• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.8
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• pp.719-727
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• 2008

A 3-D compressible Navier-Stokes solver using overlapped grids is developed to predict a flow-field around a hovering rotor. The flow solver is verified by a parametric study with the grid spacing of wake grid, spatial accuracy and turbulence model. Computations are performed with different Chimera grid systems. Computational results are compared with the experimental data of Caradonna et al. for both blade loading and the tip vortex behavior. Numerical results show good agreements with experiments for the distribution of surface pressure and tip vortex behavior. Pressure distributions over the blade have marginal differences for different numerical methods, whereas large discrepancies are seen in the prediction of the wake behavior. Results unexpectedly show that the vortex strength from an automated cut-paste Chimera grid is weaker than that from the conventional Chimera grid.

• Journal of KSNVE
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• v.6 no.5
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• pp.607-616
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• 1996

A combined computational fluid dynamics(CFD)-Kirchhoff method is presented for predicting high-speed impulsive noise generated by a hovering blade. Two types of Kirchhoff integral formula are used; one for the classical linear Kirchhoff formulation and the other for the nonlinear Kirchhoff formulation. An Euler finite difference solver is solved first to obtain the flow field close to the blade, and then this flow field is used as an input to a Kirchhoff formulation to predict the acoustic far-field. These formulas are used at Mach numbers of 0.90 and 0.95 to investigate the effectiveness of the linear and nonlinear Kirchhoff formulas for delocalized flow. During these calculiations, the retarded time equation is also carefully examined, in particular, for the cases of the control surface located outside of the sonic cylinder, where multiple roots are obtained. Predicted results of acoustic far-field pressure with the linear Kirchhoff formulation agree well with experimental data when the control surface is at the certain location(R=1.46), but the correlation is getting worse before or after this specific location of the control surface due to the delocalized nonlinear aerodynamic flow field. Calculations based on the nonlinear Kirchhoff equation using a linear sonic cylinder as a control surface show a reasonable agreement with experimental data in negative amplitudes for both tip Mach numbers of 0.90 and 0.95, except some computational integration problems over a shock. This concliudes that a nonlinear formulation is necessary if the control surface is close to the blade and the flow is delocalized.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.775-780
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• 2013

In recent years, a notebook like an ultrabook gets thinner. Its thickness causes problems in cooling fan performance, system installation condition, and so on. In this study, we installed a small turbofan in notebook system with very narrow gap in order to generate similar condition to a real product. Experiments were performed to measure the fan's performance and the flow and noise characteristics, its results were compared with computational ones. Prediction of P-Q curve using CFD showed under about 5% error in high flow rate and its trend was agreed with experimental one over the flow field. Experimental data to measure the noise at a distance of 100 mm from a rotation axis direction of an impeller corresponded well with computational ones of broadband and BPF noise. The noise experiments to measure at a distance of 100 mm from a rotation axis direction of an impeller corresponded well with computational ones of broadband and BPF noise. Especially, tip part of impeller blade and part of exit and bottom near in an analysis by a commercial program(FlowNoise).

• Journal of the Korean Geotechnical Society
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• v.22 no.6
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• pp.15-26
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• 2006

Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.