1,721,044 research outputs found
Implementation of CHyQMOM in OpenFOAM for the simulation of non-equilibrium gas-particle flows under one-way and two-way coupling
Full text linkThe modeling of dilute gas-particle flows is challenging due to particle-trajectory crossing (PTC). Lagrangian particle tracking can be used, but requires a large number of parcels resulting in high computational costs. A less costly method is the Eulerian number density function (NDF) approach, based on the Boltzmann equation, often solved in terms of lower-order moments of the NDF. In this context the conditional hyperbolic quadrature method of moments (CHyQMOM) was developed and is here implemented for the first time in the OpenFOAM-7, together with high-order advection schemes and a new operator splitting procedure. The resulting solver is used to simulate different test cases: phase segregation problems, collision-less and weakly-collisional PTC flows, asymmetric and symmetric Taylor-Green vortex flow and a dilute gas-particle riser. Results, validated against analytical solutions and predictions obtained with Lagrangian particle tracking, show that the implemented CHyQMOM can be used to handle highly non-equilibrium flows. (c) 2021 Elsevier B.V. All rights reserved
Giardini e Paradisi
No full textUn giardino può esistere non tanto per quello che vuole essere (o meglio apparire), ma soprattutto per quello che deve significare in un contesto ben preciso, finalizzato ad un'utenza ben definita. Il contributo descrive i diversi sensi del giardino:senso ambientale, vegetale ed architettonico, offrendo una panoramica sull'attuale significato dei giardini
Molecules As Building Blocks in a Novel Population Balance Model for Flash Nano-Precipitation: Investigation of the Different Good Solvents Effect on Nanoparticle Formation
No full textMARTINI coarse-grained model for poly-ε-caprolactone in acetone-water mixtures
Full text linkIn this work we present the development of a MARTINI-type coarse-graining (CG) model for poly-ε-caprolactone (PCL) dissolved in a solvent binary mixture of acetone and water. A thermodynamic/conformational procedure is adopted to build up the CG model of the system, starting from the standard MARTINI force field. The single CG bead is parametrized upon solvation free energy calculations, whereas the conformation of the whole polymer chain is optimized using the radius of gyration values calculated at different chain lengths. The model is then able to reproduce the correct thermodynamics of the system, as well as the conformation of single PCL chains, especially in pure water and acetone. The results obtained here are then used to simulate the interactions between multiple longer PCL chains in solution. The model developed here can be used in the future to achieve deeper insight into the dynamics of the polymer self-assembly
Mixing atoms and coarse-grained beads in modelling polymer melts
No full textWe present a simple hybrid model for macromolecules where the single molecules are modelled with both atoms and coarse-grained beads. We apply our approach to two different polymer melts, polystyrene and polyethylene, for which the coarse-grained potential has been developed using the iterative Boltzmann inversion procedure. Our results show that it is possible to couple the two potentials without modifying them and that the mixed model preserves the local and the global structure of the melts in each of the case presented. The degree of resolution present in each single molecule seems to not affect the robustness of the model. The mixed potential does not show any bias and no cluster of particles of different resolution has been observed. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4759504
A computational workflow to study particle transport and filtration in porous media: Coupling CFD and deep learning
Full text linkIn 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
From Computational Fluid Dynamics to Structure Interpretation via Neural Networks: An Application to Flow and Transport in Porous Media
Full text linkThe 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
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
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
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