• Journal of Aerospace System Engineering
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• v.17 no.4
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• pp.28-33
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• 2023

In this study, computational fluid dynamics was used to analyze the flow bias generated as air supplied by a fan passes through ducts, piping, and a coal separation screen. The flow bias of the air flow is mostly caused by the spatial characteristics of the fan volute and duct, and the internal baffle and the coal separation screen at the outlet cause strong pressure losses that dampen the flow bias. ANSYS CFX was used for computational fluid dynamics, and since the baffle and the coal separation screen are shaped like perforated plates with many small holes uniformly distributed, actual modeling for analysis was not possible. Therefore, the Porous Loss Model was applied. The evaluation of the flow bias was analyzed based on the velocity distribution of the Porous Loss Model at the outlet surface of the coal separation screen obtained from the computational fluid dynamics results.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.153-162
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• 2011

The minimum cut problem is to minimize c(S,T), that is, to determine source S and sink T such that the capacity of the S-T cut is minimal. The flow-based algorithm is mostly used to find the bottleneck arcs by calculating flow network, and does not presents the minimum cut. This paper suggests an algorithm that simply includes the maximum capacity vertex to adjacent set S or T and finds the minimum cut without obtaining flow network in advance. On applying the suggested algorithm to 13 limited graphs, it can be finds the minimum cut value $_{\min}c$(S, T) with simply and correctly.

• Proceedings of the KSEEG Conference
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• 2002.04a
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• pp.258-261
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• 2002

• Proceedings of the KSEEG Conference
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• 1999.04a
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• pp.251-253
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• 1999

• Tunnel and Underground Space
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• v.11 no.2
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• pp.156-166
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• 2001

A stochastic continuum(SC) modeling technique was developed to simulate the groundwater flow pathway in fractured rocks. This model was developed to overcome the disadvantageous points of discrete fracture network(DFN) modes which has the limitation of fracture numbers. Besides, SC model is able to perform probabilistic analysis and to simulate the conductive groundwater pathway as discrete fracture network model. The SC model was formulated based on the discrete fracture network(DFN) model. The spatial distribution of permeability in the stochastic continuum model was defined by the probability distribution and variogram functions defined from the permeabilities of subdivided smaller blocks of the DFN model. The analysis of groundwater travel time was performed to show the consistency between DFN and SC models by the numerical experiment. It was found that the stochastic continuum modes was an appropriate way to provide the probability density distribution of groundwater velocity which is required for the probabilistic safety assessment of a radioactive waste disposal facility.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2005.06a
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• pp.231-238
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• 2005

On the basis of flow resistance theory the conceptual model and related mathematical descriptions is proposed for resistance modeling of groundwater flow in CPM(continuum Porous medium), DFN(discrete fracture network) and fractured-porous medium. The proposed model is developed on the basis of finite volume method assuming steady-state, constant density groundwater flow. The basic approach of the method is to evaluate inter-block flow resistance values for a staggered grid arrangement, i.e. fluxes are stored at cell walls and scalars at cell centers. The balance of forces, i.e. the Darcy law, is utilized for each control volume centered around the point where the velocity component is stored. The transmissivity (or permeability) at the interface is assumed to be the harmonic average of neighboring blocks. Flow resistance theory was utilized to relate the fluxes between the grid blocks with residual pressures. The flow within porous medium is described by three dimensional equations and that within an individual fracture is described by a two dimensional equivalent of the flow equations for a porous medium. Newly proposed models would contribute to develop flow simulation techniques with various matrix characteristics.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.9
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• pp.729-738
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• 2021

Feedback flow control using an artificial neural network was numerically investigated for NACA0015 Airfoil to suppress flow separation on an airfoil. In order to achieve goal of flow control which is aimed to reduce the size of separation on the airfoil, Blowing&Suction actuator was implemented near the separation point. In the system modeling step, the proper orthogonal decomposition was applied to the pressure field. Then, some POD modes that are necessary for flow control are extracted to analyze the unsteady characteristics. NARX neural network based on decomposed modes are trained to represent the flow dynamics and finally operated in the feedback control loop. Predicted control signal was numerically applied on CFD simulation so that control effect was analyzed through comparing the characteristic of aerodynamic force and spatial modes depending on the presence of the control. The feedback control showed effectiveness in pressure drag reduction up to 29%. Numerical results confirm that the effect is due to dramatic pressure recovery around the trailing edge of the airfoil.

• Journal of the Korean Geotechnical Society
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• v.15 no.5
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• pp.229-240
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• 1999

The groundwater flow and grout flow in individual rock joint and jointed rock mass are studied using various methods of analysis such as (i) the finite difference method, (ii) channel network analysis and (iii) joint network analysis. The flow behaviour is investigated in two distinguishable scales of observation: one for a rough joint of a laboratory scale having variable aperture, and the other for field- scale rock masses having three sets of intermittent joints. In the former case, the aperture-dependent channel flow is identified for both water and grout flows. The comparison of the flow rate in a rough joint is made between the finite difference analysis and existing analytical solution. In the latter case, the effects of increasing number of joints on the groundwater inflow into a circular opening of various diameters are analyzed using both the joint network method and Goodman's analytic solution. Comparisons are made between the two methods. The boundary effects in the joint network method are discussed. The inhomogeneity of joint network and its impacts on the groundwater inflow are also discussed.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.11
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• pp.901-907
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• 2021

The present study introduces an artificial neural network (ANN) that can predict the missile aerodynamic coefficients for various missile nose shapes and flow conditions such as Mach number and angle of attack. A semi-empirical missile aerodynamics code is utilized to generate a dataset comprised of the geometric description of the nose section of the missiles, flow conditions, and aerodynamic coefficients. Data normalization is performed during the data preprocessing step to improve the performance of the ANN. Dropout is used during the training phase to prevent overfitting. For the missile nose shape and flow conditions not included in the training dataset, the aerodynamic coefficients are predicted through ANN to verify the performance of the ANN. The result shows that not only the ANN predictions are very similar to the aerodynamic coefficients produced by the semi-empirical missile aerodynamics code, but also ANN can predict missile aerodynamic coefficients for the untrained nose section of the missile and flow conditions.

• Journal of the Korean Geotechnical Society
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• v.23 no.7
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• pp.65-76
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• 2007

Recently, there have been many construction projects on soft ground with growth of industry and various construction problems concerning soft soil behavior also have been reported. Especially, foundation piles of abutments and (or) buildings which were constructed on the soft ground have been suffering from a lot of stability problems of inordinary displacement due to lateral flow of soft ground. Although many researches for this phenomena have been carried out, it is still difficult to assess the mechanism of lateral flow on soft ground quantitatively. And reliable design method for judgement of lateral flow occurrence is not established yet. In this study, PNN (probabilistic neural network) and CNN (committee neural network) theories were applied for judgment of lateral flow occurrence based on eat data compiled from Korea and Japan. Predictions of PNN and CNN models for new data which were not used during model development are compared with those predicted by conventional empirical methods. It was found that the developed PNN and CNN models can predict more precise and reliable judgment of lateral flow occurrence than conventional empirical methods.