• Ocean Systems Engineering
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• v.3 no.4
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• pp.275-294
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• 2013

Side-by-side offloading operations are widely utilized in engineering practice. The hydrodynamic interactions between two vessels play a crucial role in safe operation. This study focuses on the coupled effects between two floating bodies positioned side-by-side as a shuttle tanker-FPSO (floating production, storage and offloading) system. Several wave directions with different side-by-side distances are studied in order to obtain the variation tendency of the horizontal hydrodynamic coefficients, motion responses and mean drift forces. It is obtained that the coupled hydrodynamics between two vessels is evidently distinguished from the single body case with shielding and exaggerating effects, especially for sway and yaw directions. The resonance frequency and the peak amplitude are closely related with side-by-side separation distance. In addition, the horizontal hydrodynamics of the shuttle tanker is more susceptible to coupled effects in beam waves. It is suggested to expand the gap distance reasonably in order to reduce the coupled drift forces effectively. Attention should also be paid to the second peaks caused by hydrodynamic coupling. Since the horizontal mean drift forces are the most mainly concerned forces to be counteracted in dynamic positioning (DP) system and mooring system, prudent prediction is beneficial in saving consumed power of DP system and reducing tension of mooring lines.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.5
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• pp.1392-1400
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• 1996

The need of companies shorten the design-to-manufacturing process for new products with improved quality in cost effective manner places increasing demends on engineers to simulate the performance characteristics of a design before it is built of a prototype is developed. For theses demands CAE(Computer-Aided Engineering) offers engineers not only giving confidence of their design but also eliminating potential errors due totesting prototypes in small numbers. This paper present the method to predict the fatigue life using dynamics simulation and FEA(Finite Element Analysis) for construciton equipment in the computer before building prototype. The dynamicsimulatio is to get the load-time history corresponding to the maneuvering and driving of the construction equipment. The FEA is to build a model of the structure and then analyse to define the local stress response to applied loadings using linear static analysis.

• Journal of Power System Engineering
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• v.19 no.6
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• pp.19-25
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• 2015

The unsteady-state, incompressible and three-dimensional large eddy simulation(LES) was carried out to analyze the aerodynamic performance of three-dimensional small-size axial fan(SSAF) with the different depth of bellmouth. The static pressure coefficients analyzed by LES predict a little bit larger than measurements except stall region regardless of the installation depth between SSAF and bellmouth. Moreover, static pressure efficiencies analyzed by LES show about maximum 30% at the actual operating point ranges, but measurements do not. Therefore, if the blades of conventional SSAF have some more rigidity and complete dynamic balance, the aerodynamic performance of SSAF will be some more improved. In consequence, LES shows the best prediction performance in comparison with any other Reynolds averaged Navier-Stokes(RANS) method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2011.04a
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• pp.735-741
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• 2011

In the early phase of vehicle development, CAE is conducted as tool for vehicle performance assessment. To maintain acceptable road noise performance, solution for reduced vehicle sensitivity is required. Chassis interface dynamic stiffness characteristics are key component to isolating vibration and noise of road from the vehicle interior. This research provide how to set up the optimized dynamic characteristics under noise effect through DFSS study. CAE-based DOE is performed to build prediction math model, CMS process involves DOE to achieve very fast run times while giving results very comparable. Minimized $95^{th}$ percentile of performance distribution is applied to minimize vehicle sensitivity and road noise levels variation during the optimization process. Finally, the results of optimization were reviewed for performance and robustness.

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.1075-1084
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• 2005

The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.

• Aerospace Engineering and Technology
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• v.1 no.1
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• pp.1-7
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• 2002

This paper describes the analysis method about the dynamic characteristics and vibratory load through HDS(Helicopter Design Study). To analyze the dynamic characteristics of helicopter rotor blade, the natural frequencies and modes are calculated according to rotor operational speed(Ω). Generally the proximity of rotor natural frequency and N times of rotor operational speed is a dominant component to determine the helicopter vibration. Also we can predict the airframe vibration by calculating the airload of rotating blade exactly. We expect to establish the design procedure of rotor dynamics by describing the two major analysis methods necessary to rotor design.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.224-231
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• 2009

In this study, an aerodynamic performance analysis code has been developed as a part of rotorcraft comprehensive program. Airloads on rotor blades are calculated based on the blade element theory with look-up tables of aerodynamic coefficients of 2-D airfoils. In order to calculate rotor induced inflow, various inflow prediction methods such as linear inflow, dynamic inflow, prescribed wake and free wake model are integrated into the present module. The aerodynamic characteristics of each method are compared and validated against available experimental data such as Elliot's inflow distribution and sectional normal force coefficients of AH-1G.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.7
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• pp.674-681
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• 2011

In the early phase of vehicle development, CAE is conducted as tool for vehicle performance assessment. To maintain acceptable road noise performance, solution for reduced vehicle sensitivity is required. Chassis interface dynamic stiffness characteristics are key component to isolating vibration and noise of road from the vehicle interior. This research provide how to set up the optimized dynamic characteristics under noise effect through DFSS study. CAE-based DOE is performed to build prediction math model, CMS process involves DOE to achieve very fast run times while giving results very comparable. Minimized 95th percentile of performance distribution is applied to minimize vehicle sensitivity and road noise levels variation during the optimization process. Finally, the results of optimization were reviewed for performance and robustness.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.279-289
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• 2006

The reconstruction of low dimension nonlinear behavior from the hydrologic time series has been an active area of research in the last decade. In this study, we present the applications of a powerful state space reconstruction methodology using the method of Support Vector Machines (SVM) to the Great Salt Lake (GSL) volume. SVMs are machine learning systems that use a hypothesis space of linear functions in a Kernel induced higher dimensional feature space. SVMs are optimized by minimizing a bound on a generalized error (risk) measure, rather than just the mean square error over a training set. The utility of this SVM regression approach is demonstrated through applications to the short term forecasts of the biweekly GSL volume. The SVM based reconstruction is used to develop time series forecasts for multiple lead times ranging from the period of two weeks to several months. The reliability of the algorithm in learning and forecasting the dynamics is tested using split sample sensitivity analyses, with a particular interest in forecasting extreme states. Unlike previously reported methodologies, SVMs are able to extract the dynamics using only a few past observed data points (Support Vectors, SV) out of the training examples. Considering statistical measures, the prediction model based on SVM demonstrated encouraging and promising results in a short-term prediction. Thus, the SVM method presented in this study suggests a competitive methodology for the forecast of hydrologic time series.

• Steel and Composite Structures
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• v.47 no.1
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• pp.91-102
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• 2023

To study the evaluation standard and control limit of mortar filling layer void length, in this paper, the train sub-model was developed by MATLAB and the track-bridge sub-model considering the mortar filling layer void was established by ANSYS. The two sub-models were assembled into a train-track-bridge coupling dynamic model through the wheel-rail contact relationship, and the validity was corroborated by the coupling dynamic model with the literature model. Considering the randomness of fastening stiffness, mortar elastic modulus, length of mortar filling layer void, and pier settlement, the test points were designed by the Box-Behnken method based on Design-Expert software. The coupled dynamic model was calculated, and the support vector regression (SVR) nonlinear mapping model of the wheel-rail system was established. The learning, prediction, and verification were carried out. Finally, the reliable probability of the amplification coefficient distribution of the response index of the train and structure in different ranges was obtained based on the SVR nonlinear mapping model and Latin hypercube sampling method. The limit of the length of the mortar filling layer void was, thus, obtained. The results show that the SVR nonlinear mapping model developed in this paper has a high fitting accuracy of 0.993, and the computational efficiency is significantly improved by 99.86%. It can be used to calculate the dynamic response of the wheel-rail system. The length of the mortar filling layer void significantly affects the wheel-rail vertical force, wheel weight load reduction ratio, rail vertical displacement, and track plate vertical displacement. The dynamic response of the track structure has a more significant effect on the limit value of the length of the mortar filling layer void than the dynamic response of the vehicle, and the rail vertical displacement is the most obvious. At 250 km/h - 350 km/h train running speed, the limit values of grade I, II, and III of the lengths of the mortar filling layer void are 3.932 m, 4.337 m, and 4.766 m, respectively. The results can provide some reference for the long-term service performance reliability of the ballastless track-bridge system of HRS.