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Data driven approach to fatigue crack growth modeling

疲劳裂纹扩展建模的数据驱动方法

基本信息

批准号：
428299198
负责人：
Dr.-Ing. Pietro Carrara, Ph.D.
金额：
--
依托单位：
Institut für Angewandte Mathematik (IAM)
依托单位国家：
德国
项目类别：
Research Fellowships
财政年份：
2019
资助国家：
德国
起止时间：
2018-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/428299198?language=en
关键词：
Data driven approach fatigue crack

项目摘要

Fatigue is a key phenomenon in mechanics, and is responsible for most structural failures. However, the inherent complexity of the problem makes the development of predictive models a non-trivial task and it greatly complicates the identification of the material fatigue constitutive behavior starting from experimental results. Hence, despite the relevance of the problem, a widely accepted model with truly predictive capabilities is still lacking.Aim of the present project is to develop a data driven approach to fatigue crack growth modeling. The adoption of data driven techniques allows to directly embed into the solution of the problem a discrete set of data of experimental or numerical nature. This has the advantage of overcoming the necessity of calibrating analytical fatigue constitutive material relationships. A recent variational phase-field approach to fatigue crack growth proposed by the applicant will be adopted as a reference mechanical model. Then, taking advantage of machine learning and data mining techniques, a data driven procedure will be proposed based on the identification of the numerical constitutive behavior. To this end, a technique involving the interpolation of the material behavior data within sub-clusters of the material space with limited extension will be used. This approach allows to determine a set of combination coefficients that parameterize the numerical (fatigue) constitutive manifold of the material.The procedure will be first studied for a 1D problem and based on a numerically generated material data set. Then, it will be extended to 2-3D and to the employment of experimental data sets. The initial adoption of numerical data allows to precisely estimate the accuracy of the procedure since a reference solution is available, i.e. the results of the numerical simulations adopting available constitutive relationships. The capability of the method will be investigated by simulating standard tests used to characterize the fatigue behavior, such as the compact tension or three-point-bending tests. Here, loading and boundary conditions different that those used to train the numerical manifold detection phase will be adopted.

疲劳是力学中的一个重要现象，也是大多数结构失效的原因。然而，这一问题的内在复杂性使得建立预测模型成为一项艰巨的任务，这使得从实验结果开始识别材料疲劳本构行为变得非常复杂。因此，尽管这一问题具有相关性，但真正具有预测能力的被广泛接受的模型仍然缺乏。本项目的目的是开发一种数据驱动的疲劳裂纹扩展建模方法。通过采用数据驱动技术，可以在问题的解中直接嵌入一组离散的实验或数值数据。这具有克服了校准分析疲劳本构材料关系的必要性的优点。申请人最近提出的疲劳裂纹扩展的变分相场方法将被用作参考力学模型。然后，利用机器学习和数据挖掘技术，提出了一种基于数值本构行为识别的数据驱动方法。为此，将使用涉及在具有有限扩展的材质空间的子群集中内插材质行为数据的技术。这种方法允许确定一组组合系数，这些组合系数对材料的数值(疲劳)本构流形进行参数化。将首先研究基于数值生成的材料数据集的一维问题的过程。然后，将其扩展到2-3D和使用实验数据集。最初采用数值数据可以精确地估计程序的精度，因为有一个参考解，即采用可用的本构关系的数值模拟结果。该方法的能力将通过模拟用于表征疲劳行为的标准试验来进行，例如紧凑拉伸或三点弯曲试验。这里，将采用不同于用于训练数值流形检测阶段的加载和边界条件。