Permeability differences based on three-dimensional geometrical information of void spaces

In this study,the permeabilities of Berea and Otway sandstones were measured under different confining pressures,and porosity was investigated through mercury intrusion porosimetry(MIP).The total porosities of the Berea and Otway sandstones were approximately 17.4%and 25%,respectively.Pore size distributions of each sandstone were almost the same,but the pores in the Otway sandstone were slightly narrower.However,the permeability of the Otway sandstone was smaller than that of the Berea sandstone by one order of magnitude.Three-dimensional(3D)void geometry and geometrical properties of the void spaces relevant to flow were compared to obtain the relation between the permeability differences and porosities of the two sandstones.The 3D geometrical analysis using microfocus X-ray computed tomography(CT)was performed,and the pore geometries of both sandstones were compared using the 3D medial axis(3DMA)method.Pore and throat radii,pore coordination number,tortuosity,number of connecting paths,connecting path volume,and other factors were determined using 3DMA.The Otway sandstone was characterized by a small effective throat/pore radius ratio.Based on the fluid flow mechanism,the lower effective throat/pore radius ratio results in a lower permeability induced by the fluid energy loss,which means that the 3D geometrical shape of void spaces affects the permeability value.

Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization

Nonnegative Matrix Factorization(NMF)is one of the most popular feature learning technologies in the field of machine learning and pattern recognition.It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.This study proposes a general semi-supervised multi-view nonnegative matrix factorization algorithm.This algorithm incorporates discriminative and geometric information on data to learn a better-fused representation,and adopts a feature normalizing strategy to align the different views.Two specific implementations of this algorithm are developed to validate the effectiveness of the proposed framework:Graph regularization based Discriminatively Constrained Multi-View Nonnegative Matrix Factorization(GDCMVNMF)and Extended Multi-View Constrained Nonnegative Matrix Factorization(ExMVCNMF).The intrinsic connection between these two specific implementations is discussed,and the optimization based on multiply update rules is presented.Experiments on six datasets show that the effectiveness of GDCMVNMF and ExMVCNMF outperforms several representative unsupervised and semi-supervised multi-view NMF approaches.

STEP-based Feature Recognition System for B-spline Surface Features

The geometrical and topological information of 3D computer aided design （CAD） models should be represented as a neut- ral format file to exchange the data between different CAD systems. Exchange of 3D CAD model data implies that the companies must exchange complete information about their products, all the way from design, manufacturing to inspection and shipping. This informa- tion should be available to each relevant partner over the entire life cycle of the product. This led to the development of an international standard organization （ISO） neutral format file named as standard for the exchange of product model data （STEP）. It has been ob- served from the literature, the feature recognition systems developed were identified as planar, cylindrical, conical and to some extent spherical and toroidal surfaces. The advanced surface features such as B-spline and its subtypes are not identified. Therefore, in this work, a STEP-based feature recognition system is developed to recognize t--spline surface features and its sub-types from the 3D CAD model represented in AP203 neutral file format. The developed feature recognition system is implemented in Java programming language and the product model data represented in STEP AP203 format is interpreted through Java standard data access interface （JSDAI）. The developed system could recognize B-spline surface features such as B-Spline surface with knots, quasi uniform surface, uniform surface, rational surface and Bezier surface. The application of extracted B-spline surface features information is discussed with reference to the toolpath generation for STEP-NC （STEP AP238）.