Efficient optimization design method of jacket structures for offshore wind turbines

With the gradual implementation of offshore wind energy production, the future tendency is to expand into the deeper water. The jacket foundations will take the place of the present monopile foundations when the water depth increases. The foundations account for the majority of the construction cost for offshore wind farms, and the structural optimization of jackets will bring lucrative economic benefits. Structural optimization is a complex iterative process that requires huge computing costs. Therefore, this paper proposes a structural optimization method based on surrogate models to solve this problem effectively and swiftly obtain optimized design schemes of lightweight jackets for offshore wind turbines. The structural responses of jacket wind turbine systems under the equivalent static extreme loads with a recurrence period of 50 years are mainly considered in structural optimization design, and the key optimization variables of jackets are determined by parameter sensitivity analysis. The finite element models of jackets are transformed into surrogate models, and the genetic algorithm is employed to optimize the surrogate models directly. The optimized jackets are additionally verified through coupled dynamic analysis, besides, buckling strength and fatigue life are also checked. And local refined optimizations are carried out for the failure members. According to the optimized design schemes of lightweight jackets for 30 m, 50 m and 70 m water depths, it is demonstrated that the structural optimization design method is adequate and efficient for jackets of wind turbines. Parameter sensitivity analysis can cut the number of optimization variables in half to improve the optimization efficiency. Furthermore, the application of surrogate models can significantly speed up the optimization process by saving about 98.61% of the original time consumed. The optimization design method of the jackets for offshore wind turbines proposed in this paper is suitable for practical engineering, with high precision and efficiency.     

基于扩展有限元法的结构内部多缺陷的反演分析

基于扩展有限元法和改进的人工蜂群智能优化算法,建立了检测和量化结构中多个内部缺陷的反演分析模型。反演分析模型中,由水平集函数来表征每个缺陷的位置及尺寸,采用精英加引导策略的人工蜂群搜索方程进行全局搜索,直到达到收敛为止;提出以人工蜂群对不同数目的缺陷参数样本进行贪婪选择,无需预先知道结构内部所含的缺陷数目,迭代过程中缺陷数目可以智能改变。该模型避免了反演过程中网格重划分问题,有效地减少了迭代的计算成本。通过若干算例的分析表明:在结构内部所含缺陷数目未知情况下,建立的反演分析模型能够快速准确地识别并确认出结构内部存在的缺陷数目及其相应的位置和大小。     

基于参数化模型的船舶圆柱売结构声学优化分析方法

复杂圆柱壳结构是船舶结构的主要形式,建立其快速声学优化分析方法对促进船舶结构声学设计, 实现“分析驱动设计”理念具有重要价值。基于隐式参数化建模方法,建立船舶复杂圆柱壳结构参数化模型库,提出基于参数化模型的船舶圆柱壳结构声学优化分析方法,解决了分析流程中数据自动传输、软件调用和变量控制等问题。算例结果表明所提方法是合理的,初步满足船舶复杂圆柱壳结构声学优化设计需求。     

基于代理模型的水下航行体头型优化设计方法研究

文章采用Kriging代理模型技术针对水下航行体头型优化设计开展研究。采用CFD方法分析了不同头型水下航行体的流体动力性能,将性能参数作为初始样本建立代理模型;根据测试样本点的预测均方误差选择加点策略更新代理模型,提高代理模型预测精度;采用非劣分类遗传算法对阻力和表面压力开展多目标优化,寻找近似最优解。结果表明:基于代理模型技术的水下航行体头型多目标优化设计方法可以有效提高设计效率,获得具有良好水动特性的航行体头型。     

基于人工蜂群算法的油船舯剖面优化设计

人工蜂群算法(ABC)是模仿蜜蜂行为提出的一种优化方法,通过各人工蜂个体的局部寻优行为,最终在群体中使全局最优值凸显出来,有着较快的收敛速度[1]。本文基于HCSR规范,以中剖面净面积最小为优化目标,以区域纵骨间距个数、板厚、型材尺寸、板缝位置为设计变量,采用ABC算法,建立了适用于油船的中剖面优化设计模型。以一艘32000DWT油船为例,对其进行了优化设计,优化结果验证了人工蜂群算法用于船舶中剖面结构优化的可行性和高效性。     

基于工艺可行性的离散变量结构动力特性设计优化

基于船舶工艺可行性分析,建立了使用自适应模拟退火算法的多工况下船体结构动力特性设计优化模型;并给出了相应的设计流程。模型使用矩阵描述由板材厚度和骨材型号构成的离散变量集合,同时将全体设计变量处理为共享设计变量。将模型应用于某集装箱船艉部结构的动力特性设计优化中,优化后的结构不仅减轻了自重,而且增加了频率储备。实例分析表明该模型能应用于工程结构的实际优化设计。     

基因演算法於船体加强板架之最佳化设计应用

船体结构之最佳化设计是一个复杂非线性的混和离散问题,并且要搜寻到全域的最佳值并不容易。在复杂的设计环境下基因演算法（Genetic Algorithm;GA）却可以搜寻到近似的全域最佳值。本文主要是应用基因演算法对T加强板架（Tee stiffened panel）、平板加强板架（flat-bat stiffened Panel）等常用且最具代表性之船体结构件进行最佳化设计,使结构在满足终极破坡限制（ultimate failure constraints）与耐用破坏限制（serviceability failure constraints）等所有限制条件下,求得最佳目标函数值中各设计变之最佳组合。在过程中并考量不同族群大小、变换机率、突变机率因素对最佳化结果的影响。文中是以制造成本为目标函数,其中同时考量材料成本及劳工成本,且所得之结果与连续性线性规则（Sequential Linear Programming;SLP）最佳化结果作了比较。计算的结果显示基因演算法可以有效地与快速地获得最小重量和最低成本的目标。     

船舶管线隔振支座布局优化设计的规范法

国内关于船舶管道隔振支承布局优化设计的标准和方法目前还没有建立,有关优化模型及算法的研究有待开展。借鉴美国ASME B31规范和综合考虑强度、刚度、稳定性、固有频率及振级落差等约束条件,提出了管线系统支座布局优化设计模型及规范设计法。该方法根据ASME B31规范初步确定支座的数量,通过几何优化设计支座的间距。提出迭代优化算法求解这上述问题,获得较优的支座数目与支座间距。通过算例,研究了不同优化目标函数的选择,(如支座造价、管线最大下垂或结构应变能等),对优化结果的影响;证明了有关模型与算法的有效性和实用性。     

Evaluation and selection of the ship collaborative design resources based on AHP and genetic and simulated annealing algorithm

1Introduction Theshipdesigninvolvesshipyards,ship owner,classi ficationsociety,shipresearchinstituteandsoon,while theshipcollaborativedesignneedsthecooperationbe tweenthedesignersfromdifferentdepartmentsand thosewithdiverseknowledge.Thus,intheshipcollab o…     

高效优化算法在船舶力学中的应用研究

作为一种贝叶斯优化算法,高效全局优化算法(EGO)利用克里格模型来构造近似模型,并采用样本填充准则以寻找下一个样本点来更新近似模型.文中详细介绍了该优化算法,并将其应用于船舶力学的两个典型优化例子.其中一个是潜艇的多学科概念设计,考虑了水动力、推进、重量、性能和成本5个学科;另外一个是屈曲状态下加筋板的优化问题.与传统优化相比,高效全局优化算法不仅收敛到全局最优解,而且更加有效.结果表明高效优化算法非常适用于船舶力学中的优化问题.     

高速三体船的快速性综合优化研究

讨论了一种高速三体船的快速性综合优化设计问题,在对一型三体船进行系列模型试验的基础上建立了快速性综合优化设计数学模型.采用遗传算法作为优化方法,编制了C++优化计算程序,基于大量的计算,确定了合理的进化代数;得到了快速性指数在傅汝德数Fr为0.32~0.44之间随速度和船长的变化曲线.文中提出的优化方法及计算结果可为高速三体船初步设计提供技术支撑.     

船艉结构静动态多目标优化设计

建立用于船体结构静动力学性能一体化设计优化的2种多目标优化模型:基于多目标遗传算法的多目标优化模型和基于多学科优化技术的多目标协同优化模型。模型均使用矩阵描述由板材厚度和骨材型号构成的离散设计变量集,以结构质量最小化和最大加速度最小化组成多目标函数。以某集装箱船艉部结构为例,对其进行了结构静力学、动力特性和动力响应的计算分析与优化设计。优化后的结构不仅具有更轻的质量、更低的振动水平,而且具有更高的固有频率储备,同时仍满足强度和刚度要求。     

大型舰艇多学科设计优化船体结构子系统设计方法

多学科设计优化(MDO)是解决复杂系统工程问题的有效手段。通过对大型舰艇多学科设计优化船体结构子系统设计方法进行研究,将船体结构设计分为船体横剖面结构布置方案自动生成、船体横剖面结构方案优选、船体结构重量估算3个步骤;根据总体方案提供的主尺度、横剖面外形轮廓和分层分舱等信息,依据骨材(桁材)均匀布置和余量控制的原则,生成船体横剖面结构布置方案,并提出一套参考母型船设计,确定设计船构件尺寸初值的方法;在船体横剖面结构方案优选过程中,采用调用代理模型代替板架有限元仿真模型的方法来减少结构分析的计算时间,并采用组合法获得重量最轻的设计方案。     

工程结构的拓扑优化设计研究

本文讨论了连续体结构拓扑优化的均匀化方法及其相关理论，分别针对静力问题和特征值问题建立了相应的结构拓扑优化模型。目前有关结构拓扑优化的工程应用研究还很不成熟，尤其在国内尚尾于起步阶段。本文将结构拓扑优化设计理论应用于实际工程结构的概念设计之中，并取得了较好的优化效果。通过对经典算例和某卫星构架子结构的拓扑优化计算，表明本文建立的结构拓扑优化模型能够有效地应用于工程结构的拓扑优化设计，从而为工程结构的结构型式选取提供了有价值的概念设计方案。     

Design of the lines of underwater vehicles based on collaborative optimization

A generalized collaborative optimization (CO) framework is proposed to the optimization design of the lines of an underwater vehicle. The resistance and maneuvering performances are concerned about and taken as the optimization objectives in the optimization framework. The resistance, lateral force and yaw moment are calculated by RANS method. To improve the optimization efficiency, an automatic integration optimization platform is constructed in which a surrogate model is adopted. A SUBOFF model is taken as the verification model. The optimal results demonstrate the validity of the optimization strategy proposed.     

Uncertainty quantification of Delft catamaran resistance,sinkage and trim for variable Froude number and geometry using metamodels,quadrature and Karhunen–Loève expansion

A framework for assessing convergence and validation of non-intrusive uncertainty quantification (UQ) methods is studied and applied to a complex industrial problem in ship design, namely the high-speed Delft Catamaran advancing in calm water, with variable Froude number and geometry. Relationship between UQ studies and deterministic verification and validation is discussed. Computations are performed using high- (URANS) and low- (potential flow) fidelity simulations. Froude number has expected value and standard deviation equal to 0.5 and 0.05, respectively, on a truncated normal distribution. Geometric uncertainty is related to the research space of a simulation-based design optimization, and assessed through the Karhunen–Loève expansion (KLE). Monte Carlo method with Latin hypercube sampling (MC-LHS) is used to compute expected value, standard deviation, distribution and uncertainty intervals for resistance, sinkage and trim. MC-LHS with CFD is used as a benchmark for validating less costly UQ methods, including MC-LHS with metamodels and standard quadrature formulas. Gaussian quadrature is found the most efficient method; however, MC-LHS with metamodels is preferred since provides with confidence intervals and distributions in a straightforward way and at reasonably small computational cost. UQ results are compared to earlier deterministic single- and multi-objective optimization; reduced-dimensional KLE studies for geometric variability indicate that stochastic optimization would not be of great benefit for the present problem.     

Approximate method to determine the model parameters in a new crack growth rate model

Marine structures are subjected to complex loading histories and one of the most significant failure modes is fatigue. Accurate prediction of the fatigue life of marine structures is very important for both safe and economic design and operation. Now many researchers and engineers have realized that fatigue crack propagation theory can provide more rational basis to predict the fatigue life of metal structures. At the same time, more and more fatigue crack growth models are proposed along with a good understanding of metal fatigue mechanisms. However, it is difficult to determine a large number of model parameters, which restricts their use in practical engineering problems. Therefore, it is significant to study the approximate methods for estimating the model parameters in good fatigue crack growth models.In our previous work, an extended McEvily model for fatigue crack growth analysis of metal structures was proposed. This model shows promising capability to explain various fatigue phenomena. In order for the convenient use in estimating fatigue life of marine structures, the concepts and approximations of the model parameters are comprehensively studied in this paper. Based on that, more reasonable assumptions and empirical formulas to determine the parameters are recommended. The approximate method is validated by experimental results of several types of materials, which could be successfully used in simple and effective engineering analysis for marine structures.     

基于神经网络与遗传算法的潜艇舱壁结构优化

建立了某潜艇端部耐压平面舱壁结构优化设计的数学模型,以舱壁上加强桁(筋)结构的剖面尺寸为设计变量,结构强度、稳定性、工艺性要求为约束条件,以加强桁(筋)结构总体积为目标函数,用人工神经网络方法代替结构的有限元分析,结合遗传算法完成了舱壁的结构优化设计。在为网络生成学习样本时,自行设计了正交表,解决了因为设计变量多,无法选取合适正交表而随机取样的问题。数值仿真结果表明,优化设计方案质量较原始初步设计方案减少了18.3%。     

基于动态罚因子的多学科协同优化算法及其在船舶设计中的应用

针对标准协同优化算法求解复杂系统工程问题的缺陷,提出了一种改进的协同优化算法,并将其应用于油船总体概念设计阶段。改进协同优化算法将系统级一致性约束最优化问题通过罚函数方法转化为一个无约束优化问题。同时,给出了两种不同的基于差异信息的动态可调罚系数,以保证在优化初期,系统级设计变量与学科级共享变量相差较大时,惩罚力度也大,促使一致性差异在总目标函数中占主导地位,则一致性差异将迅速下降。随着优化的进行,罚系数变小,惩罚力度减轻,目标函数的收敛加快。通过对MDO测试函数算例与标准协同优化和其他典型的改进协同算法的比较,验证了该方法在优化结果的可靠性和稳定性等方面有优势。最后,应用改进的协同优化算法求解以油船造价为系统级目标协同浮性与稳性、快速性等4个子学科的多学科优化问题以体现其工程实用性。     

基于序贯Kriging模型的潜器型线优化设计

潜器型线优化设计是一个多目标优化问题,在型线设计过程中,阻力性能与包络体积的要求是相互冲突的。为了解决计算流体力学软件如Fluent在进行潜器的外形优化设计时效率低下问题,采用Kriging模型代替仿真模型进行潜器外形设计的策略,其基本思想是：选取设计变量和样本点,利用ICEM软件建立参数化的水动力分析模型,用Fluent软件计算得到样本点的阻力响应值,建立反映设计变量与响应之间关系的Kriging模型,将阻力和体积作为潜器外形优化的两个目标,利用多目标遗传算法求出Pareto最优解。由于采样策略对Kriging模型精度影响很大,本文提出了一种新的序贯采样方法命名为加权累积误差方法,来选取样本点以提高Kriging模型精度。结果表明提出的序贯Kriging建模技术能极大提高潜器型线优化设计效率,同时保证设计精度。