1,721,026 research outputs found
A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning
Get PDFIn this work we developed an open-source work-flow for the construction of data-driven models from a wide Computational Fluid Dynamics (CFD) simulations campaign. We focused on the prediction of the permeability of bidimensional porous media models, and their effectiveness in filtration of a transported colloidal species. CFD simulations are performed with OpenFOAM, where the colloid transport is solved by the advection–diffusion equation. A campaign of two thousands simulations was performed on a HPC cluster, the permeability is calculated from the simulations with Darcy's law and the filtration (i.e. deposition) rate is evaluated by an appropriate upscaled parameter. Finally a dataset connecting the input features of the simulations with their results is constructed for the training of neural networks, executed on the open-source machine learning platform Tensorflow (integrated with Python library Keras). The predictive performance of the data-driven model is then compared with the CFD simulations results and with traditional analytical correlations
Simulation of Mixing in Structured Fluids with Dissipative Particle Dynamics and Validation with Experimental Data
No full textStructured fluids are simulated with dissipative particle dynamics and the predictions are validated with experimental data. The structured fluid considered is a mixture of the triblock copolymer Pluronic P103 and water. This attempt follows a first investigation with the same model parameters of a mixture with another Pluronic, L64, and water. Dissipative particle dynamics simulations are applied to identify, via a clustering algorithm, the different microstructures observed at different temperatures and compositions. This algorithm is also employed to determine the cluster mass distributions and to calculate the resulting chemical potentials associated with the different microstructures. The chemical potentials are in turn used to extract the critical micellar concentration and important shape factors. Comparison of model predictions with experimental data from the literature indicates decent agreement
A Computational Workflow to Study Particle Transport in Porous Media: Coupling CFD and Deep Learning
No full textIn this work, we studied the coupling of CFD simulation with machine learning models, by using a large set of computational result as the training dataset of a simple fully-connected neural network. The focus of the CFD investigation is the flow and colloid transport in porous media models, both simple and complex, with the end result of obtaining a computationally inexpensive data-driven surrogate model able to replace the CFD simulation, while keeping the same accuracy. While considerable success was obtained in the case of simpler geometries, more sophisticated deep learning models are needed to treat cases characterized by non-trivial fluid dynamic structures
From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media
Get PDFThe modeling of flow and transport in porous media is of the utmost importance in many chemical engineering applications, including catalytic reactors, batteries, and CO2 storage. The aim of this study is to test the use of fully connected (FCNN) and convolutional neural networks (CNN) for the prediction of crucial properties in porous media systems: The permeability and the filtration rate. The data-driven models are trained on a dataset of computational fluid dynamics (CFD) simulations. To this end, the porous media geometries are created in silico by a discrete element method, and a rigorous setup of the CFD simulations is presented. The models trained have as input both geometrical and operating conditions features so that they could find application in multiscale modeling, optimization problems, and in-line control. The average error on the prediction of the permeability is lower than 2.5%, and that on the prediction of the filtration rate is lower than 5% in all the neural networks models. These results are achieved with at least a dataset of ~ 100 CFD simulations
Hydrodynamic characterization of Gyroid, Diamond and Split-P Triply Periodic Minimal Surfaces as porous medium
Get PDFTriply Periodic Minimal Surfaces are gaining significant attention as engineered porous media for applications in fluid transport and thermal management systems due to their unique geometric properties. However, accurate prediction of pressure drop across TPMS structures remains a challenge, particularly in transitioning flow regimes. This study addresses this gap by investigating the hydrodynamic behavior of Gyroid, Diamond, and Split-P geometries using computational fluid dynamics simulations across a range of Reynolds numbers, from viscous to weakly inertial regimes. Two modeling frameworks were utilized: the Ergun equation, commonly used for packed beds, and the Darcy-Forchheimer equation, enhanced with newly developed correlations for permeability and inertial drag factor. An adapted Kozeny-Carman equation was also applied for permeability prediction. The developed correlations, expressed as power-law functions of porosity and tortuosity, demonstrated high accuracy, with relative errors below 10 % for most configurations and a maximum error of 21 % for the more complex Split-P1 geometry. Validation in larger-scale geometries, such as pipes filled with TPMS, confirmed the scalability and robustness of the proposed models, even when accounting for variations in the hydraulic diameter due to wall effects. The results demonstrate the superior suitability of the Darcy-Forchheimer equation with the developed permeability and inertial drag factor models, particularly for complex geometries like Split-P. In contrast, the Ergun equation fails to accurately predict pressure drop across the investigated TPMS, underscoring its limitations for these geometries. Furthermore, while the inclusion of tortuosity in the correlations provides additional detail, it does not offer significant advantages over the simpler permeability-porosity relation for any of the investigated TPMS, making the latter a more practical choice for design and optimization applications in systems such as heat sinks and porous flow device
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
IGF-1 and IGFBP in congenital and acquired hypothyroidism after long-term replacement treatment.
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