Response Surface Methodology in Risk Analysis

Olivi, L.

doi:10.1007/978-1-4613-3036-3_16

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Both piping groups are rather flexible. The key difference is that the pseudostatic term in the total moment response plays a relatively greater role in piping group (3). This is evidenced by the coefficients of piping frequency, b f in Fig.…”

Section: Dampingmentioning

confidence: 98%

Sensitivity of piping seismic responses to input factors

O'Connell¹

1985

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Page SMACS calculates the dynamic response of structures 2 and equipment to an earthquake. The SMACS coda (Seismic Methodology Analysis Chain 3 with Statistics) has a deterministic calculational model at its core, and a statistical model to represent the variability of inputs and outputs. The linear regression analysis (LRA) treats the SMACS 5 core as a "black box". By examining a set of inputoutput pairs, the LRA produces an "explicit box" which models the behavior of the black box aa closely as possible. The product of the LRA can be used as the inner box 6 of a statistical model to generate an explicit outer box which parallels the SMACS outer box. Site plan of the Zion nuclear power plant 21 Cross section of the containment building at Zion 22 v LIST OF FIGURES Page Finite element model of a portion of the auxiliary 24 building.

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Section: Dampingmentioning

confidence: 98%

Sensitivity of piping seismic responses to input factors

O'Connell¹

1985

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“…In order to reduce calculation time, stochastic order response surface method (SRSM) is presented by using a polynomial type of function gðXÞ to replace the actual limit state function g(X). Typically a quadratic polynomial function without mixed terms is used as follows (Khuri and Cornell, 1996;Olivi, 1980;Rajashekhar and Ellingwood, 1993):…”

Section: 3mentioning

confidence: 99%

“…In order to reduce calculation time, stochastic order response surface method (SRSM) is presented by using a polynomial type of function g¯ ( X ) to replace the actual limit state function g ( X ). Typically a quadratic polynomial function without mixed terms is used as follows (Khuri and Cornell, 1996; Olivi, 1980; Rajashekhar and Ellingwood, 1993): Equation 4 where n is the number of random variables X =[ x 1 , x 2 … x n ], and a 0 , b i , and c i are the 2 n +1 unknown coefficients. The unknown coefficients are obtained from discrete evaluations of the implicit limit state function, such as through evaluation of a FE routine.…”

Section: Fatigue‐based Reliability Assessment Of the Missile Suspension Structurementioning

confidence: 99%

Fatigue life reliability based design optimization for the missile suspension structure

Liu

Zhang

Wang³

2012

Multi Modelg in Mat & Struct

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Purpose -The purpose of this paper is to optimize the key dimensions parameters of the missile suspension structure to ensure the structural fatigue life (> 10000 cycles) with the reliability of 0.995. Design/methodology/approach -The design objective is the fatigue life reliability of the structure, while the design variables are the four fatigue-sensitive dimensions. The nominal stress approach is introduced to predict the fatigue life, and it was verified by comparing with experimental data. The second respond surface method is applied to solve the reliability in a finite element-supported analysis using software MSC Patran/Nastran. A Sequential quadratic programming (SQP) algorithm is used for structure optimization. Findings -The fillet radius r is the most important factor that influences the fatigue life reliability of the structure. The four optimal dimensions parameters are obtained by a reliability-based design optimization process with the fatigue life and reliability fulfilling the demands. Originality/value -The optimal result can be used as the design values for missile suspension structure. The feasibility of the reliability-based design optimization method is validated for the design of missile suspension structure. Keywords Missile suspension structure. Reliability-based design optimization (RBDO), Fatigue life reliability. Sequential quadratic programming (SQP), Structural analysis. Design and theory.

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Response variables correlation in stochastic finite element analysis

Faravelli

1988

Meccanica

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Response Surface Methodology in Risk Analysis

Cited by 7 publications

References 22 publications

Sensitivity of piping seismic responses to input factors

Sensitivity of piping seismic responses to input factors

Fatigue life reliability based design optimization for the missile suspension structure

Response variables correlation in stochastic finite element analysis

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