Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations, such as finite element analysis (FEA), is a credible constitutive or material model. Calibration of these complex models is an essential step; however, the selection, calibration and validation of material models is often a discrete, multi-stage process that is decoupled from material characterization activities, which means the data collected does not always align with the data that is needed. To address this issue, an integrated workflow for delivering an enhanced characterization and calibration procedure (Interlaced Characterization and Calibration (ICC)) is introduced. This framework leverages Bayesian optimal experimental design (BOED) to select the optimal load path for a cruciform specimen in order to collect the most informative data for model calibration. The critical first piece of algorithm development is to demonstrate the active experimental design for a fast model with simulated data. For this demonstration, a material point simulator that models a plane stress elastoplastic material subject to bi-axial loading was chosen. The ICC framework is demonstrated on two exemplar problems in which BOED is used to determine which load step to take, e.g., in which direction to increment the strain, at each iteration of the characterization and calibration cycle. Calibration results from data obtained by adaptively selecting the load path within the ICC algorithm are compared to results from data generated under two naive static load paths that were chosen a priori based on human intuition. In these exemplar problems, data generated in an adaptive setting resulted in calibrated model parameters with reduced measures of uncertainty compared to the static settings.

翻译：计算仿真在高风险工程决策中日益得到依赖，而固体力学仿真（如有限元分析）的基础要素之一是可靠的本构模型或材料模型。这些复杂模型的校准是关键步骤，但材料模型的选择、校准和验证通常是一个离散的多阶段过程，与材料表征活动分离，这意味着采集的数据并不总是与所需数据一致。为解决这一问题，本文提出了一种集成工作流，用于实现增强型表征与校准流程（交错式表征与校准，简称ICC）。该框架利用贝叶斯最优实验设计（BOED）选择十字形试件的最优加载路径，以采集对模型校准最具信息量的数据。算法开发的关键第一步是展示针对快速模型使用模拟数据进行主动实验设计。为此，选用了模拟双轴加载条件下平面应力弹塑性材料的材料点模拟器。通过两个示例问题验证了ICC框架，其中BOED用于确定表征与校准循环中每次迭代应采取的加载步（例如，应变增量的方向）。将ICC算法中通过自适应选择加载路径获得的数据的校准结果，与基于人类直觉先验选择的两种静态加载路径生成的数据结果进行了比较。在这些示例问题中，自适应设置生成的数据使校准模型参数的不确定性度量相比静态设置有所降低。