• 한국전산유체공학회지
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• 제15권3호
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• pp.16-23
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• 2010

Shape optimization of an upper plenum of a PBMR type gas cooled nuclear reactor has been performed by using three-dimensional Reynolds-Averaged Navier-Stokes (RANS) analysis and surrogate modeling technique. The objective function is defined as a linear combination of uniformity of flow distribution in the core and pressure drop in the upper plenum and the core. The ratio of thickness of slot to diameter of rising channels, ratio of height of upper plenum to diameter of rising channels, and ratio of height of the slot at inlet to outlet, are used as design variables for optimization. Design points are selected through Latin-hypercube sampling. The optimal point is determined through surrogate-based optimization method which uses 3-D RANS analyses at design points. The results show that the optimum shape represent remarkably improved performance in flow uniformity and friction loss than the reference shape.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.753-758
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• 2004

This study is to suggest a probabilistic design determining configurations of slider air bearings with the dimensional manufacturing tolerances of the ABS. The probabilistic design problem is formulated to minimize the variation in flying height from a target value while satisfying the desired probabilities keeping the pitch and roll angles within suitable range. The proposed approach first solves the deterministic optimization problem. Then, beginning with this solution, the RBDO is continued with the probabilistic constraints affected by the random variables with a fixed standard deviation in normal distribution. The RBDO results are directly compared with the values of the initial design and the results of the deterministic optimization, respectively. The reliability analyses are performed by the descriptive sampling (DS) to show the effectiveness and accuracy of the proposed approach. It is demonstrated that the proposed RBDO approach can efficiently obtain an optimum solution satisfying all the desired probabilistic constraints.

• 한국유체기계학회 논문집
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• 제11권2호
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• pp.46-54
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• 2008

This study presents a numerical optimization to optimize an axial flow fan blade to increase the efficiency. The radial basis neural network is used as an optimization method with the numerical analysis by Reynolds-averaged Navier-Stokes equations using SST model as turbulence closure. Four design variables related to airfoil maximum camber, maximum camber location, leading edge radius and trailing edge radius, respectively, are selected, and efficiency is considered as objective function which is to be maximized. Thirty designs are evaluated to get the objective function values of each design used to train the neural network. Optimum shape shows the efficiency increased by 1.0%.

• 한국소음진동공학회논문집
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• 제14권8호
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• pp.695-701
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• 2004

This study is to suggest a Probabilistic design determining configurations of slider air bearings with the dimensional manufacturing tolerances of the ABS. The probabilistic design problem is formulated to minimize the variation in flying height from a target value while satisfying the desired probabilities keeping the pitch and roll angles within a suitable range. The proposed approach first selves the deterministic optimization problem. Then, beginning with this solution, the RBDO is continued with the probabilistic constraints affected by the random variables with a fixed standard deviation in normal distribution. The RBDO results are directly compared with the values of the initial design and the results of the deterministic optimization, respectively. The reliability analyses are performed by the descriptive sampling (DS) to show the effectiveness and accuracy of the proposed approach. It is demonstrated that the Proposed RBDO approach can efficiently obtain an optimum solution satisfying all the desired probabilistic constraints.

• 한국안전학회지
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• 제30권3호
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• pp.32-37
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• 2015

In the fields of plant layout optimization, the main goal is to minimize the construction cost including pipelines as satisfying all constraints such as safety and operating issues. However, what is the lacking of considerations in previous researches is to consider proper safety and maintenance spaces for a complex plant. Based on the mathematical programming, MILP(Mixed Integer Linear Programming) problems including various constraints can be formulated to find the optimal solution which is to achieve the best economic benefits. The objective function of this problem is the sum of piping cost, pumping cost and area cost. In general, many conventional optimization solvers are used to find a MILP problem. However, it is really hard to solve this problem due to complex inequality and equality constraints, since it is impossible to use the derivatives of objective functions and constraints. To resolve this problem, the PSO (Particle Swarm Optimization), which is one of the representative sampling approaches and does not need to use derivatives of equations, is employed to find the optimal solution considering various complex constraints in this study. The EO (Ethylene Oxide) plant is tested to verify the efficacy of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.335-352
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• 2023

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many- objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems.

• 산업경영시스템학회지
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• 제47권2호
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• pp.1-9
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• 2024

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.

• 품질경영학회지
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• 제22권1호
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• pp.96-112
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• 1994

This article studies the design of Bayesian single attribute acceptance sampling plans under dependent production processes. An economic model is constructed by extending the mathematical model developed for non-Bayesian cases for Bayesian cases. The mathematical structure of the model is analyzed and it is used to prove that optimization of the model can be achieved by applying the solution method developed for non-Bayesian models directly. The effect of dependence patterns and the types of prior distributions on the design of sampling plans is also investigated through a computational study.

• Journal of Electrical Engineering and Technology
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• 제10권2호
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• pp.641-646
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• 2015

In this paper, a zinc electrowinning cell is fully optimized to achieve a uniform current distribution at electrode surfaces. To effectively deal with an electromagnetically coupled problem with multi-dimensional design variables, a sampling-based sensitivity approach is combined with a highly tuned multiphysics simulation model. The model involves the interrelation between electrochemical reactions and electromagnetic phenomena so as to predict accurate current distributions in the electrowinning cell. In the sampling-based sensitivity approach, Kriging-based surrogate models are generated in a local window, and accordingly their sensitivity values are extracted. Such unique design strategy facilitates optimizing very complicated multiphysics and multi-dimensional design problems. Finally, ten design variables deciding the electrolytic cell structure are optimized, and then the uniformity of current distribution in the optimized cell is examined through the comparison with existing cell designs.

• 한국군사과학기술학회지
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• 제17권2호
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• pp.258-264
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• 2014

In terms of chemical detection performance related with chemical material sampling, this investigation shows optimized values, resulted from minimizing loss from air turbulence and other reasons when pressure changes on the basis of sampling flow rate Based on simulations and pressure control of the outside conditions it became possible to obtain ion mobility detection optimum values, and to derive standard pressure conditions that is appropriate for DMS characteristic.