• Coupled systems mechanics
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• v.7 no.4
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• pp.421-440
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• 2018

In this study, a computational method for wall shear stress combined with an implicit direct-forcing immersed boundary method is presented. Near the immersed boundaries, the sub-grid stress is determined by a wall model in which the wall shear stress is directly calculated from the Lagrangian force on the immersed boundary. A coupling mathematical model of the transition process for a model Francis turbine comprising turbulent flow and rotating rigid guide vanes is established. The spatiotemporal distributions of pressure, velocity, vorticity and turbulent quantity are gained with the transient process; the drag and lift coefficients as well as other forces (moments) are also obtained as functions of the attack angle. At the same time, analysis is conducted of the characteristics of pressure pulsation, velocity stripes and vortex structure at some key parts of flowing passage. The coupling relations among the turbulent flow, the dynamical force (moment) response of blade and the rotating of guide vane are also obtained.

• Structural Engineering and Mechanics
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• v.57 no.4
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• pp.587-602
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• 2016

In structural reliability analysis, the response surface method is widely adopted because of its numerical efficiency. It should be understood that the response function must approximate the actual limit state function accurately in the main region influencing failure probability where it is evaluated. However, the size of main region influencing failure probability was not defined clearly in current response surface methods. In this study, the concept of sub-region of interest is constructed, and an improved response surface method is proposed based on the sub-region of interest. The sub-region of interest can clearly define the size of main region influencing failure probability, so that the accuracy of the evaluation of failure probability is increased. Some examples are introduced to demonstrate the efficiency and the accuracy of the proposed method for both numerical and implicit limit state functions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.567-574
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• 2006

We present applications of MLS-based finite elements, which enable us to easily treat highly complex nonmatching finite element meshes and discontinuities. The shape functions of MLS-based finite element can be easily generated with the aid of Moving Least Square approximation on the parental domain. The major advantage includes that the position of element nodes as well as the number of the element nodes can be conveniently adjusted according to the nature of the problems under consideration, so that finite-element mesh is straightforwardly adapted to evolving discontinuities and. interfaces. Furthermore, we show that the present MLS-based finite elements are efficiently applied for elastic-plastic deformations, wherein the implicit constraint of incompressibility should be properly handled.

• Journal of Educational Research in Mathematics
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• v.13 no.1
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• pp.13-28
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• 2003

Practice of proof is considered in, the view of language and meta-mathematics, recognizing the role of proof that is the means of communication and development of mathematical understanding. Linguistic components in proof texts are symbol, verbal language and visual text, and contain the implicit knowledge in the meta-mathematics view. This study investigates the functions of linguistic elements according to Halliday's functional grammar and the rhetoric skills in proof texts in math textbook, teacher's note, and student's written text. We need to inquire into the aspects of language for mathematics learning process and the understanding and use of students' language.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.333-342
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• 1999

An improved optimization algorithm for multi-objective and multi-level (MO/ML) optimum design of steel frames is proposed in this paper. In order to optimize the steel frames under seismic load, two main objective functions need to be considered for minimizing the structural weight and maximizing the strain energy. For the efficiency of the proposed method, well known multi-level optimization techniques using decomposition method that separately utilizes both system-level and element-level optimizations and an artificial constraint deletion technique are incorporated in the algorithm. And also dynamic analysis is executed to evaluate the implicit function of structural strain energy at each iteration step. To save the numerical efforts, an efficient reanalysis technique through sensitivity analysis of dynamic properties is unposed in the paper. The efficiency and robustness of the improved MOML algorithm, compared with a plain MOML algorithm, is successfully demonstrated in the numerical examples.

• ETRI Journal
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• v.37 no.4
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• pp.787-792
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• 2015

To accomplish a high-speed test on low-speed automatic test equipment (ATE), a new instruction-based fully programmable memory built-in self-test (BIST) is proposed. The proposed memory BIST generates a highspeed internal clock signal by multiplying an external low-speed clock signal from an ATE by a clock multiplier embedded in a DRAM. For maximum programmability and small area overhead, the proposed memory BIST stores the unique sets of instructions and corresponding test sequences that are implicit within the test algorithms that it receives from an external ATE. The proposed memory BIST is managed by an external ATE on-the-fly to perform complicated and hard-to-implement functions, such as loop operations and refresh-interrupts. Therefore, the proposed memory BIST has a simple hardware structure compared to conventional memory BIST schemes. The proposed memory BIST is a practical test solution for reducing the overall test cost for the mass production of commodity DDRx SDRAMs.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• Computational Structural Engineering
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• v.4 no.4
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• pp.125-133
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• 1991

For the purpose of efficiently calculating the design sensitivity and the reliability for the complicated structures in which the structural responses or limit state functions are given by implicit form, the probabilistic finite element method is introduced to formulate the deterministic design sensitivity analysis method and incorporated with the second moment reliability methods such as MVFOSM, AFOSM and SORM. Also, the probabilistic design sensitivity analysis method needed in the reliability-based design is proposed. As numerical examples, two thin plates are analyzed for the cases of plane stress and plate bending. The initial yielding is defined as failure criterion, and applied loads, yield stress, plate thickness, Young's modulus and Poisson's ratio are treated as random variables. It is found that the response variances and the failure probabilities calculated by the proposed PFEM-based reliability method show good agreement with those by Monte Carlo simulation. The probabilistic design sensitivity evaluates explicitly the contribution of each random variable to probability of failure. Further, the design change can be evaluated without any difficulty, and their effect on reliability can be estimated quickly with high accuracy.

• Journal of the Korean School Mathematics Society
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• v.19 no.4
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• pp.395-415
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• 2016

In this paper, we study the recognition of in-service teachers and pre-service teachers about the concepts of liner equations and liner functions. We chose 49 in-service teachers at secondary schools in G city and 29 pre-service teachers in M university and investigate their recognition about the concepts of liner equations and liner functions. We found following facts. First, in-service teachers and pre-service teachers tend to recognize a linear equation as an equation in one known rather than an equation in two unknowns. Second, in-service teachers and pre-service teachers tend to recognize a linear function as an explicit function rather than an implicit function. Finally, the difference between in-service teachers' recognition and pre-service teachers' recognition is not statistically significant.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.1-2
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• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.