1,721,021 research outputs found
Effects of structural nonlinearity on subsonic aeroelastic characteristics of an aircraft wing with control surface
No full textThe nonlinear aeroelastic characteristics of an aircraft wing with a control surface are investigated. A doublet-hybrid method is used for the calculation of subsonic unsteady aerodynamic forces and the minimum-state approximation is used for the approximation of aerodynamic forces. A free vibration analysis is performed using the finite element and the fictitious mass methods. The structural nonlinearity in the control surface hinge is represented by both free-play and a bilinear nonlinearity. These nonlinearities are linearized using the describing function method. From the nonlinear flutter analysis, various types of limit cycle oscillations and periodic motions are observed in a wide range of air speeds below the linear flutter boundary. The effects of structural nonlinearities on aeroelastic characteristics are investigated. (C) 2004 Elsevier Ltd. All rights reserved.This research was supported by Agency for Defense Development (ADD) and the Ministry of Science and
Technology (National Research Laboratory Program) in the Republic of Korea. This support is gratefully
acknowledged. And also, the authors appreciate the review and comments of Mr Christopher O. Johnston of Virginia
Tech about this paper and express special thanks to the associate Editor Dr Earl H. Dowell and reviewers for many
valuable comments and suggestions
Active piezoelectric sensor nodes and sensor self-diagnosis for structural health monitoring
No full textStructural health monitoring using electro‐mechanical impedance sensors
No full textThis paper reports recent achievements of novel structural health monitoring (SHM) techniques for damage diagnosis for critical members of civil, mechanical and aerospace structures using electro-mechanical impedance sensors. The basic concept of this technique is to use simultaneously both high-frequency structural excitations and responses employing piezoelectric sensors to monitor the local area of a structure for changes in structural impedance that would indicate imminent damage. In this paper, several principal software and hardware issues on these topics are described. A new impedance model is proposed that incorporates the effects of sensor and bonding defects for sensor self-diagnosis. Temperature effects-free impedance-based damage detection algorithm using effective frequency shifts based on cross-correlation coefficients is presented. In a sense of tailoring wireless sensing technology to the impedance methods, an active sensor node incorporating a miniaturized impedance sensing device, an on-board microcontroller, and a radio frequency (RF) telemetry is introduced. A data compression algorithm is embedded into the on-board chip of the active sensor node to enhance its local data processing-capability. Finally, this paper concludes with a discussion of further studies and future applications
Wireless structural health monitoring for critical members of civil infrastructures using piezoelectric active sensors
No full textExtension of flutter prediction parameter for multimode flutter systems
No full textThis research was supported by the Agency for Defense Development
and was partially supported Ministry of Science and Technology
(National Research Laboratory Program) in the Republic of
Korea. This support is gratefully acknowledged. Authors express
thanks to the associate editor Franklin Eastep, and to reviewers for
many valuable comments and suggestions. Also, the authors appreciate
the review and comment of Henry A. Sodano of Virginia
Polytechnic Institute and State University about this paper
A built-in active sensing system-based structural health monitoring technique using statistical pattern recognition
No full textA piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. A built-in active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole-damage 0.5cm in diameter at the web section and transverse cut damage 7.5cm in length and 0.5cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: a) feature 1: root mean square deviations (RMSD) of impedance signatures, and b) feature II : sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining damage indices from these two damagesensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes (OSH) were successfully established by the two-step SVM classifier: Damage detection was accomplished by the first step-SVM, and damage classification was carried out by the second step-SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by thirty test patterns prepared in advance from the intact state and two damage states.The work was jointly supported by the Smart Infra-
Structure Technology Center (SISTeC) at KAIST, by
the Korea Science and Engineering Foundation and
the Infra-Structure Assessment Research Center
(ISARC), the Ministry of Construction and Transportation,
Korea, and the Railway Tech Laboratories of
The United States. This financial support is greatly
appreciated
Piezoelectric Sensor-Based Health Monitoring of Railroad Tracks Using a Two-Step Support Vector Machine Classifier
No full textA piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. A built-in active sensing system composed of two lead–zirconate–titanate patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole damage 0.5 cm in diameter at the web section and transverse cut damage 7.5 cm in length and 0.5 cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: (1) Feature I: root-mean-square deviations of impedance signatures; and (2) Feature II: sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining appropriate damage indices from these two damage-sensitive features, a two-dimensional damage feature (2D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2D DF space. As a result, optimal separable hyperplanes were successfully established by the two-step SVM classifier: damage detection was accomplished by the first step SVM, and damage classification was carried out by the second step SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by 30 test patterns obtained in advance from the experimental study.A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. A built-in active sensing system composed of two lead–zirconate–titanate patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole damage 0.5 cm in diameter at the web section and transverse cut damage 7.5 cm in length and 0.5 cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: (1) Feature I: root-mean-square deviations of impedance signatures; and (2) Feature II: sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining appropriate damage indices from these two damage-sensitive features, a two-dimensional damage feature (2D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2D DF space. As a result, optimal separable hyperplanes were successfully established by the two-step SVM classifier: damage detection was accomplished by the first step SVM, and damage classification was carried out by the second step SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by 30 test patterns obtained in advance from the experimental study
MFC-Based Structural Health Monitoring Using a Miniaturized Impedance Measuring Chip for Corrosion Detection
No full textThis article presents an experimental study using an active sensing device that consists of a miniaturized impedance-measuring chip (AD5933) and a self-sensing macrofiber composite (MFC) patch to detect corrosion in aluminum structures widely used for aerospace, civil, and mechanical systems. A simple beam structure made from a 6063 T5 aluminum alloy was selected for corrosion-detection testing. Four different corrosion cases with two different locations and two different degrees at each location were artificially inflicted on the beam using hydrochloric (HCO acid. To identify the degrees and locations of the corrosion, the electromechanical impedance-based damage-detection technique using the proposed active sensing device was investigated. Root-mean-square deviation (RMSD) metric of the real part of the impedances obtained from the MFC patch was selected as a damage-sensitive feature. Experimental results have verified that the proposed approach can be an effective tool for detection and quantification of corrosion in aluminum structures.This work was jointly supported by the Korea Research Foundation
Grant funded by the Korean Government (MOEHRD) (KRF-2005-
213-D00092), the Smart Infra-Structure Technology Center (SISTeC) at
KAIST sponsored by the Korea Science and Engineering Foundation
(KOSEF), and the Infra-Structure Assessment Research Center (ISARC) sponsored
by Ministry of Construction and Transportation (MOCT), Korea. This
material is also based upon work supported by the National Science
Foundation under Grant No. CMS 0120827. Any opinions, findings, and
conclusions or recommendations expressed in this material are those of
the authors and do not necessarily reflect the views of the National Science
Foundation. This financial support is greatly appreciated. Finally, the authors
thank Gyuhae Park of Los Alamos National Laboratory (LANL) for giving
kind guidance during the experiment
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