汽车车身耐撞性设计参数的区间稳健性优化

基于区间分析,提出了一种考虑公差的汽车车身耐撞性稳健优化设计模型,可在有效降低耐撞性能对设计参数波动敏感性的同时实现公差范围的最大化。该模型首先利用对称公差来描述汽车碰撞模型中车身关键耐撞部件的主要尺寸、位置和形状等设计参数本身的不确定性,然后将参数设计和公差设计相结合,建立了以稳健性评价指标和公差评价指标为优化目标,设计变量名义值和公差同步优化的多目标优化模型。再次,利用区间可能度处理不确定约束,将该优化模型转换为确定性多目标优化模型。最后,将该模型应用于两个汽车耐撞性优化设计问题,并通过序列二次规划法和改进的非支配排序遗传算法进行求解,结果表明该方法及稳健优化设计模型可行且实用。     

结构动力鲁棒优化设计方法综述

如何提高结构动力学性能的鲁棒性,以减小各种不确定性因素对设计结果的影响是当前学术界和工程界研究和关注的热点问题之一。该文阐述了结构动力鲁棒优化设计的基本概念,从基于Taguchi的方法、基于多目标优化的方法和基于响应面建模的方法三个方面对结构动力鲁棒优化设计的研究进行了综述。以双转子为例,从结构的动力响应要求出发,采用响应面建模、多目标优化的方法进行了设计并与采用Taguchi方法得到的结果进行比较。结果表明,基于响应面建模、多目标优化的方法能够获得多个具有鲁棒性的设计方案,在处理具有不确定性的结构动力学问题时有着很大的应用潜力。最后,对当前方法和后续研究内容作了简要总结和展望。     

汽车前纵梁吸能盒结构耐撞性多目标优化

吸能盒上诱导槽的分布形式对汽车碰撞性能有很大影响.以汽车前纵梁吸能盒结构为对象,建立整车正面碰撞仿真模型,对吸能盒结构的耐撞性进行了多目标优化设计.以吸能盒上诱导槽之间的间距为设计变量,使用Hammersley方法采集样本点后,以车辆吸能盒的最大吸能量E、最大刚性墙反力F以及车身最大加速度a为目标函数,通过krigin...     

Reliability-based robust multi-objective optimization applied to engineering system design

ABSTRACT

Probabilistic and non-probabilistic methods have been proposed to deal with design problems under uncertainties. Reliability-based design and robust design are probabilistic strategies traditionally used for this purpose. In the present contribution, reliability-based robust design optimization (RBRDO) is formulated as a multi-objective problem considering the interaction of both approaches. The proposed methodology is based on the differential evolution algorithm associated with two strategies to deal with reliability and robustness, respectively, namely inverse reliability analysis and the effective mean concept. This multi-objective optimization problem considers the maximization of reliability and robustness coefficients as additional objective functions. The effectiveness of the methodology is illustrated by two classical test cases and a rotor-dynamics application. The results demonstrate that the proposed methodology is an alternative method to solve RBRDO problems.     

Reliability-based design optimization of a car body using dimension- reduced Chebyshev polynomial

A reliability-based design optimization (RBDO) method of a car body is presented on basis of dimension-reduced Chebyshev polynomial method (DCM). To improve calculation efficiency and save computational time, complex models are often approximated by metamodels in reliability analysis. Traditional metamodels require a large number of sample points, which is time-consuming. To improve the efficiency, DCM is proposed to approximate the performance function of the car body. First, the performance function is decomposed by the dimension-reduction method into a sum of univariate functions, which are then fitted through Chebyshev polynomials. The reliability of the car body is predicted by the Taylor expansion method and the fourth-moment method. Finally, the result of RBDO is obtained using an improved adaptive genetic algorithm. The proposed method saves on the calculation time with high precision. Besides, the improved adaptive genetic algorithm reduces the number of iterations in the car body optimization and improves the efficiency.     

An uncertain multidisciplinary design optimization method using interval convex models

This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss–Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.     

未来汽车造型发展趋势研究

结合近年出现的概念车,具体分析新技术对车身总布置、底盘构造、车体结构、外部及内部空间的影响,未来生活方式会发生如何变化。另外当汽车设计步入"轻周期"时代,设计师在进行汽车设计时需遵循的三大设计原则:美观性原则、易用性原则和可持续性原则,这些设计原则在设计过程中如何具体体现。     

面向电动汽车租赁的多目标服务组合优化设计

电动汽车租赁行业迅速发展。为满足消费者和企业的综合需求,研究了面向电动汽车租赁的多目标服务组合优化设计方法。借助模糊理论确定各服务项权重,引入失望理论描述顾客的欣喜与失望感知以计算感知绩效,建立方案成本、相容度、市场竞争指数的计算方法,最终提出顾客感知绩效最大、成本最低、方案相容最优、市场竞争力最大的电动汽车租赁多目标服务组合优化设计模型。通过一种面向多阶段的多目标权重确定法对不同阶段的多目标决策设定权重。最后,通过实际项目案例对模型进行有效性验证,针对推广市场的不同阶段分别制定服务组合套餐,目标优化结果显著,为电动汽车租赁企业在服务设计方面提供了科学有效的决策方法。     

汽车零部件的可靠性稳健优化设计——理论部分*

将可靠性优化设计理论与可靠性灵敏度分析方法相结合，讨论了汽车零部件的可靠性稳健优化设计问题，提出了可靠性稳健优化设计的计算方法。把可靠性灵敏度融入可靠性优化设计模型之中，将可靠性稳健优化设计归结为满足可靠性要求的多目标优化问题。     

硅片机器人大臂结构多目标拓扑优化设计

目的为了改善硅片机器人结构的静、动力学性能,实现结构的轻量化设计。方法引入多目标优化理论,并结合层次分析法,实现硅片机器人大臂的结构优化设计。依据设计方案分析大臂的受力情况,采用固体各向同性材料惩罚模型分别构建多目标优化数值模型,并运用优化准则法进行优化求解。引入数理统计中的层次分析法确定各子目标函数的权重比,依据折衷规划组合方法构造关于静刚度和一阶固有频率的总目标函数。结果优化结果可知,大臂的柔度从26.890 mm/N降到13.221 mm/N,一阶固有频率从556.86 Hz增加到629.90 Hz,结构质量从1.48 kg减少到0.583 kg。结论多目标优化结果表明,基于多目标优化理论对硅片机器人大臂结构的改进设计,不仅有效地提高了其静刚度特性,一阶固有频率的提高说明大臂结构能抑制振动能力的提升,还实现了大臂结构轻量化设计。层次分析法的引入为多目标优化问题中各子目标函数的重要性提供了客观的理论依据。     

圆柜内机缓冲衬垫多目标优化设计

目的 基于有限元软件研究圆柜内机包装件静态载荷下的力学特性，并进行多目标优化设计。方法 利用CREO建立三维模型、ANSYS Workbench建立有限元模型，对圆柜内机包装件进行静力学分析。以上缓冲衬垫的5条筋条厚度和2侧壁厚作为设计变量，以上缓冲衬垫的质量、包装件的最大变形量和最大等效应力作为目标函数建立优化模型。基于OSF试验设计方法和Kriging模型，采用多目标遗传算法进行多目标优化设计。结果 优化前的包装件最大变形量为1.511 3 mm，最大等效应力为0.327 6 MPa。优化后包装件的筋条和侧壁厚度显著降低，上缓冲衬垫质量减少了27%，整体强度无显著降低。结论 建立的多目标优化设计方法合理且Kriging模型精度较高，在保证缓冲防护效果的基础上，实现了轻量化设计，降低了包装成本。     

A system methodology for optimization design of the structural crashworthiness of a vehicle subjected to a high-speed frontal crash

The structural crashworthiness design of vehicles has become an important research direction to ensure the safety of the occupants. To effectively improve the structural safety of a vehicle in a frontal crash, a system methodology is presented in this study. The surrogate model of Online support vector regression (Online-SVR) is adopted to approximate crashworthiness criteria and different kernel functions are selected to enhance the accuracy of the model. The Online-SVR model is demonstrated to have the advantages of solving highly nonlinear problems and saving training costs, and can effectively be applied for vehicle structural crashworthiness design. By combining the non-dominated sorting genetic algorithm II and Monte Carlo simulation, both deterministic optimization and reliability-based design optimization (RBDO) are conducted. The optimization solutions are further validated by finite element analysis, which shows the effectiveness of the RBDO solution in the structural crashworthiness design process. The results demonstrate the advantages of using RBDO, resulting in not only increased energy absorption and decreased structural weight from a baseline design, but also a significant improvement in the reliability of the design.     

Lightweight and crashworthiness design of an electric vehicle using a six-sigma robust design optimization method

Design optimization plays an important role in electric vehicle (EV) design. However, fluctuations in design variables and noise factors during the forming process affect the stability of optimization results. This study uses six-sigma robust design optimization to explore the lightweight design and crashworthiness of EVs with uncertainty. A full-scale finite element model of an EV is established. Then, multi-objective design optimization is performed by integrating optimal Latin hypercube sampling, radial basis functions and non-dominated sorting genetic algorithm-II to achieve minimum peak acceleration and mass. Finally, six-sigma robust optimization designs are applied to improve the reliability and sigma level. Robust optimization using adaptive importance sampling is shown to be more efficient than that using Monte Carlo sampling. Moreover, deformation of the battery compartment and peak acceleration of the B-pillar are greatly decreased. The EV’s safety performance is improved and the lightweight effect is remarkable, validating the strong engineering practicability of the method.     

基于鞍点逼近的机械结构可靠性稳健优化设计

将可靠性优化设计理论与可靠性灵敏度分析方法相结合,讨论了机械零部件稳健优化设计的问题.系统地推导了基于鞍点逼近的可靠性灵敏度公式,并把可靠性灵敏度计算结果融入可靠性稳健优化设计模型之中,将可靠性稳健优化设计归结为满足可靠性要求的多目标优化问题.在基本随机参数概率分布已知的前提下,应用鞍点逼近技术,得到极限状态函数的分布函数与概率密度函数,并且将此结果应用到机械零部件的可靠性灵敏度分析中,进而实现了机械零部件的可靠性稳健优化设计.通过与Monte-Carlo方法计算所得的结果相比可知,应用鞍点逼近技术可以迅速、准确地得到机械零部件可靠性稳健设计信息．     

产品操作界面元素布局多目标优化设计

目的针对产品设计中操作界面布局设计时存在的随意性、不确定性大等问题,使用多目标优化设计的方法,寻找界面元素的最优布置,以提升界面的使用舒适性和人机交互效率。方法在分析了工效学准则和界面布局美度评价准则的基础上,确立了层次性、相关性、简洁性和舒适性四个界面布局基本原则,并依据原则构建了界面元素布局多目标优化数学模型,在此模型的基础上采用改进的遗传算法,建立基于遗传算法的界面元素布局多目标优化方法。结果给出产品操作界面布局设计的基本原则,提出一种基于遗传算法的产品操作界面元素布局的多目标优化方法及流程。结论提出的布局原则和优化方法能较好地协助设计师获得满足设计需求的布局方案,实例结果表明了理论模型的合理性与遗传算法对于界面元素布局多目标优化问题求解的有效性。     

A Geodesign Method of Human-Energy-Water Interactive Systems for Urban Infrastructure Design: 10KM2 Near-Zero District Project in Shanghai

The grand challenges of climate change demand a new paradigm of urban design that takes the performance of urban systems into account, such as energy and water efficiency. Traditional urban design methods focus on the form-making process and lack performance dimensions. Geodesign is an emerging approach that emphasizes the links between systems thinking, digital technology, and geographic context. This paper presents the research results of the first phase of a larger research collaboration and proposes an extended geodesign method for a district-scale urban design to integrate systems of renewable energy production, energy consumption, and storm water management, as well as a measurement of human experiences in cities. The method incorporates geographic information system (GIS), parametric modeling techniques, and multidisciplinary design optimization (MDO) tools that enable collaborative design decision-making. The method is tested and refined in a test case with the objective of designing a near-zero-energy urban district. Our final method has three characteristics. ① Integrated geodesign and parametric design: It uses a parametric design approach to generate focal-scale district prototypes by means of a custom procedural algorithm, and applies geodesign to evaluate the performances of design proposals. ② A focus on design flow: It elaborates how to define problems, what information is selected, and what criteria are used in making design decisions. ③ Multi-objective optimization: The test case produces indicators from performance modeling and derives principles through a multi-objective computational experiment to inform how the design can be improved. This paper concludes with issues and next steps in modeling urban design and infrastructure systems based on MDO tools.     

Parametric design and optimization of high speed train nose

Aiming at shortening the design period and improve the design efficiency of the nose shape of high speed trains, a parametric shape optimization method is developed for the design of the nose shape has been proposed in the present paper based on the VMF parametric approach, NURBS curves and discrete control point method. 33 design variables have been utilized to control the nose shape, and totally different shapes could be obtained by varying the values of design variables. Based on the above parametric method, multi-objective particle swarm algorithm, CFD numerical simulation and supported vector machine regression model, multi-objective aerodynamic shape optimization has been performed. Results reveal that the parametric shape design method proposed here could precisely describe the three-dimensional nose shape of high speed trains and could be applied to the concept design and optimization of the nose shape. Besides, the SVM regression model based the multi-points criterion could accurately describe the non-linear relationship between the design variables and objectives, and could be generally utilized in other fields. No matter the simplified model or the real model, the aerodynamic performance of the model after optimization has been greatly improved. Based on the SVR model, the nonlinear relation between the aerodynamic drag and the design variables is obtained, which could provide guidance for the engineering design and optimization.     

Al/CFRP混合薄壁结构耐撞性能可靠性优化设计

轻量化是实现汽车产业向安全、节能、环保发展的一个重要途径。Al/CFRP（carbon fiber reinforced plastic，碳纤维增强复合材料）混合材料能够在提升轻量化效果的同时兼顾材料成本和结构耐撞性能。为探索方形截面Al/CFRP混合薄壁结构的最佳组合方式，首先，制备了Al方管、CFRP方管和Al/CFRP混合方管，并开展准静态压溃实验。然后，建立能够精确模拟Al/CFRP混合方管压溃响应的有限元模型。最后，将试验设计方法、代理模型技术、多目标优化算法和蒙特卡罗模拟技术相结合，对Al/CFRP混合方管分别进行多目标确定性与可靠性优化设计，并对效果较好的可靠性优化解进行仿真验证。准静态压溃实验结果表明，Al/CFRP混合方管具有优异的耐撞性能；优化结果表明，可靠性优化解的约束可靠度相比于确定性优化解提高了10.96%，大大降低了失效概率，具有更强的实用性。研究结果有望对Al/CFRP混合薄壁吸能构件的优化设计提供参考。     

An integrated reliability-based design optimization of offshore towers

After recognizing the uncertainty in the parameters such as material, loading, geometry and so on in contrast with the conventional optimization, the reliability-based design optimization (RBDO) concept has become more meaningful to perform an economical design implementation, which includes a reliability analysis and an optimization algorithm. RBDO procedures include structural analysis, reliability analysis and sensitivity analysis both for optimization and for reliability. The efficiency of the RBDO system depends on the mentioned numerical algorithms. In this work, an integrated algorithms system is proposed to implement the RBDO of the offshore towers, which are subjected to the extreme wave loading. The numerical strategies interacting with each other to fulfill the RBDO of towers are as follows: (a) a structural analysis program, SAPOS, (b) an optimization program, SQP and (c) a reliability analysis program based on FORM. A demonstration of an example tripod tower under the reliability constraints based on limit states of the critical stress, buckling and the natural frequency is presented.     

基于可调变异算子求解遗传算法的欺骗问题

高层建筑由于自振周期长、阻尼小,其高柔的特征使其对风荷载特别敏感,风荷载是沿海地区超高层建筑的主要水平控制荷载,因此在强/台风作用下,其抗风设计须在满足规范安全要求的前提下,同时又要经济实用和结构性能高效,为此,开展高层建筑抗风优化和风振控制方面的研究具有十分重要的现实意义。该文在对高层建筑抗风优化设计和风振控制研究现状做简要介绍的基础上,首先根据风荷载的特点,着重研究了考虑风速风向联合概率分布和基于可靠度及性能化的高层建筑抗风设计方法,采用最优准则法,以结构的总重或总造价为目标函数,以顶部位移、层间侧移以及顶部风致加速度为约束条件,对高层建筑结构杆件截面抗风优化设计的相关问题进行了研究。同时为提高基因遗传智能优化算法的收敛速度和获得最可能优化解,该文提出了传统基因遗传的改进算法(如基于改进罚函数及分级遗传算法)用于结构抗风优化设计。在结构拓扑抗风优化方面,则主要引入分层优化的概念,对变密度法和改进动态进化率的双向渐进拓扑优化方法,应用于抗风结构的拓扑构型优化算法进行了相关研究。通过实例分析验证了上述结构抗风优化算法的高效和正确性。在风振控制方面,该文结合摩擦摆系统和调谐质量阻尼器各自的优点,提出了摩擦摆调谐质量阻尼器(FPS-TMD)被动控制系统,对其力学和动力特性,以及高层建筑顶部带FPS-TMD系统的风振控制理论,进行了相关研究。以结构控制第三代Benchmark模型为实例,研究顶部带FPS-TMD系统的高层建筑风振控制效果,同时结合该文开发的基于小型电振动台的实时混合实验测试平台,采用风振控制实时混合实验结果与理论模拟计算结果的对比,验证了该文提出的FPS-TMD被动控制系统,应用于高层建筑风振控制的有效性。