Domain discretizations for numerical solution of fracture problems

В работе для численного моделирования хрупкого разрушения с целью повышения эффективности громоздких расчетов предлагается использовать двухуровневую процедуру построения сетки дискретизации. На верхнем уровне генерируется сетка фрагментов - локусов задаваемых размеров и произвольной случайной формы, по границам которых может происходить разрушение. На нижнем уровне каждый локус разбивается на сетку конечных элементов. Разрыв связей между конечными элементами по траектории разрушение между локусами реализуется с помощью процедуры сцепляющей среды. Для построения сетки дискретизации верхнего уровня использована диаграмма Воронова. Разработан алгоритм процедуры создания локусов на телах произвольной формы в двумерной и трехмерной постановках. Процедура реализована на языке APDL, для использования в программном комплексе ANSYS. Алгоритм протестирован при различных значениях задаваемых параметров и на объектах разнообразной формы. Численное решение задачи о разрушении цилиндрического образца из хрупкого материала по бразильскому тесту определения прочностных характеристик материала на растяжение продемонстрировало хорошее согласование полученной картины разрушения с реальной. In this paper, for numerical modeling of brittle fracture in order to increase the efficiency of complex calculations, it is proposed to use a two-level procedure for generating a discretization network. At the upper level, a network of fragments – locus’s, of specified sizes and arbitrary random shape, is generated. At the lower level, each locus is meshed by FE. The breaking of connections between finite elements along the trajectory of destruction between locus is realized using the cohesive zone procedure. The properties of the Voronov diagram are used to generate the upper-level discretization network. The algorithm of the procedure for creating locus on bodies of arbitrary shape in two-dimensional and three-dimensional formulations is developed. The procedure is implemented in the APDL, for use in the ANSYS. The algorithm is tested at various values of the specified parameters and on objects of various shapes. The numerical solution of the problem of the destruction of a cylindrical sample made of brittle material according to the Brazilian test for determining the tensile strength characteristics of the material demonstrated a good agreement of the obtained fracture pattern with the real one.

A novel hyperspectral compressive sensing framework of plant leaves based on multiple arbitrary-shape regions of interest

Massive plant hyperspectral images (HSIs) result in huge storage space and put a heavy burden for the traditional data acquisition and compression technology. For plant leaf HSIs, useful plant information is located in multiple arbitrary-shape regions of interest (MAROIs), while the background usually does not contain useful information, which wastes a lot of storage resources. In this paper, a novel hyperspectral compressive sensing framework for plant leaves with MAROIs (HCSMAROI) is proposed to alleviate these problems. HCSMAROI only compresses and reconstructs MAROIs by discarding the background to achieve good reconstructed performance. But for different plant leaf HSIs, HCSMAROI has the potential to be applied in other HSIs. Firstly, spatial spectral decorrelation criterion (SSDC) is used to obtain the optimal band of plant leaf HSIs; Secondly, different leaf regions and background are distinguished by the mask image of the optimal band; Finally, in order to improve the compression efficiency, after discarding the background region the compressed sensing technology based on blocking and expansion is used to compress and reconstruct the MAROIs of plant leaves one by one. Experimental results of soybean leaves and tea leaves show that HCSMAROI can achieve 3.08 and 5.05 dB higher PSNR than those of blocking compressive sensing (BCS) at the sampling rate of 5%, respectively. The reconstructed spectra of HCSMAROI are especially closer to the original ones than that of BCS. Therefore, HCSMAROI can achieve significantly higher reconstructed performance than that of BCS. Moreover, HCSMAROI can provide a flexible way to compress and reconstruct different MAROIs with different sampling rates, while achieving good reconstruction performance in the spatial and spectral domains.