基于改进鲸鱼算法的复合材料层合板优化设计EI北大核心CSCD

针对层合板铺层优化问题,提出一种改进鲸鱼算法(improved whale optimization algorithm,IWOA)。通过引入对立学习策略保持进化过程的多样性,同时结合非线性收敛因子和自适应阈值提高IWOA的全局搜索能力;以层合板的铺设角度为设计变量、基频及带隙为优化目标进行板的频率优化,并使用Ritz法求解。基于IWOA算法对长宽比分别为1和2的复合材料矩形层合板进行优化研究,并与鲸鱼优化算法(whale optimization algorithm,WOA)、分层优化算法(layered optimization algorithm,LOA)和差分进化算法(differential evolution algorithm,DEA)进行比较分析。结果表明,相较于LOA、DE和WOA算法,IWOA具有更好的全局收敛性,且在处理多模态问题时更具可靠性。     

铺设角度与铺层顺序对层合板稳定性的影响

以层合板结构的临界屈曲载荷系数最大化为优化目标,基于改进型模拟退火算法对层合板结构铺设角度和铺层顺序进行优化。由于层合板结构的铺层角度是离散变量,模拟退火算法适合求解离散变量的优化问题。利用模拟退火算法优化层合板铺层,在算法内采用并行计算、引入记忆功能同时设置双阈值终止准则,有效地提高了优化过程的收敛速度,同时避免优化过程中出现局部最优解。以临界屈曲载荷系数作为目标函数,选取复合材料层合板的铺设角度顺序为设计变量,采用改进的模拟退火算法得出复合材料层合板的最优铺设角度以及铺层顺序。     

面内线性变化载荷作用复合材料层合板的振动与屈曲优化

采用广义微分求积（GDQ）法开展了不同边界条件下承受面内线性变化载荷作用复合材料层合板振动与屈曲的分析与优化。针对GDQ法求解面内线性变化载荷工况复合材料层合板屈曲问题存在计算振荡、不收敛现象,提出载荷扰动策略实现了GDQ法对复合材料层合板屈曲问题的稳定高效求解。基于基础圆频率和临界屈曲载荷系数的归一化指标,分析了铺层角度对复合材料层合板综合性能的影响,并结合直接搜索模拟退火算法开展了复合材料层合板的铺层顺序优化。结果表明:铺层角度变化对屈曲性能的影响明显强于频率特性;面内线性变化载荷中,以弯曲载荷作用下复合材料层合板的优化综合性能受边界条件变化的影响最小,而优化铺层角度受边界条件变化的影响最大。研究结果为复杂载荷作用下复合材料层合板的设计提供了参考。      

基于双值参数化方法且考虑制造约束的炭纤维复合材料铺层优化

基于梯度的优化方法对炭纤维复合材料层合板的铺层数量和顺序进行优化。优化问题中以铺层质量为目标,并以刚度和制造约束为约束。采用改进双值参数化方法对铺层的材料性能进行插值,并基于凸规划对偶算法对优化问题进行求解。为了适应凸规划对偶算法的特点,将关于铺层角度的制造约束表述为少量非线性约束。同时引入离散度约束和惩罚指数以消除优化结果中的中间变量。算例结果验证了该优化方法的有效性。     

频率约束下复合材料圆柱壳的最轻重量设计

用九节点剪切变形等参壳单元进行复合材料壳体分析。推导了频率对层厚变化的敏感度公式。先用ＰＯＷＥＬＬ法，以铺层角度为设计变量，对壳的基频进行优化，然后以层厚度为设计变量，用优化准则法进行最轻重量设计，并使基频收敛于频率约束值。     

复合材料层合板及机翼格栅结构的动态特性优化设计

以复合材料层合板各单层连续变化的铺层角度为设计变量, 在有限元软件中对层合板结构的基频进行优化分析, 在四边简支和固支两种不同的边界条件下, 结构的基频分别提高了4.9%和16.2%, 并对优化前后结构的静力失效强度进行了对比分析。随后将这种优化方法应用到某无人机复合材料机翼格栅结构中, 针对格栅结构蒙皮和肋板共计24个纤维铺层角度进行了优化设计, 使结构基频提高了10.6%, 同时结构的承载能力也有了一定程度的提高。     

基于几何因子的复合材料气动弹性剪裁设计

实现了基于几何因子的复合材料层合板建模,解决了几何因子与Natran的参数输入问题,并根据工艺约束中的最小铺层比例对几何因子可行空间进行了推导补充。在此基础上,提出了一种基于几何因子和Nastran的复合材料气动弹性剪裁优化设计方法。首先以总厚度和几何因子作为设计变量以及以Nastran作为求解器,以强度、刚度、颤振和发散速度以及几何因子相关性约束作为约束条件进行结构寻优,得到最优的铺层总厚度和几何因子。其次,以最优几何因子作为目标,进行铺层结构逆问题求解,约束条件为复合材料铺层工艺约束。因几何因子为铺层厚度和铺层顺序的表达式,与传统的多级优化相比,以几何因子作为设计变量可以避免铺层厚度和铺层顺序的解耦,进而获得更大的设计空间,且得到的铺层结构可以满足工艺约束。最后,对一矩形悬臂复合材料层合板进行剪裁设计,使得铺层结构满足气动弹性约束且质量最小。结果显示,运用该优化方法可以得到质量更小且满足工艺约束的铺层结构。     

基于密度分布曲线法的复合材料变刚度铺层优化

基于梯度的优化方法对复合材料层合板进行了变刚度铺层优化设计。在优化过程中需确定铺层中各单元的密度以及角度。为了使优化结果具有可制造性,优化结果需满足制造工艺约束并且铺层角度需从预定角度中选取。为了避免在优化问题中引入过多的约束并减少设计变量的数目,提出密度分布曲线法(DDCM)对层合板中各单元的密度进行参数化。根据各单元的密度以及角度设计变量并基于Bi-value Coding Parameterization(BCP)方法中的插值公式确定各单元的弹性矩阵。优化过程中以结构柔顺度作为优化目标,结构体积作为约束,优化算法采用凸规划对偶算法。对碳纤维复合材料的算例结果表明:采用DDCM可得到较理想的优化结果,并且收敛速率较快。     

复合材料层合板稳定性的铺层优化设计

提出采用神经网络和遗传算法来优化设计复合材料层合板,建立了满足铺层结构稳定性的优化铺层体系,优化体系分两步进行优化,第一步,当给出总的铺层数时,由已建立的神经网络模型确定规定角度下的铺层数,确立基本的铺层结构,第二步,采用遗传算法优化这种铺层结构下的铺层顺序,最终在同样重量下获得了最佳的结构铺层。     

基于直接搜索方法的层合板多目标优化设计

直接多目标搜索方法(DMS)是一种不需要计算梯度信息并且能实现全局收敛的多目标优化方法。基于直接多目标搜索方法,以简支层合板铺设角度为设计变量,基频和声功率为目标函数进行层合板结构振动与声多目标优化。分别以4层、8层复合材料层合板为例,用DMS方法对其优化设计,并与传统的遗传算法(GA)对比。结果表明,对于4层复合材料层合板,DMS方法比GA方法优化速度快,且能得到全局最优解;对于8层复合材料层合板,用DMS方法比4层板优化所需时间多,但相比GA方法,DMS方法优化更快。     

Optimal design of composite wing structures with blended laminates

In this paper we formulate the problem of wing box design optimization using composite laminates with blending constraints. The use of composite laminates necessitates the inclusion of fiber orientation angle of the layers as well as total thickness of the laminate as design variables in the design optimization problem. The wing box design problem is decomposed into several independent local panel design problems. In general such an approach results in a nonblended solution with no continuity of laminate lay ups across the panels, which may not only increase the lay up cost but may also be structurally unsafe due to discontinuities. The need for a blended solution increases the complexity of the problem many fold. In this paper we impose the blending constraints globally by using a guide based design methodology within the genetic algorithm optimization scheme and compare the results with the published ones. Two different blending schemes – outer and inner blending are presented. The result shows that the optimum design obtained using the current methodology has better continuity of laminate lay ups and also the reported weight of the composite wing box is on the lower side. Finally, a parametric study of the effect of global deflection constraint on the total weight of the optimum design is presented.     

A simulated annealing approach for optimization of multi-pass turning operations

In this paper, an optimization algorithm based on the simulated annealing (SA) algorithm and the Hooke-Jeeves pattern search (PS) is developed for optimization of multi-pass turning operations. The cutting process is divided into multi-pass rough machining and finish machining. Machining parameters are determined to optimize the cutting conditions in the sense of the minimum unit production cost under a set of practical machining constraints. Experimental results indicate that the proposed nonlinear constrained optimization algorithm, named SA/PS, is effective for solving complex machining optimization problems. The SA/PS algorithm can be integrated into a CAPP system for generating optimal machining parameters.     

Fracture mechanics and optimization — a useful tool for fibre-reinforced composite design

This paper will demonstrate the application of fracture mechanics and optimization techniques for the optimum design of fibre-reinforced composite laminates (FRC). First, a boundary-value problem of a cracked composite laminate is solved within the framework of linear elastic fracture mechanics (LEFM). The solution relates the stress intensity factor at a crack tip and the crack-induced interfacial stresses to the laminate configuration. These results are then used in two types of the optimum design of fibre-reinforced composite laminates. In the first type of optimum design, namely a crack-insensitive design of the laminate, the crack driving force and interfacial principal tensile stress are both minimized by using single- and multicriterion optimization techniques. The second type of optimum design involves in situ strength design of multidirectional angle-ply laminates. In this case, a set of in situ strength parameters are proposed based on theoretical analysis and experimental observations. This optimization problem is a min {max} one and non-differentiable. A proper treatment of the non-differentiability is introduced and the min {max} optimization problem is converted into a differentiable single-criterion one using the bound-formulation technique. All the optimization problems are solved by non-linear mathematical programming. The results show that optimization can greatly enhance the load carrying capacity of the laminates.     

A multi-objective optimization approach for design of blast-resistant composite laminates using carbon nanotubes

A reliable process for the design of blast-resistance composite laminates is needed. We consider here the use of carbon nanotubes (CNTs) to enhance the mechanical properties of composite interface layers. The use of CNTs not only enhances the strength of the interface but also significantly alters stress propagation in composite laminates. A simplified wave propagation simulation is developed and the optimal CNT content in the interface layer is determined using multi-objective optimization paradigms. The optimization process targets minimizing the ratio of the stress developed in the layers to the strength of that layer for all the composite laminate layers. Two optimization methods are employed to identify the optimal CNT content. A case study demonstrating the design of five-layer composite laminate subjected to a blast event is used to demonstrate the concept. It is shown that the addition of 2% and 4% CNTs by weight to the epoxy interfaces results in significant enhancement of the composite ability to resist blast.     

面向印制电路板钻孔任务动态调度的短视策略

印制电路板钻孔任务因随机到达和工艺要求而难以调度。考虑该问题的NP难性质,提出基于优先规则和智能算法的短视策略。该策略采用事件驱动的再调度机制,在任务到达和任务完工时触发优化算法对当前未开工任务进行决策。为了高效求解每个决策时刻的优化问题,构建了嵌入局部优势定理的模拟退火和变邻域搜索算法,其初始解由优先规则获得。通过计算实验,在不同调度环境下对比两种智能算法与经典优先规则的表现。实验结果表明,智能算法在多数目标下的优化效果较优先规则可提升20%以上,变邻域搜索的优化效果略好于模拟退火,但是模拟退火的计算效率高一倍。     

SIMULATED ANNEALING AND PARALLEL PROCESSING: AN IMPLEMENTATION FOR CONSTRAINED GLOBAL DESIGN OPTIMIZATION

Global optimization becomes important as more and more complex designs are evaluated and optimized for superior performance. Often parametric designs are highly constrained, adding complexity to the design problem. In this work simulated annealing (SA), a stochastic global optimization technique, is implemented by augmenting it with a feasibility improvement scheme (FIS) that makes it possible to formulate and solve a constrained optimization problem without resorting to artificially modifying the objective function. The FIS is also found to help recover from the infeasible design space rapidly. The effectiveness of the improved algorithm is demonstrated by solving a welded beam design problem and a two part stamping optimization problem. Large scale practical design problems may prohibit the efficient use of computationally intensive iterative algorithms such as SA. Hence the FIS augmented SA algorithm is implemented on an Intel iPSC/860 parallel super-computer using a data parallel structure of the algorithm for the solution of large scale optimization problems. The numerical results demonstrate the effectiveness of the FIS as well as the parallel version of the SA algorithm. Expressions are developed for the estimation of the speedup of iterative algorithms running on a parallel computer with hyper-cube interconnection topology. Computational speedup in excess of 8 is achieved using 16 processors. The timing results given for the example problems provide guidelines to designers in the use of parallel computers for iterative processes.     

Production scheduling optimization algorithm for the hot rolling processes

The hot rolling production scheduling problem is an extremely difficult and time-consuming process, so it is quite difficult to achieve an optimal solution with traditional optimization methods owing to the high computational complexity. To ensure the feasibility of solutions and improve the efficiency of the scheduling, this paper proposes a vehicle routing problem (VRP) to model the problem and develops an easily implemented hybrid approach (QPSO-SA) to solve the problem. In the hybrid approach, quantum particle swarm optimization (QPSO) combines local search and global search to search the optimal results and simulated annealing (SA) employs certain probability to avoid getting into a local optimum. The computational results from actual production data have shown that the proposed model and algorithm are feasible and effective for the hot rolling scheduling problem.