Self-Identification Experiments Using Variable Inertia Systems for Flexible Beam Structures

2007 ◽  
Vol 130 (1) ◽  
Author(s):  
Atsuhiko Senba ◽  
Hiroshi Furuya
Keyword(s):  
Spline Interpolation ◽  
Structural Parameters ◽  
The Self ◽  
Identification Accuracy ◽  
Sensitivity Analyses ◽  
Modal Parameters ◽  
Flexible Beam ◽  
Control Input ◽  
Shape Estimation ◽  
Identification Error

The concept of self-identification and its feasibility are experimentally investigated. The modal parameters changed by the variable inertia systems, which are controlled by control input, are used to obtain linear equations about unknown structural parameters to overcome the lack of modes in vibration testing. We derive the controllability of the modal parameters as the requested conditions for implementing self-identification using sensitivity analyses of the modal parameters with respect to the control input. Also, a criterion for the self-identification is proposed to measure the controllability. To examine the present method, the self-identification experiments are performed using a flexible cantilevered beam with controllable additional mass attached to the beam. In the experiments, we simulate the self-identification of a flexible structure with variable inertia systems, where lower vibration modes are changed by the variable inertia system adapting to the lack of modes in identification of unknown parameters. It is shown that the identification error of the bending stiffness and mass per unit length of the beam are ranging from about 8% to 12% and 1% to 7%, respectively, and they depend on the mode number because the mode shape estimation from strain sensors and cubic spline interpolation also depends on the mode. Furthermore, the factor for the identification error is discussed in detail through numerical analysis, and the results show the clear relationship between the present criterion and the identification accuracy in experiments.

Self-Identification Experiments Using Variable Inertia Systems for Flexible Beam Structures

Aerospace ◽  
2006 ◽  
Author(s):  
Atsuhiko Senba ◽  
Hiroshi Furuya
Keyword(s):  
Spline Interpolation ◽  
Structural Parameters ◽  
The Self ◽  
Identification Accuracy ◽  
Sensitivity Analyses ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Flexible Beam ◽  
Control Input ◽  
Test Beam

The concept of self-identification and its feasibility are experimentally investigated. The modal parameters changed multiple times by the control input of variable inertia parameter are used to obtain linear equations about unknown structural parameters to overcome the lack of modes. We derive the controllability of the modal parameters as the requested conditions for implementing self-identification using sensitivity analyses of the modal parameters with respect to the control input. Also, a criterion for the self-identification is proposed to measure the controllability. Then, the self-identification experiments are performed to examine the present method using a flexible cantilevered beam structure with variable inertia systems, which include controllable additional mass attached to the beam. As a result, the bending stiffness and mass per unit length of the test beam are accurately identified when the controllable and observable lower modes are appropriately excited by input force and their changes due to the variable inertia are accurately estimated from the combinations of a few strain gage sensors output and a cubic spline interpolation technique. The results also indicate that the identification accuracy of higher modes is affected by the accuracy of the estimated controllable mode shape, which is sensitive to the locations of sensors. As the proposed criterion is larger, the identification accuracy becomes more insensitive to the estimation error of the mode shapes.

Nonparametric nonlinear restoring force and excitation identification with Legendre polynomial model and data fusion

2021 ◽  
pp. 147592172199474
Author(s):  
Bin Xu ◽  
Ye Zhao ◽  
Baichuan Deng ◽  
Yibang Du ◽  
Chen Wang ◽  
...  
Keyword(s):  
Data Fusion ◽  
Legendre Polynomial ◽  
Structural Parameters ◽  
Polynomial Model ◽  
Magnetorheological Damper ◽  
Dynamic Responses ◽  
Identification Accuracy ◽  
Restoring Force ◽  
Nonlinear Restoring Force ◽  
Dynamic Loadings

Identification of nonlinear restoring force and dynamic loadings provides critical information for post-event damage diagnosis of structures. Due to high complexity and individuality of structural nonlinearities, it is difficult to provide an exact parametric mathematical model in advance to describe the nonlinear behavior of a structural member or a substructure under strong dynamic loadings in practice. Moreover, external dynamic loading applied to an engineering structure is usually unknown and only acceleration responses at limited degrees of freedom of the structure are available for identification. In this study, a nonparametric nonlinear restoring force and excitation identification approach combining the Legendre polynomial model and extended Kalman filter with unknown input is proposed using limited acceleration measurements fused with limited displacement measurements. Then, the performance of the proposed approach is first illustrated via numerical simulation with multi-degree-of-freedom frame structures equipped with magnetorheological dampers mimicking nonlinearity under direct dynamic excitation or base excitation using noise-polluted measurements. Finally, a dynamic experimental study on a four-story steel frame model equipped with a magnetorheological damper is carried out and dynamic response measurement is employed to validate the effectiveness of the proposed method by comparing the identified dynamic responses, nonlinear restoring force, and excitation force with the test measurements. The convergence and the effect of initial estimation errors of structural parameters on the final identification results are investigated. The effect of data fusion on improving the identification accuracy is also investigated.

Analysis of Modal Parameters of Adhesively Bonded Double-Strap Joints by the Modal Strain Energy Method

1993 ◽  
Author(s):  
Mohan D. Rao ◽  
Krishna M. Gorrepati
Keyword(s):  
Strain Energy ◽  
Natural Frequencies ◽  
Structural Parameters ◽  
Flexural Vibration ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Adhesively Bonded ◽  
Modal Strain Energy ◽  
Joint System ◽  
Damping Material

Abstract This paper presents the analysis of modal parameters (natural frequencies, damping ratios and mode shapes) of a simply supported beam with adhesively bonded double-strap joint by the finite-element based Modal Strain Energy (MSE) method using ANSYS 4.4A software. The results obtained by the MSE method are compared with closed form analytical solutions previously obtained by the first author for flexural vibration of the same system. Good agreement has been obtained between the two methods for both the natural frequencies and system loss factors. The effects of structural parameters and material properties of the adhesive on the modal properties of the joint system are also studied which are useful in the design of the joint system for passive vibration and noise control. In order to evaluate the MSE and analytical results, some experiments were conducted using aluminum double-strap joint with 3M ISD112 damping material. The experimental results agreed well with both analytical and MSE results indicating the validity of both analytical and MSE methods. Finally, a comparative study has been conducted using various commercially available damping materials to evaluate their relative merits for use in the design of these joints.

scholarly journals Maize stalk stiffness and strength are primarily determined by morphological factors

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Christopher J. Stubbs ◽  
Ryan Larson ◽  
Douglas D. Cook
Keyword(s):  
Material Properties ◽  
Structural Strength ◽  
Biological Activities ◽  
Structural Parameters ◽  
Sensitivity Analyses ◽  
Bioenergy Crops ◽  
Tissue Properties ◽  
Three Point Bending ◽  
X Ray Computed ◽  
The Right

AbstractThe maize (Zea mays) stem is a biological structure that must balance both biotic and structural load bearing duties. These competing requirements are particularly relevant in the design of new bioenergy crops. Although increased stem digestibility is typically associated with a lower structural strength and higher propensity for lodging, with the right balance between structural and biological activities it may be possible to design crops that are high-yielding and have digestible biomass. This study investigates the hypothesis that geometric factors are much more influential in determining structural strength than tissue properties. To study these influences, both physical and in silico experiments were used. First, maize stems were tested in three-point bending. Specimen-specific finite element models were created based on x-ray computed tomography scans. Models were validated by comparison with experimental data. Sensitivity analyses were used to assess the influence of structural parameters such as geometric and material properties. As hypothesized, geometry was found to have a much stronger influence on structural stability than material properties. This information reinforces the notion that deficiencies in tissue strength could be offset by manipulation of stalk morphology, thus allowing the creation of stalks which are both resilient and digestible.

scholarly journals Meta-Analytic Findings in the Self-Controlled Motor Learning Literature: Underpowered, Biased, and Lacking Evidential Value

2021 ◽  
Author(s):  
Brad McKay ◽  
Zachary Yantha ◽  
Julia Hussien ◽  
Michael J Carter ◽  
Diane M. Ste-Marie
Keyword(s):  
Motor Learning ◽  
Weight Function ◽  
Selection Bias ◽  
The Self ◽  
Sensitivity Analyses ◽  
P Value ◽  
Function Model ◽  
Evidential Value ◽  
Significant Difference ◽  
The Impact

The self-controlled motor learning literature consists of experiments that compare a group of learners who are provided with a choice over an aspect of their practice environment to a group who are yoked to those choices. A qualitative review of the literature suggests an unambiguous benefit from self-controlled practice. A meta-analysis was conducted on the effects of self-controlled practice on retention test performance measures with a focus on assessing and potentially correcting for selection bias in the literature, such as publication bias and p-hacking. First, a naïve random effects model was fit to the data and a moderate benefit of self-controlled practice, g=.44 (k= 52,N= 3134,95%CI[.31, .56]), was found. Second, publication status was added to the model as a potential moderator, revealing a significant difference between published and unpublished findings, with only the former reporting a benefit of self-controlled practice. Third, to investigate and adjust for the impact of selectively reporting statistically significant results, a weight-function model was fit to the data with a one-tailed p-value cutpoint of .025. The weight-function model revealed substantial selection bias and estimated the true average effect of self-controlled practice as g=.107 (95%CI[.047, .18]). P-curve analyses were conducted on the statistically significant results published in the literature and the outcome suggested a lack of evidential value. Fourth, a suite of sensitivity analyses were conducted to evaluate the robustness of these results, all of which converged on trivially small effect estimates. Overall, our results suggest the benefit of self-controlled practice on motor learning is small and not currently distinguishable from zero.

Local Mass Addition and Data Fusion for Structural Damage Identification Using Approximate Models

2020 ◽  
Vol 20 (11) ◽  
pp. 2050124
Author(s):  
Jilin Hou ◽  
Zhenkun Li ◽  
Qingxia Zhang ◽  
Łukasz Jankowski ◽  
Haibin Zhang
Keyword(s):  
Data Fusion ◽  
Objective Function ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Parameters ◽  
Identification Accuracy ◽  
Fe Model ◽  
Local Damage ◽  
Approximate Models ◽  
Structural Damage Identification

In practical civil engineering, structural damage identification is difficult to implement due to the shortage of measured modal information and the influence of noise. Furthermore, typical damage identification methods generally rely on a precise Finite Element (FE) model of the monitored structure. Pointwise mass alterations of the structure can effectively improve the quantity and sensitivity of the measured data, while the data fusion methods can adequately utilize various kinds of data and identification results. This paper proposes a damage identification method that requires only approximate FE models and combines the advantages of pointwise mass additions and data fusion. First, an additional mass is placed at different positions throughout the structure to collect the dynamic response and obtain the corresponding modal information. The resulting relation between natural frequencies and the position of the added mass is sensitive to local damage, and it is thus utilized to form a new objective function based on the modal assurance criterion (MAC) and [Formula: see text]-based sparsity promotion. The proposed objective function is mostly insensitive to global structural parameters, but remains sensitive to local damage. Several approximate FE models are then established and separately used to identify the damage of the structure, and then the Dempster–Shafer method of data fusion is applied to fuse the results from all the approximate models. Finally, fractional data fusion is proposed to combine the results according to the parametric probability distribution of the approximate FE models, which allows the natural weight of each approximate model to be determined for the fusion process. Such an approach circumvents the need for a precise FE model, which is usually not easy to obtain in real application, and thus enhances the practical applicability of the proposed method, while maintaining the damage identification accuracy. The proposed approach is verified numerically and experimentally. Numerical simulations of a simply supported beam and a long-span bridge confirm that it can be used for damage identification, including a single damage and multiple damages, with a high accuracy. Finally, an experiment of a cantilever beam is successfully performed.

scholarly journals Compartmentalization of self-representations in female survivors of sexual abuse and assault, with posttraumatic stress disorder (PTSD)

2019 ◽  
Vol 50 (6) ◽  
pp. 956-963 ◽  
Author(s):  
Georgina Clifford ◽  
Caitlin Hitchcock ◽  
Tim Dalgleish
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
The Self ◽  
Self Concept ◽  
Sensitivity Analyses ◽  
Sorting Task ◽  
Control Group ◽  
Card Sorting ◽  
Negative Attributes

AbstractBackgroundThis study examined the structure of the self-concept in a sample of sexual trauma survivors with posttraumatic stress disorder (PTSD) compared to healthy controls using a self-descriptive card-sorting task. We explored whether individuals with PTSD possess a highly affectively-compartmentalized self-structure, whereby positive and negative self-attributes are sectioned off into separate components of self-concept (e.g. self as an employee, lover, mother). We also examined redundancy (i.e. overlap) of positive and negative self-attributes across the different components of self-concept.MethodParticipants generated a set of self-aspects that reflected their own life (e.g. ‘self at work’). They were then asked to describe their self-aspects using list of positive or negative attributes.ResultsResults revealed that, relative to the control group, the PTSD group used a greater proportion of negative attributes and had a more compartmentalized self-structure. However, there were no significant differences between the PTSD and control groups in positive or negative redundancy. Sensitivity analyses demonstrated that the key findings were not accounted for by comorbid diagnosis of depression.ConclusionFindings indicated that the self-structure is organized differently in those with PTSD, relative to those with depression or good mental health.

Unraveling Decisive Structural Parameters for the Self-Assembly of Supramolecular Polymer Bottlebrushes Based on Benzene Trisureas

2020 ◽  
Vol 53 (17) ◽  
pp. 7552-7560 ◽  
Author(s):  
Franka V. Gruschwitz ◽  
Mao-Chun Fu ◽  
Tobias Klein ◽  
Rintaro Takahashi ◽  
Tomoya Higashihara ◽  
...  
Keyword(s):  
Self Assembly ◽  
Structural Parameters ◽  
The Self ◽  
Supramolecular Polymer

SHAPE ESTIMATION OF A CAVITY BY INVERSE APPLICATION OF THE BOUNDARY ELEMENTS 2D ELASTOSTATICS PROBLEM

2013 ◽  
Vol 10 (06) ◽  
pp. 1350042 ◽  
Author(s):  
MOHSEN DASHTI-ARDAKANI ◽  
MAHMUD KHODADAD
Keyword(s):  
Measurement Errors ◽  
Biaxial Tension ◽  
Spline Interpolation ◽  
Fitness Function ◽  
Boundary Elements ◽  
Initial Guess ◽  
Tension Test ◽  
Shape Estimation ◽  
Boundary Elements Method ◽  
Different Shapes

The boundary elements method (BEM), manipulated genetic algorithm (MGA), conjugate gradient method (CGM), and cubic spline interpolation (CSI) are implemented to identify the shape of a cavity located inside a 2D solid body using displacements measured from a biaxial tension test. A fitness function which is defined as the squared differences between the computed and measured displacements is minimized. The BEM is used to solve the direct 2D elastostatics problem for the boundary displacements. The MGA is used as a robust explorer to find the best circular initial guess needed by the CGM to achieve convergence. The CSI is finally employed to draw the best curve through the points found by the CGM which depict the boundary of the cavity. Several example problems with different shapes of the cavity such as elliptic, pear, heart and egg shaped are solved. The effects of the size of cavity and measurement errors on the estimation process are investigated.

scholarly journals Two-stage Bayesian system identification using Gaussian discrepancy model

2020 ◽  
pp. 147592172093352
Author(s):  
Feng-Liang Zhang ◽  
Siu-Kui Au ◽  
Yan-Chun Ni
Keyword(s):  
System Identification ◽  
Structural Model ◽  
Structural Parameters ◽  
Stage Ii ◽  
Mode Shapes ◽  
Modal Parameters ◽  
Model Parameters ◽  
Modeling Error ◽  
Two Stage ◽  
Classical Statistics

System identification aims at updating the model parameters (e.g. mass and stiffness) associated with the mathematical model of a structure based on measured structural response. In this process, a two-stage approach is commonly adopted. In Stage I, modal parameters including natural frequencies and mode shapes are identified. In Stage II, the modal parameters are used to update structural parameters such as those related to stiffness, mass, and boundary conditions. A recent Bayesian formulation allows the identification results in the first stage to be incorporated in the second stage directly via Bayes’ rule without using a heuristic model (often based on classical statistics) that transfers the information from Stages I to II. This opens up opportunities for explicitly accounting for modeling error in the structural model (Stage II) through the conditional distribution of modal parameters given structural model parameters. Following this approach, this article investigates a methodology where the modeling error between the two stages is incorporated with Gaussian distributions whose statistical parameters are also updated with available data. Leveraging on special mathematical structure induced by the model, computational issues are resolved and an analytical investigation is performed that yields insights on the role of modeling error and whether its statistics can be distinguished from those of identification uncertainty (defined for given structural model). The proposed methodology is verified using synthetic data and applied to a laboratory-scale structure.

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