含区间不确定性参数的机翼气动弹性优化

提出了一种具有区间不确定性的机翼颤振优化方法.采用拉丁超立方方法建立仿真试验表,基于MSC.Nastran平台进行颤振仿真分析.获得仿真数据之后,应用Kriging方法构造了包含区间不确定性参数的机翼颤振分析代理模型,并进行有效性检验.基于建立的代理模型并按照区间序数关系,将不确定性优化目标和约束条件转化为确定性表达形式,从面形成区间不确定性的结构优化设计方法.该方法将区间法优化和代理模型相结合,同时综合有限元仿真和遗传算法的优点,计算效率较高且应用范围较广.以某复杂机翼结构为例进行了含区间不确定性的颤振优化计算.分析结果表明了所提方法的正确性和可行性.     

一种跨音速伺服气动弹性分析方法

基于CFD技术,运用系统辨识方法,建立了基于模态坐标的跨音速气动力降阶模型(ROM)。耦合气动状态方程、结构状态方程和伺服状态方程,建立了一个适合跨音速伺服气动弹性分析的数学模型。算例首先通过对比基于ROM技术的分析结果和直接仿真结果,以证明该模型的正确性和精度。在保证精度的同时,其计算效率比直接耦合CFD技术的仿真方法高1个～2个数量级。算例还研究了传感器安放位置和结构陷幅滤波器对该导弹伺服气动弹性特性的影响,结果显示结构陷幅滤波器的引入可以显著地降低开环气动弹性系统和控制系统的耦合。     

机翼气动弹性的随机不确定性分析研究

通过分析气动弹性状态方程的特征值来求解颤振临界速度,并建立颤振的安全裕度方程.针对带有操纵面机构的二元三自由度机翼颤振问题,分别采用重要抽样法和线抽样可靠性分析技术,在d维标准正态空间中对飞机机翼的气动弹性问题进行失效概率和灵敏性分析,推导得出反映随机参数对失效概率影响程度的灵敏度因子,在假设条件下的分析结果表明了模型尺寸、质量比等对颤振可靠性的影响很小,故在进一步的计算中认为它们是确定量.仅考虑固有频率为随机变量情况下的不确定性分析.以重要抽样法的结果为近似精确解,验证了线抽样不确定性分析方法的正确性和高效性.     

气动弹性系统的模型确认与鲁棒颤振分析

研究了气动弹性系统的不确定性建模和鲁棒颤振分析问题.将结构的不确定性考虑为参数形式,非定常气动力的不确定性考虑为参数和未建模动态两种形式,建立了不确定系统的线性分式变换模型.分别使用基于Carathe-dory-Fejer插值定理和Nevanlinna-Pick插值定理的模型集检验方法进行了模型确认,在时间域和频率域中对模型集的有效性进行了验证,确定了不确定性的幅值.对于模型确认得到的不确定气动弹性系统,使用μ分析方法进行了鲁棒颤振分析.计算中,飞行速度是作为给定参数而不再是作为摄动变量,由此得到的鲁棒稳定性边界是匹配点解.仿真数值结果给出了鲁棒颤振速度,表明了方法的有效性.     

非线性气动弹性系统的鲁棒稳定性分析

基于结构奇异值理论和特征多项式的值域方法,研究了带有结构和气动参数不确定性的非线性二元机翼的鲁棒稳定性问题.机翼模型包括非线性的扭转弹簧和能够反映失速效应的非线性气动模型.针对零摄动,使用肛方法分析线性化系统在平衡点的鲁棒稳定性.针对非零摄动,将平衡点位置看作关于不确定参数的函数并展开为泰勒级数,从而在μ方法的框架下考虑平衡点的不确定性.此外,计算不确定特征多项式的值域范围,并使用除零条件来判断其鲁棒稳定性.仿真数值结果给出了鲁棒颤振速度的上下界,表明了方法的有效性.     

T型尾翼颤振特性分析方法

由于T型尾翼特殊的结构和气动布局形式,其颤振特性的分析比较复杂,飞行状态和一些常规分析可以忽略的参数如上反角都会对颤振速度有很大影响。针对这些特殊问题,分析了T型尾翼的颤振特性受平尾上反角、平尾上的定常气动力和固有振动形式等因素的影响。重点研究了T型尾翼颤振计算中特殊的附加非定常气动力,建立了T型尾翼非定常气动力和颤振分析方法。在低速风洞中开展了T型尾翼缩比模型的颤振试验,验证了分析方法。结果表明T型尾翼特殊的气动效应主要影响垂尾的弯扭耦合颤振形式,颤振速度随平尾攻角增加而降低,在设计中采用一定的平尾下反角设计能够提高T型尾翼的颤振速度。通过对试验结果的理论分析,阐述了这种效应产生的机理,并对实际设计提出建议。     

主动气动弹性机翼多控制面配平综合优化设计

针对主动气动弹性机翼（active aeroelastic wing,简称AAW）,基于遗传算法发展一种可以同步考虑结构优化和配平关系优化的综合设计方法,并在采用了AAW技术的战斗机的设计中得到了应用。以结构质量最小化为目标,以控制面偏转、铰链力矩、翼根载荷和临界颤振速度为约束条件,在多种平飞滚转机动状态下对战斗机机翼...     

基于当地流活塞理论的气动弹性稳定性分析方法研究

基于改进的当地流活塞理论,推导了用模态坐标表示的弹性振动翼面的非定常广义气动力表达式。通过与非定常Euler方程的比较,证明该气动力模型具有较高的数值精度,放宽了原始活塞理论对翼型厚度、迎角、马赫数的限制。再耦合两自由度结构运动方程,在时域和频域内实现了超音速、高超音速气动弹性的稳定性分析。应用算例计算了一系列二元机翼和三维舵面的气动弹性特性。通过与耦合非定常Euler方法或实验结果的比较,证明该方法在保证精度的同时大大提高了超音速气动弹性的计算效率,在颤振分析中有较高的工程实用价值。     

不确定性简谐激励下连续体结构可靠性拓扑优化

针对不确定性简谐激励下连续体结构设计问题,提出了一种有效的考虑载荷振幅和频率不确定性的谐响应可靠性拓扑优化方法。建立了概率可靠性约束下结构体积比最小化的可靠性设计优化模型,其中极限状态函数定义为所关注自由度振幅平方和。利用伴随变量法推导了极限状态函数关于设计变量和随机变量的解析灵敏度列式,采用功能度量法实现结构可靠性分析,并基于移动渐进线方法实现设计变量的更新。最后,通过3个数值算例及蒙特卡罗仿真,验证了所提方法对不确定性简谐激励下连续体结构设计问题的有效性和稳定性,并讨论了简谐激励的振幅大小和频率不确定性、可靠度指标及变异系数对优化结果的影响。     

二元机翼非线性颤振系统的若干分析方法

颤振是气动弹性力学研究最重要的问题之一。本文综述了亚音速条件下二元机翼非线性颤振研究的若干分析方法。目前,基于二元机翼的非线性颤振分析采用的定性方法主要是常微分方程定性理论,定量方法则有等效线性化法、描述函数法、谐波平衡法以及摄动法等。对所提及的方法做了简要的评述和比较,指出了进一步研究的问题。     

应用μ-ω方法进行气动弹性稳定性分析的数值研究

结合最近提出的一种频域颤振预测μ-ω方法,将其推广应用到静气动弹性发散分析中,进行了复杂气动弹性系统的稳定性分析数值研究。首先采用该方法进行了某大展弦比后掠机翼模型的颤振分析,与p-k法得到的颤振速度相对误差为0.83％,颤振频率相对误差仅为0.39％。然后计算了某三角翼模型的静发散速度,与p-k法求得的发散速度相对误差为2.02％。结果表明μ-ω方法具有良好的求解精度,适用于复杂气动弹性系统的稳定性分析。     

A single-loop optimization method for reliability analysis with second order uncertainty

Reliability analysis may involve random variables and interval variables. In addition, some of the random variables may have interval distribution parameters owing to limited information. This kind of uncertainty is called second order uncertainty. This article develops an efficient reliability method for problems involving the three aforementioned types of uncertain input variables. The analysis produces the maximum and minimum reliability and is computationally demanding because two loops are needed: a reliability analysis loop with respect to random variables and an interval analysis loop for extreme responses with respect to interval variables. The first order reliability method and nonlinear optimization are used for the two loops, respectively. For computational efficiency, the two loops are combined into a single loop by treating the Karush–Kuhn–Tucker (KKT) optimal conditions of the interval analysis as constraints. Three examples are presented to demonstrate the proposed method.     

Research on multiple-state industrial robot system with epistemic uncertainty reliability allocation method

Reliability allocation of industrial robot (IR) system is one of the important means to improve its whole life cycle, reduce maintenance cost, and characterize weak subsystems. The IR system is not only very complex but also has strong customization; meanwhile, its sample data are small, resulting in unclear degeneration and failure. Based on the above two epistemic uncertainties, a new methodology called multiple-state IR system reliability allocation method with epistemic uncertainty (MIRS-RAM-EU) is proposed. First, the Dempster-Shafer (D-S) evidence theory is used to quantify the epistemic uncertainty. Then, the Kolmogorov differential equations of MIR's subsystems are calculated. The reliability index of MIRS is allocated based on Birnbaum importance degree theory, and the reliability allocation coefficient of each IR subsystem is clearly expressed by this method. Finally, compared with traditional importance allocation method, the MIRS-RAM-EU is more efficient and accurate. This method is usefully directive for reliability evaluation of IR.     

Belief rule-based dependence assessment method under interval uncertainty

Human reliability analysis (HRA) is of great significance for probabilistic risk assessment, and the technique for human error rate prediction (THERP) has been widely applied to assess the dependence among HRA. However, uncertainties in analyst's judgment and experts' knowledge, especially interval uncertainty in analyst's judgment, have been ignored by existing methods. To this end, the belief rule-based system is employed to model uncertainties in experts' knowledge in this paper, and the interval belief distribution is used to model interval uncertainty in analyst's judgment. Then, a new belief rule-based dependence assessment method is proposed, and two case studies are used to illustrate the effectiveness of the proposed method. Experimental results demonstrate that the proposed method could not only model uncertainty using belief rules and interval belief distributions, but also provide a novel and effective way for human reliability analysis.     

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.     

Integration method for reliability assessment with multi-source incomplete accelerated degradation testing data

ABSTRACT

During the product life cycle, the lifetime information will be collected at each stage, mainly from different tests at the R&D phase, field usage, and maintenance. To comprehensively conduct reliability assessments, it generally requires the integration of multi-source datasets, even that from similar products. In this article, we considered the scenario that products have been arranged with several accelerated degradation tests (ADT) under different types of accelerated stresses with dependency. The obtained data is called incomplete ADT dataset with incomplete stress conditions which fails the traditional integration method for reliability assessments. A novel method is proposed to accomplish this task through mutually exclusive set (MES) theory. The probability assignments for each dataset are given through the union set of several MESs. Then, the multi-source ADT datasets are integrated with the assigned weights of probabilities. Finally, a simulation study and a real application are given to illustrate the effectiveness of the proposed methodology.     

Collaborative optimization with inverse reliability for multidisciplinary systems uncertainty analysis

This article introduces a method which combines the collaborative optimization framework and the inverse reliability strategy to assess the uncertainty encountered in the multidisciplinary design process. This method conducts the sub-system analysis and optimization concurrently and then improves the process of searching for the most probable point (MPP). It reduces the load of the system-level optimizer significantly. This advantage is specifically more prominent for large-scale engineering system design. Meanwhile, because the disciplinary analyses are treated as the equality constraints in the disciplinary optimization, the computation load can be further reduced. Examples are used to illustrate the accuracy and efficiency of the proposed method.     

An efficient hybrid reliability analysis method with random and interval variables

Random and interval variables often coexist. Interval variables make reliability analysis much more computationally intensive. This work develops a new hybrid reliability analysis method so that the probability analysis (PA) loop and interval analysis (IA) loop are decomposed into two separate loops. An efficient PA algorithm is employed, and a new efficient IA method is developed. The new IA method consists of two stages. The first stage is for monotonic limit-state functions. If the limit-state function is not monotonic, the second stage is triggered. In the second stage, the limit-state function is sequentially approximated with a second order form, and the gradient projection method is applied to solve the extreme responses of the limit-state function with respect to the interval variables. The efficiency and accuracy of the proposed method are demonstrated by three examples.     

IFQP: A hybrid optimization method for filter management in fluid power systems under uncertainty

An interval-fuzzy quadratic programming (IFQP) method is developed for the assessment of filter allocation and replacement strategies in fluid power systems (FPS) under uncertainty. It can directly handle uncertainties expressed as interval values and/or fuzzy sets that exist in the left-hand and right-hand sides of constraints, as well as in the objective function. Multiple control variables are used to tackle independent uncertainties in the model's right-hand sides and thus optimize the overall satisfaction of the system performance. The IFQP method is applied to a case of planning filter allocation and replacement strategies under uncertainty for an FPS with a single circuit. A piecewise linearization approach is firstly employed to convert the nonlinear FPS problem into a linear one. The generated decision alternatives can help decision makers to identify desired policies for contamination control under various total costs, satisfaction degrees, and system-failure risks under different contaminant-ingression/generation rates.