In this study,non-linear thermal-mechanical stability and vibration analyses of different end-shaped single-walled carbon nanotube conveying viscous nano-magnetic fluid embedded in non-linear visco-elastic foundation ...In this study,non-linear thermal-mechanical stability and vibration analyses of different end-shaped single-walled carbon nanotube conveying viscous nano-magnetic fluid embedded in non-linear visco-elastic foundation under the influence of magnetic fields are presented.The development of the equation of motion was based on Euler-Bernoulli theory,Hamilton principle and nonlocal elasticity theory.The results of the analytical solutions using Galerkin decomposition differential transform method(GDDTM)were validated with existing experimental results.From the parametric studies,it was shown that decreasing the temperature difference as well as increasing the downstream angle decreased the system's stability for pre-bifurcation analysis but increased stability of the system for post bifurcation analysis.Also,the results obtained from the dynamic behaviour of the system indicated that the magnetic effect had an attenuating impact of about 45%on the system's response at any mode and for any boundary condition considered.It is hoped that this work will enhance the design and optimization of nano-devices with I,V,Y,L,K and T-shaped junctions under the influence of thermal-magneto-mechanical flow induced vibration.展开更多
具有单连续变量的背包问题(Knapsack Problem with a single Continuous variable,KPC)是标准0-1背包问题的一个新颖扩展形式,它既是一个NP完全问题,又是一个带有连续变量S的新颖组合优化问题,求解难度非常大.为了快速高效地求解KPC问题...具有单连续变量的背包问题(Knapsack Problem with a single Continuous variable,KPC)是标准0-1背包问题的一个新颖扩展形式,它既是一个NP完全问题,又是一个带有连续变量S的新颖组合优化问题,求解难度非常大.为了快速高效地求解KPC问题,该文提出了利用演化算法求解KPC的新思路,并给出了基于离散差分演化算法求解KPC的两个有效方法.首先,介绍了基本差分演化算法和具有混合编码的二进制差分演化算法(HBDE)的原理,给出了HBDE的算法伪代码描述,并分析了KPC的基本数学模型KPCM1的计算复杂度.然后,在基于降维法消除KPCM1中连续变量S的基础上,建立了KPC的一个新离散数学模型KPCM2;随后在基于贪心策略提出处理不可行解的有效算法基础上,基于单种群HBDE给出了求解KPC的第一个离散演化算法S-HBDE.第三,通过把连续变量S的取值范围划分为两个子区间将KPC分解为两个子问题,并基于降维法建立了KPC的适于并行求解的第二个数学模型KPCM3;在利用贪心策略给出处理子问题不可行解的两个有效算法基础上,基于双种群HBDE提出了求解KPC的第二个离散演化算法B-HBDE.最后,在给出四类大规模KPC实例的基础上,利用S-HBDE和B-HBDE分别求解这些实例,并与近似算法AP-KPC、遗传算法和离散粒子群优化算法的计算结果、耗费时间和稳定性等指标进行比较,比较结果表明S-HBDE和B-HBDE不仅在求解精度和稳定性方面均优于其它3个算法,而且求解速度很快,非常适于在实际应用中快速高效地求解大规模KPC实例.展开更多
文摘In this study,non-linear thermal-mechanical stability and vibration analyses of different end-shaped single-walled carbon nanotube conveying viscous nano-magnetic fluid embedded in non-linear visco-elastic foundation under the influence of magnetic fields are presented.The development of the equation of motion was based on Euler-Bernoulli theory,Hamilton principle and nonlocal elasticity theory.The results of the analytical solutions using Galerkin decomposition differential transform method(GDDTM)were validated with existing experimental results.From the parametric studies,it was shown that decreasing the temperature difference as well as increasing the downstream angle decreased the system's stability for pre-bifurcation analysis but increased stability of the system for post bifurcation analysis.Also,the results obtained from the dynamic behaviour of the system indicated that the magnetic effect had an attenuating impact of about 45%on the system's response at any mode and for any boundary condition considered.It is hoped that this work will enhance the design and optimization of nano-devices with I,V,Y,L,K and T-shaped junctions under the influence of thermal-magneto-mechanical flow induced vibration.
文摘具有单连续变量的背包问题(Knapsack Problem with a single Continuous variable,KPC)是标准0-1背包问题的一个新颖扩展形式,它既是一个NP完全问题,又是一个带有连续变量S的新颖组合优化问题,求解难度非常大.为了快速高效地求解KPC问题,该文提出了利用演化算法求解KPC的新思路,并给出了基于离散差分演化算法求解KPC的两个有效方法.首先,介绍了基本差分演化算法和具有混合编码的二进制差分演化算法(HBDE)的原理,给出了HBDE的算法伪代码描述,并分析了KPC的基本数学模型KPCM1的计算复杂度.然后,在基于降维法消除KPCM1中连续变量S的基础上,建立了KPC的一个新离散数学模型KPCM2;随后在基于贪心策略提出处理不可行解的有效算法基础上,基于单种群HBDE给出了求解KPC的第一个离散演化算法S-HBDE.第三,通过把连续变量S的取值范围划分为两个子区间将KPC分解为两个子问题,并基于降维法建立了KPC的适于并行求解的第二个数学模型KPCM3;在利用贪心策略给出处理子问题不可行解的两个有效算法基础上,基于双种群HBDE提出了求解KPC的第二个离散演化算法B-HBDE.最后,在给出四类大规模KPC实例的基础上,利用S-HBDE和B-HBDE分别求解这些实例,并与近似算法AP-KPC、遗传算法和离散粒子群优化算法的计算结果、耗费时间和稳定性等指标进行比较,比较结果表明S-HBDE和B-HBDE不仅在求解精度和稳定性方面均优于其它3个算法,而且求解速度很快,非常适于在实际应用中快速高效地求解大规模KPC实例.
