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There are lots of uncertainties in variable-stiffness composite materials such as material properties, fibre volume fraction, geometries at various scale and matrix porosity. Commonly, these uncertainties are not always mutually independent and there exist correlations among these random input variables. These correlations may affect the output of composite significantly. To address these correlations, a novel approach for uncertainty analysis based on copula function assisted by reanalysis method is suggested. The Copula function is utilized to address the correlations of random input variables. Monte Carlo simulation (MCS) is employed to obtain the uncertainty analysis. Therefore, a large number of samples should be generated and the expensive computational cost is not feasible when the popular finite element (FE) model is utilized. To save the computational cost and make the uncertainty analysis feasible in practice, an efficient fast computation method, reanalysis method is integrated in the frame. The numerical test demonstrates that the proposed approach is an efficient uncertainty analysis tool for the practical engineering problems.

Although the Efficient Global Optimization (EGO) algorithm has been widely used in multi-disciplinary optimization, it is still difficult to handle multiple constraint problems. In this study, to increase the accuracy of approximation, the Least Squares Support Vector Regression (LSSVR) is suggested to replace the kriging model for approximating both objective and constrained functions while the variances of these surrogate models are still obtained by kriging. To enhance the ability to search the feasible region, two criteria are suggested. First, a Maximize Probability of Feasibility (MPF) strategy to handle the infeasible initial sample points is suggested to generate feasible points. Second, a Multi-Constraint Parallel (MCP) criterion is suggested for multiple constraints handling, parallel computation and validation, respectively. To illustrate the efficiency of the suggested EGO-based method, several deterministic benchmarks are tested and the suggested methods demonstrate a superior performance compared with two other constrained algorithms. Finally, the suggested algorithm is successfully utilized to optimize the fiber path of variable-stiffness beam and lightweight B-pillar to demonstrate the performance for engineering applications. [ABSTRACT FROM AUTHOR]

In this study, a thermal optimization framework for the platform inertial navigation system (PINS) is built. To reduce the local high temperature of the PINS, a pin-fin heat sink with staggered arrangement is designed. To reduce the dimension of the inputs and improve the optimization efficiency, a feasible global sensitivity analysis (GSA) based on kriging–high-dimensional model representation with DIviding RECTangles (DIRECT-KG-HDMR) sampling strategy is proposed. According to the GSA results, the filtered structural parameters are used to optimize the thermal performance of the heat sink, and several optimization algorithms (genetic algorithm, differential evolution, teaching–learning-based optimization, particle swarm optimization and efficient global optimization) are used for optimization. The steady thermal response of the PINS with the optimized heat sink is also studied, and the result shows that the maximum temperature of the high-temperature region of the platform is reduced by 1.08°C compared with the PINS without the heat sink. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to suggest and develops a global sensitivity analysis-assisted multi-level sequential optimization method for the heat transfer problem. Design/methodology/approach: Compared with other surrogate-assisted optimization methods, the distinctive characteristic of the suggested method is to decompose the original problem into several layers according to the global sensitivity index. The optimization starts with the several most important design variables by the support vector regression-based efficient global optimization method. Then, when the optimization process progresses, the filtered design variables should be involved in optimization one by one or the setting value. Therefore, in each layer, the design space should be reduced according to the previous optimization result. To improve the accuracy of the global sensitivity index, a novel global sensitivity analysis method based on the variance-based method incorporating a random sampling high-dimensional model representation is introduced. Findings: The advantage of this method lies in its capability to solve complicated problems with a limited number of sample points. Moreover, to enhance the reliability of optimum, the support vector regression-based global efficient optimization is used to optimize in each layer. Practical implications: The developed optimization tool is built by MATLAB and can be integrated by commercial software, such as ABAQUS and COMSOL. Lastly, this tool is integrated with COMSOL and applied to the plant-fin heat sink design. Compared with the initial temperature, the temperature after design is over 49°. Moreover, the relationships among all design variables are also disclosed clearly. Originality/value: The D-MORPH-HDMR is integrated to obtain the coupling relativities among the design variables efficiently. The suggested method can be decomposed into multiplier layers according to the GSI. The SVR-EGO is used to optimize the sub-problem because of its robustness of modeling. [ABSTRACT FROM AUTHOR]