• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.4
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• pp.378-385
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• 2004

Viscous-driven pumping is a very promising type in microscale applications. However, there exist a few disadvantages such as low efficiency and small volume flow rate. In the present study, a pump with tandem rotating cylinders and its optimum synthesis are proposed fur enhancing pumping performance. First, using an unstructured grid CFD method, we investigate the effects of geometrical parameters and then the performance of the pump with tandem cylinders is evaluated. Next, an optimum design synthesis tool is constructed by combining the aforementioned CFD analysis model with the mathematical optimization model, namely, Modified Method of Feasible Directions (MMFD). This technique is used to optimize the geometrical parameters of the pump, fur maximizing pumping efficiency. From the optimization results, it is believed that the present optimum synthesis is robust and has a potential fur other microfluidic device design.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.403-407
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• 2012

The purpose of this study is to find the optimum skirt size for a composite pressure vessel using optimum analysis technique. The size optimization for skirt shape of a composite pressure vessel was conducted using sub-problem approximation method and batch processing codes programmed by APDL(ANSYS Parametric Design Language). The thickness and length of skirt part were selected as design variables for the optimum analysis. The objective function and constraints were chosen as weight and displacement of skirt part, respectively. The numerical results showed that the weight of skirt of a composite pressure vessel would be saved by maximum 4.38% through the size optimization analysis for the skirt shape.

• Advances in nano research
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• v.14 no.2
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• pp.155-164
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• 2023

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1529-1539
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• 2000

A newly developed optimization method which uses the genetic algorithm combined with the sensitivity analysis is presented in this paper. The genetic algorithm is a probabilistic method, searching the optimum at several points simultaneously, requiring only the values of the object and constraint functions. It has therefore more chances to find global solution and can be applied various problems. Nevertheless, it has such shortcomings that even it approaches the optimum rapidly in the early stage, it slows down afterward and it can't consider the constraints explicitly. It is only because it can't search the local area near the current points. The traditional method, on the other hand, using sensitivity analysis is of great advantage in searching the near optimum. Thus the combination of the two techniques makes use of the individual advantages, that is, the superiority both in global searching by the genetic algorithm and in local searching by the sensitivity analysis. Application of the method to the several test functions verifies that the method suggested is very efficient and powerful to find the global solutions, and that the constraints can be considered properly.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1110-1116
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• 1993

A study on optimum operation performances of power efficiency, economy and exhaust emissions for a tractor was conducted. A mathematical model of multiple degree polynomial equation was applied to established the function of solid multiple parameter curves for specific fuel consumption (ge), cabon monoxide (CO) ,hydrcarbons (HC) and cabonaceous smoke (Rb). The optimum operation theorems for economy operation indicated by ge and for exhaust emissions described by Co , HC and Rb were obtained from analytical method and performance test data. The optimum operation theorems could exhibit optimum operation working points, curves, and regions. The optimum matching relations of engine speed and transmission parameters were analyzed by using computer simulation methods in accordance with the tractor specifications , actual farm working conditions in a typical drawbar pull work such as plowing , the optimum operation objective function, the ideal transmission ratio, practical gear shif ing positions and practical travel speed of the tractor TN55 medel. The results of the anlayzes indicated clearly that the optimum power efficient operation, energy saving and pollution reducing would be realized if the tractor would be operated according to theoptimum operation methods.

• Journal of Korean Association for Spatial Structures
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• v.5 no.4 s.18
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• pp.61-70
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• 2005

In paper the discrete optimum design program was developed using the continuous and discrete optimum algorithms based on the SUMT and genetic algorithms. In this paper, the objective function is the weight of structures and the constraints are limits state design limits method. The design variables are diameter and thick of steel pipe. Design examples are given to show the applicability of the optimum design using the continuous and discrete optimum algorithms based on the SUMT and genetic algorithms of this study.

• Journal of Korean Association for Spatial Structures
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• v.1 no.1 s.1
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• pp.125-134
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• 2001

The objective of this study is the development of size discrete optimum design algorithm which is based on the GAs(genetic algorithms). The algorithm can perform size discrete optimum designs of space trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of space trusses and the constraints are limite state design codes(1998) and displacements. The basic search method for the optimum design is the GAs. The algorithm is known to be very efficient for the discrete optimization. This study solves the problem by introducing the GAs. The GAs consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the current design points. The evolutionary process evaluates the survivability of the design points selected from the genetic process. In the genetic process of the simple GAs, there are three basic operators: reproduction, cross-over, and mutation operators. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying GAs to optimum design examples.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.30 no.2
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• pp.86-96
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• 1994

In order to estimate the optimum harvesting mesh size of multispecies, the 24 species of catching data which were taken by fishing trial of trawl gear in Korean Southern Coast and East China Sea during 1991-1993 year were grouped and divided by the Cluster analysis method, considering first maturity length and body width, body height, body girth based on the first maturity length. With the same method, the above groups were subdivided by the potential escape such as possible escape index, range factor and selection factor. In case of the species devoid of selection parameters, these species were first subdivided by the use of possible escape index and length range factor. Next, the optimum harvesting mesh size of multispecies was properly classified according to the optimal mesh size of a fish estimated by first maturity length against selection factor. The results obtained are summarized as follows: 1. Each optimum harvesting mesh size of Psenopsis anomala, Priacanthus macra-canthus, Trachurus japonicus, Argyrosomus argentatus was 71.1-79.5mm, and Saurida undosquamis was 65.5mm. 2. Each optimum harvesting mesh size of Scomber japonicus, Pseudosciaena crosea, Pseudosciaena Polyactis, Sebastes thompsoni, Doderleinia berycoides was 78.5-85.6mm, and Bembras japonicus, Sphyraena pinguis was 48.4-51.3mm. 3. Each optimum harvesting mesh size of Zeus faber, Pampus argenteus, Zenopsis nebulosan was 118.4-124.1mm, and Caranx equula was 91.4mm, and Thamnaconus modestus was 131.2mm, and Pagrus major was 149.4mm. 4. Each optimum harvesting mesh size of Upeneus bensasi, Callanthias japonicus, Sardinops melanosticata, Konosirus punctatus was 36.8-42.8mm, and Acropoma japonicum was 21.2mm, and Apogon lineatus was 26.3mm.

• Journal of Korean Society of Steel Construction
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• v.19 no.2
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• pp.171-179
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• 2007

The greenhouse discrete optimum design program was developed using discrete optimum algorithm based on the genetic algorithm. The basic search method for the optimum design is the genetic algorithm, which is known to be very efficient for discrete optimization. In this paper, the objective function was the weight of the greenhouse structures and the constraints were the limits state design method. The design variables were galvanized steel pipes for plastic housing KSD 3760. Objective criteria were presented for the design of economic greenhouse structure and evaluation of its stability. The standardizations of greenhouse structure were used, as well as the normalization of greenhouse-related materials. Design examples were given to show the applicability of the optimum design using the discrete optimum algorithm based on the genetic algorithm of this study.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2005.11a
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• pp.173-178
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• 2005

In this paper, an efficient and robust analysis system for the flutter optimization of laminated composite wings has been developed using the coupled computational method based on the genetic algorithm. General three-dimensional doublet-lattice method is efficiently used to compute generalized aerodynamic forces of T-tail configuration in the frequency domain. Structural dynamic analyses of laminated composite T-tail models are conducted using finite clement method. The classical P-k flutter analysis technique is applied to effectively solve the aeroelastic governing equations in the frequency domain. Optimum design studies using genetic algorithm have been conducted in order to obtain maximum flutter stability of a composite T-tail configuration. The results show that flutter stability can be significantly increased using composite materials with proper optimum design concepts even for the same weight and shape condition. In the view point of engineering design, it is also importantly shown that the optimization of the vertical wing part is highly effective comparing to the optimization of horizontal wing part.