• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.5
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• pp.41-52
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• 2004

In this study, parameters of long and short term runoff model were optimized using genetic algorithm as a basic research for integrated water management in a watershed. In case of Korea where drought and flood occurr frequently, the integrated water management is necessary to minimize possible damage of drought and flood. Modified TANK model was optimized as a long term runoff model and storage-function model was optimized as a short term runoff model. Besides distinguished parameters were applied to modified TANK model for supplementing defect that the model estimates less runoff in the storm period. As a result of application, simulated long and short term runoff results showed 7% and 5% improvement compared with before optimized on the average. In case of modified TANK model using distinguished parameters, the simulated runoff after optimized showed more interrelationship than before optimized. Therefore, modified TANK model can be applied for the long term water balance as an integrated water management in a watershed. In case of storage-function model, simulated runoff in the storm period showed high interrelationship with observed one. These optimized models can be applied for the runoff analysis of watershed.

• Journal of Drive and Control
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• v.20 no.4
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• pp.54-63
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• 2023

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.2B no.4
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• pp.156-161
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• 2002

This paper proposes an optimized universal motor for improving its performance using the finite element method (FEM) with the (1＋1) Evolution Strategy (ES) algorithm. To do this, various design parameters are modified, such as air gap length, shape of motor slot, pole shoe, pole width, and rotor shaft diameter. Two parameters (arc length of stator pole and thickness of pole shoe) are chosen and optimized using the program, and the optimized model is built and tested with a performance measuring system. The measured values of the model are compared with those of the initial and the optimized model to prove the algorithm. As a result, the final model improves its performance compared with those of the initial model.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.163-168
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• 2013

A construction workspace is considered as a critical factor to secure constructability and safety of a project. Specially, optimized size of each workspace helps to minimize any conflicts between workspaces, works and resources within a workspace in the construction site. However, since an existing method for making a decision workspace's size depends on generally experiences of managers and work conditions of activity, it is difficult to perform safe works considering feasible workspace size. The workspace size is changed according to the quantity of resources allocated into each activity as time progresses. Accordingly, it is desirable that optimized workspace size considering input size of resources is determined. To solve these issues, this study configures an optimized model for deciding standard size of workspaces by simple regression analysis and develops a visualized scenario model for simulating the optimized workspace shape in order to support BIM (Building Information Modeling) environment. For this, this study determines an optimized resource shape size considering maximum working radius of each resource and constructs its visual model. Subsequently, input size of resources for each activity is estimated considering safety execution area of resources and workspaces. Based on this, an optimized 3D workspace shape is generated as a VR simulation model of a BIM system based on the suggested methodologies. Moreover, operational feasibility of the developed system is evaluated through a case study for a bride project. Therefore, this study provides a visualized framework so that project managers can establish an efficient workspace planning in BIM environment. Besides, it is expected that constructability, productivity and safety of the project will be improved by minimizing conflicts between workspace and congestions between resources within a workspace in the construction phase.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Nuclear Engineering and Technology
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• v.40 no.5
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• pp.387-396
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• 2008

Many experimental analyses for annular film dryouts, which is one of the Critical Heat Flux (CHF) mechanisms, have been performed because of their importance. Numerical approaches must also be developed in order to assess the results from experiments and to perform pre-tests before experiments. Various thermal-hydraulic codes, such as RELAP, COBRATF, MARS, etc., have been used in the assessment of the results of dryout experiments and in experimental pre-tests. These thermal-hydraulic codes are general tools intended for the analysis of various phenomena that could appear in nuclear power plants, and many models applying these codes are unnecessarily complex for the focused analysis of dryout phenomena alone. In this study, a numerical model was developed for annular film dryout using the drift-flux model from uniform heated tube geometry. Several candidates of models that strongly affect dryout, such as the entrainment model, deposition model, and the criterion for the dryout point model, were tested as candidates for inclusion in an optimized annular film dryout model. The optimized model was developed by adopting the best combination of these candidate models, as determined through comparison with experimental data. This optimized model showed reasonable results, which were better than those of MARS code.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4134-4145
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• 2022

The collimator is one of the high-heat-flux components used to avoid a series of vacuum and thermal problems. In this paper, the heat load distribution throughout the collimator is first calculated through experimental data, and a transient thermodynamic simulation analysis of the original model is carried out. The error of the pipe outlet temperature between the simulated and experimental values is 1.632%, indicating that the simulation result is reliable. Second, the model is optimized to improve the heat transfer performance of the collimator, including the contact mode between the pipe and the flange, the pipe material and the addition of a twisted tape in the pipe. It is concluded that the convective heat transfer coefficient of the optimized model is increased by 15.381% and the maximum wall temperature is reduced by 16.415%; thus, the heat transfer capacity of the optimized model is effectively improved. Third, to adapt the long-pulse steady-state operation of the experimental advanced superconducting Tokamak (EAST) in the future, steady-state simulations of the original and optimized collimators are carried out. The results show that the maximum temperature of the optimized model is reduced by 37.864% compared with that of the original model. The optimized model was changed as little as possible to obtain a better heat exchange structure on the premise of ensuring the consumption of the same mass flow rate of water so that the collimator can adapt to operational environments with higher heat fluxes and long pulses in the future. These research methods also provide a reference for the future design of components under high-energy and long-pulse operational conditions.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.4
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• pp.361-369
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• 2008

The optimized distance between skegs and angle of the skeg for a standard twin-skeg type LNG carrier were presented using the CFD and model tests. The evaluation method of self-propulsion performance was derived based on the results of CFD and confirmed the validity through model tests. The analyses to assess self-propulsion performance using CFD were shown by flow line patterns on the skeg surface, nominal wake distribution in the propeller plane and the evaluation for flow balance around stern skegs. The optimized ship that was applied to the optimized two design parameters in stern skeg arrangement for target ship was derived in this work. Finally speed performance of mother ship which is existing ship and optimized ship were compared through CFD and model tests. And the usefulness about the evaluation method of self-propulsion performance was reconfirmed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.12
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• pp.693-699
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• 2013

This paper describes a method for size optimization of the major design variables for solar water heating systems at the stage of concept design. The widely used RETScreen simulation tool was used for optimization. Currently, the RETScreen tool itself does not provide a function for optimization of the design parameters. In this study, an optimizer was combined with the software. A comparative study was performed to evaluate the RETScreen-based approach with the case study of a solar heating system in an office building. The optimized results using the RETScreen model were compared to previously published results with the TRNSYS model. The objective function of the optimization is the life-cycle cost of the system. The optimized design results from the RETScreen model showed good agreement with the optimized TRNSYS results for the solar collector area and storage volume, but presented a slight difference for the collector slope angle in terms of the converged direction of the solutions. The energy cost, life-cycle cost, and thermal performance regarding collector efficiency, system efficiency, and solar fraction were compared as well, and the RETScreen model showed good agreement with the TRNSYS model for the conditions of the base case and optimized design.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.40-45
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• 2008

This paper presents an optimal design of a permanent magnet actuator(PMA) using a parallel genetic algorithm. Dynamic characteristics of permanent magnet actuator model are analyzed by coupled electromagnetic-mechanical finite element method. Dynamic characteristics of PMA such as holding force, operating time, and peak current are obtained by no load test and compared with the analyzed results by coupled finite element method. The permanent magnet actuator model is optimized using a parallel genetic algorithm. Some design parameters of vertical length of permanent magnet, horizontal length of plunger, and depth of permanent magnet actuator are predefined for an optimal design of permanent magnet actuator model. Furthermore dynamic characteristics of the optimized permanent magnet actuator model are analyzed by coupled finite element method. A displacement of plunger, flowing current of the coil, force of plunger, and velocity of plunger of the optimized permanent magnet actuator model are compared with the results of a primary permanent magnet actuator model.