Probabilistic Identification of Parameters in Dynamic Fracture Propagation

ABSTRACT In this paper, we propose a novel multiphase approach for identifying input parameters in dynamic fracture propagation. Often, such parameters are partially known and uncertain with incomplete input data, resulting in challenges in predicting a reliable dynamic failure response. To address this, we employ a stochastic Bayesian inverse method to estimate input parameters in three distinct phases of a fracture model. As a case study, we analyze a virtual version of Kalthoff's dynamic fracture propagation test using a finite element model enhanced with embedded strong discontinuities, where cracks propagate in a mixed‐mode manner, to demonstrate the effectiveness and robustness of the proposed method. The approach successfully identifies six material parameters, including the bulk modulus, shear modulus, tensile strength, shear strength, and the modes I and II fracture energies. Through different time intervals and measurements in each phase, our results show that the computed posterior mean values are closely aligned with the true parameters of the material, validating the reliability and accuracy of the method.