• Journal of Mechanical Science and Technology
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• v.19 no.spc1
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• pp.265-271
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• 2005

Randomness exists in engineering. Tolerance, assemble-error, environment temperature and wear make the parameters of a mechanical system uncertain. So the behavior or response of the mechanical system is uncertain. In this paper, the uncertain parameters are treated as random variables. So if the probability distribution of a random parameter is known, the simulation of mechanical multibody dynamics can be made by Monte-Carlo method. Thus multibody dynamics simulation results can be obtained in statistics. A new concept called functional reliability is put forward in this paper, which can be defined as the probability of the dynamic parameters(such as position, orientation, velocity, acceleration etc.) of the key parts of a mechanical multibody system belong to their tolerance values. A flexible mechanical arm with random parameters is studied in this paper. The length, width, thickness and density of the flexible arm are treated as random variables and Gaussian distribution is used with given mean and variance. Computer code is developed based on the dynamic model and Monte-Carlo method to simulate the dynamic behavior of the flexible arm. At the same time the end effector's locating reliability is calculated with circular tolerance area. The theory and method presented in this paper are applicable on the dynamics modeling of general multibody systems.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.2
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• pp.187-193
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• 2009

Dynamic response of an elastic pendulum system under random excitations was studied by using the Lagrangian equations of motion which uses the kinetic and potential energy of a target system. The responses of random excitations were calculated by using Monte Carl simulation which uses the series of random numbers. The procedure of Monte Carlo simulation is generation of random numbers, system model, system output, and statistical management of output. When the levels of random excitations were changed, the expected responses of the pendulum system showed various responses.

• Smart Structures and Systems
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• v.11 no.1
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• pp.53-67
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• 2013

In this paper we apply Monte Carlo Filter to identifying dynamic parameters of structural systems and improve the efficiency of this algorithm. The algorithms using Monte Carlo Filter so far has not been practical to apply to structural identification for large scale structural systems because computation time increases exponentially as the degrees of freedom of the system increase. To overcome this problem, we developed a method being able to reduce number of particles which express possible structural response state vector. In MCF there are two steps which are the prediction and filtering processes. The idea is very simple. The prediction process remains intact but the filtering process is conducted at each node of structural system in the proposed method. We named this algorithm as relaxation Monte Carlo Filter (RMCF) and demonstrate its efficiency to identify large degree of freedom systems. Moreover to increase searching field and speed up convergence time of structural parameters we proposed an algorithm combining the Genetic Algorithm with RMCF and named GARMCF. Using shaking table test data of a model structure we also demonstrate the efficiency of proposed algorithm.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.331-343
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• 2019

Dynamic thermal rating of the overhead transmission lines is affected by many uncertain factors. The ambient temperature, wind speed and wind direction are the main sources of uncertainty. Measurement uncertainty is an important parameter to evaluate the reliability of measurement results. This paper presents the uncertainty analysis based on Monte Carlo. On the basis of establishing the mathematical model and setting the probability density function of the input parameter value, the probability density function of the output value is determined by probability distribution random sampling. Through the calculation and analysis of the transient thermal balance equation and the steady- state thermal balance equation, the steady-state current carrying capacity, the transient current carrying capacity, the standard uncertainty and the probability distribution of the minimum and maximum values of the conductor under 95% confidence interval are obtained. The simulation results indicate that Monte Carlo method can decrease the computational complexity, speed up the calculation, and increase the validity and reliability of the uncertainty evaluation.

• Structural Engineering and Mechanics
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• v.58 no.3
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• pp.443-458
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• 2016

In this paper, we propose a method for taking into account uncertainties based on the projection on polynomial chaos. Due to the manufacturing and assembly errors, uncertainties in material and geometric properties, the system parameters including assembly defect, damping coefficients, bending stiffness and traction-compression stiffness are uncertain. The proposed method is used to determine the dynamic response of a one-stage spur gear system with uncertainty associated to gear system parameters. An analysis of the effect of these parameters on the one stage gear system dynamic behavior is then treated. The simulation results are obtained by the polynomial chaos method for dynamic analysis under uncertainty. The proposed method is an efficient probabilistic tool for uncertainty propagation. The polynomial chaos results are compared with Monte Carlo simulations.

• Structural Engineering and Mechanics
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• v.53 no.4
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• pp.819-831
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• 2015

In this paper, we propose a new method for taking into account uncertainties based on the projection on polynomial chaos. The new approach is used to determine the dynamic response of a spur gear system with uncertainty associated to gear system parameters and this uncertainty must be considered in the analysis of the dynamic behavior of this system. The simulation results are obtained by the polynomial chaos approach for dynamic analysis under uncertainty. The proposed method is an efficient probabilistic tool for uncertainty propagation. It was found to be an interesting alternative to the parametric studies. The polynomial chaos results are compared with Monte Carlo simulations.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.7 s.124
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• pp.579-586
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• 2007

Analytical method to analyze the effect of tolerance on the modal characteristic of multi-body systems in dynamic equilibrium position is suggested in this paper. Monte-Carlo method is conventionally employed to perform the tolerance analysis. However, Monte-Carlo method spends too much time for analysis and has a greater or less accuracy depending on sample condition. To resolve these problems, an analytical method is suggested in this paper. Sensitivity equations for damped natural frequencies and the transfer function are derived at the dynamic equilibrium position. By employing the sensitivity information of mass, damping and stiffness matrices, the sensitivities of damped natural frequencies and the transfer function can be calculated.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.109-114
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• 2007

Analytical method to analyze the effect of tolerance on the modal characteristic of multi-body systems in dynamic equilibrium position is suggested in this paper. Monte-Carlo Method is conventionally employed to perform the tolerance analysis. However, Monte-Carlo Method spends too much time for analysis and has a greater or less accuracy depending on sample condition. To resolve these problems, an analytical method is suggested in this paper. By employing the sensitivity information of mass, damping and stiffness matrices, the sensitivities of damped natural frequencies and the transfer function can be calculated at the dynamic equilibrium position. The effect of tolerance on the modal characteristic can be analyzed from tolerance analysis method.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.53-60
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• 2015

본 연구에서는 random walk하는 입자와 암세포 확산을 비교하여 Fick's law를 따르는 확산 모형과 암세포 확산의 차이를 밝힌다. 암세포 확산은 암 전이 메커니즘을 이해하는데 매우 중요하다. 하지만 아직까지 암세포 확산은 정확하게 이해되지 않고 있다. 따라서 이번 연구에서는 가장 간단한 2차원 random walk와 암세포 확산을 비교하고, 동역학적인 차이를 규명해 암세포 확산을 이해하고자 한다. Random walk하는 입자는 EDISON 전산화학 전문센터의 프로그램 중 dynamic Monte Carlo(dynamic MC) 전산 모사 소프트웨어를 이용하여 2차원에서 움직이는 레나드-존스 입자의 운동을 통해 살펴보았다. 암세포 확산은 실제 암세포의 시간에 따른 위치 변화 정보 (세포의 궤적)를 직접 구하여 분석하였다. Dynamic MC 결과는 Fickian 확산 모형을 잘 따르는 것을 평균 제곱 거리와 밀도 함수를 통해 확인할 수 있었다. 암세포 확산의 경우 평균 제곱 거리는 시간에 대해서 선형적으로 비례하지만 밀도 함수는 가우시안 형태로 나오지 않으며 Fick's law를 따르는 확산 모형과 다른 확산 형태를 보인다. 이러한 확산 형태는 암세포의 동역학적인 다양성 때문에 나타나며 각각의 암세포가 다른 운동성을 가지는 것에 기인하는 것으로 보인다.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.519-527
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• 2020

Dynamic analysis of a vehicle-track coupling system is important to structural design, damage detection and condition assessment of the structural system. Deterministic analysis of the vehicle-track coupling system has been extensively studied in the past, however, the structural parameters of the coupling system have uncertainties in engineering practices. It is essential to treat the parameters of the vehicle-track coupling system with consideration of uncertainties. In this paper, a method for predicting the bounds of the vehicle-track coupling system responses with uncertain parameters is presented. The uncertain system parameters are modeled as fuzzy variables instead of conventional random variables with known probability distributions. Then, the dynamic response functions of the coupling system are transformed into a component function based on the high dimensional representation approximation. The Lagrange interpolation method is used to approximate the component function. Finally, the bounds of the system's dynamic responses can be predicted by using Monte Carlo method for the interpolation polynomials of the Lagrange interpolation function. A numerical example is introduced to illustrate the ability of the proposed method to predict the bounds of the system's dynamic responses, and the results are compared with the direct Monte Carlo method. The results show that the proposed method is effective and efficient to predict the bounds of the system's dynamic responses with fuzzy variables.