• Composites Research
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• v.23 no.2
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• pp.40-47
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• 2010

In this paper, the hybrid response surface method(HRSM) is proposed and examined. Hybrid response surface method calculate a approximate model repeatedly based on MPP coordinates. To verify the performance, probability of failure, MPP(Most Probable failure Point) and reliability index are calculated for nonlinear function and composite laminated plate by using reliability analysis method and compared with results by using typical response surface method(RSM). Probability of failure is calculated under the assumption of the nonlinear limit state equation and given failure criterion. The results of proposed method shows performance improvement in estimating the probability of failure.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.27 no.6
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• pp.635-642
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• 2014

In this paper, an efficient technique is developed to predict failure probability of three failure modes(case rupture, fracture and bolt breakage) related to solid rocket motor case due to the inner pressure during the mission flight. The overall procedure consists of the steps: 1) design parameters affecting the case failure are identified and their uncertainties are modelled by probability distribution, 2) combustion analysis in the interior of the case is carried out to obtain maximum expected operating pressure(MEOP), 3) stress and other structural performances are evaluated by finite element analysis(FEA), and 4) failure probabilities are calculated for the above mentioned failure modes. Axi-symmetric assumption for FEA is employed for simplification while contact between bolted joint is accounted for. Efficient procedure is developed to evaluate failure probability which consists of finding first an Most Probable Failure Point(MPP) using First-Order Reliability Method(FORM), next making a response surface model around the MPP using Latin Hypercube Sampling(LHS), and finally calculating failure probability by employing Importance Sampling.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.12
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• pp.1464-1470
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• 2009

RBDO approach based on a sampling method with the Kriging metamodel and Constraint Boundary Sampling (CBS), which is sequential sampling method to generate metamodels is proposed. The major advantage of the proposed RBDO approach is that it does not require Most Probable failure Point (MPP) which is essential for First-Order Reliability Method (FORM)-based RBDO approach. The Monte Carlo Sampling (MCS), most well-known method of the sampling methods for the reliability analysis is used to assess the reliability of constraints. In addition, a Cumulative Distribution Function (CDF) of the constraints is approximated using Moving Least Square (MLS) method from empirical distribution function. It is possible to acquire a probability of failure and its analytic sensitivities by using an approximate function of the CDF for the constraints. Moreover, a concept of inactive design is adapted to improve a numerical efficiency of the proposed approach. Computational accuracy and efficiency of the proposed RBDO approach are demonstrated by numerical and engineering problems.