基于集成数字图像相关的微力学参数识别的贝叶斯方法
Bayesian Approach to Micromechanical Parameter Identification Using Integrated Digital Image Correlation
AI总结：
针对IDIC方法对边界误差敏感且参数识别有限的问题，提出基于Metropolis-Hastings算法的贝叶斯方法，通过概率分布估计微力学参数，在纤维增强复合材料上验证了其鲁棒性和多参数优化优势。
摘要：
具有异质结构的材料表现出诸如分层、裂纹和塑性等复杂物理过程，这些过程需要微力学参数来理解。然而，由于微观-宏观长度尺度差异、边界条件的不明确性以及所需的高分辨率等问题，识别这些参数具有挑战性。虽然集成数字图像相关（IDIC）方法被广泛用于此目的，但它对边界数据误差敏感，并且在适定问题中参数识别能力有限。为解决这些问题，提出了一种贝叶斯方法用于微力学参数识别，使用Metropolis--Hastings算法（MHA）来估计体积模量和剪切模量以及边界条件参数的概率分布。所提出的方法与两种确定性IDIC版本在人为引入误差下进行了比较。尽管MHA在计算上更昂贵，并且在某些情况下不如BE-IDIC准确，但它提供了显著优势，包括能够优化大量参数、获得统计特征以及对单个参数关系的洞察。本文强调了使用MHA进行非归一化参数识别方法的优势，与确定性IDIC相比，该方法在处理边界噪声方面显著提高了鲁棒性。研究考虑了平面应变假设下的纤维增强复合材料样本。
英文摘要：
Materials with heterogeneous structures exhibit complex physical processes such as delamination, cracks, and plasticity, which require micromechanical parameters for understanding. However, identifying these parameters is challenging due to micro-macro length scale differences, ambiguity in boundary conditions, and required high resolution, among others. While the Integrated Digital Image Correlation (IDIC) method is widely used for this purpose, it suffers from sensitivity to boundary data errors and limited identification of parameters within well-posed problems. To address these issues, a Bayesian approach is proposed for micromechanical parameter identification using the Metropolis--Hastings Algorithm (MHA) to estimate probability distributions of bulk and shear moduli and boundary condition parameters. The proposed approach is compared to two versions of deterministic IDIC method under artificially introduced errors. Although MHA is computationally more expensive and in certain cases less accurate than BE-IDIC, it offers significant advantages, including the ability to optimize a large number of parameters, obtain statistical characterization and insights into individual parameter relationships. The paper highlights the benefits of the non-normalized approach to parameter identification with MHA, which significantly improves the robustness in handling boundary noise, compared to deterministic IDIC. The study considers a fiber-reinforced composite sample under plane strain assumption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
