CFD study of filtration process in moulded filters within a vacuum pump

Air/Oil filtration through filters is commonly utilised in the vacuum industry where oil lubricated pumps are used across a number of different applications such as food and packaging, industrial, pharmaceutical, R&D, forming and drying. The air/oil filters are crucial in the reduction of exhaust emissions, which, when suspended as fine particulate matter can cause great harm to the environment, climate, equipment life and public health. However, the behaviour of flow through the filters is not fully understood and much of the design and development work is based on historical know-how and experimental studies. Computational Fluid Dynamics (CFD) is a powerful tool to understand the flow characteristics and droplet trajectory through the filters which is challenging through experimental techniques. In this study, a CFD model is developed by using the commercial ANSYS FLUENT code. Oil droplets from the pump entering the filter are treated as a discrete phase. Euler-Lagrangian frame is used to characterise the multiphase flow, K-Ɛ as a turbulence model, Rosin-Rammler distribution of oil droplets, User Defined Functions (UDF) are written for droplet injection, distribution and deposition. Various methodologies and tests were developed to obtain the required data to feed into the model and validate the data predicted by the computational model. The obtained computational data agrees well with the experimental data

Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations

The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of reactor is vital to ensure enhanced mixing characteristics and feasible manufacturability. Here we present a framework to rapidly solve this nonlinear, computationally expensive, and derivative-free problem, enabling the fast prototype of novel reactor parameterizations. We take advantage of Gaussian processes to adaptively learn a multi-fidelity model of reactor simulations across a number of different continuous mesh fidelities. The search space of reactor geometries is explored through an amalgam of different, potentially lower, fidelity simulations which are chosen for evaluation based on weighted acquisition function, trading off information gain with cost of simulation. Within our framework we derive a novel criteria for monitoring the progress and dictating the termination of multi-fidelity Bayesian optimization, ensuring a high fidelity solution is returned before experimental budget is exhausted. The class of reactor we investigate are helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics, have the potential to be highly parameterized, and are easily manufactured using 3D printing. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming its mixing performance. In doing so we demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimization problems, supporting the design of the next generation of highly parameterized chemical reactors.Comment: 22 Pages with Appendi

Machine Learning-Assisted Discovery of Novel Reactor Designs via CFD-Coupled Multi-fidelity Bayesian Optimisation

Additive manufacturing has enabled the production of more advanced reactor geometries, resulting in the potential for significantly larger and more complex design spaces. Identifying and optimising promising configurations within broader design spaces presents a significant challenge for existing human-centric design approaches. As such, existing parameterisations of coiled-tube reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. Given algorithmic improvements and the onset of additive manufacturing, we propose two novel coiled-tube parameterisations enabling the variation of cross-section and coil path, resulting in a series of high dimensional, complex optimisation problems. To ensure tractable, non-local optimisation where gradients are not available, we apply multi-fidelity Bayesian optimisation. Our approach characterises multiple continuous fidelities and is coupled with parameterised meshing and simulation, enabling lower quality, but faster simulations to be exploited throughout optimisation. Through maximising the plug-flow performance, we identify key characteristics of optimal reactor designs, and extrapolate these to produce two novel geometries that we 3D print and experimentally validate. By demonstrating the design, optimisation, and manufacture of highly parameterised reactors, we seek to establish a framework for the next-generation of reactors, demonstrating that intelligent design coupled with new manufacturing processes can significantly improve the performance and sustainability of future chemical processes.Comment: 11 pages, 8 figure

Analysis of Oil-Injected Twin-Screw Compressor with Multiphase Flow Models

Growing demands for energy are motivating researchers to conduct in-depth analysis of positive displacement machines such as oil-injected screw compressors which are frequently used in industrial applications like refrigeration, oil and gas and air compression. The performance of these machines is strongly dependent on the oil injection. Optimisation of oil has a great energy saving potential by both increasing efficiency and reducing other impacts on the environment. Therefore, a three-dimensional, transient computational fluid dynamics study of oil injection in a twin-screw compressor is conducted in this research. This study explores pseudo single-fluid multiphase (SFM) models of VOF (Volume of Fluid) and a mixture for their capability to predict the performance of the oil-injected twin screw compressor and compare this with the experimental values. SCORGTM (Screw Compressor Rotor Grid Generator) is used to generate numerical grids for unstructured solver Fluent with the special interface developed to facilitate user defined nodal displacement (UDND). The performance predictions with both VOF and mixture models provide accurate values for power consumption and flow rates with low deviation between computational fluid dynamics (CFD) and the experiment at 6000 RPM and 7.0 bar discharge pressure. In addition, the study reflects on differences in predicting oil distribution with VOF, mixture and Eulerian-Eulerian two-fluid models. Overall, this study provides an insight into multiphase flow-modelling techniques available for oil-injected twin-screw compressors comprehensively accounting for the details of oil distribution in the compression chamber and integral compressor performance

Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows

The separation of liquid-liquid dispersions in horizontal pipes is common in many industrial sectors. It remains challenging, however, to predict the separation characteristics of the flow evolution due to the complex flow mechanisms. In this work, Computational Fluid Dynamics (CFD) simulations of the silicone oil and water two-phase flow in a horizontal pipe are performed. Several cases are explored with different mixture velocities and oil fractions (15%-60%). OpenFOAM (version 8.0) is used to perform Eulerian-Eulerian simulations coupled with population balance models. The 'blending factor' in the multiphaseEulerFoam solver captures the retardation of the droplet rising and coalescing due to the complex flow behaviour in the dense packed layer (DPL). The blending treatment provides a feasible compensation mechanism for the mesoscale uncertainties of droplet flow and coalescence through the DPL and its adjacent layers. In addition, the influence of the turbulent dispersion force is also investigated, which can improve the prediction of the radial distribution of concentrations but worsen the separation characteristics along the flow direction. Although the simulated concentration distribution and layer heights agree with the experiments only qualitatively, this work demonstrates how improvements in drag and coalescence modelling can be made to enhance the prediction accuracy. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows

The separation of liquid-liquid dispersions in horizontal pipes is common in many industrial sectors. It remains challenging, however, to predict the separation characteristics of the flow evolution due to the complex flow mechanisms. In this work, Computational Fluid Dynamics (CFD) simulations of the silicone oil and water two-phase flow in a horizontal pipe are performed. Several cases are explored with different mixture velocities and oil fractions (15%-60%). OpenFOAM (version 8.0) is used to perform Eulerian-Eulerian simulations coupled with population balance models. The 'blending factor' in the multiphaseEulerFoam solver captures the retardation of the droplet rising and coalescing due to the complex flow behaviour in the dense packed layer (DPL). The blending treatment provides a feasible compensation mechanism for the mesoscale uncertainties of droplet flow and coalescence through the DPL and its adjacent layers. In addition, the influence of the turbulent dispersion force is also investigated, which can improve the prediction of the radial distribution of concentrations but worsen the separation characteristics along the flow direction. Although the simulated concentration distribution and layer heights agree with the experiments only qualitatively, this work demonstrates how improvements in drag and coalescence modelling can be made to enhance the prediction accuracy. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)