. Additionally, upon a series of scheduled experimental data collection, a permanent output-only vibration SHM system could be installed and even a proper dynamic balancing could be investigated and designed

Vibration-Based Damage Detection Using Finite Element Modeling and the Metaheuristic Particle Swarm Optimization Algorithm

The continuous development of new materials and larger and/or more complex structures drives the need for the development of more robust, accurate, and sensitive Structural Health Monitoring (SHM) techniques. In the present work, a novel vibration-based damage-detection method that contributes into the SHM field is presented using Metaheuristic algorithms coupled with optimal Finite Element Models that can effectively localize damage. The proposed damage-detection framework can be applied in any kind of detailed structural FE model, while requiring only the output information of the dynamic response of the structure. It can effectively localize damage in a structure by highlighting not only the affected part of the structure but also the specific damaged area inside the part. First, the optimal FE model of the healthy structure is developed using appropriate FE model updating techniques and experimental vibration measurements, simulating the undamaged condition. Next, the main goal of the proposed method is to create a damaged FE model that approximates the dynamic response of the damaged structure. To achieve this, a parametric area is inserted into the FE model, changing stiffness and mass to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed damage-detection framework. On this specific implementation of the framework, the Particle Swarm Optimization (PSO) algorithm is selected which has been used for a wide variety of optimization problems in the past. On the PSO’s search space, two parameters control the stiffness and mass of the damaged area while additional location parameters control the exact position of the damaged area through the FE model. For effective damage localization, the Transmittance Functions from acceleration measurements are used which have been shown to be sensitive to structural damage while requiring output-only information. Finally, with proper selection of the objective function, the error that arises from modeling a physical damage with a linear damaged FE model can be minimized, thus creating a more accurate prediction for the damaged location. The effectiveness of the proposed SHM method is demonstrated via two illustrative examples: a simulated small-scale model of a laboratory-tested vehicle-like structure and a real experimental CFRP composite beam structure. In order to check the robustness of the proposed method, two small damage scenarios are examined for each validation model and combined with random excitations

Model-Based Damage Localization Using the Particle Swarm Optimization Algorithm and Dynamic Time Wrapping for Pattern Recreation

Vibration-based damage detection methods are a subcategory of Structural Health Monitoring (SHM) methods that rely on the fact that structural damage will affect the dynamic characteristic of a structure. The presented methodology uses Finite Element Models coupled with a metaheuristic optimization algorithm in order to locate the damage in a structure. The search domains of the optimization algorithm are the variables that control a parametric area, which is inserted into the FE model. During the optimization procedure, this area changes location, stiffness, and mass to simulate the effect of the physical damage. The final output is a damaged FE model which can approximate the dynamic response of the damaged structure and indicate the damaged area. For the current implementation of this Damage Detection Framework, the Particle Swarm Optimization algorithm is used. As an effective metric of the comparison between the FE model and the experimental structure, Transmittance Functions (TF) are used that require output only acceleration signals. As with most model-based methods, a common concern is the modeling error and how this can be surpassed. For this reason, the Dynamic Time Wrapping (DTW) algorithm is applied. When damage occurs in a structure it creates some differences between the Transmittance Functions (TF) of the healthy and the damaged state. With the use of DTW, the damaged pattern is recreated around the TF of the FE model, while creating the same differences and, thus, minimizing the modeling error. The effectiveness of the proposed methodology is tested on a small truss structure that consists of Carbon-Fiber Reinforced Polymer (CFRP) filament wound beams and aluminum connectors, where four cases are examined with the damage to be located on the composite material

Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function

A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity

Response-Only Damage Detection Approach of CFRP Gas Tanks Using Clustering and Vibrational Measurements

The advancements in the automotive, aviation, and aerospace industry have led to an increased usage of CFRP high-pressure gas tanks. In order to avoid any fatal accidents, the inspection procedures require accuracy, but also practicality, to be used in the industry. The presented work focuses on response-only metrics from vibrational experimental measurements of the CFRP tank. The power spectral density and transmittance function curves are both compared for the accuracy and ability to be used as metrics for damage detection. Along with the selection of the proper metric, an appropriate clustering algorithm that can accurately group similar states of the structure is of high importance. Two clustering algorithms, agglomerative hierarchical and spectral clustering, are employed and compared for their performance. A small Type V CFRP tank is used as an experimental structure on this benchmark problem. In order to create realistic material damage, the tank is placed on an impact system multiple times where different damage magnitudes are created. After each new state and damage magnitude on the tank, vibrational experimental data are collected. Using the collected data, all the combinations of the mentioned metrics and algorithms are executed and properly compared to evaluate their accuracy

Composite Material Elastic Effective Coefficients Optimization by Means of a Micromechanical Mechanical Model

The presented research work demonstrates an efficient methodology based on a micromechanical framework for the prediction of the effective elastic properties of strongly bonded long-fiber-reinforced materials (CFRP) used for the construction of tubular structures. Although numerous analytical and numerical micromechanical models have been developed to predict the mechanical response of CFRPs, either they cannot accurately predict complex mechanical responses due to limits on the input parameters or they are resource intensive. The generalized method of cells (GMC) is capable of assessing more detailed strain fields in the vicinity of fiber–matrix interfaces since it allows for a plethora of material and structural parameters to be defined while being computationally effective. The GMC homogenization approach is successfully combined with the covariance matrix adaptation evolution strategy (CMA–ES) to identify the effective elasticity tensor Cij of CFRP materials. The accuracy and efficiency of the proposed methodology are validated by comparing predicted effective properties with previously measured experimental data on CFRP cylindrical samples made of 3501-6 epoxy matrix reinforced with AS4 carbon fibers. The proposed and validated method can be successively used in both analyzing the mechanical responses of structures and designing new optimized composite materials

Optimal Modeling of an Elevator Chassis under Crash Scenario Based on Characterization and Validation of the Hyperelastic Material of Its Shock Absorber System

A wide variety of hyperelastic rubber-like materials, exhibiting strong nonlinear stress–strain relations under large deformations, is applied in various industrial mechanical systems and engineering applications involving shock and vibration absorbers. An optimal design procedure of an elevator chassis crashing on a hyperelastic shock absorber in a fail scenario, applicable in large-scale mechanical systems or industrial structures of high importance under strong nonlinear dynamic excitation, is presented in this work. For the characterization of the hyperelastic absorber, a Mooney–Rivlin material model was adopted, and a series of in-lab compression quasi-static tests were conducted. Applying a fully parallelizable state-of-the-art stochastic model updating methodology, coupled with robust, accurate and efficient Finite Element Analysis (FEA) software, the hyperelastic behavior of the shock absorber was validated under uniaxial large deformation, in order to tune all material parameters and develop a high-fidelity FE model of the shock absorber system. Next, a series of in situ full-scale experimental trials were carried out using a test-case elevator chassis, representing the crash scenario on the buffer absorber system, after a controlled free fall. A limited number of sensors, i.e., triaxial accelerometers and strain gauges, were placed at characteristic points of the real structure of the elevator chassis recording experimental data. A discrete Finite Element (FE) model of the experimentally tested arrangement involving the elevator chassis and updated buffer absorber system along with all boundary conditions was developed and used in explicit nonlinear analysis of the crash scenario. Steel material properties and the characterized updated Mooney–Rivlin material model were assigned to the elevator chassis and buffer, respectively. A direct comparison of the numerical and experimental data validated the reliability and accuracy of the methodology applied, whereas results of the analysis were used in order to redesign and optimize a new-design elevator chassis, achieving minimum design stresses and satisfying serviceability limit states

Optimal Modeling of an Elevator Chassis under Crash Scenario Based on Characterization and Validation of the Hyperelastic Material of Its Shock Absorber System

A wide variety of hyperelastic rubber-like materials, exhibiting strong nonlinear stress–strain relations under large deformations, is applied in various industrial mechanical systems and engineering applications involving shock and vibration absorbers. An optimal design procedure of an elevator chassis crashing on a hyperelastic shock absorber in a fail scenario, applicable in large-scale mechanical systems or industrial structures of high importance under strong nonlinear dynamic excitation, is presented in this work. For the characterization of the hyperelastic absorber, a Mooney–Rivlin material model was adopted, and a series of in-lab compression quasi-static tests were conducted. Applying a fully parallelizable state-of-the-art stochastic model updating methodology, coupled with robust, accurate and efficient Finite Element Analysis (FEA) software, the hyperelastic behavior of the shock absorber was validated under uniaxial large deformation, in order to tune all material parameters and develop a high-fidelity FE model of the shock absorber system. Next, a series of in situ full-scale experimental trials were carried out using a test-case elevator chassis, representing the crash scenario on the buffer absorber system, after a controlled free fall. A limited number of sensors, i.e., triaxial accelerometers and strain gauges, were placed at characteristic points of the real structure of the elevator chassis recording experimental data. A discrete Finite Element (FE) model of the experimentally tested arrangement involving the elevator chassis and updated buffer absorber system along with all boundary conditions was developed and used in explicit nonlinear analysis of the crash scenario. Steel material properties and the characterized updated Mooney–Rivlin material model were assigned to the elevator chassis and buffer, respectively. A direct comparison of the numerical and experimental data validated the reliability and accuracy of the methodology applied, whereas results of the analysis were used in order to redesign and optimize a new-design elevator chassis, achieving minimum design stresses and satisfying serviceability limit states

Composite Material Elastic Effective Coefficients Optimization by Means of a Micromechanical Mechanical Model

The presented research work demonstrates an efficient methodology based on a micromechanical framework for the prediction of the effective elastic properties of strongly bonded long-fiber-reinforced materials (CFRP) used for the construction of tubular structures. Although numerous analytical and numerical micromechanical models have been developed to predict the mechanical response of CFRPs, either they cannot accurately predict complex mechanical responses due to limits on the input parameters or they are resource intensive. The generalized method of cells (GMC) is capable of assessing more detailed strain fields in the vicinity of fiber–matrix interfaces since it allows for a plethora of material and structural parameters to be defined while being computationally effective. The GMC homogenization approach is successfully combined with the covariance matrix adaptation evolution strategy (CMA–ES) to identify the effective elasticity tensor Cij of CFRP materials. The accuracy and efficiency of the proposed methodology are validated by comparing predicted effective properties with previously measured experimental data on CFRP cylindrical samples made of 3501-6 epoxy matrix reinforced with AS4 carbon fibers. The proposed and validated method can be successively used in both analyzing the mechanical responses of structures and designing new optimized composite materials