计算机网络可靠性优化设计问题探析

阐述了计算机网络可靠性设计的原则,围绕计算机网络可靠性优化设计方法进行了探讨与分析,希望能够从理论层面上为我国计算机网络技术的发展提供一点指导与支持.     

面向复杂装备精密产品质量特性的Kriging-RBDO可靠性优化设计

不确定设计参数情形下的复杂装备柔顺机构精密产品质量特性波动与可靠性疲劳退化是精密微机电系统领域的基础性工程难题.针对这一基础性工程难题,提出一种面向复杂装备柔顺机构精密产品可靠性优化设计模型.利用拉丁超立方试验设计(Latin hypercube design,LHD)构建试验设计组合方案,通过有限元数值模拟获取各试验设计组合方案的质量特性值.据此,采用Kriging代理模型建立质量特性与不确定设计参数之间复杂非线性函数关系模型.在此基础上,引入基于可靠性优化设计(Reliability-based design optimization,RBDO)策略,构建面向复杂装备柔顺机构精密产品Kriging-RBDO可靠性优化设计模型.算例表明,所提出的方法在不确定设计参数情形下的复杂装备柔顺机构精密产品早期质量设计方面具有良好的抗疲劳退化特性.     

卫星载荷安装平台结构轻量优化与仿真分析

结构轻量化是目前航天器设计的发展趋势.以国内某高精度成像卫星有效载荷安装平台为对象,为了降低卫星平台的质量,同时满足其在复杂外热流环境下的热变形要求,分别采用拓扑优化与铺层优化两种方法对卫星安装平台进行轻量优化设计.在整星有限元热变形分析基础上,以载荷安装平台的质量最轻为目标,对结构进行优化设计,并对整星进行模态分析以验证设计方案的可靠性.两种优化方法均满足结构热变形设计要求,相比拓扑优化方法,铺层优化方法可以大幅降低安装平台质量,并且可行性高.     

通用数据采集平台ADC电路的可靠性设计

ADC电路作为基于DSP的通用数据采集平台的重要组成部分．其可靠性设计的好坏直接影响平台的可靠性和数据采集的准确性。本数据采集平台通过对A／D转换器优化设计．对电路进行电磁兼容设计，确保可靠性和采集数据的准确性。     

基于非线性规划的开关电源最优设计技术

随着电力电子技术的不断发展,要求开关电源有更高的频率、更高的功率密度、更高的效率、更高的功率因数和更高的可靠性,同时还要求其电路相对简洁,并有较小的体积,这样才更具实用性.在进行单相Boost PFC变换器初步设计的基础上,运用非线性规划技术对Boost PFC变换器进行了优化设计.同时采用遗传算法对该非线性模型进行求解.结果表明,优化设计方案在满足各项性能指标的情况下,实现了设计目标的最优.     

提高环境控制系统可靠性的方法

根据环境控制系统所处的特殊气候条件,对环境控制系统的拓扑结构进行了优化设计.对组成环境控制系统的主要部件RS485总线、自动控制装置、水冷机组以及冷却水管路的可靠性进行了分析、设计.同时,辅以必要的三防处理技术,提高了环境控制系统的可靠性和适应性.     

基于EA方法的飞行控制律设计与仿真

在飞行控制系统优化设计中,由于系统存在多输入、多输出的模型结构,古典控制方法无法应用到MIMO系统中,尤其现代控制方法设计过程中一次性计算出反馈控制器,导致应用过程中可靠性降低,对古典飞行控制系统控制结构进行优化设计.控制系统内回路采用EA方法,外回路适当引入误差积分控制和前馈比例控制,进而针对广义被控对象直接进行控制律设计与仿真.运用根轨迹方法对控制系统进行验证分析,并根据实际需求对控制律进行适当改进.仿真结果表明,特征结构配置方法能够较好的与古典控制理论相结合,完成指令跟踪控制律设计,且具有很好的鲁棒性和动态特性,新的控制结构应用起来方便有效.     

基于准则法的飞机薄壁结构可靠性优化设计

针对传统的安全因数法考虑载荷和强度不确定性因素对飞机安全性的影响太笼统,无法从根本上减轻飞机结构质量的问题,提出一种飞机结构可靠性优化设计方法.根据应力-强度干涉模型计算结构单元的可靠度,建立以单元满可靠度为准则的优化模型,实现包含可靠性约束的结构尺寸优化.该功能已经加入到COMPASS中.算例结果表明该方法能够减轻结构质量,改善结构可靠度.该方法为开展飞机结构不确定性设计提供技术手段.     

构造已知核度的最短网络的近似算法

在网络的优化设计中,可靠性和经济性是测度网络优劣的两个重要标准.而可靠性与经济性是此消彼长的关系,即可靠性越高,成本越大,经济性越差.该文在系统核与核度理论的基础上,给出度量网络可靠性的一种新方法.并在网络的可靠性一定--即核度已知的条件下,给出构造最短网络的近似算法,算法的时间复杂性是O(n).这对网络优化设计和网络的组织规划具有重要的理论指导意义.     

旋翼复合材料桨叶参数化及有限元建模

直升机旋翼桨叶结构建模通常只考虑内部结构的几何特征,在进行优化设计时,没有考虑设计参数对初始静强度设计的影响,为优化设计,提出采用有限元离散方法,面向多数直升机采用的C型梁结构,将桨叶内部结构分为带设计控制点的三大类构型模块,建立了在设计过程中能够保证桨叶大梁设计强度不变的参数化模型,并有针对性的提出背景网格与层结构网格结合的快速有限元离散方法,生成高效的旋翼复合材料桨叶剖面参数化有限元模型.通过对“海豚”直升机桨叶剖面建模,验证参数化建模方法的可靠性和有效性.     

Optimum values of structural safety factors for a predefined reliability level with extension to multiple limit states

In the field of deterministic structural optimization, the designer reduces the structural cost without taking into account uncertainties concerning materials, geometry and loading. This way, the resulting optimum solution may represent a lower level of reliability and thus a higher risk of failure. It is the objective of reliability-based design optimization (RBDO) to design structures that should be both economic and reliable. The coupling between mechanical modeling, reliability analyses and optimization methods leads to very high computational costs and weak convergence stability. Since the traditional RBDO solution is achieved by alternating between reliability and optimization iterations, the structural designers performing deterministic optimization do not consider the RBDO model as a practical tool for the design of real structures. Fortunately, a hybrid method based on simultaneous solution of the reliability and the optimization problem, has successfully reduced the computational time problem. The hybrid method allows us to satisfy a required reliability level, but the vector of variables here contains both deterministic and random variables. The hybrid RBDO problem is thus more complex than that of deterministic design. The major difficulty lies in the evaluation of the structural reliability, which is carried out by a special optimization procedure. In this paper a new methodology is presented with the aim of finding a global solution to RBDO problems without additional computing cost for the reliability evaluation. The safety factor formulation for a single limit state case has been used to efficiently reduce the computational time . This technique is fundamentally based on a study of the sensitivity of the limit state function with respect to the design variables. In order to demonstrate analytically the efficiency of this methodology, the optimality condition is then used. The efficiency of this technique is also extended to multiple limit state cases. Two numerical examples are presented at the end of the paper to demonstrate the applicability of the new methodology.     

Efficient reliability-based design optimization using a hybrid space with application to finite element analysis

The design of high technology structures aims to define the best compromise between cost and safety. The Reliability-Based Design Optimization (RBDO) allows us to design structures which satisfy economical and safety requirements. However, in practical applications, the coupling between the mechanical modelling, the reliability analyses and the optimization methods leads to very high computational time and weak convergence stability. Traditionally, the solution of the RBDO model is achieved by alternating reliability and optimization iterations. This approach leads to low numerical efficiency, which is disadvantageous for engineering applications on real structures. In order to avoid this difficulty, we propose herein a very efficient method based on the simultaneous solution of the reliability and optimization problems. The procedure leads to parallel convergence for both problems in a Hybrid Design Space (HDS). The efficiency of the proposed methodology is demonstrated on the design of a steel hook, where the RBDO is combined with Finite Element Analysis (FEA).     

Design sensitivity and reliability-based structural optimization by univariate decomposition

This paper presents a new univariate decomposition method for design sensitivity analysis and reliability-based design optimization of mechanical systems subject to uncertain performance functions in constraints. The method involves a novel univariate approximation of a general multivariate function in the rotated Gaussian space for reliability analysis, analytical sensitivity of failure probability with respect to design variables, and standard gradient-based optimization algorithms. In both reliability and sensitivity analyses, the proposed effort has been reduced to performing multiple one-dimensional integrations. The evaluation of these one-dimensional integrations requires calculating only conditional responses at selected deterministic input determined by sample points and Gauss–Hermite integration points. Numerical results indicate that the proposed method provides accurate and computationally efficient estimates of the sensitivity of failure probability, which leads to accurate design optimization of uncertain mechanical systems.     

Efficient strategies for reliability-based design optimization of variable stiffness composite structures

This study investigates efficient design optimization frameworks for composite structures with uncertainties related to material properties and loading. The integration of two decoupled reliability-based design optimization methodologies with a decoupled discrete material optimization is proposed to determine material and fiber orientation for three-dimensional composite structures. First, a deterministic and decoupled discrete material optimization is used for baseline comparison. The objective is to minimize the cost of composite structures with the design variables comprising of the piecewise patch orientations and material properties of the fiber reinforced composites. The reliability-based design optimization includes a hybrid method, and also the sequential optimization and reliability assessment method. In the sequential optimization and reliability assessment method, the inverse reliability analysis is evaluated using a stochastic response surface method and a first order reliability approach. Comparing the methods based on the optimal material and fiber orientations, the uncertainties in loads and material properties lead to different optimal layouts compared to the deterministic solutions. The numerical results also reveal that the hybrid method applied in reliability based designs results in negligible additional computational cost.     

Topology optimization of dynamic stress response reliability of continuum structures involving multi-phase materials

This paper proposes a methodology for maximizing dynamic stress response reliability of continuum structures involving multi-phase materials by using a bi-directional evolutionary structural optimization (BESO) method. The topology optimization model is built based on a material interpolation scheme with multiple materials. The objective function is to maximize the dynamic stress response reliability index subject to volume constraints on multi-phase materials. To solve the defined topology optimization problems, the sensitivity of the dynamic stress response reliability index with respect to the design variables is derived for iteratively updating the structural topology. Subsequently, an optimization procedure based on the BESO method is developed. Finally, a series of numerical examples of both 2D and 3D structures are presented to demonstrate the effectiveness of the proposed approach.

     

Sequential optimization and reliability assessment for multidisciplinary systems design

With higher reliability and safety requirements, reliability-based design has been increasingly applied in multidisciplinary design optimization (MDO). A direct integration of reliability-based design and MDO may present tremendous implementation and numerical difficulties. In this work, a methodology of sequential optimization and reliability assessment for MDO is proposed to improve the efficiency of reliability-based MDO. The central idea is to decouple the reliability analysis from MDO with sequential cycles of reliability analysis and deterministic MDO. The reliability analysis is based on the first-order reliability method (FORM). In the proposed method, the reliability analysis and the deterministic MDO use two MDO strategies, the multidisciplinary feasible approach and the individual disciplinary feasible approach. The effectiveness of the proposed method is illustrated with two example problems.     

基于VB机械优化设计软件的研究与实现

机械优化设计方法是现代机械设计过程中寻求最优化设计的一种重要手段。利用VB作为开发平台,以常用的最优化计算方法为基础,开发适用于机械设计过程中的优化软件,运用该软件能够满足机械设计过程中的优化要求。     

Collaborative robust optimization under uncertainty based on generalized dynamic constraints network

Uncertainties exist in every aspect of a collaborative multidisciplinary design process. These uncertainties will have a great influence on design negotiations between various disciplines and may force designers to make conservative decisions. In this paper, a novel collaborative robust optimization (CRO) method based on constraints network under uncertainty is presented. The generalized dynamic constraints network (GDCN) is developed to analysis and management of uncertainties, and to ensure the parameter consistency in the collaborative design process. Given the feasible consistent parameter region, The CRO is formulated as a multi-criteria optimization problem, which brings both the objective robustness and the feasibility robustness of the constraint into account simultaneously. The CRO based on GDCN could bring both the design parameters dynamic consistency management and robust optimization into account simultaneously, which assures a product’s reliability and quality robustness. The efficiency of proposed method is evaluated in the design of crank and connecting rod in one V6 engine.     

Reliability-based design optimization of aeroelastic structures

Aeroelastic phenomena are most often either ignored or roughly approximated when uncertainties are considered in the design optimization process of structures subject to aerodynamic loading, affecting the quality of the optimization results. Therefore, a design methodology is proposed that combines reliability-based design optimization and high-fidelity aeroelastic simulations for the analysis and design of aeroelastic structures. To account for uncertainties in design and operating conditions, a first-order reliability method (FORM) is employed to approximate the system reliability. To limit model uncertainties while accounting for the effects of given uncertainties, a high-fidelity nonlinear aeroelastic simulation method is used. The structure is modelled by a finite element method, and the aerodynamic loads are predicted by a finite volume discretization of a nonlinear Euler flow. The usefulness of the employed reliability analysis in both describing the effects of uncertainties on a particular design and as a design tool in the optimization process is illustrated. Though computationally more expensive than a deterministic optimum, due to the necessity of solving additional optimization problems for reliability analysis within each step of the broader design optimization procedure, a reliability-based optimum is shown to be an improved design. Conventional deterministic aeroelastic tailoring, which exploits the aeroelastic nature of the structure to enhance performance, is shown to often produce designs that are sensitive to variations in system or operational parameters.