• 대한기계학회논문집
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• 제16권3호
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• pp.527-539
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• 1992

본 연구에서는 터빈익렬의 입구유동면에 주어지는 끝벽 경계층유동에 의하여 익렬 내의 유동에서 발생하는 여러 와류들에 의한 2차 유동과 이와 연관된 여러가지 3차원 점성유동 현상 그리고 이에 따른 유동손실을 보다 정확히 예측하기 위한 수치해 석적 연구를 수행하였으며, 이에 필요한 수치해석적 연구를 수행하였으며, 이에 필요 한 수치해석코드를 작성하였다.유동특성에 대하여 상세한 연구결과가 보고되어 있 는 UTRC(United Technologies Research Center) 평면 터빈익렬을 연구대상으로 채택하 여 익렬 내의 3차원 유동특성을 연구하고 계산한 결과를 기존의 결과와 비교 검토하였 다. 강한 2차유동이 존재하는 경우에 발생하는 수치확산을 감소시키기 위하여 대류 항에 대하여 2차 정확도(second-order accuracy)의 선형상류도식(linear upwind sche- me)을 사용하여 일반적으로 널리 사용되는 하이브리드도식(hybrid scheme)에 의한 해 석결과와 비교하였다. 터빈익렬 내의 난류 유동은 익렬의 회전과 유선의 만곡 등에 의한 영향으로 복잡한 유동현상을 나타내지만, 터빈익렬 내의 난류유동 특성에 대한 실험결과가 아직까지는 부족하고 또한 본 연구에서는 평균유동값의 정확한 해석에 중 점을 두었으므로 표준 k-.epsilon. 모델을 사용하였다.

• 한국소음진동공학회논문집
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• 제12권7호
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• pp.510-519
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• 2002

The present study addresses a computational work of the weak shock wave propagating inside an automobile exhaust muffler. Several different types of the silencer systems are employed to investigate the magnitude of the shock wave during propagating through them. The Initial shock wave Mach number $M_s$ is varied between 1.01 and 1.30, and a normal shock wave is given at the inlet of the silencer systems. The second order total variation diminishing scheme Is employed to solve the two dimensional, compressible, unsteady Euler equations. The present computational results are compared with the previous experimental ones available. The present computations predict the experimental results with a quite good accuracy. Of the four silencer systems applied. the most desirable silencer system to reduce the peak pressure at the exalt of the exhaust pipe is discussed from the Point of view of the engineering design of the silencer systems.

• 한국멀티미디어학회논문지
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• 제17권10호
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• pp.1171-1181
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• 2014

In this paper, we propose improved methods which are image conversion and extraction method of watershed seed using morphological characteristic of teeth on complement image. Conventional tooth segmentation methods are occurred low detection ratio at molar region and over, overlap segmentation owing to specular reflection and morphological feature of molars. Therefore, in order to solve the problems of the conventional methods, we propose the image conversion method and improved extraction method of watershed seed. First, the image conversion method is performed using RGB, HSI space of tooth image for to extract boundary and seed of watershed efficiently. Second, watershed seed is reconstructed using morphological characteristic of teeth. Last, individual tooth segmentation is performed using proposed seed of watershed by watershed algorithm. Therefore, as a result of comparison with marker controlled watershed algorithm and the proposed method, we confirmed higher detection ratio and accuracy than marker controlled watershed algorithm.

• Wind and Structures
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• 제27권5호
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• pp.293-309
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• 2018

The prediction of multimode flutter relies, to a larger extent than bimodal flutter, on accurate modeling of the self-excited forces since it is challenging to perform experimental validation by using aeroelastic tests for a multimode case. This paper sheds some light on the accuracy of predicted self-excited forces by comparing numerical predictions of self-excited forces with measured forces from wind tunnel tests considering the flutter vibration mode. The critical velocity and the corresponding flutter vibration mode of the Hardanger Bridge are first determined using the classical multimode approach. Then, a section model of the bridge is forced to undergo a motion corresponding to the flutter vibration mode at selected points along the bridge, during which the forces that act upon it are measured. The measured self-excited forces are compared with numerical predictions to assess the uncertainty involved in the modeling. The self-excited lift and pitching moment are captured in an excellent manner by the aerodynamic derivatives. The self-excited drag force is, on the other hand, not well represented since second-order effects dominate. However, the self-excited drag force is very small for the cross-section considered, making its influence on the critical velocity marginal. The self-excited drag force can, however, be of higher importance for other cross-sections.

• Structural Engineering and Mechanics
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• 제73권1호
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• pp.65-73
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• 2020

The present work describes a new time marching procedure for structural dynamics analyses. In this novel technique, time integration parameters are automatically evaluated according to the properties of the model. Such parameters are locally defined, allowing the user to input a numerical dissipation property for each element, which defines the amount of numerical dissipation to be introduced. Since the integration parameters are locally defined as a function of the structural element itself, the time marching technique adapts according to the model, providing enhanced accuracy. The new methodology is based on displacement-velocity relations and no computation of accelerations is required. Furthermore, the method is second order accurate, it has guaranteed stability, it is truly self-starting and it allows highly controllable algorithm dissipation in the higher modes. Numerical results are presented and compared to those provided by the Newmark and the Bathe methods, illustrating the good performance of the new time marching procedure.

• Structural Engineering and Mechanics
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• 제76권4호
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• pp.435-449
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• 2020

This work presents the formulation of the isogeometric collocation method to solve the strong form equation of a unified and integrated approach of Reissner Mindlin plate theory (UI-RM). In this plate theory model, the total displacement is expressed in terms of bending and shear displacements. Rotations, curvatures, and shear strains are represented as the first, the second, and the third derivatives of the bending displacement, respectively. The proposed formulation is free from shear locking in the Kirchhoff limit and is equally applicable to thin and thick plates. The displacement field is approximated using the B-splines functions, and the strong form equation of the fourth-order is solved using the collocation approach. The convergence properties and accuracy are demonstrated with square plate problems of thin and thick plates with different boundary conditions. Two approaches are used for convergence tests, e.g., increasing the polynomial degree (NELT = 1×1 with p = 4, 5, 6, 7) and increasing the number of element (NELT = 1×1, 2×2, 3×3, 4×4 with p = 4) with the number of control variable (NCV) is used as a comparable equivalent variable. Compared with DKMQ element of a 64×64 mesh as the reference for all L/h, the problem analysis with isogeometric collocation on UI-RM plate theory exhibits satisfying results.

• Computers and Concrete
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• 제22권6호
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• pp.527-538
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• 2018

Buckling of longitudinal bars is a brittle failure mechanism, often recorded in reinforced concrete (RC) structures after an earthquake. Studies in the literature highlights that it often occurs when steel is in the post elastic range, by inducing a modification of the engineered stress-strain law of steel in compression. A proper evaluation of this effect is of fundamental importance for correctly evaluating capacity and ductility of structures. Significant errors can be obtained in terms of ultimate bending moment and curvature ductility of an RC section if these effects are not accounted, as well as incorrect evaluations are achieved by non-linear static analyses. This paper presents a numerical investigation aiming to evaluate the engineered stress-strain law of reinforcing steel in compression, including second order effects. Non-linear FE analyses are performed under the assumption of local buckling. A role of key parameters is evaluated, making difference between steel with strain hardening or with perfectly plastic behaviour. Comparisons with experimental data available in the literature confirm the accuracy of the achieved results and make it possible to formulate recommendations for design purposes. Finally, comparisons are made with analytical formulations available in the literature and based on obtained results, a modification of the stress-strain law model of Dhakal and Maekawa (2002) is proposed for fitting the numerical predictions.

• Steel and Composite Structures
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• 제29권6호
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Structural Engineering and Mechanics
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• 제77권4호
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• 한국인공지능학회지
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• 제10권1호
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• pp.9-14
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• 2022

The demand for public bicycles operated by the Seoul Metropolitan Government is increasing every year. The size of the Seoul public bicycle project, which first started with about 5,600 units, increased to 3,7500 units as of September 2021, and the number of members is also increasing every year. However, as the size of the project grows, excessive budget spending and deficit problems are emerging for public bicycle projects, and new bicycles, rental office costs, and bicycle maintenance costs are blamed for the deficit. In this paper, the Azure Machine Learning Studio program and the Boosted Decision Tree Regression technique are used to predict the number of public bicycle rental over environmental factors and time. Predicted results it was confirmed that the demand for public bicycles was high in the season except for winter, and the demand for public bicycles was the highest at 6 p.m. In addition, in this paper compare four additional regression algorithms in addition to the Boosted Decision Tree Regression algorithm to measure algorithm performance. The results showed high accuracy in the order of the First Boosted Decision Tree Regression Algorithm (0.878802), second Decision Forest Regression (0.838232), third Poison Regression (0.62699), and fourth Linear Regression (0.618773). Based on these predictions, it is expected that more public bicycles will be placed at rental stations near public transportation to meet the growing demand for commuting hours and that more bicycles will be placed in rental stations in summer than winter and the life of bicycles can be extended in winter.