The high computational demands of multiscale modeling necessitate advanced parallel and adaptive strategies. To address this challenge, we introduce an adaptive method that utilizes two microscale models based on an offline database for multiscale modeling of curved interfaces (e.g., adhesive layers). This database employs nonlinear classifiers, developed using Support Vector Machines from microscale sampling data, as a preprocessing step for multiscale simulations. Next, we develop a new parallel network library that enables seamless model selection with customized communication layers, ensuring scalability in parallel computing environments. The correctness and effectiveness of the hierarchically parallel solver are verified on a crack propagation problem within the curved adhesive layer. Finally, we predict the ultimate bending moment and adhesive layer failure of a wind turbine blade and validate the solver on a difficult large-scale engineering problem.

中文翻译：

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用于模拟工程规模系统中的内聚破坏的自适应并行多尺度框架


多尺度建模的高计算要求需要先进的并行和自适应策略。为了应对这一挑战，我们引入了一种自适应方法，该方法利用两个基于离线数据库的微尺度模型对弯曲界面（例如粘合层）进行多尺度建模。该数据库采用非线性分类器，该分类器是使用支持向量机根据微尺度采样数据开发的，作为多尺度模拟的预处理步骤。接下来，我们开发了一个新的并行网络库，可以通过定制通信层实现无缝模型选择，从而确保并行计算环境中的可扩展性。在弯曲粘合层内的裂纹扩展问题上验证了分层并行求解器的正确性和有效性。最后，我们预测风力涡轮机叶片的极限弯矩和粘合层失效，并在困难的大型工程问题上验证求解器。