Optimization of a Two-Stage Transonic Axial Fan to Enhance Aerodynamic Stability

2016 ◽  
Author(s):  
Sang-Bum Ma ◽  
Arshad Afzal ◽  
Kwang-Yong Kim ◽  
Jaeho Choi ◽  
Wonsuk Lee
Keyword(s):  
Rotational Speed ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Latin Hypercube Sampling ◽  
Objective Functions ◽  
Axial Fan ◽  
Aerodynamic Stability ◽  
Stall Margin ◽  
Multi Objective ◽  
Design Variables

In this paper, a multi-objective optimization of a transonic axial fan to enhance aerodynamic stability has been conducted using three-dimensional Reynolds-Averaged Navier-Stokes equations, surrogate modeling and multi-objective genetic algorithm (MOGA). Hub radius and first rotor chord length of the axial fan were chosen as design variables for the optimization. Peak adiabatic efficiency of the axial fan and stall margin at 60% of the designed rotational speed, were used as objective functions. Latin Hypercube Sampling (LHS) method was used to select design points in the design space. The objective functions were formulated using the response surface approximation (RSA) model. Three LHS samples with different distributions of twelve design points were tested to study their effects on prediction accuracy of the RSA model and optimization results. MOGA with the RSA models based on the best LHS sample, was used to obtain the Pareto-optimal front. As a result of optimization, an improvement of 17.2% in the stall margin at 60% of the designed rotational speed and 2.96% in peak adiabatic efficiency were obtained compared to the reference design. It was also found that distribution of the design points generated by LHS affects the effectiveness of the surrogate-based optimization.

Design Optimization of a Centrifugal Fan with Splitter Blades

2015 ◽  
Vol 32 (2) ◽  
Author(s):  
Man-Woong Heo ◽  
Jin-Hyuk Kim ◽  
Kwang-Yong Kim
Keyword(s):  
Stokes Equations ◽  
Reference Model ◽  
Three Dimensional ◽  
Pressure Rise ◽  
Latin Hypercube Sampling ◽  
Aerodynamic Characteristics ◽  
Navier Stokes ◽  
Centrifugal Fan ◽  
Multi Objective ◽  
Design Variables

AbstractMulti-objective optimization of a centrifugal fan with additionally installed splitter blades was performed to simultaneously maximize the efficiency and pressure rise using three-dimensional Reynolds-averaged Navier-Stokes equations and hybrid multi-objective evolutionary algorithm. Two design variables defining the location of splitter, and the height ratio between inlet and outlet of impeller were selected for the optimization. In addition, the aerodynamic characteristics of the centrifugal fan were investigated with the variation of design variables in the design space. Latin hypercube sampling was used to select the training points, and response surface approximation models were constructed as surrogate models of the objective functions. With the optimization, both the efficiency and pressure rise of the centrifugal fan with splitter blades were improved considerably compared to the reference model.

Design Optimization of Circumferential Casing Grooves for a Transonic Axial Compressor to Enhance Stall Margin

2010 ◽  
Author(s):  
Kwang-Jin Choi ◽  
Jin-Hyuk Kim ◽  
Kwang-Yong Kim
Keyword(s):  
Design Optimization ◽  
Numerical Solutions ◽  
Stokes Equations ◽  
Pressure Ratio ◽  
Three Dimensional ◽  
Axial Compressor ◽  
Latin Hypercube Sampling ◽  
Optimum Shape ◽  
Data Set ◽  
Stall Margin

This paper presents a design optimization of an axial compressor with NASA Rotor 37 and five circumferential casing grooves for enhancement of stall margin. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results is performed in comparison with experimental data for pressure ratio and adiabatic efficiency. The Latin-hypercube sampling as design-of-experiments is used to generate the twelve design points within the design space. A stall margin parameter is considered as an objective function with two design variables defining the geometry of the circumferential casing grooves. The radial basis neural network method employed as a surrogate model for the design optimization of the circumferential casing grooves is trained on the numerical solutions by carrying out leave-one-out cross-validation for the data set. The results show that the stall margin of the optimum shape is enhanced considerably by the design optimization compared to the cases with smooth casing and the reference grooves.

Multi-Objective Optimization of Inclined Discrete Cavities in a Centrifugal Compressor Using Hybrid Optimization Techniques

2018 ◽  
Author(s):  
Sang-Bum Ma ◽  
Kwang-Yong Kim
Keyword(s):  
Centrifugal Compressor ◽  
Parametric Study ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Leading Edge ◽  
Latin Hypercube Sampling ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Geometrical Parameters ◽  
Objective Functions

In order to extend the operating range of a centrifugal compressor, inclined discrete cavities located upstream of the impeller leading edge were optimized in this work. Aerodynamic performance analysis was performed using three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model. A parametric study on aerodynamic performances of the centrifugal compressor with the inclined discrete cavities was conducted with six geometrical parameters. Through the parametric study, three geometric parameters were selected as design variables for optimization. Peak adiabatic efficiency and stall margin were selected as objective functions. The Latin hypercube sampling method was used to select the design points, and the radial basis neural network was used to construct surrogate models of the objective functions. A hybrid method combining the particle swarm optimization showed better overall performance in finding global optimum than the genetic algorithm. Pareto-optimal solutions provided the designs which enhance considerably both the performance parameters compared to the reference design.

Multi-Objective Shape Optimization on the Inlet Pipe of a Vertical Inline Pump

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Ji Pei ◽  
Xingcheng Gan ◽  
Wenjie Wang ◽  
Shouqi Yuan ◽  
Yajing Tang
Keyword(s):  
Genetic Algorithm ◽  
Cross Sections ◽  
Great Influence ◽  
Latin Hypercube Sampling ◽  
Installation Space ◽  
Objective Functions ◽  
Inlet Pipe ◽  
Maximum Increase ◽  
Multi Objective ◽  
Design Variables

Vertical inline pump is a single-stage single-suction centrifugal pump with a bent pipe before the impeller, which is usually used where installation space is a constraint. In this paper, with three objective functions of efficiencies at 0.5 Qd, 1.0 Qd, and 1.5 Qd, a multi-objective optimization on the inlet pipe of a vertical inline pump was proposed based on genetic algorithm with artificial neural network (ANN). In order to describe the shape of inlet pipe, the fifth-order and third-order Bezier curves were adopted to fit the mid curve and the trend of parameters of cross sections, respectively. Considering the real installation and computation complexity, 11 variables were finally used in this optimization. Latin hypercube sampling (LHS) was adopted to generate 149 sample cases, which were solved by CFD code ANSYS cfx 18.0. The calculation results and design variables were utilized to train ANNs, and these surrogate models were solved for the optimum design using multi-objective genetic algorithm (MOGA). The results showed the following: (1) There was a great agreement between numerical results and experimental results; (2) The ANNs could accurately fit the objective functions and variables. The maximum deviations of efficiencies at 0.5 Qd, 1.0 Qd, and 1.5 Qd, between predicted values and computational values, were 1.94%, 2.35%, and 0.40%; (3) The shape of inlet pipe has great influence on the efficiency at part-load and design conditions while the influence is slight at overload condition; (4) Three optimized cases were selected and the maximum increase of the efficiency at 0.5 Qd, 1.0 Qd, and 1.5 Qd was 4.96%, 2.45, and 0.79%, respectively; and (5) The velocity distributions of outflow in the inlet pipe of the three optimized cases were more uniform than the original one.

scholarly journals Hydrodynamic Optimization for Design of a Submersible Axial-Flow Pump with a Swept Impeller

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3053
Author(s):  
Youn-Sung Kim ◽  
Man-Woong Heo ◽  
Hyeon-Seok Shim ◽  
Bong-Soo Lee ◽  
Dong-Hwan Kim ◽  
...  
Keyword(s):  
Performance Test ◽  
Stokes Equations ◽  
Three Dimensional ◽  
Axial Flow ◽  
Grid System ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Axial Flow Pump ◽  
Flow Pump

Submersible pumps are now in high demand due to the sporadic occurrence of recent torrential rains. The current study was carried out to investigate the hydraulic characteristics of a submersible axial-flow pump with a swept impeller and to optimize the impeller and diffuser shapes of the pump to enhance the hydraulic performance. Three-dimensional Reynolds-averaged Navier–Stokes equations were solved with the shear stress transport turbulence model. The governing equations were discretized using the finite volume method, and unstructured tetrahedral and hexahedral meshes were used in the grid system. The optimal grid system was selected through a grid dependency test. A performance test for the submersible axial-flow pump was carried out experimentally, and the results of the numerical analysis were validated against the experimental results. The hydraulic efficiency and the total head were used as objective functions. For the first optimization, a multi-objective optimization was carried out to simultaneously improve the objective functions through a hybrid multi-objective evolutionary algorithm coupled with a response surface approximation by varying the swept angle and pitch angle of the blades of the rotating impeller. The second multi-objective optimization was performed using two design variables, i.e., the inlet angle and the length of the diffuser vanes, to simultaneously increase the objective functions. Clustered optimum designs in the Pareto optimal solutions yielded significant increases in the objective function values as compared with the reference design.

scholarly journals Optimized Blade Design of Counter-Rotating-Type Pump-Turbine Unit Operating in Pump and Turbine Modes

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Jin-Woo Kim ◽  
Jun-Won Suh ◽  
Young-Seok Choi ◽  
Kyoung-Yong Lee ◽  
Toshiaki Kanemoto ◽  
...  
Keyword(s):  
Flow Rate ◽  
Turbine Unit ◽  
Stokes Equations ◽  
Flow Analysis ◽  
Three Dimensional ◽  
Latin Hypercube Sampling ◽  
Low Flow ◽  
Pump Turbine ◽  
Design Variables ◽  
Turbine Mode

In this study, a counter-rotating-type pump-turbine unit was optimized to improve the pump and turbine mode efficiencies simultaneously. Numerical analysis was carried out by solving three-dimensional Reynolds-averaged Navier–Stokes equations using the shear stress turbulence model. The hub and tip blade angles of the rear impeller (in the pump mode) were selected as the design variables by conducting a sensitivity test. An optimization process based on steady flow analysis was conducted using a radial basis neural network surrogate model with Latin hypercube sampling. The pump and turbine mode efficiencies of the unit were selected as the objective functions and they combined into a single specific objective function with the weighting factors. Consequently, the pump and turbine mode efficiencies of the optimum design increased simultaneously at overall range of flow rate, except for low flow rate of turbine mode, compared to the reference design.

Multi-Objective Design Problem of Tire Wear and Visualization of Its Pareto Solutions2

2006 ◽  
Vol 34 (3) ◽  
pp. 170-194 ◽  
Author(s):  
M. Koishi ◽  
Z. Shida
Keyword(s):  
Inverse Problems ◽  
Conceptual Design ◽  
Dimensional Space ◽  
Design Stage ◽  
Objective Functions ◽  
Pareto Solutions ◽  
Design Problems ◽  
Multi Objective ◽  
Design Engineers ◽  
Design Variables

Abstract Since tires carry out many functions and many of them have tradeoffs, it is important to find the combination of design variables that satisfy well-balanced performance in conceptual design stage. To find a good design of tires is to solve the multi-objective design problems, i.e., inverse problems. However, due to the lack of suitable solution techniques, such problems are converted into a single-objective optimization problem before being solved. Therefore, it is difficult to find the Pareto solutions of multi-objective design problems of tires. Recently, multi-objective evolutionary algorithms have become popular in many fields to find the Pareto solutions. In this paper, we propose a design procedure to solve multi-objective design problems as the comprehensive solver of inverse problems. At first, a multi-objective genetic algorithm (MOGA) is employed to find the Pareto solutions of tire performance, which are in multi-dimensional space of objective functions. Response surface method is also used to evaluate objective functions in the optimization process and can reduce CPU time dramatically. In addition, a self-organizing map (SOM) proposed by Kohonen is used to map Pareto solutions from high-dimensional objective space onto two-dimensional space. Using SOM, design engineers see easily the Pareto solutions of tire performance and can find suitable design plans. The SOM can be considered as an inverse function that defines the relation between Pareto solutions and design variables. To demonstrate the procedure, tire tread design is conducted. The objective of design is to improve uneven wear and wear life for both the front tire and the rear tire of a passenger car. Wear performance is evaluated by finite element analysis (FEA). Response surface is obtained by the design of experiments and FEA. Using both MOGA and SOM, we obtain a map of Pareto solutions. We can find suitable design plans that satisfy well-balanced performance on the map called “multi-performance map.” It helps tire design engineers to make their decision in conceptual design stage.

Shape Optimization of Forward-Curved-Blade Centrifugal Fan with Navier-Stokes Analysis

2004 ◽  
Vol 126 (5) ◽  
pp. 735-742 ◽  
Author(s):  
Kwang-Yong Kim ◽  
Seoung-Jin Seo
Keyword(s):  
Response Surface ◽  
Stokes Equations ◽  
Computing Time ◽  
Relative Size ◽  
Three Dimensional ◽  
Navier Stokes ◽  
Centrifugal Fan ◽  
Navier Stokes Equations ◽  
Design Variables ◽  
Curved Blade

In this paper, the response surface method using a three-dimensional Navier-Stokes analysis to optimize the shape of a forward-curved-blade centrifugal fan is described. For the numerical analysis, Reynolds-averaged Navier-Stokes equations with the standard k-ε turbulence model are discretized with finite volume approximations. The SIMPLEC algorithm is used as a velocity–pressure correction procedure. In order to reduce the huge computing time due to a large number of blades in forward-curved-blade centrifugal fan, the flow inside of the fan is regarded as steady flow by introducing the impeller force models. Four design variables, i.e., location of cutoff, radius of cutoff, expansion angle of scroll, and width of impeller, were selected to optimize the shapes of scroll and blades. Data points for response evaluations were selected by D-optimal design, and a linear programming method was used for the optimization on the response surface. As a main result of the optimization, the efficiency was successfully improved. Effects of the relative size of the inactive zone at the exit of impeller and momentum fluxes of the flow in scroll on efficiency were further discussed. It was found that the optimization process provides a reliable design of this kind of fan with reasonable computing time.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

Multi-Objective Design Optimization of a Mixed-Flow Pump

2009 ◽  
Author(s):  
Yumiko Takayama ◽  
Hiroyoshi Watanabe
Keyword(s):  
Performance Characteristic ◽  
Performance Characteristics ◽  
Inverse Design ◽  
Surface Model ◽  
Objective Functions ◽  
Mixed Flow ◽  
Multi Objective ◽  
Design Variables ◽  
Flow Head ◽  
Flow Pump

In most cases of high specific speed mixed-flow pump applications, it is necessary to satisfy more than one performance characteristic such as deign point efficiency, shut-off power/head and non-stall characteristic (no positive slope in flow-head curve). However, it is known that these performance characteristics are in relation of trade-offs. As a result, it is difficult to optimize these performance characteristics by conventional way such as trial and error approach by modifying geometrical parameters. This paper presents the results of the multi-objective optimization strategy of mixed-flow pump design by means of three dimensional inverse design approach, Computational Fluid Dynamics (CFD), Design of Experiments (DoE), response surface model (RSM) and Multi Objective Genetic Algorism (MOGA). The parameters to control blade loading distributions and meridional geometries for impeller and diffuser blades in inverse design were chosen as design variables of the optimization process. Pump efficiency, maximum slope in flow-head curve and shut-off power/head were selected as objective functions. Objective functions of pumps, designed by design variables specified in DoE, were evaluated by using CFD. Then, trade-off relations between objective functions were analyzed by using Pareto fronts obtained by MOGA. Some pumps which have specific performance characteristic (non-stall, low shut-off power, high efficiency etc.) designed along the Pareto front were numerically evaluated.

Sign in / Sign up

Export Citation Format

Share Document