MULTI-LEVEL K-d TREE-BASED DATA-DRIVEN COMPUTATIONAL METHOD FOR THE DYNAMIC ANALYSIS OF MULTI-MATERIAL STRUCTURES

The model-free distance-minimizing data-driven computational method has recently become a novel paradigm for solving various mechanics problems. However, the paradigm may suffer from low efficiency since tremendous iterative searches of key data points in the material dataset are needed during the solution process. A fast data-driven solver is therefore proposed here for the accurate and efficient analysis of multi-material structural responses to dynamic loading. In the proposed approach, a multi-material database (MMD) with different kinds of constituents is constructed, and a multi-level K-d tree (MKT) is developed for effective data addition and fast data search in the MMD. An efficient data-driven dynamics solver (DDDS) is then designed based on the MMD/MKT, which can deal with the complicated dynamic analysis of different structures containing multiple material datasets. Representative types of dynamic problems are considered to verify and demonstrate the capability of the proposed approach. Numerical results demonstrate that the MMD/MKT and the corresponding DDDS possess high accuracy and efficiency, which might be further developed for the dynamic analysis of composite structures containing constituents at different scales.