各向异性损伤的多方位层析识别

Cognition on anisotropic void damage by multi-directional tomography

建立各向异性孔洞损伤的多方位层析识别数学模型。通过引进孔洞分布密度及形状参数作为损伤特征量，给出损伤参数与多方位层析信号的均值和方差的关系，表明可由多方位层析信号的均值来识别孔洞损伤程度，可由多方位层析信号的方差来识别孔洞损伤的各向异性特征。

A mathematical cognition model on anisotropic void damage by multi directional tomography is established in the present paper. The volumetric density and the shape parameters of the voids are adopted as the meso-damage characterization. These damage parameters are related to the statistical distribution of the multi-directional tomography spectra. The analysis indicates that the extent of the void damage can be resolved through the mean value of multi-directional tomography spectra and the anisotropy through the standard deviations.