Bammann-Chiesa-Johnson粘塑性本构模型的参数识别方法与验证

Bammann-Chiesa-Johnson(BCJ)粘塑性本构模型对材料力学响应的再现和预测能力强烈依赖于其模型参数的确定,而模型参数的确定往往是通过反分析方法来进行。由于BCJ粘塑性模型包含了应变、应变率和温度耦合效应以及加载路径和温度历史,其常数多达18个,所以寻找最佳的模型参数识别值十分繁琐。针对BCJ本构模型参数复杂、识别困难的问题,本文基于参数的物理意义,在准静态、蠕变及动态加载试验基础上,通过模型参数解耦分离、粒子群智能优化的方法分6步对18个材料常数进行识别,并用识别结果对1060纯铝动态加载试验力学响应进行模拟,模拟结果与试验结果符合良好。通过定量化误差分析,证明了BCJ粘塑性模型对实验数据的预测具有较高精度,该模型参数识别方法科学可行。     

Damage Dependent Constitutive Behavior and Energy Release Rate for a Cohesive Zone in a Thermoviscoelastic Solid

In this paper a cohesive zone is introduced ahead of a crack tip in order to avoid the singularity at the crack tip. By applying thermodynamics to the cohesive zone and the surrounding body, a fracture criterion will be established so that the inelastic energy dissipation both in the cohesive zone and the surrounding bulk material can be distinguished from the energy released by fracture, and the propagation of crack can be predicted. In addition, the cohesive zone constitutive equation is constructed utilizing the Helmholtz free energy in the form of a single hereditary integral for a nonlinear viscoelastic material. The resulting constitutive model for the cohesive zone contains an internal state variable which represents the damage state within the cohesive zone. When the cohesive zone opening displacement is known, the energy release rate is thus history dependent, which is expressed in terms of the damage state, the length of separation in the cohesive zone and the geometric configuration of the cohesive zone opening displacement. Example results contained herein demonstrate this effect. This revised version was published online in July 2006 with corrections to the Cover Date.     

混凝土结构材料非线性本构参数识别算法研究

针对混凝土材料特性离散性较大、本构参数不确定性较强的问题,本研究提出了基于RSM-CMA的钢筋混凝土结构材料非线性本构参数的识别算法。首先,讨论分析了ABAQUS混凝土损伤塑性模型与规范提供的混凝土本构模型的统一方法,开发了混凝土材料子程序,获取了影响混凝土单轴应力-应变关系的敏感待识别参数,给出了损伤塑性模型屈服面函数与塑性势函数等非识别参数的建议取值。其次,基于响应面法(RSM),实现了结构宏观响应与细观本构参数间隐性关系的显式表达,基于带约束的最小二乘原理,构建目标函数,利用混沌猴群算法(CMA),实现本构参数的优化识别。最后,以钢筋混凝土拱构件的数值仿真为算例,验证了本研究所提算法的准确性。结果表明:抗压强度、峰值压应变是混凝土本构敏感性参数;抗压强度为影响结构极限承载力的最显著本构参数,弹性模量次之,峰值压应变影响最小;本研究所提算法能够较为准确地识别材料的本构参数,有助于获取精确的结构层次宏观响应,可为有限信息下材料本构参数的辨识、获取提供参考。     

考虑加/卸载速率影响的Ti-Ni形状记忆合金简化本构模型

系统研究了Ti-Ni形状记忆合金丝(SMA)应力-应变曲线、特征点应力、耗能能力、等效阻尼比随材料直径、应变幅值、加载速率、加载循环次数的变化规律;针对SMA唯象Brinson本构模型无法描述SMA动态力学性能的缺点,结合前述试验结果,提出了一种可考虑加/卸载速率影响的SMA简化本构模型。应用该模型对试验用SMA丝进行模拟,所得应力-应变曲线各特征点平均误差仅为3%,结果表明:所建立的速率相关SMA简化本构模型可较为精确地描述SMA在应力诱发相变过程中的超弹性力学行为,同时可反映加/卸载速率和应变幅值等主要因素对其动力本构模型的影响;该模型结构形式简单,具有较好的工程应用前景。     

A Higher Order Synergistic Damage Model for Prediction of Stiffness Changes due to Ply Cracking in Composite Laminates

A non-linear damage model is developed for the prediction of stiffness degradation in composite laminates due to transverse matrix cracking. The model follows the framework of a recently developed synergistic damage mechanics (SDM) approach which combines the strengths of micro-damage mechanics and continuum damage mechanics (CDM) through the so-called constraint parameters. A common limitation of the current CDM and SDM models has been the tendency to over-predict stiffness changes at high crack densities due to linearity inherent in their stiffness-damage relationships. The present paper extends this SDM approach by including higher order damage terms in the characterization of ply cracking damage inside the material. Following the SDM procedure, predictions are aided by suitable micromechanical computations of crack opening displacements. A nonlinear SDM model is developed and applied for multiple classes of composite laminate layups. Stiffness predictions for damaged laminates using the developed model are compared with the experimental data for cross-ply ([0m/90n]s), angle-ply ([±θm/90n]s), off-axis ([0/±θ4/01/2]s) and quasi-isotropic ([0/90/±45]s) laminates. A comparison with current linear damage models showcases the usefulness of the proposed nonlinear SDM approach.     

An Integrated Optimization Model for Inventory and Quality Control Problems

In this paper, we develop an integrated economic model for inventory and quality control problems, extending the work of Rahim ( IIE Transactions 1994; 26(6): 2–11) and Rahim and Ben-Daya (IJPR 1998; 36(1): 277–289). The production process is subject to an assignable cause which shifts the process from an in-control state to an out-of-control state. We consider the shifts in both the process mean and the process variance. When a signal for an assignable cause is triggered, a search is initiated and is terminated upon finding the cause within a pre-specified target time. The process is then brought back to an in-control state by repair. However, if the assignable cause is not discovered within the pre-specified time, production is allowed to continue until the next sampling or warning, whichever occurs first. In this case, either the alarm is considered to be false with a probability of Type I error, or the assignable cause has not been eliminated with a probability of Type II error. In the latter case, the process produces products in an out-of-control state until the next sampling or warning, whichever occurs first. However, this state does not indicate any severe damage to the system. Joint X and R charts are used for monitoring both process mean and variance. Under these conditions, a generalized economic model for the joint determination of production quantity, an inspection schedule, and the design of the X and R control charts are developed. A direct search optimization method is used to determine the optimal decision variables of the economic model.