摘要
Conventional fracture mechanics asserts that the relevant physics governing small crack growth occurs near the crack front.However,for fatigue,computing these physics for each crack-growth increment over the entire microstructurally small crack regime is computationally intractable.Properly trained deep-learning surrogate models canmassively accelerate fatigue crack-growth predictions by virtually propagating an initial crack using micromechanical fields corresponding to just the initially cracked microstructure.As the predicted crack front advances,however,the fields no longer reflect relevant near-crack-front physics,leading to error and uncertainty accumulation.To address this,we present an interleaved physics-based deep-learning(PBDL)framework,where updates to the crack representation in the physics-based model are triggered intermittently using model uncertainty,thereby updating micromechanical fields passed to the deep-learning model.We show that this framework,representing a novel cycle-jumping approach,effectively limits error accumulation in history-dependent fatigue crack evolution and forms a template for other time-series applications in materials.
基金
supported by the National Science Foundation under Grant No. CMMI-1752400. The authors would like to acknowledge the computational resources provided by the University of Utah's Center for High Performance Computing.
