• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.127-134
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• 1995

The dynamic characteristics of a system can be critically influenced by system uncertainty, so the dynamic system must be analyzed stochastically in consideration of system uncertainty. This study presents a generalized stochastic model of dynamic system subjected to bot external and parametric nonstationary stochastic input. And this stochastic system is analyzed by a new stochastic process closure method and moment equation method. The first moment equation is numerically evaluated by Runge-Kutta method. But the second moment equation is founded to constitute an infinite coupled set of differential equations, so this equations are numerically evaluated by cumulant neglect closure method and Runge-Kutta method. Finally the accuracy of the present method is verified by Monte Carlo simulation.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3367-3382
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• 2023

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian computational fluid dynamics method that has been widely used in the analysis of physical phenomena characterized by large deformation or multi-phase flow analysis, including free surface. Despite the recent implementation of eddy-viscosity models in SPH methodology, sophisticated turbulent analysis using Lagrangian methodology has been limited due to the lack of computational performance and numerical consistency. In this study, we implement the standard and dynamic Smagorinsky model and dynamic Vreman model as sub-particle scale models based on a weakly compressible SPH solver. The large eddy simulation method is numerically identical to the spatial discretization method of smoothed particle dynamics, enabling the intuitive implementation of the turbulence model. Furthermore, there is no additional filtering process required for physical variables since the sub-grid scale filtering is inherently processed in the kernel interpolation. We simulate lid-driven flow under transition and turbulent conditions as a benchmark. The simulation results show that the dynamic Vreman model produces consistent results with experimental and numerical research regarding Reynolds averaged physical quantities and flow structure. Spectral analysis also confirms that it is possible to analyze turbulent eddies with a smaller length scale using the dynamic Vreman model with the same particle size.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.121-122
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• 2006

In the case of mechanical control systems, it is highly useful to be able to provide a compact model of the mechanical system to control engineers using the smallest number of variables, while still providing an accurate model. The reduced mechanical model can then be inserted into the complete mechanical control system models and used for system-level dynamic simulation. In this paper, a moment-matching based model order reduction (MOR) which reduces the number of degrees of freedom of an original finite element model via the Arnoldi process is considered to study the dynamic responses of a HDD actuator and suspension system.

• Earthquakes and Structures
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• v.5 no.1
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• pp.111-127
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• 2013

This paper describes effects of infill walls on behavior of RC frame with low strength, including numerical modeling, modal testing and finite-element model updating. For this purpose full scaled, one bay and one story RC frame is produced and tested for plane and brick in-filled conditions. Ambient-vibration testis applied to identify dynamic characteristics under natural excitations. Enhanced Frequency Domain Decomposition and Stochastic Subspace Identification methods are used to obtain experimental dynamic characteristics. A numerical modal analysis is performed on the developed two-dimensional finite element model of the frames using SAP2000 software to provide numerical frequencies and mode shapes. Dynamic characteristics obtained by numerical and experimental are compared with each other and finite element model of the frames are updated by changing some uncertain modeling parameters such as material properties and boundary conditions to reduce the differences between the results. At the end of the study, maximum differences in the natural frequencies are reduced on average from 34% to 9% and a good agreement is found between numerical and experimental dynamic characteristics after finite-element model updating. In addition, it is seen material properties are more effective parameters in the finite element model updating of plane frame. However, for brick in-filled frame changes in boundary conditions determine the model updating process.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.754-759
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• 1993

An adaptive model predictive control (AMPC) strategy using auto-regression moving-average (ARMA) models is presented. The characteristic features of this methodology are the small computer memory requirement, high computational speed, robustness, and easy handling of nonlinear and time varying MIMO systems. Since the process dynamic behaviors are expressed by ARMA models, the model parameter adaptation is simple and fast to converge. The recursive least square (RLS) method with exponential forgetting is used to trace the process model parameters assuming the process is slowly time varying. The control performance of the AMPC is verified by both comparative simulation and experimental studies on distillation column control.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.263-268
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• 2017

A hot forming process is required for Mg alloys to enhance the formability and plastic workability due to the insufficient formability at room temperature. Mg alloy undergoes dynamic recrystallization (DRX) during the hot working process, which is a restoration or softening mechanism that reduces the dislocation density and releases the accumulated energy to facilitate plastic deformation. The flow stress curve shows three stages of complicated strain hardening and softening phenomena. As the strain increases, the stress also increases due to work hardening, and it abruptly decreases work softening by dynamic recrystallization. It then maintains a steady-state region due to the equilibrium between the work hardening and softening. In this paper, an efficient optimization process is proposed for the material model of the dynamic recrystallization to improve the accuracy of the flow curve. A total of 18 variables of the constitutive equation of AZ80 alloy were systematically optimized at an elevated forming temperature($300^{\circ}C$) with various strain rates(0.001, 0.1, 1, 10/sec). The proposed method was validated by applying it to the constitutive equation of AZ61 alloy.

• Journal of the Korean Statistical Society
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• v.27 no.3
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• pp.279-288
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• 1998

We propose a multiprocess dynamic Poisson model for the analysis of Poisson process with the covariates. The algorithm for the recursive estimation of the parameter vector modeling time-varying effects of covariates is suggested. Also the algorithm for forecasting of numbers of events at the next time point based on the information gathered until the current time is suggested.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1705-1711
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• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.10
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• pp.1034-1042
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• 2009

In this paper, the magic formula model was applied for a friction damper in a drum-type washing machine. To describe characteristics of the hysteretic damping force, Physical tests were first carried out to get experimental results using an MTS machine. Then, parameters for the magic formula model were determined from the experimental curves. The ADAMS and MATLAB programs were used for the multibody modeling of the damper and process for parameter identification. The model of drum-type washing machine was applied for a dynamic model of friction damper, in which the accuracy of the proposed damper model was verified.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.174-182
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• 2002

System identification and controller formulation are essential in dynamic process control. In system identification, data for system identification are obtained, and then they are analyzed so that the system model of the process is built, identified, and diagnosed. In controller formulation, the control equation is derived based on the result of the system identification. There has been much theoretical research on system identification and controller formulation. These theories are very useful when they are appropriately applied. To our regret, however, these theories are not always effectively applied in practice because the engineers and the operators who manage the process often do not have the necessary understanding of required time series analysis methods. On the other hand, because of widespread use of statistical packages, system identification such as estimating ARMA models can be done with little understanding of time series analysis methods. Therefore, it might be said that the most theoretically difficult part in practice is the controller formulation. In this paper, lists of control equations are proposed as a useful tool for practitioners to use. The tool supports bridging the gap between theory and practice in dynamic process control. Also, for some models, the generalized control equations are obtained.