NONLINEAR STABILITY OF TRUNCATED SHALLOW CONICAL SANDWICH SHELL WITH VARIABLE THICKNESS

The theory of nonlinear stability for a truncated shallow conical shell with variable thickness under the action of uniform pressure was presented. The fundamental equations and boundary conditions were derived by means of calculus of variations. An analytic solution for the critical buckling pressure of the shell with a hyperbolically varying thickness is obtained by use of modified iteration method. The results of numerical calculations are presented in diagrams, which show the influence of geometrical and physical parameters on the buckling behavior.

NONLINEAR STABILITY OF TRUNCATED SHALLOW SPHERICALSHELL WITH VARIABLE THICKNESS UNDERUNIFORMLY DISTRIBUTED LOAD

In this paper, to begin with. the nonlinear differential equations of a truncaled shallow spherical shell with variable thickness under uniformal distributed load are linearized by step-by-step loading method. The linear differential equations can be solved by spline collocanon method. Critical loads have been obtained accordingly.

Adaptive Radial Based Sampling for Reliability and Reliability Sensitivity Analysis Involving Truncated Correlated Normal Variables

The reliability and reliability sensitivity （ RS ） models are presented for the engineering problem involving truncated correlated normal variables （CNV）, and in the case an adaptive radial based sampling is used to analyze the reliability and the RS. In the presented models, the truncated CNV is transformed to general CNV, and the value domains of the truncated CNV are treated as multiple failure modes, then the reliability and the RS with the truncated CNV are transformed to the general cases, on which an e^cient radial based sampling is used to analyze the trans- formed reliability and RS. An adaptive strategy is employed to search for the optimal radial in the sampling, by which the robustness of the method is improved. After the model concepts and the detailed implementation are given, several examples are presented to demonstrate the feasibility of the model and the efficiency of the solutions.

ABCLS method for high-reliability aerospace mechanism with truncated random uncertainties

The random variables are always truncated in aerospace engineering and the truncated distribution is more feasible and effective for the random variables due to the limited samples available.For high-reliability aerospace mechanism with truncated random variables, a method based on artificial bee colony（ABC） algorithm and line sampling（LS） is proposed.The artificial bee colony-based line sampling（ABCLS） method presents a multi-constrained optimization model to solve the potential non-convergence problem when calculating design point（is also as most probable point, MPP） of performance function with truncated variables; by implementing ABC algorithm to search for MPP in the standard normal space, the optimization efficiency and global searching ability are increased with this method dramatically.When calculating the reliability of aerospace mechanism with too small failure probability, the Monte Carlo simulation method needs too large sample size.The ABCLS method could overcome this drawback.For reliability problems with implicit functions, this paper combines the ABCLS with Kriging response surface method,therefore could alleviate computational burden of calculating the reliability of complex aerospace mechanism.A numerical example and an engineering example are carried out to verify this method and prove the applicability.