下采样体素网格技术在角联锁复合材料损伤失效预测中的应用研究

Research on downsampling voxel technique in failure prediction of angle interlocking composites

为解决角联锁复合材料在损伤失效预测时计算量大、计算效率低的问题,采用图像处理方法中常用的下采样体素网格技术,将原体素网格模型中的4~3个网格打包成一个代表性体元进行计算,从而大幅度降低计算量和计算时间。从复合材料的弹性性能、损伤状态和计算时间三个方面与全网格模型进行比较,数值试验结果表明,采用下采样体素网格技术的模型与全网格模型不仅在刚度预测中完全一致,且损伤状态也相仿,同时具有千万级网格模型的CPU时间降低约85%,适用于工程用角联锁结构的高精度、高速率计算。

In order to solve problems of large amount of calculation and low efficiency in failure prediction of angle interlock composites,43 voxels in the original model are packed into one representative volume element by adopting the down-sampling voxel technique commonly used in image processing methods,thus greatly reducing the calculation amount and time.The full-voxel model is compared in terms of elastic property,damage state of composites and CPU time.Numerical results show that the model using down-sampling voxel technique is not only identical to the full-voxel model in stiffness prediction,but mimic the full-voxel model in damage state.At the same time,the CPU time is reduced by almost 85%in the model computation with tens of millions voxels,thus it is suitable for high-precision and high-speed prediction of engineering angle interlock composites.