• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2008년도 정기 학술대회
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• pp.65-70
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• 2008

The capacity spectrum method (CSM) is a deterministic seismic analysis approach wherein the expected seismic response of a structure is established as the intersection of the demand and capacity curves. Recently, there are a few studies about a probabilistic CSM where uncertainties in design factors such as material properties, loads, and ground motion are being considered. However, researches show that soil-structure interaction also affects the seismic responses of structures. Thus, their uncertainties should also be taken into account. Therefore, this paper presents a probabilistic approach of using the CSM for seismic analysis considering uncertainties in soil properties. For application, a reinforced concrete bridge column structure is employed as a test model. Considering the randomness of the various design parameters, the structure's probability of failure is obtained. Monte Carlo importance sampling is used as the tool to assess the structure's reliability when subjected to earthquakes. In this study, probabilistic CSM with and without consideration of soil uncertainties are compared and analyzed. Results show that the analysis considering soil structure interaction yields to a greater probability of failure, and thus can lead to a more conservative structural design.

• Structural Engineering and Mechanics
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• 제63권6호
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• pp.779-788
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• 2017

System identification and damage detection for structural health monitoring have received considerable attention. Various time domain analysis methodologies based on measured vibration data of structures have been proposed. Among them, recursive least-squares estimation of structural parameters which is also known as parametric Kalman filter (PKF) approach has been studied. However, the conventional PKF requires that all the external excitations (inputs) be available. On the other hand, structural uncertainties are inevitable for civil infrastructures, it is necessary to develop approaches for probabilistic damage detection of structures. In this paper, a parametric Kalman filter with unknown inputs (PKF-UI) is proposed for the simultaneous identification of structural parameters and the unmeasured external inputs. Analytical recursive formulations of the proposed PKF-UI are derived based on the conventional PKF. Two scenarios of linear observation equations and nonlinear observation equations are discussed, respectively. Such a straightforward derivation of PKF-UI is not available in the literature. Then, the proposed PKF-UI is utilized for probabilistic damage detection of structures by considering the uncertainties of structural parameters. Structural damage index and the damage probability are derived from the statistical values of the identified structural parameters of intact and damaged structure. Some numerical examples are used to validate the proposed method.

• 대한기계학회논문집A
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• 제20권6호
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• pp.1882-1892
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• 1996

This paper is concerned with the position control of the ond degree-of freedom manipulator using pneumatic artificial muscle actuator which is built to have a proper compliance. For t his pneumatic artificial muscle actuator though, it is difficult to make an effective control scheme due to the nonlinearity and uncertainties on the dynamics of the actuator. In this paper, a third-order equation of motion is derived for the actuator including the dynamics of the pneumatic servovalve. Later, various modeling uncertainties due to the nonlinearity and unmodeled dynamics of the servo vlave and the actuator are taken care of, as a trade-off between the closed-loop performance of the controlled system and its robustness to uncertainties. A controller using .mu. synthesis thchnique is designed, and robust performance against measurement noise, various modeling uncertainties due to the dynamics of the servo valve and actuator is achieved. The effectiveness of the proposed control methods is illustrated through simulations and experiments.

• 한국산학기술학회논문지
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• 제11권9호
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• pp.3304-3310
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• 2010

연구는 가격 및 수요 불확실성하의 강건한 (robust) 생산 및 수송 전략을 수립함으로써 수요 및 가격 불확실성이 존재하는 TFT-LCD 제조업 공급사슬망의 의사결정 문제를 해결하고자 한다. 품질로 구분되는 제품들의 생산, 재고 및 물류에 관한 의사결정을 조정하기 위해, 본 연구에서는 생산용량 제약, 해상/항공 수송 리드타임 및 용량 제약 등의 현실적인 제약조건들을 반영하는 확정적 모델을 정의하고, 시나리오 모델을 이용하여 수요 및 가격 불확실성을 함께 반영하는 확률적 혼합정수선형계획법모형들을 개발한다. 또한 개발된 확률적 모형들의 robust 솔루션을 도출하기 위한 휴리스틱 알고리즘을 제안한다. 그리고 이들 모형들로부터 산출된 솔루션의 성능을 실험을 통하여 다양한 시나리오 하에서 평가하도록 한다.

• 대한기계학회논문집A
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• 제33권10호
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• Structural Engineering and Mechanics
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• 제25권3호
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• pp.311-330
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• 2007

Model-based predictions of structural behavior are negatively affected by uncertainties of various type and in various stages of the structural analysis. The present paper focusses on dynamic analysis and addresses the effects of uncertainties concerning material and geometric parameters, mainly in the context of modal analysis of large-scale structures. Given the large number of uncertain parameters arising in this case, highly scalable simulation-based methods are adopted, which can deal with possibly thousands of uncertain parameters. In order to solve the reliability problem, i.e., the estimation of very small exceedance probabilities, an advanced simulation method called Line Sampling is used. In combination with an efficient algorithm for the estimation of the most important uncertain parameters, the method provides good estimates of the failure probability and enables one to quantify the error in the estimate. Another aspect here considered is the uncertainty quantification for closely-spaced eigenfrequencies. The solution here adopted represents each eigenfrequency as a weighted superposition of the full set of eigenfrequencies. In a case study performed with the FE model of a satellite it is shown that the effects of uncertain parameters can be very different in magnitude, depending on the considered response quantity. In particular, the uncertainty in the quantities of interest (eigenfrequencies) turns out to be mainly caused by very few of the uncertain parameters, which results in sharp estimates of the failure probabilities at low computational cost.

• Nuclear Engineering and Technology
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• 제46권3호
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• pp.343-352
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• 2014

Uncertainty and sensitivity analyses with respect to nuclear data are performed with depletion calculations for BWR and PWR fuel assemblies specified in the framework of the UAM-LWR Benchmark Phase II. For this, the GRS sampling based tool XSUSA is employed together with the TRITON depletion sequences from the SCALE 6.1 code system. Uncertainties for multiplication factors and nuclide inventories are determined, as well as the main contributors to these result uncertainties by calculating importance indicators. The corresponding neutron transport calculations are performed with the deterministic discrete-ordinates code NEWT. In addition, the Monte Carlo code KENO in multi-group mode is used to demonstrate a method with which the number of neutron histories per calculation run can be substantially reduced as compared to that in a calculation for the nominal case without uncertainties, while uncertainties and sensitivities are obtained with almost the same accuracy.

• 대한기계학회논문집A
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• 제25권3호
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• pp.369-380
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• 2001

Although extensive efforts have been devoted to the optimal design of composite laminated plates in recent years, some practical issues still need further research. Two of them are: the handling of the ply angle as either continuous or discrete; and that of the uncertainties in material properties, which were treated as continuous and ignored respectively in most researches in the past. In this paper, an algorithm for stacking sequence optimization which deals with discrete ply angles and that for thickness optimization which considers uncertainties in material properties are used for a two step optimization of composite laminated plates. In the stacking sequence optimization, the branch and bound method is modified to handle discrete variables; and in the thickness optimization, the convex modeling is used in calculating the failure criterion, given as constraint, to consider the uncertain material properties. Numerical results show that the optimal stacking sequence is found with fewer evaluations of objective function than expected with the size of feasible region taken into consideration; and the optimal thickness increases when the uncertainties of elastic moduli considered, which shows such uncertainties should not be ignored for safe and reliable designs.

• 항공우주시스템공학회지
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• 제14권2호
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• pp.20-27
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• 2020

Several uncertainties in the landing environment of an aircraft are not considered, such as the falling speed, ambient temperature, and sensor noise. These uncertainties negatively affect the performance of the controller applied to a landing gear. The sliding mode control (SMC) method, which maintains the optimal performance of a controller under uncertainties, is used in this study. The landing gear is equipped with a magnetorheological damper that changes the yield shear stress according to the applied magnetic field. The applied controller employs a hybrid control combining Skyhook control and force control. The SMC maintains the optimal performance of the hybrid control by minimizing the tracking error of the damper force, even in various landing environments where parameter uncertainties are applied. The effect of SMC is verified through co-simulation results from Simscape and Simulink.

• Nuclear Engineering and Technology
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• 제48권3호
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• pp.684-701
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• 2016

The Gaussian process model (GPM) is a flexible surrogate model that can be used for nonparametric regression for multivariate problems. A unique feature of the GPM is that a prediction variance is automatically provided with the regression function. In this paper, we estimate the safety margin of a nuclear power plant by performing regression on the output of best-estimate simulations of a large-break loss-of-coolant accident with sampling of safety system configuration, sequence timing, technical specifications, and thermal hydraulic parameter uncertainties. The key aspect of our approach is that the GPM regression is only performed on the dominant input variables, the safety injection flow rate and the delay time for AC powered pumps to start representing sequence timing uncertainty, providing a predictive model for the peak clad temperature during a reflood phase. Other uncertainties are interpreted as contributors to the measurement noise of the code output and are implicitly treated in the GPM in the noise variance term, providing local uncertainty bounds for the peak clad temperature. We discuss the applicability of the foregoing method to reduce the use of conservative assumptions in best estimate plus uncertainty (BEPU) and Level 1 probabilistic safety assessment (PSA) success criteria definitions while dealing with a large number of uncertainties.