Multi-objective optimization of cyclone separators in series based on computational fluid dynamics

Multiple cyclones working in series are sufficient for heavy separation tasks at the cost of significant energy consumption. The majority of researchers conducting optimization studies on multiple cyclones have attempted to find the best possible compromise between pressure drop and collection efficiency by optimizing the geometry of the cyclones. In a departure from this approach, we report a novel design. Using the proposed method, exit gas is divided into dirty and clean gas by inserting a secondary vortex finder (SVF) into the primary vortex finder (PVF) of the previous cyclone. The dirty gas flows into the succeeding cyclone, whereas the clean gas passes over the succeeding cyclone and directly flows into the device farther behind. Methodologically, the separation performance was tested experimentally, and computational fluid dynamics (CFD) simulations were employed. The Reynolds Stress Model (RSM) for turbulence and the Discrete Phase Model (DPM) were used to simulate the turbulent two-phase flow within the gas–solid separator, capturing the three-dimensional, transient, and turbulent characteristics of the flow. Our performance test results showed that the new series configuration clearly reduces energy consumption while not hindering overall efficiency. The CFD simulation was used to optimize the SVF’s diameter and length. The results indicated that having an SVF with optimal dimensions significantly enhances the vortex flow in the separation space and thus improves the efficiency of the previous cyclone. In addition, an equation was established that describes the volume of gas flowing into a succeeding cyclone.

Cyclone separators are commonly employed in the solid particle separation process due to their simplicity and low-cost manufacture. Collection efficiency and pressure drop are two of the most important factors. Small-size cyclone separator has outperformed collection efficiency. However, due to its small size, it’s hard to detailed flow investigation experimentally. So, the target of this work is to perform a numerical investigation of the flow characteristics as well as the collection efficiency and pressure drop of small cyclone separators, using Ansys Fluent software. Discrete Phase Model is included to simulate the particle-flow interaction. Different values of velocity inlet and particle size are considered. Typical flow characteristics of cyclone separators with a strong vortex are revealed. Results also show that the larger particle size or the stronger inlet velocity provides higher collection efficiency. Particularly, for a typical particle diameter of 2.96 micrometers, the collection efficiency of the cyclone separator is always higher than 91% when the inlet velocity surpasses 15 m/s. All these results provide a valuable framework for the operation of cyclone separators.

The purpose of this paper is to study the pressure drop of an axial flow cyclone separator as a function of inlet velocities using experimental and computational fluid dynamics (CFD) methods. First, the resistance performance of the separator was acquired under ambient pressure and temperature with little change by wind tunnel experiments. Then, numerical simulations were carried out in CFD code Fluent 6.3 under standard operating conditions. A comparison between the experimental and CFD data demonstrates that the CFD method can predict the pressure drop of the axial cyclone separator excellently. Additionally, the results show that the axial flow cyclone separators have a pressure drop coefficient of approximately 7.5. To study the effect of ambient pressure and temperature on pressure drops, the same CFD method was employed to predict the resistance performance under various operating conditions. Then the numerical results were compared with the data of a normalization process method of pressure drops raised in this paper. Their comparison demonstrated that the normalization method had a high precision in predicting the influence of ambient operating parameters on pressure drops of an axial flow cyclone separator.

Modeling and multi-objective Pareto optimization of new cyclone separators using CFD, ANNs and NSGA II algorithm

Cyclone separators are suitable to remove particles in extreme conditions with high pressure and temperature during the gas-solid separation process. The important performance of cyclone separators is determined by the separation efficiency and pressure drop. The inlet parameters including the number of inlets, inlet geometry and inlet gas velocity affects the performance of cyclone separators. In this paper, the experimental investigation has been conducted to determine the effects of dual inlet to the separation efficiency of cyclone separators. The cyclone separator was fabricated using the acrylic materials to accommodate the flow observation and trajectories inside the chamber. The experiments were conducted at 9, 11 and 13 m/s of inlet velocity using three particle sizes at 277.5, 42.5 and 625 um. Results showed that as the particle sizes increases, the separation efficiency also increases up to 94.5% for 625 um particles. The findings indicate that the dual inlet cyclone separators increases the performance of cyclone separators especially to remove fly ash in the biomass processing plant and other gas-solid process.

Nowadays, Computational Fluid Dynamics (CFD) simulations play an increasingly important role for turbine airfoil design. This high-fidelity approach is capable to provide accurate information of flow fields. Meanwhile, the calculation accuracy is always gained at the expense of numerical cost. This gap limits opportunities for design space exploration. To address this problem, surrogate models (also known as metamodels) are introduced to approximate high-fidelity CFD models. However, traditional surrogate models, such as Kriging or Radial Basis Function, construct response surface on a design space with limited dimensions. This prevents users from predicting the flow fields directly from the geometry and performing interactive design of airfoil. In the present work, we propose a Convolutional Neural Network (CNN) based surrogate model to predict flow properties on turbine vane airfoil surface from 3D airfoil profile defined by point cloud. The proposed CNN architecture adopts a symmetric expanding path that is similar to the so-called U-Net. The geometries in the training and testing dataset are generated via varying the parameters defined by the Free-Form Deformation approach. The corresponding flow fields are obtained through high-fidelity CFD simulations performed in a finite volume context. Furthermore, a gaussian process based Bayesian optimization technique is utilized to tune automatically the hyperparameters of the network. In this work, we trained the CNN based surrogate model with static pressure and temperature on the mean section of turbine vane airfoil surface. The trained model is able to predict in a reliable and efficient way the corresponding property directly from the 3D geometry, which allows engineers to agilely adjust their airfoil design.

Gas cyclone has two main parameters for evaluating separation performance, separation efficiency and pressure drop through cyclone. They are closely influenced by the geometrical design variables of the cyclone. This study performed optimization of the cyclone performance for the cyclone shape based on computational fluid dynamics (CFD), GMDH type neural network and genetic algorithm (GA). First, CFD was used to obtain the data of the cyclone performance parameters. As result of the CFD validation, the errors of the reference model and CFD were 0.5 % and 2 % for the pressure drop and the separation efficiency. Secondly, the meta-model of the cyclone performance was derived by using GMDH algorithm based on supervised learning of machine learning. The fitness of the modelling results was shown using the correlation coefficient. As results of the GMDH, the correlation coefficients of meta models of the separation efficiency and the pressure drop were 98.9 %, 99.7 %, respectively. Finally, we performed optimization of the meta model by applying GA. When the optimal point was compared with the reference model, the performance of the optimal point was improved by 24.31 % and 8.32 % for pressure drop and the separation efficiency, respectively.

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power.

Simulation of tidal turbine array using coupled Computational Fluid and Rigid Body Dynamics

In this paper, in order to Pareto design of cyclone separators, pressure drop and collection efficiency Δp and η in a set of cyclones are numerically investigated using Computational Fluid Dynamic (CFD) techniques. Then, the neural network is used to obtain polynomial models for the effects of geometrical parameters of the cyclones on both the pressure drop and the collection efficiency. The obtained simple polynomial models are used to find the best possible combinations of the pressure drop and collection efficiency, known as the Pareto front based on a novel multi-objective algorithm.

Numerical investigation of effects of inner cone on flow field, performance and erosion rate of cyclone separators

The effect of operational and geometrical parameters on the short-cut flow in cyclone separators has been investigated computationally using the Reynolds stress model (RSM). The motion of solid particles in the flow field was simulated using the Eulerian-Lagrangian approach with one way discrete phase method (DPM). Eleven cyclones with different cone tip diameters, vortex finder lengths and diameters were studied and the simulation results were analyzed in terms of velocity fields, pressure drops, cut-off diameters and short-cut flows. The numerical simulation was verified with the published experimental results. The results obtained demonstrate that all three parameters, particularly, vortex finder diameter, have significant effects on the cut-off diameter (collection efficiency), the short-cut flow and the pressure drop.

The particle collection performance of the square and cylindrical type of cyclone separator is reviewed for the study. The fabricated setup is designed and processed by Solid works and TIG welding respectively. The geometrical variations observed in the structure of setup sequentially arranged in parallel condition of Square-Cylinder- Square-Cylinder arrangement. The significant rise in pressure drop is observed with the study for collection efficiency. The performance carried out in larger scale for the conclusive segments with the aid of variable frequency diaphragm. The speed controlling device which optimise the speed condition for the setup through blower. The centrifugal action inside the separator will impinges the fine particles away from the surface and follows the vortex patter on the middle. The coarse particles will largely collect to the bottom of the vortex and collected on the bin. The variant's mixed configuration has a higher pressure drop and better collecting effectiveness. Only for tiny particles does the percentage of short-circuiting particles rise with an increase in intake velocity. There are noticeable discrepancies in the collection efficiency curves as a result of the fact that when particle size increases, the fraction of short-circuiting particles declines more quickly at higher intake velocities. The findings from the review research will allows to recommend the suitable arrangement setup for better collection efficiency and also provides the convene model to study of particulates.

The pressure drop and mass transfer efficiency for two‐phase flow in a structured packed column were simulated using a commercial CFD package, CFX version 10. The distillation of the methanol/isopropanol system was carried out in a 0.073 m diameter column, with an element composed of a ceramic structured packing and 0.053 m in height. The Height Equivalent to Theoretical Plate (HETP) value varied from 0.106–0.146 m. Pressure drop experiments were measured with an air/water system. The pressure drops at the flooding and loading points were ca. 173 and 580 Pa/m of packing, respectively. HETPs and pressure drops calculated from the Computational Fluid Dynamics (CFD) model were compared to their experimental counterparts. The average relative error between CFD predictions and the experimental data for the prediction of dry pressure drop, irrigated pressure drop and mass transfer efficiency are 20.3 %, 23 % and 9.15 %, respectively. In all cases, the CFD predictions show a good agreement with the experimental data, indicating that CFD is a reliable, cost saving and suitable technique for the design and optimization of separation processes.

Cold flow model of a PFBC cyclone