• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.926-931
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• 2014

This paper presents the torque calculation and parametric analysis of synchronous permanent magnet couplings (SPMCs). Based on a magnetic vector potential, we obtained the analytical magnetic field solutions produced by permanent magnets (PMs). Then, the analytical solutions for a magnetic torque were obtained. All analytical results were extensively validated with the non-linear a two-dimensional (2D) finite element analysis (FEA). In particular, test results such as torque measurements are presented that confirm the analysis. Finally, using the derived analytical magnetic torque solutions, we carried out a parametric analysis to determine the influence of the design parameters on the SPMC's behavior.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.4
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• pp.22-25
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• 2008

The dynamic response of the head arm assembly (HAA) of a hard disk drive to an impact load was obtained from a 3D non-linear finite element model using ANSYS/LS-DYNA and from experiments using a modified levitation mass method (LMM). In the finite element model, the impact load was created by modeling the mass as a rigid body and making it collide with the HAA. The velocity, displacement, acceleration, and inertial force of the mass were then obtained from the time history data of the finite element analysis. In the LMM, a mass that was levitated with an aerostatic linear bearing, and hence encountered negligible friction, was made to collide with the actuator arm, resulting in a dynamic bending test for the arm. During the collision, the Doppler frequency shift of the laser beam reflected from the mass was accurately measured with an optical interferometer. The velocity, displacement, acceleration, and inertial force of the mass were accurately calculated from the measured time-varying Doppler frequency shift. A good correlation between the experimental data and FEA results was observed. The FEA was also used to investigate the dynamic response of the HAA to impact by different masses.

• Journal of Ocean Engineering and Technology
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• v.23 no.5
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• pp.45-53
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• 2009

In this study, Finite Element Analysis (FEA) was used to decide three kinds of material property of vibration proof rubber with the unique characteristic of non-linear and large deformation. As well, three types of hardness (Hs 50, 55, 60) were compared with the result of fatigue tests, fatigue life was able to be predicted. The request for fatigue life becomes strict more and more as increasing stress under conditions like a compaction, high load and high temperature for parts because it is main characteristics of rubber mount for automotive. Regarding to the fatigue life under dynamic deformation condition, it can be predicted as checking forced deformation extends and its frequency and its strain-life curve. As for material property tests of uniaxial tension test, uniaxial compression test, pure shear test, Ogden model was used for FEA by observing relations between stress and strain's rate as curve fitting. As a result of FEA, fatigue life for rubber mount was predicted and accorded well with the experimental data of fatigue test with hourglass specimens. In addition, its property of the predictable fatigue life method suggested in this study was accorded well with the experimental data by comparing the predicted fatigue life of FEA with the result of fatigue test for rubber component of engine rubber mount.

• Proceedings of the Korea Concrete Institute Conference
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• 2009.05a
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• pp.121-122
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• 2009

Currently, the design methods for coping of concrete piers predict over-reinforcements. In this study comparison and analysis of internal and external design codes is performed. Non-linear analysis using FEA and strut-tie model was done to reduce reinforcements embedded in coping of concrete piers.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.824-825
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• 2015

본 논문은 Switched Reluctance Motor(SRM)의 보다 정확한 제어를 위한 인덕턴스 측정에 대해 설명한다. 기존의 인덕턴스 측정방법에는 등가회로를 이용하여 측정하는 방법과 시정수를 이용하여 측정하는 방법이 사용되어왔고 본 논문에서는 기존의 인덕턴스 측정방법과 자동화된 전압적분법을 이용하여 인덕턴스를 측정하였고 실제 측정 데이터 및 유한요소해석(FEA : Finite Element Analysis) 데이터와의 비교를 통하여 타당성을 입증하였다.

• Proceedings of the KSR Conference
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• 1999.05a
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• pp.124-131
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• 1999

The axle spring is used in primary suspension component of railway vehicles. The most important factor of axle spring is to have adequate spring constant for comfortable feeling of ride. The non-linear properties of rubber which were important parameter to evaluate of rubber components, were determined by the characteristic test. The finite element analysis of the axle spring are executed to predict the behavior of deformation and stiffness by using commercial FEA code.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.942-944
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• 2003

Detent force produced in permanent linear machines generally causes an undesired effect that contributes to the output ripple of machine, vibration and noise. This paper analyzes detent force and thrust in Permanent Magnet Linear Synchronous Motor(PMLSM) by using various detent force minimization techniques. A two dimensional Finite Element Analysis(FEA) is used to Predict detent force and thrust due to structural factors and non-linearity.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.325-342
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• 2020

The current study is focused on the evaluation of the ultimate strength of stiffened panels found in ship hull structures that are subjected to combined uniaxial thrust, in-plane and out-of-plane bending moments. This loading condition, which is in general ignored when performing buckling checks, applies to representative control geometries (stiffener with attached plating) as a consequence of the linearly varying normal stresses along the ship's depth induced by the hull-girder vertical bending moment. The problem is generalized by introducing a non-uniform thrust described by a displacement ratio and rotation angle and by introducing the slenderness ratios, within the practical range of interest. The formed design space is explored through methods sourcing from Design of Experiments and by applying non-linear finite element procedures. Surrogate empirical models have been constructed through regression analysis and Response Surface Methods. An additional empirical model is provided to the literature for predicting the ultimate strength under uniaxial thrust. The numerical experimentation has shown that is a significant influence on the ultimate strength of stiffened panels as the thrust non-uniformity increases.

• Structural Engineering and Mechanics
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• v.64 no.5
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• pp.621-640
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• 2017

This paper reports the numerical investigation conducted to study the influence of Local-Distortional (L-D) interaction mode buckling on post buckling strength erosion in fixed ended lipped channel cold formed steel columns. This investigation comprises of 81 column sections with various geometries and yield stresses that are carefully chosen to cover wide range of strength related parametric ratios like (i) distortional to local critical buckling stress ratio ($0.91{\leq}F_{CRD}/F_{CRL}{\leq}4.05$) (ii) non dimensional local slenderness ratio ($0.88{\leq}{\lambda}_L{\leq}3.54$) (iii) non-dimensional distortional slenderness ratio ($0.68{\leq}{\lambda}_D{\leq}3.23$) and (iv) yield to non-critical buckling stress ratio (0.45 to 10.4). The numerical investigation is carried out by conducting linear and non-linear shell finite element analysis (SFEA) using ABAQUS software. The non-linear SFEA includes both geometry and material non-linearity. The numerical results obtained are deeply analysed to understand the post buckling mechanics, failure modes and ultimate strength that are influenced by L-D interaction with respect to strength related parametric ratios. The ultimate strength data obtained from numerical analysis are compared with (i) the experimental tests data concerning L-D interaction mode buckling reported by other researchers (ii) column strength predicted by Direct Strength Method (DSM) column strength curves for local and distortional buckling specified in AISI S-100 (iii) strength predicted by available DSM based approaches that includes L-D interaction mode failure. The role of flange width to web depth ratio on post buckling strength erosion is reported. Then the paper concludes with merits and limitations of codified DSM and available DSM based approaches on accurate failure strength prediction.

• Steel and Composite Structures
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• v.25 no.6
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.