Note: most of my projects are under NDA so I am unable to share those projects. If there is anything you would like to see further, please feel free to contact me.

Evaluation of geometric tortuosity for 3D digitally generated porous media considering the pore size distribution and the A-star algorithm

Porous materials are of great interest in multiple applications due to their usefulness in energy conversion devices and their ability to modify structural and diffusive properties. Geometric tortuosity plays an important role in characterizing the complexity of a porous medium. The literature on several occasions has related it as a parameter dependent on porosity only. However, due to its direct relationship with the morphology of the medium, a deeper analysis is necessary. For this reason, in the present study, the analysis of the geometric tortuosity is proposed considering the porosity and the pore size distribution. Geometric tortuosity in artificially generated digital porous media is estimated using the A-star algorithm and the Pore Centroid method. By performing changes in the size of the medium and the distribution of the pore size, results are obtained that indicate that the geometric tortuosity does not only depend on …

Publication url =>

An alternative methodology to compute the geometric tortuosity in 2D porous media using the A-Star pathfinding algorithm

Geometric tortuosity is an essential characteristic to consider when studying a porous medium’s morphology. Knowing the material’s tortuosity allows us to understand and estimate the different diffusion transport properties of the analyzed material. Geometric tortuosity is useful to compute parameters, such as the effective diffusion coefficient, inertial factor, and diffusibility, which are commonly found in porous media materials. This study proposes an alternative method to estimate the geometric tortuosity of digitally created two-dimensional porous media. The porous microstructure is generated by using the PoreSpy library of Python and converted to a binary matrix for the computation of the parameters involved in this work. As a first step, porous media are digitally generated with porosity values from 0.5 to 0.9; then, the geometric tortuosity is determined using the A-star algorithm. This approach, commonly used in pathfinding problems, improves the use of computational resources and complies with the theory found in the literature. Based on the obtained results, the best geometric tortuosity–porosity correlations are proposed. The selection of the best correlation considers the coefficient of determination value (99.7%) with a confidence interval of 95%.

Publication url =>

A-Asterisk Algorithm as an Alternative to Evaluate the Geometric Tortuosity in Digitally Created SOFC Anodes

A solid oxide fuel cell (SOFC) contains complex materials that facilitate the energy conversion process. The diffusion media play an important role in facilitating the reactant gases to reach the electrochemical active regions. Porosity and tortuosity are crucial parameters describing the diffusion to be analyzed in a SOFC anode. This paper aims to evaluate the feasibility of using the A-asterisk algorithm to compute the geometric tortuosity within SOFC anodes. A three-dimensional structure, which is digitally created, represents the SOFC anode, in which the possible paths that follow the fluid flow are analyzed. A-asterisk algorithm is used to generate possible paths, and therefore the geometric tortuosity can be computed considering an averaged distance. A tortuosity-porosity correlation is proposed, and the results are compared with previous studies. Results show that the A-asterisk algorithm is a capable algorithm to …

Publication url =>

Malware Detection using API Calls Visualisations and Convolutional Neural Networks

This research explores and analyzes different API Calls sequence transformation methods into images to train deep learning models and determine which combination of these methods and models performs better. We generated images from API Calls sequences using Simhash and FreqSeq. The results were compared by training two well-known Convolutional Network architectures (ResNet50v2 and MobileNetv2). This work presents our experience running these experiments highlighting the results obtained and the challenges we faced.

Publication url =>