Zongzhan Gao

Experiment and Simulation Study on the Creep Behavior of PMMA at Different Temperatures

In the present study, creep experiments at different stress and different temperatures were carried out to study the creep behavior of PMMA (MDYB-3). The results show that the creep behavior of PMMA is significantly dependent on temperature in the temperature range of 20–75°C. It was indicated that the duration curves of creep could be divided into three phases. The Chen theory, Norton formula and exponential expression can be used to describe three phases of creep behaviors, respectively. Furthermore, the creep constitutive model based on the experiment results was introduced and implemented into a user subroutine UMAT of software ABAQUS. The finite element analysis (FEA) results prove that the constitutive model introduced in the paper can successfully simulate the creep behavior of structure made of PMMA.

Experimental and Constitutive Investigation on Tensile and Compressive Behavior of MDYB-3 at Different Temperatures and Loading Rates

In the present study, tensile tests and compressive tests at different loading rates and different temperatures were carried out to study the mechanical behaviors of MDYB-3 systematically. The experimental results show that the stress-strain behavior and mechanical performance of MDYB-3 material under tension differ significantly from it under compressive response. The tension failure strength, elastic modulus and yield strength increase basically with the rise of temperature, while the critical strain decreases with the rise of temperature. Furthermore, a tensile constitutive model was proposed for MDYB-3 material expressing in terms of elastic, viscous and plastic deformation.

Structure and System Nonprobabilistic Reliability Solution Method Based on Enhanced Optimal Latin Hypercube Sampling

Using the convex model approach, the bounds of uncertain variables are only required rather than the precise probability distributions, based on which it can be made possible to conduct the reliability analysis for many complex engineering problems with limited information. This paper aims to develop a novel nonprobabilistic reliability solution method for structures with interval uncertainty variables. In order to explore the entire domain represented by interval variables, an enhanced optimal Latin hypercube sampling (EOLHS) is used to reduce the computational effort considerably. Through the proposed method, the safety degree of a structure with convex modal uncertainty can be quantitatively evaluated. More importantly, this method can be used to deal with any general problems with nonlinear and black-box performance functions. By introducing the suggested reliability method, a convex-model-based system reliability method is also formulated. Three numerical examples are investigated to demonstrate the efficiency and accuracy of the method.

Structural reliability assessment based on low-discrepancy adaptive importance sampling and artificial neural network:

In the field of structural reliability, the estimation of failure probability often requires large numbers of time-consuming performance function calls. It is a great challenge to keep the number of function calls to a minimum extent. The aim of this paper is to propose an approach to assess the structural reliability in an efficient way. The proposed method could be viewed as a hybrid reliability method which combines the advantages of adaptive importance sampling, low-discrepancy sampling and artificial neural network. In the proposed method, artificial neural network is introduced to alleviate the computational burden of deterministic and boring engineering analysis, and its introduction guarantees the computational efficiency of the proposed method. While the Markov chain process is adopted to generate the experimental samples which are used to construct the artificial neural network, the introduction of Markov chain process guarantees the adaptivity of the proposed method and makes the proposed method applicable for various reliability problems. The proposed method is shown to be very efficient as the estimated failure probability is very accurate and only a small number of calls to the actual performance function are needed. The effectiveness and engineering applicability of the proposed method are demonstrated by several test examples.

A Practical Method of Nonprobabilistic Reliability and Parameter Sensitivity Analysis Based on Space-Filling Design

Using the convex model approach, the bounds of uncertain variables are only required rather than the precise probability distributions, based on which it can be made possible to conduct the reliability analysis for many complex engineering problems with limited information. In this paper, three types of convex model including interval, ellipsoid, and multiellipsoid convex uncertainty model are investigated, and a uniform model of nonprobabilistic reliability analysis is built. In the reliability analysis process, an effective space-filling design is introduced to generate representative samples of uncertainty space so as to reduce the computational cost and provide an accurate depiction of possible model outcome. Finally, Spearman’s rank correlation coefficient is used to perform parameters global sensitivity analysis. Three numerical examples are investigated to demonstrate the feasibility and accuracy of the presented method.