Strength design of composite beam using gradient and particle swarm optimization

This work addresses the optimum design of a composite box-beam structure subject to strength constraints. Such box-beams are used as the main load carrying members of helicopter rotor blades. A computationally efficient analytical model for box-beam is used. Optimal ply orientation angles are sought which maximize the failure margins with respect to the applied loading. The Tsai–Wu–Hahn failure criterion is used to calculate the reserve factor for each wall and ply and the minimum reserve factor is maximized. Ply angles are used as design variables and various cases of initial starting design and loadings are investigated. Both gradient-based and particle swarm optimization (PSO) methods are used. It is found that the optimization approach leads to the design of a box-beam with greatly improved reserve factors which can be useful for helicopter rotor structures. While the PSO yields globally best designs, the gradient-based method can also be used with appropriate starting designs to obtain useful designs efficiently.     

Particle swarm-based structural optimization of laminated composite hydrokinetic turbine blades

Composite blade manufacturing for hydrokinetic turbine application is quite complex and requires extensive optimization studies in terms of material selection, number of layers, stacking sequence, ply thickness and orientation. To avoid a repetitive trial-and-error method process, hydrokinetic turbine blade structural optimization using particle swarm optimization was proposed to perform detailed composite lay-up optimization. Layer numbers, ply thickness and ply orientations were optimized using standard particle swarm optimization to minimize the weight of the composite blade while satisfying failure evaluation. To address the discrete combinatorial optimization problem of blade stacking sequence, a novel permutation discrete particle swarm optimization model was also developed to maximize the out-of-plane load-carrying capability of the composite blade. A composite blade design with significant material saving and satisfactory performance was presented. The proposed methodology offers an alternative and efficient design solution to composite structural optimization which involves complex loading and multiple discrete and combinatorial design parameters.     

基于遗传算法的大型风机复合材料叶片根部强度优化设计

为了提高复合材料叶片承担载荷的能力, 尤其是承受最大弯矩的叶片根部的承载能力, 研究了遗传算法的优化原理并将遗传算法应用到复合材料叶片根部铺层的优化设计中。针对复合材料层压结构遗传算法优化设计中, 层压结构参数具有离散型的特点, 提出了适合复合材料层压结构遗传算法优化设计的整数编码策略, 以整数来表征层压结构参数。在分析层压结构强度的基础上, 针对结构强度优化的目标构造了可用于遗传算法的适应度函数。同时参考了一定的铺层规则, 在铺层角度限制为工程中常用的四种角度的前提下, 应用遗传算法对叶片根部进行了铺层优化设计。结果表明, 由于遗传算法特有的处理离散型问题的优势, 在叶片根部的铺层优化设计中应用遗传算法是可行和可信的。     

弹性耦合对直升机复合材料桨叶稳定性的影响

研究了弹性耦合对复合材料桨叶动特性和气弹稳定性的影响, 所采用的结构模型考虑了剪切变形、剖面面外翘曲变形和复合材料弹性耦合。推导出同时考虑剪切和翘曲影响的小应变、中等变形梁的应变2位移关系,并构造出21 个自由度梁单元, 应用Hamilton 原理推导出桨叶运动的有限元方程。在此基础上, 对三种不同构型的复合材料桨叶进行固有频率计算和气弹稳定性分析。计算结果表明: 尽管复合材料弹性耦合对桨叶固有频率的影响非常小, 但却改变了固有振型分布, 使桨叶挥舞-摆振-扭转运动之间存在耦合; 弹性耦合对桨叶气弹稳定性有很大的影响; 正的挥舞2扭转耦合使得摆振一阶稳定性增加, 负的挥舞2扭转耦合却使摆振一阶稳定性下降。      

Optimal design of composite ChamberCore structures

An optimization procedure has been developed to uniquely and efficiently determine the “best” local geometry design of a new composite ChamberCore structure. This procedure is based on minimization of the total mass of a single composite ChamberCore subject to a set of design and stress constraints. The stress constraints are obtained in closed form based on the composite box-beam model for various composite lamination designs and loading conditions. The optimization problem statement is constructed and then solved using the VMCON optimization program, which is an iterative sequential quadratic programming (SQP) technique based on Powell's algorithm. The sensitivity of the solution of the optimal geometry to the values of parameters that characterize the structural durability and the failure mechanism is discussed.     

Optimization of aeroelastic composite structures using evolutionary algorithms

The flutter/divergence speed of a simple rectangular composite wing is maximized through the use of different ply orientations. Four different biologically inspired optimization algorithms (binary genetic algorithm, continuous genetic algorithm, particle swarm optimization, and ant colony optimization) and a simple meta-modeling approach are employed statistically on the same problem set. In terms of the best flutter speed, it was found that similar results were obtained using all of the methods, although the continuous methods gave better answers than the discrete methods. When the results were considered in terms of the statistical variation between different solutions, ant colony optimization gave estimates with much less scatter.     

基于粒子群算法的高黏度大比重物料无轴螺旋输送机构优化设计

 为解决高黏度大比重物料无轴螺旋输送机的螺旋叶片变形问题,利用粒子群优化算法,以无轴螺旋叶片刚度变形最小为优化设计目标,构建了基于粒子群算法的无轴螺旋叶片优化设计模型.应用构建的理论模型,进行了实例设计,并通过实验研究进一步证实该设计方法具有先进性和实用性．将智能算法应用于机械优化设计,为该领域研究提供新思路．     

Optimization of laminated composite structure considering uncertainty effects

In this paper, the most conservative Tsai–Wu failure envelopes are obtained for laminated composite considering material as well as ply angle uncertainty. The uncertainty analysis is performed using Monte Carlo simulation (MCS). The obtained failure envelopes are then used as the constraint functions to perform the minimum weight design optimization problem using particle swarm optimization (PSO). Results show increase in weight of the laminate from the deterministic results and it varies from 4% to 50% depending upon the stacking sequence and loading condition. Substantial effects of uncertainty on the failure envelope and optimal design are quantified.     

A surrogate-assisted particle swarm optimization algorithm based on efficient global optimization for expensive black-box problems

Evolutionary algorithms cannot effectively handle computationally expensive problems because of the unaffordable computational cost brought by a large number of fitness evaluations. Therefore, surrogates are widely used to assist evolutionary algorithms in solving these problems. This article proposes an improved surrogate-assisted particle swarm optimization (ISAPSO) algorithm, in which a hybrid particle swarm optimization (PSO) is combined with global and local surrogates. The global surrogate is not only used to predict fitness values for reducing computational burden but also regarded as a global searcher to speed up the global search process of PSO by using an efficient global optimization algorithm, while the local one is constructed for a local search in the neighbourhood of the current optimal solution by finding the predicted optimal solution of the local surrogate. Empirical studies on 10 widely used benchmark problems and a real-world structural design optimization problem of a driving axle show that the ISAPSO algorithm is effective and highly competitive.     

直升机旋翼桨叶鸟撞动态响应计算

基于旋翼综合气弹分析程序,求解出直升机旋翼桨叶在飞行过程中的稳态响应。以此作为鸟体撞击桨叶的初始状态,采用非线性流-固耦合算法,建立了直升机旋翼桨叶鸟撞动力学方程,利用直接数值积分方法求解桨叶的动态响应。并讨论了鸟体速度、质量、撞击位置、桨叶根部约束和离心力等参数对桨叶动态响应的影响,从而为直升机桨叶抗鸟撞设计提供一些理论依据。     

Contact analysis of mechanically fastened joints in composite laminates by linear complementarity problem formulation

The contact phenomena and stress distributions in the vicinity of the hole on mechanically fastened joints are investigated in composite laminates exhibiting nonlinear elastic behavior. The effects of fastener stiffness, friction force between the fastener and the composite laminates, geometrical nonlinearities due to concentrated fastener load around the hole edge for the snug and clearance-fit in the [0 °]_S, [± 45 °]_S, [0°/± 45 °]_S and laminates are also considered. An efficient numerical procedure is developed for the solution of the contact problems with Coulomb friction between the fastener and the hole based on a linear complementarity problem (LCP) formulation in an incremental form. A nonlinear finite element code for stress analysis of laminated composites using Lemke's complementary pivoting algorithm is developed for solving the LCP. The nonlinear analysis is composed of the combination of updated Lagrangian formulation of the frictional contact problem and the nonlinear shear stress-strain relation in each ply. The accuracy, applicability and computational efficiency of the proposed method are confirmed by comparison with previous researches.     

Response surface approach to aerodynamic optimization design of helicopter rotor blade

This paper describes a hovering rotor blade design through the suitable combination of flow analysis and optimization technique. It includes a parametric study concerned with the influence of design variables and different design conditions such as objective functions and constraints on the rotor performance. Navier–Stokes analysis is employed to compute the hovering rotor performance in subsonic and transonic operating conditions. Response surface method based on D‐optimal 3‐level factorial design and genetic algorithm are applied to obtain the optimum solution of a defined objective function including the penalty terms of constraints. The designs of the rotor airfoil geometry and the rotor tip shape are performed in subsonic and transonic conditions, and it is observed that the new rotor blades optimized by various objective functions and constraints have better aerodynamic characteristics than the baseline rotor blade. The influence of design variables and their mutual interactions on the rotor performance is also examined through the optimization process. Copyright © 2005 John Wiley & Sons, Ltd.     

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。     

基于拓扑优化的直升机旋翼桨叶剖面设计

提出了一种基于拓扑优化的直升机旋翼桨叶剖面设计方法。采用了有限元方法计算直升机旋翼桨叶剖面刚度特性, 截面考虑了剪切和翘曲变形, 并消除了翘曲位移和刚体位移之间的耦合作用。基于SIMP拓扑优化算法, 以旋翼桨叶平均柔度或者剖面刚度为设计目标, 桨叶重量为约束函数, 建立了旋翼桨叶拓扑优化模型。提出的敏度求解算法具有较高的计算精度, 采用序列线性规划算法对旋翼桨叶剖面进行优化设计。结果表明在展长较小并且承受均布升力载荷情况下, Ⅱ型截面梁的柔度最小, 而当展长增大时, 工字梁截面具有最小的柔度。此外, 旋翼桨叶外载荷等对优化结果也有较大的影响。提出的拓扑优化方法适合于概念设计阶段的直升机旋翼桨叶剖面设计。     

Multi-objective optimization of engineering systems using game theory and particle swarm optimization

This study proposes particle swarm optimization (PSO) based algorithms to solve multi-objective engineering optimization problems involving continuous, discrete and/or mixed design variables. The original PSO algorithm is modified to include dynamic maximum velocity function and bounce method to enhance the computational efficiency and solution accuracy. The algorithm uses a closest discrete approach (CDA) to solve optimization problems with discrete design variables. A modified game theory (MGT) approach, coupled with the modified PSO, is used to solve multi-objective optimization problems. A dynamic penalty function is used to handle constraints in the optimization problem. The methodologies proposed are illustrated by several engineering applications and the results obtained are compared with those reported in the literature.     

Probabilistic optimal design of laminates using improved particle swarm optimization

A new approach to the particle swarm optimization (PSO) is proposed for the solution of non-linear optimization problems with constraints, and is applied to the reliability-based optimum design of laminated composites. Special mutation-interference operators are introduced to increase swarm variety and improve the convergence performance of the algorithm. The reliability-based optimum design of laminated composites is modelled and solved using the improved PSO. The maximization of structural reliability and the minimization of total weight of laminates are analysed. The stacking sequence optimization is implemented in the improved PSO by using a special coding technique. Examples show that the improved PSO has high convergence and good stability and is efficient in dealing with the probabilistic optimal design of composite structures.     

Wind Turbine Efficiency Under Altitude Consideration Using an Improved Particle Swarm Framework

In this work, the concepts of particle swarm optimization-based method, named non-Gaussian improved particle swarm optimization for minimizing the cost of energy (COE) of wind turbines (WTs) on high-altitude sites are introduced. Since the COE depends on site specification constants and initialized parameters of wind turbine, the focus was on the design optimization of rotor radius, hub height and rated power. Based on literature, the COE is converted to the Saudi Arabia context. Thus, the constrained wind turbine optimization problem is developed. Then, non-Gaussian improved particle swarm optimization is provided and compared with the conventional particle swarm optimization for solving the optimization design in wind turbine efficiency under different altitudes ranging from 2500 to 4000 m. The results show that as altitude rises, the optimal rotor radius grows, but the optimal hub height and rated power drop, resulting in an increase in COE. Further, the non-Gaussian method display a faster convergence compared to the classical particle swarm optimization. These findings will be useful as a reference for wind turbine design at high altitudes. Thus, it could be employed to optimize the initialized parameter of wind turbine for the planned and largest wind farm in Saudi Arabia in Dumat Al-Jandal selected site.

     

A parallel particle swarm optimization algorithm for multi-objective optimization problems

In this article, a new proposal of using particle swarm optimization algorithms to solve multi-objective optimization problems is presented. The algorithm is constructed based on the concept of Pareto dominance, as well as a state-of-the-art ‘parallel’ computing technique that intends to improve algorithmic effectiveness and efficiency simultaneously. The proposed parallel particle swarm multi-objective evolutionary algorithm (PPS-MOEA) is tested through a variety of standard test functions taken from the literature; its performance is compared with six noted multi-objective algorithms. The computational experience gained from the first two experiments indicates that the algorithm proposed in this article is extremely competitive when compared with other MOEAs, being able to accurately, reliably and robustly approximate the true Pareto front in almost every tested case. To justify the motivation behind the research of the parallel swarm structure, the computational results of the third experiment confirm the PPS-MOEA's merit in solving really high-dimensional multi-objective optimization problems.     

基于信息熵改进的粒子群算法在桁架结构

提出了信息熵改进的粒子群优化算法用于解决有应力约束、位移约束的桁架结构杆件截面尺寸优化设计问题.首先介绍了信息熵基本理论和基本粒子群优化算法理论,然后对粒子群优化算法作了合理的参数设置,并将信息熵引入粒子群优化算法的适应函数和停机判别准则中.最后对2个经典的优化问题进行求解并与其他算法进行了比较.数据结果表明信息熵改进后的粒子群优化算法在桁架结构优化设计中优于其他同类算法.     

Matrix crack-induced delamination in composite laminates under transverse loading

Fibre-reinforced multidirectional composite laminates are observed in experiments under transverse static or low-velocity impact loading to suffer considerable delamination damage. The intensity of this damage depends on the difference in the ply angles above and below the interface. In this paper a fracture mechanics model is presented for investigating the role of matrix cracks in triggering delaminations and the influence of ply angles in adjacent plies on delamination cracking. The fracture mechanics analysis shows that for a graphite fibre-reinforced composite laminate containing a transverse intraply crack, the crack-induced largest interfacial principal tensile stress is a maximum when the difference between the ply angles across the interface is 90 °, and it attains a minimum when the difference is 40 °. When the crack tips touch the interfaces, the minimum mode II stress singularity, which is weaker than the usual square-root type, appears when the difference between the ply angles is about 45 ° for one glass fibre-reinforced laminate and three graphite fibre-reinforced laminates. These results are in agreement with the experimental observation that the largest delaminations appear at the interface across which the difference between the ply angles is the largest i.e. 90 °.