Updating the Finite Element Model of the Aerostructures Test Wing Using Ground Vibration Test Data

Lung, Shun-Fat; Pak, Chan-gi

doi:10.2514/6.2009-2528

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…EFI is defined in [30] to be a sensor placement algorithm that starts with all possible sensor positions and reaches the wanted number of locations by gradually removing those that have the minimum contributions to the linearly independent manifestation of the fixture faults. While EFI-DPR is a composition of EFI method and an energetic approach, called the driving-point residue (DPR) [31]. A sensor with low energy can be selected in EFI method, and results with loss of information.…”

Section: Sensor Placement Based On Civil Engineering and Network Requmentioning

confidence: 99%

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

El-Qawasma

Elfouly

Ahmed

2019

IET wirel. sens. syst.

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Nowadays, wireless sensor networks (WSNs) are considered an essential candidate to apply structural health monitoring (SHM). An important problem in this area is sensor placement optimization. In many research works, solving this problem focuses only on the network properties and requirements such as energy consumption, network coverage, …etc., without considering the civil engineering requirements. However, there are other research works that consider network and civil requirements while optimizing the sensor placement. Unfortunately, although minimizing the number of sensors is important, it has never been addressed. This could be noticed from the limited literature found that addresses this problem while considering both the civil and the network requirements. As a result, in this thesis we study the problem of minimizing the number of sensors for SHM in WSNs. The idea behind this research is to reduce the network size, which can solve some problems such as the scalability, installation time and cost. Our contribution in this work is not limited to the mathematical model of the mentioned problem, but will extend to solve the problem using different methods: the exhaustive search, genetic algorithm (GA), and a heuristic algorithm that applies the binary search. The problem is then solved for different number of sensors as well as different placements in many conducted experiments. Finally, the time complexity is evaluated to compare between all the applied methods. The obtained results showed that minimizing the number of sensors becomes more significant with big structures. Furthermore, the binary search algorithm is the best to use to solve the problem for small buildings. But, For larger buildings, there is a trade-off between the performance, and time complexity, where

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Section: Sensor Placement Based On Civil Engineering and Network Requmentioning

confidence: 99%

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

El-Qawasma

Elfouly

Ahmed

2019

IET wirel. sens. syst.

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“…The extended version of the model-tuning tool required a significant number of performance indices because individual offdiagonal terms, as shown in Eqs. (15) and (16), were performance indices instead of using a single norm value of off-diagonal terms as in Eq. (17), which was used in the previous version of the modeltuning tool [2,16].…”

Section: A Weight Submodulementioning

confidence: 99%

“…(15) and (16), were performance indices instead of using a single norm value of off-diagonal terms as in Eq. (17), which was used in the previous version of the modeltuning tool [2,16]. With the extended version, it was much easier to improve each off-diagonal term element as opposed to the previous version.…”

Section: A Weight Submodulementioning

confidence: 99%

“…Supporting the ARMD guidelines, NASA AFRC has developed a computer code for FE model tuning [2,16] using the object-oriented optimization (O 3 ) tool [17] together with Nastran [18], an FE analysis computer software program. Three optimizer algorithms have been incorporated into the O 3 tool: design optimization tools (DOTs) [19], based on a gradient-based algorithm; the genetic algorithm (GA) [20], which is a class of stochastic and global optimization; and bigbang-big-crunch algorithms [21][22][23][24].…”

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confidence: 99%

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Creating a Test-Validated Finite-Element Model of the X-56A Aircraft Structure

Pak

Truong

2015

Journal of Aircraft

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Small modeling errors in a finite-element model will eventually induce errors in the structural flexibility and mass, thus propagating into unpredictable errors in the unsteady aerodynamics and the control law design. One of the primary objectives of the X-56A Multi-Utility Technology Testbed aircraft is the flight demonstration of active flutter suppression and, therefore, in this study, the identification of the primary and secondary modes for the structural model tuning based on the flutter analysis of the X-56A aircraft. The ground-vibration test-validated structural dynamic finite-element model of the X-56A aircraft is created in this study. The structural dynamic finite-element model of the X-56A aircraft is improved using a model-tuning tool. In this study, two different weight configurations of the X-56A aircraft have been improved in a single optimization run. Frequency and the cross-orthogonality (mode shape) matrix were the primary focus for improvement, whereas other properties such as c.g. location, total weight, and off-diagonal terms of the mass orthogonality matrix were used as constraints. The end result was an improved structural dynamic finite-element model configuration for the X-56A aircraft. Improved frequencies and mode shapes in this study increased average flutter speeds of the X-56A aircraft by 7.6% compared to the baseline model.

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“…The detailed derivations of equation (4) are listed in Appendix A. Let the basis AIC matrix  Q be defined as shown in equation (5),…”

Section: Basis Function Approximationmentioning

confidence: 99%

Application of Approximate Unsteady Aerodynamics for Flutter Analysis

Pak

Li²

2010

51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials ConferenceSelf Cite

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A technique for approximating the modal aerodynamic influence coefficient (AIC) matrices by using basis functions has been developed. A process for using the resulting approximated modal AIC matrix in aeroelastic analysis has also been developed. The method requires the unsteady aerodynamics in frequency domain, and this methodology can be applied to the unsteady subsonic, transonic, and supersonic aerodynamics. The flutter solution can be found by the classic methods, such as rational function approximation, k, p-k, p, root locus et cetera. The unsteady aeroelastic analysis using unsteady subsonic aerodynamic approximation is demonstrated herein. The technique presented is shown to offer consistent flutter speed prediction on an aerostructures test wing (ATW) 2 and a hybrid wing body (HWB) type of vehicle configuration with negligible loss in precision. This method computes AICs that are functions of the changing parameters being studied and are generated within minutes of CPU time instead of hours. These results may have practical application in parametric flutter analyses as well as more efficient multidisciplinary design and optimization studies.

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Updating the Finite Element Model of the Aerostructures Test Wing Using Ground Vibration Test Data

Cited by 11 publications

References 11 publications

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

Minimising number of sensors in wireless sensor networks for structure health monitoring systems

Creating a Test-Validated Finite-Element Model of the X-56A Aircraft Structure

Application of Approximate Unsteady Aerodynamics for Flutter Analysis

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