Nano-particle capture is a key process in filtration, separation, and biomedical applications. Here we explored the mechanisms of soft particle capture using nanofiber networks. We identified possible states of the ca...Nano-particle capture is a key process in filtration, separation, and biomedical applications. Here we explored the mechanisms of soft particle capture using nanofiber networks. We identified possible states of the capture process, which are defined by their structural and material parameters. By performing numerical analysis, we provided a phase diagram in the parametric space of the network structure and interracial adhesion. The work provides a conceptual model for rational design of synthetic materials in related applications that focus on the protection against or removal of virus, as well as other soft particles.展开更多
In this paper, artificial neural networks are used for predicting single fiber efficiency in the process of removing smaller particles from gas stream by fiber filters. For this, numerical simulations are obtained of ...In this paper, artificial neural networks are used for predicting single fiber efficiency in the process of removing smaller particles from gas stream by fiber filters. For this, numerical simulations are obtained of a classic model of literature for fiber efficiency, which is numerically solved along with the convection diffusion equation in polar coordinates for particle concentration, with associated initial and boundary conditions. A sufficient number of examples from two numerical simulations are employed to construct a database, from which parameters of a novel neural model are adjusted. This model is constructed based on the back propagation algorithm in order to map two features, namely Peclet number and packing density, which are extracted from the numerical simulations into the corresponding single fiber efficiency. The results indicate that the developed neural model can be trained in a reasonable computational time and is capable of estimating single fiber efficiency from examples of the test set with a maximum error of 1.7%.展开更多
基金supported by the Boeing Company,the National Natural Science Foundation of China (11222217 and 11002079)Tsinghua University Initiative Scientific Research Program (2011Z02174)the Tsinghua National Laboratory for Information Science and Technology of China
文摘Nano-particle capture is a key process in filtration, separation, and biomedical applications. Here we explored the mechanisms of soft particle capture using nanofiber networks. We identified possible states of the capture process, which are defined by their structural and material parameters. By performing numerical analysis, we provided a phase diagram in the parametric space of the network structure and interracial adhesion. The work provides a conceptual model for rational design of synthetic materials in related applications that focus on the protection against or removal of virus, as well as other soft particles.
文摘In this paper, artificial neural networks are used for predicting single fiber efficiency in the process of removing smaller particles from gas stream by fiber filters. For this, numerical simulations are obtained of a classic model of literature for fiber efficiency, which is numerically solved along with the convection diffusion equation in polar coordinates for particle concentration, with associated initial and boundary conditions. A sufficient number of examples from two numerical simulations are employed to construct a database, from which parameters of a novel neural model are adjusted. This model is constructed based on the back propagation algorithm in order to map two features, namely Peclet number and packing density, which are extracted from the numerical simulations into the corresponding single fiber efficiency. The results indicate that the developed neural model can be trained in a reasonable computational time and is capable of estimating single fiber efficiency from examples of the test set with a maximum error of 1.7%.
基金supported by the National Natural Science Foundation of China(51875544,91963127,51675503,51805509,61805230,)USTC Research Funds of the Double First-Class Initiative(YD2090002005)Fundamental Research Funds for the Central Universities(WK2090000024)
