Comparative Study on Reliability Estimation Using Monte Carlo Simulation with Application to Cylindrical Pressure Vessel

TL;DR

This study compares different burst pressure criteria for cylindrical pressure vessels using Monte Carlo simulation to evaluate failure probabilities, analyzing the impact of variable variations and sensitivities on reliability estimates.

Contribution

It introduces a comparative analysis of multiple burst pressure criteria with Monte Carlo simulation for reliability estimation in pressure vessel design.

Findings

Different criteria show varying sensitivity to design variable changes.

Monte Carlo simulation effectively estimates failure probabilities under variable uncertainties.

Standard deviation analysis highlights key factors influencing burst pressure reliability.

Abstract

One of the methods to design products is reliability-based design, in which failure probability is usually used instead of safety factors. In the technique, it should not be less than a predetermined value. Choosing the proper design criterion is a challenging problem for designers who are dealing with the technique, particularly, when there are various criteria. One of these kinds of products is a cylindrical pressure vessel which has diverse criteria proposed in the literature to calculate the burst pressure as a start point of the design. In this paper, we are going to evaluate and compare the performances of various burst pressure criteria in estimating failure probability which is used for a sample pressure vessel. For each criterion, Monte Carlo simulation has been employed to calculate the probability of failure due to variations related to major design variables. The design parameters include material properties and operating pressure. First, the effects of variations in standard deviations of the design variables on the calculated burst probabilities have been determined by standard deviation analysis. Then, sensitivity analyses were carried out to assess the sensitivity of each burst pressure criterion against changes in the magnitude of design variables.