Computational fluid dynamics analysis of a novel axial flow hydrocyclone

A comparative CFD investigation was carried out for a mini axial, a large axial and a large reverse flow hydrocyclone. The diameters selected were 5 mm and 75 mm for the mini and large hydrocyclones, respectively. The simulations were conducted using a large eddy simulation (LES) turbulence model with various subgrid scale models, and the results of the reverse flow hydrocyclone were validated against published LDV data. The numerical results confirmed that the LES-Smagorinsky model provides good prediction relative to the other subgrid scale models studied, giving an error percentage of 0.45% for the water split ratio. Numerical investigation of mini axial and reverse flow hydrocyclones were performed. The Lagrangian discrete phase model (DPM) was used to track the soda-lime glass particles released from the inlet surface. The soda-lime glass particles had a density of 2520 kg/m3 with a particle diameter range of 10 µm to 150 µm. The results indicate that the axial flow hydrocyclone gave a lower pressure drop and a higher cut size than the reverse flow hydrocyclone for inlet velocities ranging from 1-10 m/s. Thus, the axial flow hydrocyclone is an effective particle separator. The effect of inlet dimensions, vortex finder diameter and length on the performance and flow pattern was then investigated. Thirteen mini axial hydrocyclones separators were investigated for a fixed inlet velocity of 2 m/s. The simulations showed that changing the vortex finder diameter and length had a more pronounced effect on the separation efficiency and velocity profiles than changing the inlet dimensions. Decreasing the diameter of vortex finder translates to higher separation efficiency at the cost of higher pressure drop. The results showed that lengthening the vortex finder increases the separation efficiency but decreases the cut size as the vortex strength decreases with vortex finder length. The axial flow hydrocyclone can be a serious competitor to the reverse flow hydrocyclone for industrial use. A comprehensive study of 75 mm axial flow hydrocyclone enormously improved the understanding of the possibility of its use for industrial applications. A comparison of the axial and reverse flow hydrocyclones of 75 mm diameter showed that the cut size of the axial flow hydrocyclone was larger than the reverse flow hydrocyclone at particle concentrations of 4.88% and 10.47%. However, the pressure drop was significantly lower for axial flow hydrocyclone. The CFD-based investigation showed that the axial flow hydrocyclone could be successfully used to classify particles in the industry with a substantially lower pressure drop and pumping energy requirement

Optimal design of sand blown wind tunnel

This work investigates the airflow driven by dual axial-flow fans in an atmospheric boundary layer (ABL) wind tunnel and the expected entrainment of sand movement together. The present study is conducted via 3D numerical simulation based on modelling the entire wind tunnel, including the power fan sections. Three configurations of dual fans in the tunnel are proposed. Simulation results show that the airflow in the tunnel with dual-fan configuration can satisfy the logarithmic distribution law for ABL flows. The airflow driven by the dual fans placed together at the tunnel outlet is highly similar to that in the tunnel with single fans. Although the boundary layer thickness is reduced, the maximum airflow velocity (53.393 m/s) and turbulence intensity (12.02%), which are respectively 1.75 and 1.49 times higher than those under the single-fan configuration, can be reached when dual fans are separately placed at the tunnel inlet and outlet. The simulation and experiment manifest that the separated arrangement of dual fans in the tunnel should be suitable for the experimental study of aeolian sand transport. Some measures, such as wind tunnel construction adjustment and optimal roughness element arrangement, are necessary to guarantee the required boundary layer thickness in the wind tunnel

Performance of multicell, axial-entry cyclones for industrial gas cleaning

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Multi-objective Bayesian shape optimization of an industrial hydrodynamic separator using unsteady Eulerian-Lagrangian simulations

This is the author accepted manuscript.Availability of data and materials: No data for public archival are reported in this study. This study does not report any data of this kind.The shape of a hydrodynamic particle separator has been optimized using a parallelized and robust formulation of Bayesian optimization, with data from an unsteady Eulerian flow field coupled with Lagrangian particle tracking. The uncertainty due to the mesh, initial conditions, and stochastic dispersion in the Eulerian-Lagrangian simulations was minimized and quantified. This was then translated across to the error term in the Gaussian process model and the minimum probability of improvement infill criterion. An existing parallelization strategy was modified for the infill criterion and customized to prefer exploitation in the decision space. In addition, a new strategy was developed for hidden constraints using Voronoi penalization. In the approximate Pareto Front, an absolute improvement over the base design of 14% in the underflow collection efficiency and 10% in the total collection efficiency was achieved. The corresponding designs were attributed to the effective distribution of residence time between the trays via the removal of a vertical plume. The plume also reduced both efficiencies by creating a flow path in a direction that acted against effective settling. This demonstrates the value of Bayesian optimization in producing non-intuitive designs, which resulted in the filing of a patent.Innovate UKEngineering and Physical Sciences Research Council (EPSRC

Development and Application of Rotation and Curvature Correction to Wray-Agarwal Turbulence Model

Computational Fluid Dynamics (CFD) is increasingly playing a significant role in the analysis and design of aircrafts, turbomachines, automobiles, and in many other industrial applications. In majority of the applications, the fluid flow is generally turbulent. The accurate prediction of turbulent flows to date remains a challenging problem in CFD. In almost all industrial applications, Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model are employed for simulation and prediction of turbulent flows. Currently the one-equation (namely the Spalart-Allmaras (SA) and Wray-Agarwal (WA) and two-equation (namely the k-ε and Shear Stress Transport k-ω) turbulence models remain the most widely used models in industry. However, improvements and new developments are needed to improve the accuracy of the turbulence models for wall bounded flows with separation in the presence of adverse pressure gradients, and for flows with rotation and curvature (RC) such as those encountered in turbomachinery, centrifugal pumps and the rotating machinery in other industrial devices. The goal of this research is to enable the eddy-viscosity type turbulence models to accurately account for the rotation and curvature effects. To date, there have been two approaches for inclusion of RC effects in turbulence models, which can be categorized as the “Modified Coefficients Approach” which parameterizes the model coefficients such that the growth rate of turbulent kinetic energy is either suppressed or enhanced depending upon the effect of system rotation and streamline curvature on the pressure gradient in the flow and the “Bifurcation Approach” which parameterizes the eddy-viscosity coefficient such that the equilibrium solution bifurcates from the main branch to decaying solution branches. In this research, the uncertainty quantification (UQ) is applied to examine the sensitivity of RC correction coefficients and the coefficients are modified based on the UQ analysis to improve the model’s behavior. Both these approaches are applied to the widely used turbulence models (SA, SST k-ω and WA) and they show some improvement in predictions of turbulent flow in all benchmark test cases considered, namely the flow in a 2D curved duct, flow in a 2D U-turn duct, fully developed turbulent flow in a 2D rotating channel, fully developed turbulent flow in a 2D rotating backward-facing step, flow in a rotating cavity, flow in a stationary and rotating serpentine channel, flow in a rotor-stator cavity and in a hydrocyclone as well as two wall-unbounded turbulent flow cases. All the simulations are conducted using the commercial software ANSYS Fluent and the open source CFD software OpenFOAM. The success of this research should enhance the ability of the RANS modeling for more accurate prediction of complex turbulent flows with rotation and curvature effects. In addition to the RANS modeling of RC effects, a new DES model incorporating the WA2017m-RC turbulence model (referred to as the WA2017m-RC-DES model) is developed and validated against experimental and DNS data. Further improvements are obtained with the DES model in some test cases

Synthesis, optimisation and control of crystallization systems

Process systems engineering has provided with a range of powerful tools to chemical engineers for synthesis, optimisation and control using thorough understanding of the processes enhanced with the aid of sophisticated and accurate multi-faceted mathematical models. Crystallization processes have rarely benefited from these new techniques, for they lack in models that could be used to bridge the gaps in their perception before utilising the resulting insight for the three above mentioned tasks. In the present work, first a consistent and sufficiently complex models for unit operations including MSMPR crystallizer, hydrocyclone and fines dissolver are developed to enhance the understanding of systems comprising these units. This insight is then utilised for devising innovative techniques to synthesise, optimise and control such processes. A constructive targeting approach is developed for innovative synthesis of stage-wise crystallization processes. The resulting solution surpasses the performance obtained from conventional design procedure not only because optimal temperature profiles are used along the crystallizers but also the distribution of feed and product removal is optimally determined through non-linear programming. The revised Machine Learning methodology presented here for continual process improvement by analysing process data and representing the findings as zone of best average performance, has directly utilised the models to generate the data in the absence of real plant data. The methodology which is demonstrated through KNO₃ crystallization process flowsheet quickly identifies three opportunities each representing an increase of 12% on nominal operation. An optimal multi-variable controller has been designed for a one litre continuous recycle crystallizer to indirectly control total number and average size of crystals from secondary process measurements. The system identification is solely based on experimental findings. Linear Quadratic Gaussian method based design procedure is developed to design the controller which not only shows excellent set-point tracking capabilities but also effectively rejects disturbance in the simulated closed loop runs

Advances in Hydraulics and Hydroinformatics Volume 2

This Special Issue reports on recent research trends in hydraulics, hydrodynamics, and hydroinformatics, and their novel applications in practical engineering. The Issue covers a wide range of topics, including open channel flows, sediment transport dynamics, two-phase flows, flow-induced vibration and water quality. The collected papers provide insight into new developments in physical, mathematical, and numerical modelling of important problems in hydraulics and hydroinformatics, and include demonstrations of the application of such models in water resources engineering

Bayesian probabilistic numerical methods

The increasing complexity of computer models used to solve contemporary inference problems has been set against a decreasing rate of improvement in processor speed in recent years. As a result, in many of these problems numerical error is a challenge for practitioners. However, while there has been a recent push towards rigorous quantification of uncertainty in inference problems based upon computer models, numerical error is still largely required to be driven down to a level at which its impact on inferences is negligible. Probabilistic numerical methods have been proposed to alleviate this; these are a class of numerical methods that return probabilistic uncertainty quantification for their numerical error. The attraction of such methods is clear: if numerical error in the computer model and uncertainty in an inference problem are quantified in a unified framework then careful tuning of numerical methods to mitigate the impact of numerical error on inferences could become unnecessary. In this thesis we introduce the class of Bayesian probabilistic numerical methods, whose uncertainty has a strict and rigorous Bayesian interpretation. A number of examples of conjugate Bayesian probabilistic numerical methods are presented before we present analysis and algorithms for the general case, in which the posterior distribution does not posess a closed form. We conclude by studying how these methods can be rigorously composed to yield Bayesian pipelines of computation. Throughout we present applications of the developed methods to real-world inference problems, and indicate that the uncertainty quantification provided by these methods can be of significant practical use