近年来,复合材料层合板结构被广泛地应用于航空航天、军工、建筑工程等领域。但是,由于其几何尺寸的不准确性、材料参数的分散性、载荷环境的波动性等不确定性因素的影响,可能会对复合材料层合板结构的可靠性和安全性,以及系统的输出响...近年来,复合材料层合板结构被广泛地应用于航空航天、军工、建筑工程等领域。但是,由于其几何尺寸的不准确性、材料参数的分散性、载荷环境的波动性等不确定性因素的影响,可能会对复合材料层合板结构的可靠性和安全性,以及系统的输出响应产生重大影响。由于复合材料层合板的层间黏结不良、外部应力集中等因素,当复合材料层合板结构的能量释放速率达到层间断裂韧性时,就会发生分层。因此对复合材料层合板结构的分层可靠性进行分析具有重要的意义。目前,对于复合材料层合板结构的可靠性分析主要是采用一阶可靠性方法(first order reliability method,FORM)、二阶可靠性方法(second order reliability method,SORM)和重要性抽样方法(importance sampling,IS)等传统可靠性分析方法,并将其和蒙特卡罗模拟(Monte Carlo simulation,MCS)对比。但是,当复合材料结构不确定性维度高且复杂时,这些方法不仅计算效率太低,而且不能保证其计算精度。相比于传统的可靠性分析方法,可以利用基于自适应Kriging模型集成策略和主动学习函数结合蒙特卡罗模拟(adaptive Kriging-based Monte Carlo simulation,AK-MCS)的方法,对复合材料层合板结构进行可靠性分析。而直接概率积分方法(direct probability integral method,DPIM)具有更高的计算效率和精度,特别是对于高维度和复杂的可靠性分析问题。所以,本文采用AK-MCS方法和DPIM对模式Ⅰ、模式Ⅱ和混合Ⅰ/Ⅱ模式下的复合材料层合板结构分层的可靠度进行了研究。结果表明:DPIM和AK-MCS与传统可靠性分析方法相比具有更高的计算精度和计算效率,但是DPIM以其高效的计算效率脱颖而出,尽管其精度略低于AK-MCS,但在处理随机变量更多、非线性程度更高的混合Ⅰ/Ⅱ模式下的层合板结构分层的可靠性时展现出明显优势。综合考虑精度与时效性的平衡,DPIM能够准确地评估复合材料结构的可靠度,保障其在航天航空装备等领域的安全运行。展开更多
An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the bucklin...An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.展开更多
文摘近年来,复合材料层合板结构被广泛地应用于航空航天、军工、建筑工程等领域。但是,由于其几何尺寸的不准确性、材料参数的分散性、载荷环境的波动性等不确定性因素的影响,可能会对复合材料层合板结构的可靠性和安全性,以及系统的输出响应产生重大影响。由于复合材料层合板的层间黏结不良、外部应力集中等因素,当复合材料层合板结构的能量释放速率达到层间断裂韧性时,就会发生分层。因此对复合材料层合板结构的分层可靠性进行分析具有重要的意义。目前,对于复合材料层合板结构的可靠性分析主要是采用一阶可靠性方法(first order reliability method,FORM)、二阶可靠性方法(second order reliability method,SORM)和重要性抽样方法(importance sampling,IS)等传统可靠性分析方法,并将其和蒙特卡罗模拟(Monte Carlo simulation,MCS)对比。但是,当复合材料结构不确定性维度高且复杂时,这些方法不仅计算效率太低,而且不能保证其计算精度。相比于传统的可靠性分析方法,可以利用基于自适应Kriging模型集成策略和主动学习函数结合蒙特卡罗模拟(adaptive Kriging-based Monte Carlo simulation,AK-MCS)的方法,对复合材料层合板结构进行可靠性分析。而直接概率积分方法(direct probability integral method,DPIM)具有更高的计算效率和精度,特别是对于高维度和复杂的可靠性分析问题。所以,本文采用AK-MCS方法和DPIM对模式Ⅰ、模式Ⅱ和混合Ⅰ/Ⅱ模式下的复合材料层合板结构分层的可靠度进行了研究。结果表明:DPIM和AK-MCS与传统可靠性分析方法相比具有更高的计算精度和计算效率,但是DPIM以其高效的计算效率脱颖而出,尽管其精度略低于AK-MCS,但在处理随机变量更多、非线性程度更高的混合Ⅰ/Ⅱ模式下的层合板结构分层的可靠性时展现出明显优势。综合考虑精度与时效性的平衡,DPIM能够准确地评估复合材料结构的可靠度,保障其在航天航空装备等领域的安全运行。
基金Vietnam National Foundation for Science and Technology Development(NAFOSTED)under Grant number 107.02-2019.330.
文摘An effective hybrid optimization method is proposed by integrating an adaptive Kriging(A-Kriging)into an improved partial swarm optimization algorithm(IPSO)to give a so-called A-Kriging-IPSO for maximizing the buckling load of laminated composite plates(LCPs)under uniaxial and biaxial compressions.In this method,a novel iterative adaptive Kriging model,which is structured using two training sample sets as active and adaptive points,is utilized to directly predict the buckling load of the LCPs and to improve the efficiency of the optimization process.The active points are selected from the initial data set while the adaptive points are generated using the radial random-based convex samples.The cell-based smoothed discrete shear gap method(CS-DSG3)is employed to analyze the buckling behavior of the LCPs to provide the response of adaptive and input data sets.The buckling load of the LCPs is maximized by utilizing the IPSO algorithm.To demonstrate the efficiency and accuracy of the proposed methodology,the LCPs with different layers(2,3,4,and 10 layers),boundary conditions,aspect ratios and load patterns(biaxial and uniaxial loads)are investigated.The results obtained by proposed method are in good agreement with the literature results,but with less computational burden.By applying adaptive radial Kriging model,the accurate optimal resultsebased predictions of the buckling load are obtained for the studied LCPs.
