Elasto-plastic material parameter identification by inverse methods: Calculation of the sensitivity matrix

Inverse methods offer a powerful tool for the identification of elasto-plastic material properties of metals. The basic principle of the inverse method we are studying, is to compare an experimentally measured strain field with a strain field computed by a finite element (FE) model. The material parameters in the FE model are iteratively tuned in such a way that the numerically computed strain field matches the experimentally measured field as closely as possible. One of the building blocks in this identification procedure is the optimization algorithm for the material parameters in the numerical model. The key problem of this optimization algorithm is the determination of a sensitivity matrix, which expresses the sensitivities of the strains with respect to the material parameters. This paper presents an analytical method for the calculation of this sensitivity matrix in the case of a tensile test with non-rotating principal axes of strain.     

基于泰勒级数展开的区间反演方法

实际结构或构件的几何与材料参数总包含不确定性,在对结构计算模型进行精确分析时,有时需要对参数不确定性进行量化。本文提出了一种用于区间参数识别的反演方法,即基于泰勒级数展开式分别建立参数与响应的区间中值、区间半径的对应函数关系,并通过构建两个反演问题来分步识别参数区间中值和半径,以避免区间扩张现象和简化优化反演过程。通过数值质-弹系统初步验证了方法的可行性,然后基于一组钢板的动测数据,识别了钢板的几何及材料特性参数的区间范围。研究结果表明,本文方法具有良好的区间反演精度,能有效地避免区间扩张现象,可以用于实际工程区间问题的求解。     

Determination of Ductile Damage Parameters by Local Deformation Fields: Measurement and Simulation

This work comprises the development, implementation and application of methods for the parameter identification of damage mechanical constitutive laws. Ductile damage is described on a continuum mechanical basis by extension of the von Mises yield condition with the Gurson–Tvergaard–Needleman as well as with the Rousselier model. The classical Rousselier model is complemented by accelerated void growth and void nucleation. The non-linear boundary and initial value problem is solved by the finite element system SPC–PMHP, which was developed in the frame of the special research program SFB393 for parallel computers. The material parameters are identified by locally measured displacement fields and measured force–displacement curves. For the material parameter identification a non-linear optimization algorithm is used, which renders the objective function to a minimum by means of a gradient based method. A useful strategy to identify the material parameters was found by careful numerical studies. Finally, using the object grating method the local displacement fields as well as the force–displacement curves are measured at notched flat bar tension specimens made of StE 690 and the parameters of the material are identified.     

A Novel Method for Uncertainty Inverse Problems and Application to Material Characterization of Composites

A novel method is suggested to deal with so-called uncertainty inverse problems (UIPs) which are a class of inverse problems with uncertainty in the system parameters of the forward model. Interval which represents a closed bounded set of real numbers is used to model and characterize the uncertainty in our formulation, and hence only the bounds of the uncertainty of the system parameters are needed. For a specific input vector, the possible values of the outputs form an interval vector because of the uncertainty. An error function is defined using the measured interval vector of the outputs and those computed using a forward model. The UIP is then formulated as an optimization problem which minimizes the error function. To improve the optimization efficiency, an interval forward model is constructed based on the interval analysis method which can calculate very efficiently the bounds of the outputs caused by the uncertainty of the system parameters. The present method is applied to a complex inverse problem, namely material characterization of composite laminates using elastic waves. Uncertainty of load is considered, and the hybrid numerical method (HNM) is used to compute the transient displacement responses. The engineering constants of two kinds of laminates are successfully identified using the simulated measurements of the outputs.     

基于填充函数与DCD的全局优化算法及其反演

岩土工程中以监测位移为已知信息的反演问题可通过带未知变量约束空间的优化模型去求解.该模型中的优化函数常具有非线性、非凸性等特点,使得反演结果容易陷入局部最优的困境.为了应对在运用优化算法反演此类问题时存在的困境,并提高其算法效率,依据填充函数优化思想与DCD(Dynamic Canonical Descent)思想在反...     

遗传算法在随机参数刚架结构概率优化设计中的应用

对随机参数刚架结构在随机荷载作用下基于概率的优化设计进行了研究。同时考虑结构的物理参数和作用荷载等的随机性；建立以杆截面积为设计变量、结构质量均值极小化为目标函数、具有刚度和强度可靠性约束的优化设计数学模型；通过可靠性约束等价显式化处理和引入罚函数，将原概率约束优化问题转化为无约束优化问题，利用遗传算法求解。算例表明：文中提出的模型和方法是合理有效的。     

Inverse parameter identification of cohesive zone model for simulating mixed-mode crack propagation

Inverse analysis is widely applied to the identification of material properties or model parameters. In order to improve the computational efficiency of the inverse method based on the genetic algorithm, an interpolation scheme upon the response surface constructed by the finite element simulation has been adopted in this paper. Meanwhile, a gradual homogenization treatment scheme has also been presented to improve the convergence of the inverse method based on the Kalman filter algorithm. Both methods are proven effective in dealing with the single-objective inverse problem. However, literature studies show that the adoption of multiple types of experimental information is useful to improve the accuracy of inverse analysis. In this case, it turns into a multiple-objective inverse problem. Our practice proved that the above-mentioned two methods might not yield a proper result if the sensitivity issue of different types of information is not considered. Therefore, another multi-objective inverse method, in combination of the above two optimization algorithms and a weight-estimating scheme that can consider such sensitivity, has been further presented. Finally, by using a mixed-mode crack propagation simulation and two types of experimental information (loading-displacement response curve and crack path profile), the parameters of the cohesive zone model were inversely identified and its simulation results are in good agreement with the experiment.     

接触力优化设计的熵正则化方法

包含单侧接触的机械和结构系统中,接触面间的局部接触力很大时,容易导致结构局部磨损和破坏。利用结构优化的手段极小化接触面上的最大接触力是调整接触面上接触力分布的一个有效途径。然而在数学上,目标函数的不可微和约束中的接触条件导致该结构优化问题属于非光滑和非凸优化问题,因而常用优化算法的优化结果是不可靠的。为此,本文提出接触力优化的一个熵正则化模型,并利用基于正则化过程的序列二次结构优化算法求解。所提的方法在数值上避免了处理不可微问题,而且正则化参数的调整还有助于获得更小的局部最优解。以离散结构为例的数值算例验证了本文的模型和算法。     

传热结构的多目标拓扑优化设计研究

深入分析了传热结构多目标拓扑优化设计中的几个关键问题。提出了基于结构柔度最小化和结构散热弱度最小化的多目标拓扑优化设计方法,建立了传热结构的多目标拓扑优化设计模型,推导了传热结构多目标拓扑优化中用于迭代分析求解的优化准则算法和敏度分析方程。通过数值计算验证了理论和算法的有效性。     

基于Kriging代理模型的迭代更新高效反演方法

为提高混凝土坝等大体积结构参数反演效率和精度,减少由于应用有限元进行大量正分析而产生的计算机时,建立了一种结合Kriging代理模型和粒子群优化(PSO)算法的迭代更新反演方法。通过拉丁超立方抽样(LHS)方法确定初始样本点的空间分布,并使用有限元正分析获取对应的响应值,构建粗糙的初始代理模型,结合具有全局寻优能力的PSO算法,反演大体积结构的分区弹性模量,随之再代入有限元模型中,计算获取新的位移响应,并将其作为新样本加入到样本集中,通过迭代更新获得局部更高精度的代理模型。工程实际算例表明,该方法对混凝土坝等大体积结构参数反演精度较高和适用性好,且能大幅减少传统有限元模型反演方法所需消耗的正分析机时,提高反演效率。     

A dynamic optimization theoretical method for heavy loaded vibrating screens

In the present work, an innovative design strategy for the optimization of the dynamic performances and the structural loads of heavy loaded vibrating screens is presented. A dynamic model of a vibrating screen for the selection of inert materials in an asphalt plant is proposed, and a numerical optimization procedure is applied to selected design parameters and geometrical features. The algorithm provides a tool to improve the dynamic behavior of vibrating screens of different geometric and inertial properties. The results are analyzed, in order to find the parameters apt to minimize the pitching angle of the examined screen during stationary working conditions, thus providing a better material selection by reducing gravel throwback. A numerical FEM model analysis and an experimental strain-gage campaign have been conducted on a realization of the vibrating screen, testing two optimized and un-optimized configurations, to verify the FEM model results. The complete work gives the machine designer a powerful tool, validated by means of full scale experimental tests, to optimize the dynamic behavior of the screen and to verify its fatigue resistance.     

Bammann-Chiesa-Johnson粘塑性本构模型材料参数的一种识别方法

粘塑性本构模型能否成功模拟金属高应变率大应变变形过程依赖于材料参数识别结果的好坏。由于BCJ模型考虑了应变率、温度与材料硬化之间的耦合效应以及应变率、温度历史效应,同时模型中包含了多个材料参数,因此很难通过试验直接识别模型的材料参数。本文针对BCJ模型中的耦合效应和历史效应,基于对模型中材料参数物理涵义的界定,给出了一种对材料参数解耦、分离并进行估计的方法,获得了模型材料参数估计公式,估计了材料参数的取值范围。在此基础上,编制了BCJ模型应力积分径向返回算法和粒子群优化算法的计算程序,应用重新设计了BCJ模型耦合效应和历史效应的反分析方法,在参数取值范围内对材料参数进行了优化识别。以OFHC Cu为例,应用提出的识别方法对BCJ模型的材料参数进行了识别,计算结果和试验结果符合较好。     

Design optimization of consolidated granular-solid polymer prismatic beam using metamorphic development

Topologically optimized granular-solid structures from additive manufacturing processes have impending applications as lightweight load bearing structures. To facilitate application, a procedure for characterizing optimal structural performance is devised for the design optimization of ‘end-use’ functional structures. An approach capable of realizing this objective is presented and demonstrated for horizontal prismatic beams produced from Nylon-12 granular-solid polymer, by Selective Laser Sintering (SLS). It combines topology (or layout) optimization, calibration of material parameters, and finite element (FE) modelling. The metamorphic development (MD) method forms the basis of an adaptive growth and degeneration optimization by material distribution approach. Experimental measurements are used to calibrate a bimodulus constitutive Drucker–Prager (D–P) model. Simply-supported three-point bending (3PB) tests are used to assess the fidelity of optimized beams on the basis of numerically predicted performance.     

高斯牛顿技术求解偶应力反问题

建立了便于敏度分析的偶应力反问题数值求解模型,给出了直接法和伴随法两种敏度计算格式.在反演计算中采用了高斯牛顿技术对未知本构参数进行识别,探讨了测点数目、初值选取和数据噪音对反演结果的影响,数值算例给出了令人满意的结果.     

RELIABILITY-BASED DESIGN OF COMPOSITES UNDER THE MIXED UNCERTAINTIES AND THE OPTIMIZATION ALGORITHM

This paper proposed a reliability design model for composite materials under the mixture of random and interval variables. Together with the inverse reliability analysis technique, the sequential single-loop optimization method is applied to the reliability-based design of composites. In the sequential single-loop optimization, the optimization and the reliability analysis are decoupled to improve the computational efficiency. As shown in examples, the minimum weight problems under the constraint of structural reliability are solved for laminated composites. The Particle Swarm Optimization （PSO） algorithm is utilized to search for the optimal solutions. The design results indicate that, under the mixture of random and interval variables, the method that combines the sequential single-loop optimization and the PSO algorithm can deal effectively with the reliability-based design of composites.     

APPLICATION OF SURROGATE BASED PARTICLE SWARM OPTIMIZATION TO THE RELIABILITY-BASED ROBUST DESIGN OF COMPOSITE PRESSURE VESSELS

A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method （performed by software ANSYS）. An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.     

Parameter determination and response analysis of viscoelastic material

The parameter determination of viscoelastic material is a multi-variable, multi-aim nonlinear optimization problem, which made the optimization process very complicated. In this paper a hybrid optimal algorithm was proposed to determine the viscoelastic parameters in the constitutive relation according to the experimentally obtained mechanical properties. This algorithm merges the Broydon–Fletcher–Goldfarb–Shanno search into a genetic algorithm framework as a basic operator in order to enhance the local search capability. The proposed hybrid algorithm not only can reduce the iterative times greatly but can abolish the limitation of initial parameter values. Nonlinear material characteristic curve-fitting was carried out using the proposed algorithm and other existing approaches. And the comparison results show this algorithm is accurate and effective. The numerical simulation and experimental study of viscoelastic cantilever beam also indicates that the finite element formulation and the calculative viscoelastic model parameters are reliable. The proposed optimization method can be extended to further complex parameter estimation researches.     

Elastic characterization of orthotropic plates of any shape via static testing

The paper presents an inverse procedure for identifying elastic properties of isotropic or orthotropic materials from the full-field measurement of the surface displacements of plates under flexural loading configurations. The procedure is based on a numerical–experimental optimisation process which minimizes an error function defined by subtracting the experimental data from the outputs of the numerical analysis. In each iteration the optimisation process updates the values of the elastic constants in a finite element model of the specimen used in the experimental tests. The unknown parameters are simultaneously identified by a single test and without damaging the structural integrity of the specimen. The possibility of using the methodology for characterizing any-shaped plates was investigated. The applicability and the robustness of the procedure were carried out on aluminum and unidirectional Graphite/PEEK laminate specimens. Phase-shifting speckle interferometry was employed to detect the out-of-plane displacement field of a portion of the observed surface of the specimen.     

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.The project supported by the National Natural Science Foundation of China (10472025). The English text was polished by Yunming Chen.     

时域自适应算法求解移动荷载下梁的多宗量反问题

通过一种时域自适应算法,建立了求解变速移动荷载下梁的多宗量反问题的数值模型,可同时识别移动荷载和梁的物性参数.正问题采用时域自适应算法和FEM建模,并可由此方便地推导敏度公式;在反问题求解中采用Levenberg-Marquardt法,计算表明该方法具有较好的抗不适定性.通过两个算例,对所提算法进行了数值验证,并探讨了噪声和测点的变化对反演结果的影响,结果令人满意.