Development of a GPU parallel computational framework for impact debonding of coating-substrate interfaces

Single-particle tests are normally used to evaluate the impact resistance performance of automotive coatings. Impact-induced debonding is one of the major failure patterns for coating-substrate structures of a vehicle. Currently, it remains a challenging task to accurately and efficiently simulate progressive debonding of automotive coating-substrate interfaces under single particle impact. The main purpose of this work is to develop a graphics processing unit (GPU)-based parallel computational framework to achieve this end. An efficient coating finite element model in three dimensions is established, where solid elements with good aspect ratios and solid-shell elements are respectively used to discretize the impact contact region and the rest region of a coating. An intrinsic cohesive zone model coupled with a mortar-based contact algorithm is used to accurately describe the progressive debonding behavior of coating-substrate interfaces. The computational efficiency of the developed method is dramatically enhanced by recourse to the GPU parallel computing technique. Three benchmark tests are carried out to validate the effectiveness and efficiency of the developed computational framework. Finally, the novel computational framework is successfully applied to the debonding analysis of an organic coating bonded with an aluminum substrate under single-particle impact. Results show that the GPU parallel computational framework can achieve a total speedup of 136.5, which provides a powerful tool for coating debonding analysis.

Automated summary

This study aims to develop a GPU-based parallel computational framework to accurately and efficiently simulate the progressive debonding behavior of automotive coating-substrate interfaces under single-particle impact. By establishing an efficient finite element model and utilizing an intrinsic cohesive zone model coupled with a mortar-based contact algorithm, combined with GPU parallel computing technique, the computational efficiency is significantly improved.