• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.11
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• pp.1854-1862
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• 2001

In this paper, power flow characteristics of a hydromechanical transmission(HMT) are investigated using network analysis. The HMT used in this study consist of a hydrostatic unit(HSU), planetary gear sets, clutches and brakes providing forward 4 speeds and backward 2 speeds. Since the HMT power flows showing a closed loop and the HSU efficiency varies depending on the pressure and speed, a systematic approach is required to analyze the power transmission characteristics of the HMT. In order to analyze the closed loop power flow and the HSU power loss which changes depending on the pressure and speed, network model is constructed fur each speed range. In addition, an algorithm is proposed to calculate an accurate HSU loss corresponding to the experimental results. It is found from the network analysis that the torque and speed of each transmission element including the HSU can be obtained as well as direction of the power flow by the proposed algorithm. It is expected that the network analysis can be used in the design of relatively complicated transmission system such as HMT.

• Transactions of Materials Processing
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• v.27 no.4
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• pp.227-235
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• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.22-24
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• 2008

This paper presents a numerical model of internal flows in a lubricant supplying and scavenging flow path of an aero-engine lubrication system. The numerical model was built in the General Analysis Software of Aero-engine Lubrication System, GASLS, developed by Northwestern Polytechnical University. The lubricant flow flux, pressure and temperature distribution at steady state were calculated. GASLS is a general purpose computer program employed a 1-D steady state network algorithm for analyzing flowrates, pressures and temperatures in a complex flow network. All kinds of aero-engine lubrication systems can be divided into finite correlative typical elements and nodes from which the calculation network be developed in GASLS. Special emphasis is on how to use combined elements which is a type of typical elements to replace some complex components like bearing bores, accessory gearboxes or heat exchangers. This method can reduce network complexity and improve calculation efficiency. Final computational results show good agreement with experimental data.

• Proceedings of the Technology Innovation Conference
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• 1997.12a
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• pp.133-158
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• 1997

This paper analyzes the characteristics of technological knowledge flow-structure of Korean manufacturing in dynamic perspective. In doing that, the concept of the knowledge network is introduced which is defined as a set of industries and their interaction(knowledge flow) or linkage. The analysis of the inter-industrial knowledge flows is based on the technological similarity by using R&D researchers'academic background in the year of 1984, 1987, 1990. The analysis is carried out by such methodology as network analysis, indicator analysis and simple statistical analysis. And the final results are drawn both in absolute terms(dimension effect) and in relative terms (proportion effect) respectively. The main findings are as follow. First, the Korean manufacturing knowledge network appears to strengthen existing inter-industrial knowledge linkages rather than to construct new linkages. Second, the network seems to form a dualistic structure in that some high-technology sectors (knowledge production sectors) emerge along with traditional sectors (knowledge absorbing sectors). Third, since the mid-1980s, an inter-industrial fusion is witnessed among technologically intensive sectors, indicating that some sophisticated innovation modes are emerging in Korean manufacturing system.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.7 s.178
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• pp.1753-1762
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• 2000

An inverse method is presented to obtain material's flow properties by using small punch test. This procedure employs, as the objective function of inverse analysis, the balance of measured load-di splacement response and calculated one during deformation. In order to guarantee convergence to global minimum, simulated annealing method was adopted to optimize the current objective function. In addition, artificial neural network was used to predict the load-displacement response under given material parameters which is the most time consuming and limits applications of global optimization methods to these kinds of problems. By implementing the simulated annealing for optimization along with calculating load-displacement curve by neural network, material parameters were identified irrespective of initial values within very short time for simulated test data. We also tested the present method for error-containing experimental data and showed that the flow properties of material were well predicted.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1289-1302
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• 2019

In this paper, an optimal allocation method of a hybrid active power filter in an active distribution network is designed based on the differential evolution algorithm to resolve the harmonic generation problem when a distributed generation system is connected to the grid. A distributed generation system model in the calculation of power flow is established. An improved back/forward sweep algorithm and a decoupling algorithm are proposed for fundamental power flow and harmonic power flow. On this basis, a multi-objective optimization allocation model of the location and capacity of a hybrid filter in an active distribution network is built, and an optimal allocation scheme of the hybrid active power filter based on the differential evolution algorithm is proposed. To verify the effect of the harmonic suppression of the designed scheme, simulation analysis in an IEEE-33 nodes model and an experimental analysis on a test platform of a microgrid are adopted.

• 한국전산유체공학회:학술대회논문집
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• 2005.10a
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• pp.39-44
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• 2005

In HANARO, a multi-purpose research reactor of 30 MWth, the emergency water supply system consists essentially of an emergency water storage tank located in the level of about thirteen meter (13 m) above the reactor core, a three inch ('3\%') diameter water injection pipe line including injection valves from the tank to the reactor cooling inlet pipe and a test loop to do periodic system performance test. When the water level of the reactor pool comes down to the extremely low due to a loss of reactor pool water accident the emergency water stored in the tank should be fed to the core by the gravity force and at that time the design flow rate is eleven point four kilogram per second (11.4 kg/s). But it is impossible periodically to measure the injection flow rate under the emergency condition because the normal water level should be maintained during the reactor operation. This paper describes a flow network analysis to simulate the flow rate under the emergency condition. As results, it was confirmed through the analysis results that the calculated flow rate agrees with the design requirement under the emergency condition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.44
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• pp.81-92
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• 1997

The purpose of this paper is to develop a method of paramatric analysis that can be applied to an optimal solution of a maximum flow problem. We first define the transformed network corresponding to a given network. In such a network, we conduct paramatric analysis by determining changes in the optimal solution precipitated by changes in the capacity as the arc capacity varies from 0 to infinite. By this method we can easily calculate not only the characteristic region where the given optimal solution remains unchanged, but also the characteristic region where the value of the maximal flow gradually increases or decreases. The proposed method is demonstrated by numerical example.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.825-833
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• 1997

The purpose of this paper is to develop a method of postoptimality analysis that can be applied to an optimal solution of a maximum flow problem. We first use the transformed network corresponding to a given network. In such a network we conduct postoptimality analysis by determining changes in the optimal solution precipitated by changes in the capacity as the arc capacity varies from 0 to infinite. By this method we can easily calculate not only the characteristic region where the given optimal solution remains unchanged, but also the characteristic region where the value of the maximal flow gradually increases or decreases. The proposed method is demonstrated by numerical example.