含随机缺陷复合材料结构强度分布的贝叶斯评估框架
A Bayesian Framework for Assessing the Strength Distribution of Composite Structures with Random Defects
摘要：
本文提出了一种新型随机框架，用于量化试样层面面外皱纹导致的强度下降。关键的创新是一个马尔可夫链蒙特卡罗算法，严格推导出随机分布的皱纹缺陷直接从图像数据的缺陷。该方法显着降低了随机数值研究的缺陷的影响的参数化的不确定性。为了证明我们的方法，我们提出了一个原始的随机研究，以确定随机面外褶皱缺陷的拐角弯曲样品的强度分布。缺陷参数化的随机随机场定义使用Karhunen-Loéve（KL）模式。KL系数的分布是从使用多场图像分析的修改版本从B扫描数据提取的未对准数据推断的。强度分布估计，通过嵌入皱纹高保真FE模拟使用高性能工具箱"沙丘复合材料"，我们观察到严重击倒的概率为$1/200$的$74\%$。在文献的支持下，我们的研究结果突出了最大错位和击倒在试样强度之间的强相关性。这种观察使我们能够定义一个代理模型，提供快速评估的预测强度通知随机模拟利用观察到的皱纹数据和高保真有限元模型。
英文摘要：
This paper presents a novel stochastic framework to quantify the knock down in strength from out-of-plane wrinkles at the coupon level. The key innovation is a Markov Chain Monte Carlo algorithm which rigorously derives the stochastic distribution of wrinkle defects directly informed from image data of defects. The approach significantly reduces uncertainty in the parameterization of stochastic numerical studies on the effects of defects. To demonstrate our methodology, we present an original stochastic study to determine the distribution of strength of corner bend samples with random out-plane wrinkle defects. The defects are parameterized by stochastic random fields defined using Karhunen-Loéve (KL) modes. The distribution of KL coefficients are inferred from misalignment data extracted from B-Scan data using a modified version of Multiple Field Image Analysis. The strength distribution is estimated, by embedding wrinkles into high fidelity FE simulations using the high performance toolbox 'dune-composites' from which we observe severe knockdowns of $74\%$ with a probability of $1/200$. Supported by the literature our results highlight the strong correlation between maximum misalignment and knockdown in coupon strength. This observations allows us to define a surrogate model providing fast assessment of predicted strength informed from stochastic simulations utilizing both observed wrinkle data and high fidelity finite element models.
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。
