Global reliability sensitivity analysis by Sobol-based dynamic adaptive kriging importance sampling

Cadini, Francesco; Lombardo, Simone Salvatore; Giglio, Marco

doi:10.1016/j.strusafe.2020.101998

Cited by 28 publications

(9 citation statements)

References 37 publications

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“…Step 2: Calculate the true responses of the initial sample using the constructed S-FEM of sealing performance and establish an initial KG model based on the N i groups of samples. The prediction sum of squares (PRESS) 18 of the KG model is calculated by using cross validation method, and whether the prediction error value is less than the set prediction error standard R s is determined. If the conditions are satisfied, go directly to Step 3; otherwise, obtain another n us groups of new samples using the LHS method and use the S-FEM to calculate the true responses of the new samples.…”

Section: Sensitivity Analysis Of Sealing Performance Based On the Kg-sobol Methodsmentioning

confidence: 99%

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Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

Zeng

Sun

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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The deep-water pipeline connector (DPC) is a key piece of equipment for the connection of multiple facilities located in different positions of the subsea production system. Its sealing reliability affects the safety of the production system directly during oil and gas exploration. Although the reliability assessment methods of the DPC have been investigated, previous studies have mostly been carried out under a single mechanical field. To determine the DPC reliability considering thermo-mechanical coupling, this paper establishes random load models of working pressure and temperature by using the Gaussian function, and the stochastic finite element model of the DPC is constructed to obtain the sealing performance under coupling conditions. A Kriging–Sobol sensitivity analysis method is developed to obtain the main variables influencing the sealing performance for the purpose of decreasing the computational cost of the ordinary sensitivity analysis. A reliability assessment approach based on the cross entropy-importance sampling-Kriging method is introduced to analyze the DPC reliability, and a sealing reliability assessment method of DPC under thermo-mechanical coupling conditions is finally formed. An engineering case is taken to verify the effectiveness of the proposed method. The results show that the reliability analysis accuracy of the proposed method is almost agreed with the Monte Carlo method, but the computational cost can be reduced 85.61%, which indicates that the proposed method provides designers with a fast method with an acceptable computational cost to assess the reliability of the DPC connector under coupling conditions.

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Section: Sensitivity Analysis Of Sealing Performance Based On the Kg-sobol Methodsmentioning

confidence: 99%

“…Its key point is the calculation of the main and total effects of samples. 18 Taking a response with n-dimensional independent variables as an example, k(2 + n) calculations are needed to obtain the main effects of the analysis, and the value of k is usually . 500.…”

Section: High Computational Cost Of the Sensitivity Analysismentioning

confidence: 99%

Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

Zeng

Sun

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…In this respect, Sensitivity Analysis (SA) can aid the understanding of how the uncertainty in the model is apportioned among the model input parameters uncertainties [10], [11]. In other words, through SA, one can identify the most sensitive parameters and better focus the uncertainty analysis without losing accuracy [12]. Different SA techniques have been proposed in literature, which can be sorted into three main categories: local, regional, and global [13].…”

Section: Introductionmentioning

confidence: 99%

“…They are usually computed via a double-loop Monte Carlo Simulation (MCS), with computational cost equal to 𝐶 = 𝑣 • 𝑛 1 • 𝑛 2 , where 𝑣 is the number of input variables, 𝑛 1 the sample size for estimating the inner loop and 𝑛 2 the sample size for the outer loop [24], [25]. When the model runs are time consuming, the computational cost is high and strategies have been proposed to reduce it, including: reduced order models calibrated on input-output data obtained by few runs of the original model, e.g., Bayesian approaches [26], kriging [12], [27],and polynomial chaos expansion [28], [29]; sampling schemes tailored to efficiently characterize the sensitivity of the model inputs, e.g., Fourier Amplitude Sensitivity Test (FAST) [30], [31] and Effective Algorithm for computing global Sensitivity Indices (EASI) [32]; finally, data-driven approaches allow exploiting available datasets to calculate sensitivity measures [22], [33], [34], [35], which is quite of interest for many practical applications in which an input-output dataset is available and models to perform a GSA using MCS-based methods cannot be run [22], [36].…”

Section: Introductionmentioning

confidence: 99%

A Bootstrapped Modularised method of Global Sensitivity Analysis applied to Probabilistic Seismic Hazard Assessment

et al. 2023

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“…An intelligent optimization algorithm-the improved chaotic particle swarm optimization algorithm [20][21][22][23]was used to analyze the stochastic sensitivity of the angle beam-column joint with a gusset plate.…”

Section: Introductionmentioning

confidence: 99%

Research on Mechanical Properties and Parameter Identification of Beam‐Column Joint with Gusset Plate Angle Using Experiment and Stochastic Sensitivity Analysis

Dong

Wang

2021

Advances in Civil Engineering

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Based on stochastic sensitivity analysis, a new style of joint structure with greater ductility and higher strength—the beam-column joint with gusset plate angle (JGA) steel—was proposed. Research on the static and hysteretic behavior of the JGA was performed using finite element analysis and experimental methods. The research results indicated that adding a seat angle could increase the positive and negative initial rotational stiffness and strength and provide a better energy consumption performance of the joint. An improved chaotic particle swarm optimization (ICPSO) neural network algorithm was used to study the stochastic sensitivity. Seven important parameters that influence the bending stiffness and strength of the JGA, namely, the beam height, beam flange width, beam web thickness, gusset plate thickness, connection angle steel thickness, connection angle steel width, and seat angle steel thickness, were investigated by stochastic sensitivity analysis. Moreover, the beam height, connection angle steel, and seat angle steel thickness, which had significant influences on the mechanical properties of the joints, were studied in depth by finite element analysis. Within the range of the parameters of the joint, the higher the beam height was, the larger the connection angle thickness was; the smaller the connection angle width was, the better the joint performance was. A reasonable design of the JGA is proposed: a beam with the SH2 section (250 × 125 × 6 × 9 mm) and a 10 mm thick and 75 mm long angle steel connection.

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Global reliability sensitivity analysis by Sobol-based dynamic adaptive kriging importance sampling

Cited by 28 publications

References 37 publications

Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

A Bootstrapped Modularised method of Global Sensitivity Analysis applied to Probabilistic Seismic Hazard Assessment

Research on Mechanical Properties and Parameter Identification of Beam‐Column Joint with Gusset Plate Angle Using Experiment and Stochastic Sensitivity Analysis

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