Computational Fluid Dynamics Optimization Research Articles

CFD simulation and optimization of slurry erosion of PDC bits

Polycrystalline diamond compact (PDC) bits are subjected to harsh erosive conditions under downhole. The erosion resistance partly determines the rate of penetration and footage of the bit. This work aims to investigate the effects of key factors on the erosion resistance of PDC bits through numerical simulation and field trials, and then to optimize the bit hydraulic designs. Computational Fluid Dynamics (CFD) and Discrete Phase Model (DPM) model were used to simulate the evacuation and impingement of cuttings on the bit body at the bottom of the well. The reliability of the numerical simulation was verified by the bit conditions after the field testing. Fuzzy grey relational analysis (FGRA) was then employed to rank the correlation degree of various affecting factors. The FGRA results show that the mass flow rate has the greatest effect on the average erosion rate of PDC bits, followed by the flow rate. The most erosion-vulnerable areas on the bit body are the webbings between cutters as well as the blade front. To improve the erosion resistance from the perspective of the bit hydraulic design, three novel hydraulic structures were proposed: the optimized nozzle orientation, the multi-ridge guide, and the extended curved nozzle. Numerical simulations indicate that all of these novel designs have great potential to enhance the erosion resistance of PDC bits with the customized extended curved nozzle showing the best.

Computational fluid dynamics optimization of gas drainage technology in gas-mining areas

Coal mining results in strata movement and surrounding rock failure. Eventually, manual mining space will be occupied by the destructed coal rock, making it difficult to conduct field tests of the coal seam to explore gas seepage and transport patterns. Therefore, computational fluid dynamics (CFD) numerical computation is an important tool for such studies. From the aspect of gas pre-drainage, for layer-through boreholes in the floor roadway of the 8,406 working face in Yangquan Mine 5 in China, reasonable layout parameters were obtained by CFD optimization. For effectively controlling the scope of boreholes along coal seam 9 in the Kaiyuan Mine, CFD computation was performed. The results revealed that the horizontal spacing between boreholes should be ≤2 m when a tri-quincuncial borehole layout is used. Optimization of the surface well position layout for the fault structure zone in the Xinjing Mine of the Yangquan mining area indicated that the horizontal distance between the surface well and the fault plane should be <150 m. From the aspect of gas drainage with mining-induced pressure relief, CFD computation was performed for pressure-relieved gas transport in the K8205 working face of Yangquan Mine 3. The results showed that forced roof caving should be used before the overhang length of hard roof reaches 25 m in the K8205 working face to avoid gas overrun. From the aspect of gas drainage from the abandoned gob, surface well control scopes at different surface well positions were computed, and an O-ring fissure zone is proposed as a reasonable scope for the surface well layout. CFD computation has been widely applied to coal and gas co-extraction in the Yangquan mining area and has played a significant role in guiding related gas drainage engineering practice.

Numerical investigation of nanofluid flow using CFD and fuzzy-based particle swarm optimization

This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al2O3/water turbulent flow in a heated pipe. One of the challenging problems in CFD modeling is the lost data for a specific boundary condition. For example, the CFD data are available for wall heat fluxes of 75, 85, 105, and 125 w/m2, but there is no data for the wall heat flux of 95 w/m2. So, the PSOFIS learns the available CFD data, and it predicts the velocity profile for where the data is not available (i.e., wall heat flux of 95 w/m2). The intelligence of PSOFIS is checked by the coefficient of determination (R2 pattern) for different values of accept ratio (AR) and inertia weight damping ratio (IWDR). The best intelligence is obtained for the AR and IWDR of 0.7 and 0.99, respectively. At this condition, the velocity profile predicted by both CFD and PSOFIS is compatible. As the performance of the PSOFIS, for learning time of 268 s, the prediction of the CFD data lost was negligible (~ 1 s). In contrast, the CFD calculation takes around 600 s for each simulation.

Numerical investigation of water forced convection inside a copper metal foam tube: Genetic algorithm (GA) based fuzzy inference system (GAFIS) contribution with CFD modeling

Computational fluid dynamics (CFD) is an accurate method, but it is not always simple. In this approach, the partial differential governing equations are discretized and solved for many divisions in the domain of the fluid. The solution to such equations takes so much time and needs a computer with high hardware capacity. Although many studies have focused on the accuracy improvement of the CFD method, a few investigations have considered CFD optimization, especially in prediction time. The machine learning (ML) of the CFD data using artificial intelligence (AI) algorithms could reduce CFD calculations. For the first time, this study aims to analyze the efficiency of an AI method, namely the genetic algorithm-based fuzzy inference system (GAFIS), in contribution to CFD modeling. A turbulent flow of water through a heated metal foam tube is considered. The wall heat flux and water inlet velocity are constant, while the water inlet temperature changes from 295 ok to 335 ok. To precisely predict the pressure, the intelligence status of the GAFIS must be evaluated. So, the effects of GAFIS parameters, including the number of populations, number of clusters, and percentage of mutation, on the prediction error are investigated. For validation, the GAFIS performance is verified by the fuzzy adaptive network (ANFIS).The results revealed that the best intelligence condition (regression number of 0.996) is related to the number of populations of 30, the number of clusters of 10, and the percentage of mutation of 0.6. The intelligent GAFIS predicted the same pressure as the CFD. The ANFIS and the GAFIS showed the same predictions as the CFD data. The GAFIS learning and prediction times were 223 and 345 s, respectively, while those times were 3 and 10 s for the ANFIS. The AI algorithms reduced the computational time comparing to the CFD calculation time (∼ 5,409 s).

Numerical simulation and optimization of centrifugal compressor return channels: methods and results

The authors actively use their engineering Universal Modelling Method for gas-dynamic design of centrifugal compressors for industrial partners. Currently, there are two approaches to improving the Method: improving the preliminary design and increasing the accuracy of gas-dynamic characteristics calculation. Computational Fluid Dynamics (CFD) methods give good results for the flow path stator part. Recently, massive CFD calculations of the vaneless diffusers (VLD) characteristics were generalized by a system of algebraic equations, which replaced the previous more complex mathematical model in the Method. Then, based on CFD optimization of a large series of return channels (RCh), corrections were made to the preliminary design of this element of the flow path.This paper presents the results of the joint CFD optimization of a vaneless diffuser and a return channel. Stator elements have many geometric parameters. For a stage with the flow rate coefficient of 0.0597 and the loading factor of 0.60, only the VLD relative radial length D 4/D 2, the number and the inlet angle of the return channel vanes were optimized. Engineering calculations and design experience predicted an optimal value of D 4/D2 within the interval 1.9 - 2.0. CFD optimization demonstrated almost linear reduction of the total head loss towards the end of the investigated range D 4/D2 = 2.3. After careful optimization of the U-bend, the optimization of D 4/D2 was repeated. The influence of on the loss coefficient has decreased, but the value of D 4/D 2 = 2.3 is still far from optimum. It significantly exceeds technically acceptable radial size. The result obtained influenced the design plan of future calculation experiments with a large series of stator elements of the stages in a practically significant range of design parameters.

Performance analysis of a H-Darrieus wind turbine for a Naca 0015 Airfoil: CFD and RSM-based design optimization

With the increasing demand for electrical energy and the depletion of fossil fuel reserves, this situation forces humans to look for alternative renewable energy. The Indonesian region has a very large wind energy potential because it is located in the tropics. The wind turbine is alternative energy based on kinetic energy into electricity. The purpose of this study was to analyze the effect of the number of blades, wind speed, and angle of attack on turbine performance in the form of the efficiency of the H-Darrieus wind turbine using Airfoil NACA 0015. Variations used in this study were the number of blades (3; 4; 5 pieces) and angle of attack (5°; 15°; 25°). The method used in this research is the Computational Fluid Dynamics (CFD) method and the response surface methodology (RSM). The CFD method is used to find the moment pressure and tangential velocity values, while the RSM method is used to find the most optimum variation. The results of this study indicate that a wind turbine with a number of blades 3 pieces, and a turbine angle of attack 11º is the most optimum H-Darrieus wind turbine design with an efficiency of 5.38%.

Analysis of Hydrocyclone Geometry via Rapid Optimization Based on Computational Fluid Dynamics

AbstractHydrocyclones exploit density gradients for the centrifugal separation of dispersions in a continuous liquid. Selection of the geometrics for optimal separation is case specific, like the media characteristics. The existing optimization method based on computational fluid dynamics (CFD) provides a powerful analytical tool but requires long computational times. The most common praxis for CFD optimization is via the single‐factor optimization method (SFOM). In this study, a novel approach is presented as an improved rapid optimization method that implements a dynamic‐mesh and user‐defined function optimization method (DUOM). The DUOM adapts the dynamic‐mesh approach from other applications to the optimization analysis of hydrocyclones. The DUOM reduced the computational time by 31.1 %, compared to the SFOM.

Design space optimisation of an unmanned aerial vehicle submerged inlet through the formulation of a data-fusion-based hybrid model

ABSTRACTThe research focuses on the design space optimisation of National Advisory Committee for Aeronautics (NACA) submerged inlets through the formulation of a hybrid data fusion methodology. Submerged inlets have drawn considerable attention owing to their potential for good on-design performance, for example during cruise flight conditions. However, complexities due to the geometrical topology and interactions among various design variables remain a challenge. This research enhances the current design knowledge of submerged inlets through the utilisation of data mining and Computational Fluid Dynamics (CFD) methodologies, focusing on design space optimisation. A two-pronged approach is employed where the first step encompasses a low-fidelity model through data mining and surrogate modelling to predict and optimise the design parameters, while the second step uses the Design of Experiments (DOE) approach based on the CFD results for the candidate design geometry to construct a surrogate model with high fidelity for design refinement. The feasibility of the proposed methodology is demonstrated for the optimisation of the total pressure recovery of a NACA submerged inlet for the subsonic flight regime. The proposed methodology is found to provide good agreement between the surrogate and CFD-based model and reduce the optimisation processing time by half in comparison with conventional (global-based) CFD optimisation approaches.

Effects of vortexes in feed header on air flow distribution of PEMFC stack: CFD simulation and optimization for better uniformity

Distribution uniformity is critical to achieve high performance for proton exchange membrane fuel cell (PEMFC) stacks. In this work, air flow distribution of a 30 kW “U-type” stack of 100 unit cells is investigated by computational fluid dynamics (CFD) simulation, Burgers vortex model is introduced to describe the mechanism of flow maldistribution. Three cases are presented to evaluate the effects of vortexes on the distribution uniformity under various average current densities, and obtain optimized design for better uniformity. The results show that the vortexes usually generate in the feed header, the flow vortex structures are governed by the feed header’s configuration. The distribution uniformity reduces dramatically with the increase of current density and the generation of vortexes. The cells that locate in the positions of vortexes get less flow allocation than other cells, distribution uniformity can improve by optimizing the feed header’s configuration to change the vortex structure of flow field.

Calorimeter conceptual design for Neutral Beam Injector of DTT - CFD optimisation and thermal stress analysis

A conceptual design of the calorimeter for the Neutral Beam Injector (NBI) of the Divertor Tokamak Test facility (DTT, a new tokamak whose construction is starting in Frascati, Italy) has been developed. The DTT NBI calorimeter features two beam stopping panels made of CuCrZr cooled by pressurized water flowing through deep drilled cooling channels with twisted tape insertions. The proposed design is based primarily on the expected beam power distribution of DTT NBI, thermal-hydraulic factors (maximum temperature of the structure and coolant, pressure drop) and geometrical constraints (beam cross-section, panel inclination angle, available space for the calorimeter, etc.). Main design choices are made on the reduced model, representing a section of the panel with a single cooling channel. Detailed computational fluid dynamics (CFD) simulations of several designs were performed, leading to an optimized design in terms of minimizing the structure and coolant temperatures while respecting the operating conditions, allowed pressure drop in the cooling loop and overall space constraints.The mechanical response of the calorimeter structures, in terms of deformations and stresses, has been also analysed for the optimized design. To obtain realistic results, the whole panel (not only the reduced section of it) with appropriate boundary conditions has been considered. To simulate the temperature distribution over the whole panel, a simplified strategy using only the solid domain was developed. Before applying it on the whole panel, it was verified on the reduced model. The follow-up thermo-mechanical simulation of the whole panel use the heat transfer coefficient on the cooling channel interface that has been previously calculated from the accurate fluid-solid CFD simulation of the reduced model. It has been shown that the thermal loads induced by the beam neutral particles dominate the deformations and stresses in the calorimeter.

CFD Optimization of the Pre-Chamber Geometry for a Gasoline Spark Ignition Engine

In the present paper, an efficient optimization method based on Bayesian updating strategy is developed for the design of a spark-ignition engine equipped with pre-chamber. 3D computational fluid dynamics (CFD) simulation coupled with strategies including design of experiment, genetic algorithm, and machine learning methods is used to optimize the pre-chamber with desired combustion phasing. The optimization process starts from a design of experiment matrix of 11 design parameters, which are used to analytically characterize the pre-chamber geometry and set up the 3D combustion CFD. Taking CA50 as the single objective, the CFD results are then used to train the machine learning models. Different machine learning models are evaluated based on their Root Mean Square Error. Five machine learning models from five different categories are selected for second round evaluation. The trained machine learning model is used in the genetic algorithm optimization, which yields the optimized configuration and is again justified by CFD. The new CFD results based on the optimized design are added into the database to further refine the machine learning model. After 24 iterations for each selected machine learning models, the medium Gaussian support vector machine model is identified as the best method for the present application. Iterations using the medium Gaussian support vector machine model continue until a satisfactory result is achieved. Detailed combustion analysis is conducted to investigate the physical mechanism about how the design of pre-chamber influences the engine's performance. It is found that larger volume of the upper part of the pre-chamber results in stronger jet flow and turbulent intensity which further accelerates the flame propagation inside the pre-chamber, dominating the contrary effects from reduced pressure and temperature. Regression analysis shows that the radius of the pre-chamber is the most influential design parameter. The current work not only sheds light on the optimization of engine design, but also has demonstrated a general strategy applicable to the purpose of arbitrary engine optimization and mechanical system design.

CFD Analysis and Optimization of Effect of Shroud with Multi-outlets on Airflow Uniformity in a Frost-Free Refrigerator

The shroud is a key component of the frost-free refrigerator and its geometric parameters have great influence on the aerodynamic performance of the whole system. Previous researches mainly focused on the effect of other components, such as the fan, shelves, or plate-evaporator. In this paper, the influence of the shroud with multi-outlets on the flow distributions of a frost-free refrigerator is studied thoroughly with the help of Computational Fluid Dynamics (CFD) tools. A 1/2 3-D CFD model is developed, where the verification of turbulence models and mesh independence tests are performed by comparing the mass flow rate obtained by different model configurations. The standard k-epsilon is deemed as the most suitable turbulence model choice and a mesh with Fine level is considered as mesh independence. To obtain the boundaries of the developed CFD model, an airflow velocity test rig is built and constructed. To convert the measured data to CFD model, Structural Response Vector (SRV) method is implemented for velocity profile fitting, and the fitted surface is assigned by User Defined Functions (UDF) macros in simulations. A series of simulations are carried out with the developed model, and the results indicates that no streamline in the middle two cavities of the original freezer compartment and the airflow velocities at the three outlets of the investigated shroud show a certain difference. To optimize the flow distribution, the agent model based on the BP neural network is established, in which four critical parameters of the shroud are adopted as design variables. The results show that the velocity streamlines in the middle two cavities are significantly increased after optimization and the value of the mean square error model constructed in optimization has a reduction of 61.09% compared with the original design.

3D CFD Modeling and Optimization of a Cylindrical Porous Bed Reactor for Hydrogen Production using Steam Reforming of Methane

Steam Reforming of Methane, which converts natural gas into products with higher economic value in the presence of a suitable catalyst bed reformer, is the most economical method for hydrogen production in petroleum refineries. This study focuses on developing a Computational Fluid Dynamics (CFD) model of a steam methane reformer. To this purpose, a steady-state heterogeneous 3 Dimensional model that was composed of mass, species, momentum, and energy balances was developed. It compares two different geometrical porous bed reformers with different heating tube configurations for better heat transfer and reforming. Effects of heating tubes inlet temperature, the ratio of inlet CH4/H2O, and the configuration of the heating tube are studied and optimized. The results show that conversion of methane will be promoted by increasing inlet temperature of the heating tube as well as the number of heating tubes in the reformer when CH4/H2O ratio is about 0.2. In this platform, the conversion of methane is not affected by the porosity below 0.35. Also, the simulations results are shown to be in agreement with typical data reported in the literature. So, this study can be used to develop industrial natural gas reformers.

CFD simulation design and optimization of a novel zigzag wave-plate mist eliminator with perforated plate

A zigzag wave-plate mist eliminator with an internally improved structure of perforated plate was proposed in this work, which applied in particle removal process for lime-stone wet flue desulfurization (WFGD) system. Its performance was evaluated by Computational Fluid Dynamics (CFD) methods with three criteria, overall collection efficiency, system pressure drop, and impact factor. The realizable k-ε turbulence model and Eulerian-Lagrangian methodology were adopted to describe the motion of the continuous phase and dispersed phase. The CFD results showed that the main factors affecting the performance of perforated plate mist eliminator are the number of perforated plates N, the perforated plate interval spacing I, the hole diameter d1, the distance between two holes d2, porosity ε, installation angle α, and the height of the perforated plate h. Compared with the wave-plate mist eliminator with porous foam layers, the mist eliminator with perforated plate is found to have better performance in particle removal. The overall particle collection efficiency is increased by more than 2.3%, the pressure drop is reduced by more than 27.4% while the porosity of the perforated plate is small. When ε is large, the particle collection efficiency of the porous plate with large porosity decreased by 0.3–1.2%, the pressure drop was reduced by more than 34.7%. Besides, it has a simple structure and can be processed by integrated technology. All these advantages make the perforated plate mist eliminator a promising candidate for fine particle removal in the WFGD system.

Scalable gradient–enhanced artificial neural networks for airfoil shape design in the subsonic and transonic regimes

Airfoil shape design is one of the most fundamental elements in aircraft design. Existing airfoil design tools require at least a few minutes to analyze a new shape and hours to perform shape optimization. To drastically reduce the computational time of both analysis and design optimization, we use machine learning to create a model of a wide range of possible airfoils at a range of flight conditions, making it possible to perform airfoil design optimization in a few seconds. The machine learning consists of gradient-enhanced artificial neural networks where the gradient information is phased in gradually. This new gradient-enhanced artificial neural network approach is trained to model the aerodynamic force coefficients of airfoils in both subsonic and transonic regimes. The aerodynamics is modeled with Reynolds-averaged Navier–Stokes (RANS)-based computational fluid dynamics (CFD). The proposed approach outperforms an existing airfoil model that uses a mixture of experts technique combined with a gradient-based kriging surrogate model. The approach yields to similar airfoil shape optimization solutions than high-fidelity CFD optimization solutions with a difference of 0.01 count and 0.12 count for Cd in subsonic and transonic regimes, respectively. Airfoil optimization problems are solved in a few seconds (instead of hours using CFD-based optimization), making the design process much more interactive, as demonstrated in the Webfoil airfoil design optimization tool.

CFD optimization of return air ratio and use of upper room UVGI in combined HVAC and heat recovery system

Several strategies and techniques have been recently developed to decrease energy consumption in heating, ventilation, and air-conditioning (HVAC) systems. One method is to recirculate a fraction of return air and make use of the remaining part (exhaust) to preheat fresh air in order to reduce the heating load. However, limitations must be imposed to the return fraction to maintain acceptable indoor air quality (IAQ).The aim of this paper is to develop a computational fluid dynamics (CFD) model to predict the dispersion of CO2 and airborne bacteria in all-air HVAC systems with aforementioned heat recovery system for known return air ratios as well as the disinfection of indoor air by upper room Ultraviolet Germicidal Irradiation (UVGI).The developed model can be put to practical use for estimating the maximum allowable return that minimizes energy cost of the integrated system while maintaining acceptable IAQ. It can also be used to predict the minimum required UV output to eliminate any excess in bacteria count in the breathing zone without the need for additional fresh air intake at the supply.

Optimized design of an autonomous underwater vehicle, for exploration in the Caribbean Sea

Abstract The needing to explore the depths of the sea is currently increasing. However, human exploration can be compromised due to dangers that divers face at great depths. The Autonomous Underwater Vehicle (AUV) has allowed underwater research to make a great stride and avoid human accidents. In such situation, designing an optimized AUV's hull is essential. To maximize periods of operation, AUV's hull must be outfitted to withstand harsh environmental conditions and minimize hydrodynamic drag. In this paper, a variant of torpedo body is proposed, with four Degrees Of Freedom (DoF) and capable of exploring the Caribbean Sea at great depths. Computational Fluid Dynamics (CFD) analysis and empirical methods are used to investigate the hydrodynamic resistance of the designed hull and estimate the drag, employing an optimization process and proving the effectiveness of the optimization algorithm with the profile efficiency analysis. The results demonstrate that CFD optimization methods have an influence on drag estimation. Furthermore, this paper shows that main factors impact the AUV design are the pressure on its body and the hydrodynamic drag. With results obtained based on CFD and empirical estimations, it was possible to achieve optimal parameters concerning the protection of internal hardware, the buoyancy of the vehicle and minimum drag to reach optimal energy consumption. Finally, the variation made to the typical torpedo body does not increase the drag of the vehicle.

Novel trends in modelling techniques of Pelton Turbine bucket for increased renewable energy production

Pelton turbines have been used for harvesting clean energy from water jet for over 100 years. The wide range of their applicability and the robustness with minimal onsite monitoring and carbon free energy production makes them one of the most desired hydro turbines for renewable energy production. Pelton turbine buckets are subject to complex turbulent multiphase flows with free surfaces, thus received lot of attention from Computational Fluid Dynamics (CFD) researchers to visualise the flow patterns. In addition, the bucket geometry optimization has been a prevalent research stream using analytical and graphical methods. Both of these investigations fields have resulted in significant improvement in the performance of the Pelton Turbine system. However, the design investigations for each feature are carried out independently due to the complexity in incorporating large number of design parameters. Thus, analysing the complex fluid flow on each pre-optimized design was rather challenging due to expensive computational needs of CFD and limited manufacturing possibilities. Today, development of accurate and inexpensive CFD models and innovative manufacturing technology such as rapid prototyping has made complex freeform shape possible to simulate and manufacture. Hence, any bucket designs disregarded in optimization on the basis of manufacturing feasibility can now be possible to manufacture and is worth investigating. This will require the combination of CFD and design optimization field of investigation with novel analysis approach. This paper examines these fields of studies with the view to establish the background for such novel approach. This approach could be a foundation for design optimization of turbomachinery or any reaction surface leading to increased production of renewable and sustainable energy from existing resources.

CFD Optimization Process of a Lateral Inlet/Outlet Diffusion Part of a Pumped Hydroelectric Storage Based on Optimal Surrogate Models

The lateral inlet/outlet plays a critical role in the connecting tunnels of a water delivery system in a pumped hydroelectric storage (PHES). Therefore, the shape of the inlet/outlet was improved through computational fluid dynamics (CFD) optimization based on optimal surrogate models. The CFD method applied in this paper was validated by a physical experiment that was carefully designed to meet bidirectional flow requirements. To determine a good compromise between the generation and pump mode, reasonable weights were defined to better evaluate the overall performance. In order to find suitable surrogate models to improve the optimization process, the best suited surrogate models were identified by an optimal model selection method. The optimal configurations of the surrogate model for the head loss and the velocity distribution coefficient were the Kriging model with a Gaussian kernel and the Kriging model with an Exponential kernel, respectively. Finally, a multi-objective surrogate-based optimization method was used to determine the optimum design. The overall head loss coefficient and velocity distribution coefficients were 0.248 and 1.416. Compared with the original shape, the coefficients decrease by 6.42% and 40.28%, respectively. The methods and findings of this work may provide practical guidelines for designers and researchers.

CFD Simulation and Optimization of Gas-Solid Phase Temperature of Isothermal Acetylene Hydrogenation Reactor

Abstract Gas-solid coupled heat transfer in an industrial isothermal acetylene hydrogenation reactor was carried out using computational fluid dynamics (CFD). A two-temperature porous medium model was established by adding source terms to energy equations of the solid and gas phases. The combination of a genetic algorithm with CFD methods is applied to optimization of the kinetic and process parameters of the reaction. The model was validated by comparing the simulated results with those obtained from a one-temperature porous medium model, a two-temperature porous medium model, and industrial data. The optimal hydrogen-to-acetylene ratio and inlet temperature are 1.78 and 326K, respectively. The optimized ethylene yield increase and hydrogenation selectivity are 0.53 % and 0.18 % higher than the values before optimization, respectively. Finally, the effects of the hydrogen-to-acetylene ratio and inlet temperature on the increase in ethylene yield and hydrogenation selectivity are analyzed. Therefore, the hydrogen-to-acetylene ratio and inlet temperature should be reasonably controlled during production.