3D FEA based surrogate modeling in fatigue crack growth life assessment

Runtime efficient models designed for damage tolerant life assessment are desired in Structural Health Management and Digital Twin development. While FEM is commonly used in the industry to assess health of a nominal structure design while in service, in probabilistic assessments, reduced order models are preferred due to lower runtime compared to the deterministic models but at the cost of solution accuracy. Readily available machine learning algorithms coupled with deterministic 3D simulations for modeling fatigue crack growth provide a feasible path to reach a better runtime-accuracy compromise. In this study, a fatigue crack growth testing procedure along with measurement data are used for validation purposes and for laying out details of the modeling process. Accuracy and solution runtime of the 3D FEA based surrogate models are assessed to demonstrate the efficiency of the method. (C) 2021 The Authors. Published by Elsevier B.V.