部分加速度测量下结构恢复力与质量非参数化识别方法

传统的基于扩展卡尔曼滤波方法的结构非线性行为识别方法往往要求结构质量以及结构非线性恢复力的参数化模型已知。该研究为解决非线性结构质量,结构参数,非线性恢复力的识别问题,提出了一种两阶段识别方法;为提高计算效率采用遗忘因子扩展卡尔曼滤波算法结合等效线性模型实现结构非线性位置的定位,随后采用无迹卡尔曼滤波算法与恢复力的二重切比雪夫多项式非参数化模型识别结构参数,质量与恢复力。在对一个含形状记忆合金(SMA)阻尼器的多自由度体系的数值模型进行了数值模拟验证的基础上,设计了一个含SMA阻尼器的四自由度框架开展动力试验,验证了所提出方法对结构质量以及恢复力的识别效果。     

改进的平行六面体凸模型识别动力学不确定参数区间的方法

提出了一种改进的平行六面体凸模型,以便更普遍地描述不确定参数之间的相关性。结合逆响应面模型,建立了一种动力学不确定参数区间识别方法。首先,采用改进的平行六面体二维模型描述结构动力学响应频率的不确定域相关分布,再构建其多维模型,并通过仿射坐标变换将其转化为标准参数空间中的区间形式;然后,以此参数空间内的响应频率为输入,结构不确定参数为输出,构建能避免区间扩张问题的完全平方项区间逆响应面模型;进而进行区间运算,识别动力学不确定参数区间;最后,以一个3自由度质量弹簧系统仿真算例和一个螺栓连接框架结构模态实验进行了验证。结果说明:此方法能够有效性地识别结构的动力学不确定参数区间,具有较高的识别精度。     

基于区间B样条小波基函数时变多变量AR模型的时变结构参数识别

本文研究的是时变动力学结构的参数识别问题。仅利用线性时变结构的多个加速度响应测量数据,本文将区间B样条小波函数作为时变基函数,构造了基于区间B样条小波基函数的时变多变量自回归模型,并推导了时变结构瞬时频率的识别方法。文中利用该法对一个三自由度线性时变结构进行了仿真研究,针对结构瞬时频率周期变化,突变和线性变化三种不同的情况分别进行了瞬时频率识别,并在加速度响应数据中加入了不同信噪比的高斯白噪声以检验识别方法的抗噪能力。最后,文中对一个具有质量时变特性的悬臂梁结构进行了实验研究,实验结果充分说明了区间B样条小波基函数时变多变量自回归模型对时变结构参数识别的可行性,有效性和良好的抗噪能力。     

连续体结构裂纹识别的Kriging代理模型求解策略

本文提出了一种基于代理模型的裂纹识别方法,利用初始样本构造Kriging代理模型,建立裂纹模型参数与结构响应的关系,来代替结构的原有的结构参数与动力响应关系,最大程度地减少了反演优化迭代过程中反复网格剖分和冗繁的有限元计算次数。使用最优设计加点准则进行代理模型修正,以改进初始代理模型的准确性。为了识别连续体结构上的裂纹模型参数,采用随机粒子群优化方法搜索代理模型多极值域下的全局最优解。数值算例对具有裂纹的悬臂梁和板结构进行了裂纹识别。结果表明,该方法能有效地识别裂纹参数,并且具有良好的抗噪性能。此外,初始样本数量对裂纹识别效率及识别结果的影响也进行了讨论。     

基于随机减量法的非平稳激励下模态参数识别

针对现有结构模态参数识别方法平稳激励假定的不足,提出了一种非平稳激励下的结构模态参数识别方法。以白噪声调幅激励、调幅相关激励和频率幅值调制共三类非平稳随机激励为对象,采用演化谱理论推导了随机激励分段叠加后的均值和方差表达式,然后在此基础上对传统的随机减量法进行拓展,将非平稳激励下模态参数识别问题转化为结构自由振动曲线的模态参数识别,最后利用特征实现算法识别结构的模态参数。通过一个悬臂梁数值模型和两层实验室钢框架进行验证,识别结果表明运用该算法可以准确识别出非平稳激励下系统的固有频率和振型,且固有频率的识别误差在2%以内,具有较高的识别精度。     

针对柔性转子系统的电磁轴承支承刚度阻尼参数识别

为了用传统转子动力学的理论和方法分析支承在电磁轴承上的转子系统动力特性,电磁轴承支承的等效刚度和等效阻尼是必须获知的重要参数。已有研究提出的电磁轴承等效刚度和等效阻尼的识别方法大多都是建立在单自由度模型或刚性转子模型上的。针对支承在电磁轴承上的柔性转子系统,提出了其等效刚度和等效阻尼参数的识别方法。首先,设计了静态力和动态力两种自由度凝聚方法,把柔性转子有限元模型凝聚到所需要的自由度,减少了模型中未知数的数量,并提取凝聚后转子模型中的激励与响应关系;然后建立电磁轴承激励和加配重激励两种情况下的电磁轴承等效刚度和等效阻尼的识别方程;最后,通过一个电磁轴承-柔性转子系统算例验证了识别方法的有效性。算例分析了由于柔性转子振动相位的变化和增加识别方程主自由度数量对识别精度和效果的影响。     

一种磁流变弹性体模型参数识别新方法及其应用研究

为了定量描述不同应变幅值、频率和磁场下磁流变弹性体(magnetorheological elastomer, MRE)的力学特性,提出了一种新的参数识别方法。采用Bouc-Wen模型表征MRE的非线性滞回特性,通过试验数据验证了模型有效性,基于对模型参数与应变幅值、频率和磁场的耦合关系分析,提出了多工况参数识别方法。将仿真值与试验值之间的误差作为目标函数,利用GA-PSO混合算法对Bouc-Wen模型参数进行识别,选用多种工况的试验数据验证识别结果。结果表明,多工况参数识别方法得到的模型参数表达式在较宽的应变幅值、频率和磁场范围内是准确的,能够反映MRE的力学特性。识别和非识别工况下MRE滞回曲线的仿真值与试验值吻合率高于93%,证明了多工况参数识别方法的有效性,该方法也可用于其他模型的参数识别。     

设备振动内源参数识别方法

针对设备振动内源参数识别问题,分为设备不平衡激扰力单独作用、不平衡激扰力及不平衡激扰力矩联合作用两种情况,建立"设备-隔振器-基础"多自由度耦合振动模型,提出设备振动内源参数识别方法。基于Kirchhoff定律,假设设备内源特性参数(设备等效激励力及等效质量)不受基座、隔振器等外部参数影响,通过改变隔振器类型开展设备台架试验,并通过线性回归方法对设备内源特性参数(设备等效激励力及等效质量)辨识,获得设备内源特性参数。在此基础上,开展试验研究,验证方法有效性。     

一种静态转子系统连接参数辨识方法

针对转子系统连接参数辨识问题,研究一种转子系统连接参数的辨识方法。以某型含膜盘联轴器的转子试验器为研究对象,建立转子系统有限元模型,将支承刚度、支承位置和膜盘联轴器连接刚度作为辨识参数,并进行参数灵敏度分析。基于大量仿真样本,采用支持向量回归(SVR)算法建立连接参数和固有频率之间的计算代理模型。在模态试验基础上,采用第二代非支配排序遗传算法(NSGA-II),基于多阶固有频率建立多目标函数,利用多目标优化法,寻找转子连接参数在修正区间中的Pareto最优解,最终识别出转子系统的多个连接参数。与试验结果相比,修正后的动力学模型固有频率仿真结果:第1阶的精度达到97.62%;第2阶的精度达到99.70%。     

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.     

A Robust Inverse Method Based on Least Square Support Vector Regression for Johnson-cook Material Parameters

The purpose of this study is to propose a robust inverse method for estimating Johnson-Cook material parameters. The method is shown through illustrative examples for two different advanced high strength steel (AHSS) materials (DP980 and TRIP780) using set of data from impact experiments with different velocities. Compared with widely mixed numerical experimental methods, the suggested inverse method has the capability to guarantee the robustness of the obtained parameters by considering uncertainties. The inverse problem is converted into multi-objective optimization problems. Furthermore, in order to improve the performance in efficiency and accuracy, metamodeling techniques and global optimization method are integrated. The final results demonstrate that the experimental and simulation curves are well matched based on identified by the suggested robust inverse method.     

Optimal Operation of Integrated Heat and Electricity Systems: A Tightening McCormick Approach

Combined heat and electricity operation with variable mass flow rates promotes flexibility, economy, and sustainability through synergies between electric power systems (EPSs) and district heating systems (DHSs). Such combined operation presents a highly nonlinear and nonconvex optimization problem, mainly due to the bilinear terms in the heat flow model—that is, the product of the mass flow rate and the nodal temperature. Existing methods, such as nonlinear optimization, generalized Benders decomposition, and convex relaxation, still present challenges in achieving a satisfactory performance in terms of solution quality and computational efficiency. To resolve this problem, we herein first reformulate the district heating network model through an equivalent transformation and variable substitution. The reformulated model has only one set of nonconvex constraints with reduced bilinear terms, and the remaining constraints are linear. Such a reformulation not only ensures optimality, but also accelerates the solving process. To relax the remaining bilinear constraints, we then apply McCormick envelopes and obtain an objective lower bound of the reformulated model. To improve the quality of the McCormick relaxation, we employ a piecewise McCormick technique that partitions the domain of one of the variables of the bilinear terms into several disjoint regions in order to derive strengthened lower and upper bounds of the partitioned variables. We propose a heuristic tightening method to further constrict the strengthened bounds derived from the piecewise McCormick technique and recover a nearby feasible solution. Case studies show that, compared with the interior point method and the method implemented in a global bilinear solver, the proposed tightening McCormick method quickly solves the heat–electricity operation problem with an acceptable feasibility check and optimality.     

Comparison of the born iterative method and tarantola's method for an electromagnetic time-domain inverse problem

Two methods of solving the nonlinear two-dimensional electromagnetic inverse scattering problem in the time domain are considered. These are the Born iterative method and the method originally proposed by Tarantola for the seismic reflection inverse problems. The former is based on Born-type iterations on an integral equation, whereby at each iteration the problem is linearized, and its solution is found via a regularized optimization. The latter also uses an iterative method to solve the nonlinear system of equations. Although it linearizes the problem at each stage as well, no optimization is carried out at each iteration; rather the problem as a whole is posed as a (regularized) optimization. Each method is described briefly and its computational complexity is analyzed. Tarantola's method is shown to have a lower numerical complexity compared to the Born iterative method for each iteration, but in the examples considered, required more iterations to converge. Both methods perform well when inverting a smooth profile; however, the Born iterative method gave better results in resolving localized point scatterers.     

Reduction of springback by intelligent sampling-based LSSVR metamodel-based optimization

This paper deals with the variable blank holder force in sheet metal forming in order to reduce springback effects after forming. A structural risk minimization principle-based metamodeling technique, least square support vector regression (LSSVR) method is applied to optimization. In order to improve the efficiency, an intelligent sampling strategy proposed by Wang et al. (Mater Des 30:1468–1479, 2009a) is integrated with the LSSVR. Therefore, the proposed strategies establish an adaptive metamodeling optimization system. The optimization procedure can be carried out automatically. To valid the flexibility of this system, the presented method is used to optimize the variable blank force parameters of the models from NUMISHEET’96 and torsion rail model. Compared with other popular metamodel-based optimization methods, the test results demonstrate the potential capability for nonlinear engineering problems.     

一个瞬态温度场区间上下界估计的数值方法

提出一种求解带有区间不确定性参数的瞬态热传导问题的数值方法。利用Taylor展开和区间分析技术,建立温度区间量与不确定参数区间量的确定性关系,然后采用时域分段自适应算法和有限元技术,递推计算温度场的区间半径及区间中值,以估算温度场不确定区间的上下界。自适应计算可根据时间步长的变化,使计算在各离散时段达到给定的计算精度,从而保证整个时域的计算精度。在算例分析中,通过与组合方法、概率方法的比较,说明了所提方法的有效性,并探讨了Taylor展开阶次与计算步长对计算结果的影响。     

High fidelity multidisciplinary design optimization of a wing using the interaction of low and high fidelity models

This paper presents an efficient metamodel building technique for solving collaborative optimization (CO) based on high fidelity models. The proposed method is based on a metamodeling concept, that is designed to simultaneously utilize computationally efficient (low fidelity) and expensive (high fidelity) models in an optimization process. A distinctive feature of the method is the utilization of interaction between low and high fidelity models in the construction of high quality metamodels both at the discipline level and system level of the CO. The low fidelity model is tuned in such a way that it approaches the same level of accuracy as the high fidelity model; but at the same time remains computational inexpensive. In this process, the tuned low fidelity models are used in the discipline level optimization process. In the system level, to handle the computational cost of the equality constraints in CO, model management strategy along with metamodeling technique are used. To determine the fidelity of metamodels, the predictive estimation of model fidelity method is applied. The developed method is demonstrated on a 2D Airfoil design problem, involving tightly coupled high fidelity structural and aerodynamic models. The results obtained show that the proposed method significantly reduces computational cost, and improves the convergence rate for solving the multidisciplinary optimization problem based on high fidelity models.     

偶应力反问题参数识别

利用有限元技术和最小二乘原理,建立了便于敏度分析的偶应力反问题数值求解模型,并采用高斯牛顿方法进行求解。模型考虑了材料的非均质、各向异性等因素,并在计及测量误差的情形下,对其进行了数值验证,得到了令人满意的结果。此外,还通过反问题的求解,对偶应力本构等效的网格材料的等效本构参数进行了估算,并给出相应算例。     

Chemical process dynamic optimization based on the differential evolution algorithm with an adaptive scheduling mutation strategy

To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process. The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem.     

基于流固耦合的弹载电子设备瞬态热仿真分析方法

在导弹飞行过程中由于工况环境剧烈变化,其整流罩蒙皮温度和热载荷急剧变化,这将对导弹的作战性能产生重大影响。为保证弹载电子设备的设计满足精度要求,提出了一种基于流固耦合非定常数值模拟的设计计算方法。针对弹载电子设备在高速运行过程中的瞬态热载荷是一个典型流固耦合问题,通过求解流体与固体表面的热传递方程,计算获得流场内各点的能量分段函数。运用MATLAB软件计算拟合出流场每块结构微元对应的瞬态功率曲线分段函数,采用将功率曲线分段函数加载至Fluent的热仿真分析方法,直观预测整个弹载电子设备的工作温度及其环境温度的瞬态变化。通过对某型号反辐射导引头及其弹载电子设备气动加热问题进行具体计算分析,获得了该型号装备在各个时刻流场内导引头及其弹载电子设备的瞬态温度空间分布,所得结果与实际有较好的吻合度。结果表明该方法对同类电子设备的瞬态热分析和相应散热设计具有一定参考作用。     

Efficient Pareto Frontier Exploration using Surrogate Approximations

In this paper we present an efficient and effective method of using surrogate approximations to explore the design space and capture the Pareto frontier during multiobjective optimization. The method employs design of experiments and metamodeling techniques (e.g., response surfaces and kriging models) to sample the design space, construct global approximations from the sample data, and quickly explore the design space to obtain the Pareto frontier without specifying weights for the objectives or using any optimization. To demonstrate the method, two mathematical example problems are presented. The results indicate that the proposed method is effective at capturing convex and concave Pareto frontiers even when discontinuities are present. After validating the method on the two mathematical examples, a design application involving the multiobjective optimization of a piezoelectric bimorph grasper is presented. The method facilitates multiobjective optimization by enabling us to efficiently and effectively obtain the Pareto frontier and identify candidate designs for the given design requirements.