No abstract

A radial basis function (RBF) based sequential surrogate reliability method (SSRM) is proposed, in which a special optimization problem is solved to update the surrogate model of the limit state function (LSF) iteratively. The objective of the optimization problem is to find a new point to maximize the probability density function (PDF), subject to the constraints that the new point is on the approximated LSF and the minimum distance to the existing points is greater than or equal to the given distance. By updating the surrogate model with the new points, the surrogate model of the LSF becomes more and more accurate in the important region with a high failure probability and on the LSF boundary.Moreover, the accuracy of the unimportant region is also improved within the iteration due to the minimum distance constraint. SSRM takes advantage of the information of PDF and LSF to capture the failure features, which decreases the number of the expensive LSF evaluations. Six numerical examples show that SSRM improves the accuracy of the surrogate model in the important region around the failure boundary with small number of samples and has better adaptability to the nonlinear LSF, hence increases the accuracy and efficiency of the reliability analysis.Reliability analysis is essentially a high-dimensional integration of complex and implicit limit state function (LSF). The numerical integration and Monte Carlo Simulation (MCS) with the original LSF face enormous computational challenges [1-2], therefore different approximations of the LSF are adopted to improve the computational efficiency.

No abstract

No abstract

No abstract

No abstract

Reduction to the mechanical properties of fiber-reinforced polymer composites occurs when the material is exposed to radiant heat flux and compressive loading. A thermo-mechanical model was developed to predict the compressive strength and the failure time of silica fiber-reinforced phenolic composites. The coupling heat and mass transfer processes, generation of pyrolysis gases, and their subsequent diffusion process were considered in the model. The thermal softening, thermal decomposition of the matrix material, and phase transition of the reinforced fibers, which reduce the strength of the material, were also taken into account in the formulation of the model. Pyrolysis kinetics of phenolic resin, volume fraction of phase component, temperature profile, compressive strength, and time-to-failure of silica/ phenolic composites were predicted using the developed model. The calculated temperature-dependent strength curve was compared with the experimental results measured by a high-temperature compression testing, and the agreement is good. The material fracture morphology was analyzed for silica-phenolic composite specimen after high-temperature compression testing.

No abstract