Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

一种可预测非饱和土壤中入渗和汇水的水力耦合模型

非饱和土壤在自然和工程实践中广泛存在,进一步了解非饱和多孔介质渗透过程中的水力耦合效应,对岩土工程和水文学界有着重要意义。本文提出一个非线性的完全水力耦合模型,用于控制可变形多孔介质中非饱和水的流动。该模型依赖于有限元(FE)求解器来计算受瞬态流作用下土壤中的水力耦合响应。使用已有的水力耦合解析解和积水入渗试验数据作为参考,对模型模拟结果的准确性进行验证。模拟结果表明,该模型在非稳定降雨条件和地表积水条件下能够准确模拟出降雨过程中的土壤入渗量和地表汇水量。此外,分别考虑吸力弹性模量、渗透模型的控制参数、饱和渗透系数以及边坡角度四个因素的变化而进行入渗和汇水过程的模拟结果进一步表明,土壤的膨胀性延迟了孔隙水压力的增长,而土壤的湿陷性则促进了孔隙水压力的增长;土壤孔径分布越粗糙,水力耦合效应越明显;土壤饱和渗透系数增大会导致更大的降雨入渗量和土体变形,并延缓地表开始积水的时间。边坡坡度对入渗过程的影响则存在争议,在本研究中,边坡坡度增大,土壤中的降雨入渗量减少,地表流失水增加,有待进一步探索。长远来看,在水力耦合系统中进一步考虑弹塑性耦合法则是更全面了解多孔介质中降雨入渗和地表汇水过程有用的方法。