Fracture characterization and permeability prediction by pore scale variables extracted from X-ray CT images of porous geomaterials

Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples subjected to triaxial compression are used to investigate the permeability and fracture characteristics.A novel double threshold segmentation algorithm is proposed to segment cracks,pores and grains,and pore scale variables are defined and extracted from these X-ray CT images to study the geometric characteristics of microstructures of porous geomaterials.Moreover,novel relations among these pore scale variables for permeability prediction are established,and the evolution process of cracks is investigated.The results indicate that the porescale permeability is prominently improved by cracks.In addition,excellent agreements are found between the measured and the estimated pore scale variables and permeability.The established correlations can be employed to effectively identify the hydraulic properties of porous geomaterials.

Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization

To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model.

Quantitative two/three-dimensional spatial characterization and fluid transport prediction of macro/micropores in Gaomiaozi bentonite

The sealing performance of a bentonite barrier is highly dependent on its seepage characteristics, which are directly related to the characteristics of its pore structure. Based on scanning electron microscopy(SEM) and focused ion beam-SEM(FIB-SEM), the pore structure of bentonite was characterized at different scales. First, a reasonable gray threshold was determined through back analysis, and the image was binarized based on the threshold. In addition, binary images were used to analyze bentonite’s pore structure(porosity and pore size distribution). Furthermore, the effects of different algorithms on the pore structure characterization were evaluated. Then, permeability calculations were performed based on the previous pore structure characteristics and a modified permeability prediction model. For permeability prediction based on the three-dimensional model, the effect of pore tortuosity was also considered. Finally, the accuracy of numerical calculations was verified by conducting macroscopic gas and alcohol permeability experiments. This approach provides a better understanding of the microscale mechanism of gas transport in bentonite and the importance of pore structures at different scales in determining its seepage characteristics.

Development of Human in vitro Brain-blood Barrier Model from Induced Pluripotent Stem Cell-derived Endothelial Cells to Predict the in vivo Permeability of Drugs

An in vitro blood-brain barrier(BBB) model is critical for enabling rapid screening of the BBB permeability of the drugs targeting on the central nervous system.Though many models have been developed, their reproducibility and renewability remain a challenge. Furthermore, drug transport data from many of the models do not correlate well with the data for in vivo BBB drug transport.Induced-pluripotent stem cell(i PSC) technology provides reproducible cell resources for in vitro BBB modeling.Here, we generated a human in vitro BBB model by differentiating the human i PSC(hi PSC) line GM25256 into brain endothelial-type cells. The model displayed BBB characteristics including tight junction proteins(ZO-1,claudin-5, and occludin) and endothelial markers(von Willebrand factor and Ulex), as well as high transendothelial electrical resistance(TEER)(1560 X.cm2±230 X.cm2) and c-GTPase activity. Co-culture with primary rat astrocytes significantly increased the TEER of the model(2970 X.cm2 to 4185 X.cm2). RNAseq analysis confirmed the expression of key BBB-related genes in the hi PSC-derived endothelial cells in comparison with primary human brain microvascular endothelial cells,including P-glycoprotein(Pgp) and breast cancer resistant protein(BCRP). Drug transport assays for nine CNS compounds showed that the permeability of non-Pgp/BCRP and Pgp/BCRP substrates across the model was strongly correlated with rodent in situ brain perfusion data for these compounds(R2= 0.982 and R2= 0.9973,respectively), demonstrating the functionality of the drug transporters in the model. Thus, this model may be used to rapidly screen CNS compounds, to predict the in vivo BBB permeability of these compounds and to study the biology of the BBB.