Porosity Of Porous Structure Research Articles

On the Various Numerical Techniques for the Optimization of Bone Scaffold.

As the application of bone scaffolds becomes more and more widespread, the requirements for the high performance of bone scaffolds are also increasing. The stiffness and porosity of porous structures can be adjusted as needed, making them good candidates for repairing damaged bone tissues. However, the development of porous bone structures is limited by traditional manufacturing methods. Today, the development of additive manufacturing technology has made it very convenient to manufacture bionic porous bone structures as needed. In the present paper, the current state-of-the-art optimization techniques for designing the scaffolds and the settings of different optimization methods are introduced. Additionally, various design methods for bone scaffolds are reviewed. Furthermore, the challenges in designing high performance bone scaffolds and the future developments of bone scaffolds are also presented.

Predicting Effective Diffusivity of Porous Media from Images by Deep Learning

We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topology, large variation of porosity (0.28–0.98), and effective diffusivity spanning more than one order of magnitude (0.1 ≲ De < 1), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, especially for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed analysis of the performance of CNN models in the present work.

Influence of Porosity on the Mechanical Properties of Cu/LDPE Porous Composites for Intrauterine Contraceptive Devices

As a novel Cu-IUD material, it is necessary for copper/low-density polyethylene (Cu/LDPE) porous composite to have some suitable mechanical properties if it is used to prepare a clinical Cu-IUD with frame. However, it is not clear how the porosity of the porous structure affects the mechanical properties of Cu/LDPE porous composites. Therefore, the influence of the porosity of porous structure on the mechanical properties of Cu/LDPE porous composites is investigated in the present paper. The results show that the porosity has significant effects on the mechanical properties of Cu/LDPE porous composites. The mechanical properties of the Cu/LDPE porous composites decrease with the increasing of porosity, for the effective load area decreases, the chance for occurrence of cracks increases and the crystallinity degree of LDPE matrix increases with the increasing of porosity. These results are very important and they can be applied to guide the design of Cu/LDPE porous composite IUD for use in the future clinical application.