Design of Industrial Axial Compressor Blade Sections for Optimal Range and Performance

2004 ◽  
Vol 126 (2) ◽  
pp. 323-331 ◽  
Author(s):  
Frank Sieverding ◽  
Beat Ribi ◽  
Michael Casey ◽  
Michael Meyer
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbines ◽  
Fitness Function ◽  
Optimization Technique ◽  
Axial Compressor ◽  
Axial Flow ◽  
High Volume ◽  
Volume Flow ◽  
Design System ◽  
Operating Range

Background: The blade sections of industrial axial flow compressors require a wider range from surge to choke than typical gas turbine compressors in order to meet the high volume flow range requirements of the plant in which they operate. While in the past conventional blade profiles (NACA65 or C4 profiles) at moderate Mach number have mostly been used, recent well-documented experience in axial compressor design for gas turbines suggests that peak efficiency improvements and considerable enlargement of volume flow range can be achieved by the use of so-called prescribed velocity distribution (PVD) or controlled diffusion (CD) airfoils. Method of approach: The method combines a parametric geometry definition method, a powerful blade-to-blade flow solver and an optimization technique (breeder genetic algorithm) with an appropriate fitness function. Particular effort has been devoted to the design of the fitness function for this application which includes non-dimensional terms related to the required performance at design and off-design operating points. It has been found that essential aspects of the design (such as the required flow turning, or mechanical constraints) should not be part of the fitness function, but need to be treated as so-called “killer” criteria in the genetic algorithm. Finally, it has been found worthwhile to examine the effect of the weighting factors of the fitness function to identify how these affect the performance of the sections. Results: The system has been tested on the design of a repeating stage for the middle stages of an industrial axial compressor. The resulting profiles show an increased operating range compared to an earlier design using NACA65 profiles. Conclusions: A design system for the blade sections of industrial axial compressors has been developed. Three-dimensional CFD simulations and experimental measurements demonstrate the effectiveness of the new profiles with respect to the operating range.

Design of Industrial Axial Compressor Blade Sections for Optimal Range and Performance

2003 ◽  
Author(s):  
Frank Sieverding ◽  
Beat Ribi ◽  
Michael Casey ◽  
Michael Meyer
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Axial Compressor ◽  
Design System ◽  
Flow Solver ◽  
Axial Compressors ◽  
Mechanical Constraints ◽  
And Performance ◽  
Operating Points

A design system for the blade sections of industrial axial compressors has been developed. The method combines a parametric geometry definition method, a powerful blade-to-blade flow solver (MISES) and an optimization technique (breeder genetic algorithm) with an appropriate fitness function. Particular effort has been devoted to the design of the fitness function for this application which includes non-dimensional terms related to the required performance at design and off-design operating points. It has been found that essential aspects of the design (such as the required flow turning, or mechanical constraints) should not be part of the fitness function, but need to be treated as so-called “killer” criteria in the genetic algorithm. Finally, it has been found worthwhile to examine the effect of the weighting factors of the fitness function to identify how these affect the performance of the sections. The system has been tested on the design of a repeating stage for the middle stages of an industrial axial compressor. The resulting profiles show an increased operating range compared to an earlier design using NACA65 profiles.

Research on Optimization of the Design(Inverse) Issue of S2 Surface of Axial Compressor Based on Genetic Algorithm

2020 ◽  
Author(s):  
Zijing Chen ◽  
Bo Liu ◽  
Xiaoxiong Wu
Keyword(s):  
Genetic Algorithm ◽  
Total Pressure ◽  
Fitness Function ◽  
Pressure Ratio ◽  
Axial Compressor ◽  
Local Data ◽  
Real Coded Genetic Algorithm ◽  
Overall Efficiency ◽  
Total Pressure Ratio ◽  
Peak Value

Abstract In order to further improve the effectiveness of design(inverse) issue of S2 surface of axial compressor, a design method of optimization model based on real-coded genetic algorithm is instructed, with a detailed description of some important points such as the population setting, the fitness function design and the implementation of genetic operator. The method mainly takes the pressure ratio, the circulation as the optimization variables, the total pressure ratio and the overall efficiency of the compressor as the constraint condition and the decreasing of the diffusion factor of the compressor as the optimization target. In addition, for the propose of controlling the peak value of some local data after the optimization, a local optimization strategy is proposed to make the method achieve better results. In the optimization, the streamline curvature method is used to perform the iterative calculation of the aerodynamic parameters of the S2 flow surface, and the polynomial fitting method is used to optimize the dimensionality of the variables. The optimization result of a type of ten-stage axial compressor shows that the pressure ratio and circulation parameters have significant effect on the diffusion factor’s distribution, especially for the rotor pressure ratio. Through the optimization, the smoothness of the mass-average pressure ratio distribution curve of the rotors at all stages of the compressor is improved. The maximum diffusion factors in spanwise of rotor rows at the first, fifth and tenth stage of the compressor are reduced by 1.46%, 12.53% and 8.67%, respectively. Excluding the two calculation points at the root and tip of the blade because of the peak value, the average diffusion factors in spanwise are reduced by 1.28%, 3.46%, and 1.50%, respectively. For the two main constraints, the changes of the total pressure ratio and overall efficiency are less than 0.03% and 0.032%, respectively. In the end, a 3-d CFD numerical result is given to testify the effects of the optimization, which shows that the loss in the compressor is decreased by the optimization algorithm.

CFD Aerodynamic Investigation of Air-Water Trajectories on Rotor-Stator Blade of an Axial Compressor for Online Washing

2006 ◽  
Author(s):  
Zainol Mustafa ◽  
Pericles Pilidis ◽  
Joao A. Amaral Teixeira ◽  
Kamarul Arifin Ahmad
Keyword(s):  
Flow Pattern ◽  
Gas Turbines ◽  
Initial Conditions ◽  
Axial Compressor ◽  
Axial Flow ◽  
Droplet Size Distribution ◽  
Numerical Approach ◽  
Realistic Model ◽  
Washing Process ◽  
Droplet Sizes

In multistage axial-flow compressors, the droplet size distribution of the injected fluid depends upon the entry conditions. This paper presents the numerical approach used in the development of a preliminary analytical tool to investigate droplet flow pattern during fluid injection into an axial compressor. An investigation was carried out using the commercial CFD code CFX-TASCflow on the aerodynamics of an axial-flow compressor designed for operation with air while operating with an air-fluid droplet mixture during online washing. A test matrix involving different initial droplet diameters was investigated with the machine rotating at constant shaft speed. The range of droplet sizes used in these simulations is representative of the droplet dimensions used during online washing. The liquid employed in this study was water. The main objective of the investigation is to gain added physical insight into the washing process, through the assembly of a realistic model, describing the movement of liquid phase in an industrial axial compressor. The results are presented to show how varying droplet sizes affect the flow pattern of a representative 260MW Gas Turbines axial compressor during online washing. Furthermore, results are presented for the amount of water collected by the blades due to these tests as well as the quantity of water evaporated. This study shows that the droplet dispersion and flow pattern inside the compressor is strongly influenced by the initial conditions of the droplets during online compressor washing.

Research on GA-ANN Integration and Its Applications to Cold Extrusion Process Design

2006 ◽  
Vol 532-533 ◽  
pp. 897-900
Author(s):  
Ting Wei Ji ◽  
Jun Gao ◽  
Guo Qun Zhao ◽  
Cheng Rui Zhang
Keyword(s):  
Genetic Algorithm ◽  
Process Design ◽  
Binary Code ◽  
Fitness Function ◽  
Extrusion Process ◽  
Selection Method ◽  
Cold Extrusion ◽  
Design System ◽  
Roulette Wheel Selection ◽  
Roulette Wheel

Based on the research of the functions of ANN-based cold extrusion process design system, genetic algorithm (GA) is proposed to optimize the topology and parameters of artificial neural networks (ANN), in order to improve the running efficiency of the networks. The binary encoding approach is implemented to represent the GA chromosome. The code string or the chromosome was divided into three parts: the first part is the binary code of the cold extruded part; the second part is the binary code of the topology and parameters of ANN; the last is the binary code of the semi-cold-extruded-part or the billet. The 1/F(X) function is selected as the fitness function in GA, where, X represents the binary code of the cold extruded part, F(X) represents the error between the real outputs of ANN and the desired results; the biased roulette wheel selection method is used for selecting operation in this paper; two-point crossover and one-point mutation are selected for these two types of genetic operations. Finally, the typical cold extruded part is used for verification as an example by using the optimized ANN, the result shows that ANN optimized by GA has efficiency and validity in the cold extrusion process design system.

scholarly journals Optimization of Biofilter Size for Aquaponics Using Genetic Algorithm

2021 ◽  
Vol 25 (5) ◽  
pp. 632-638
Author(s):  
Amir A. Bracino ◽  
Jason L. Española ◽  
Argel A. Bandala ◽  
Elmer P. Dadios ◽  
Edwin Sybingco ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Loading Rate ◽  
Fitness Function ◽  
Optimization Technique ◽  
Water Flow Rate ◽  
Nitrifying Bacteria ◽  
Design Parameters ◽  
Heuristic Solution ◽  
Hydraulic Loading Rate ◽  
Hydraulic Loading

Unlike a media-filled aquaponic system, the nutrient film technique (NFT) and deep water culture (DWC) require the installation of an external biofilter to provide sufficient area for nitrifying bacteria colonization, which is essential for the conversion of toxic ammonia from fish waste into nitrate that is easily assimilated by plants. Given the importance of biofilters, it is imperative to properly design this tank to effectively support the nitrification process. Several factors need to be considered for the biofilter design. Thus, an optimization algorithm can be used to obtain combinations of the design parameters. The genetic algorithm (GA) is a heuristic solution search or optimization technique based on the Darwinian principle of genetic selection. The main goal of this study was to obtain the optimal biofilter size for a given fishpond volume and the amount of ammonia to be treated. The conversion coefficient in the Michaelis–Menten equation was used as the fitness function in this study. The parameters optimized using GA include the hydraulic loading rate, height of the biofilter, and predicted ammonia concentration. For the given assumption of a 60 kg feed introduced to the system and a 1500 L fishpond, the hydraulic loading rate, biofilter height, and final concentration of ammonia were 0.17437 m, 0.58585 m, and 0.01026 ppm, respectively. Using the values obtained from running the GA, the optimum biofilter volume for the system was 0.4608 m3, whereas the water flow rate was 0.03 L/min. For recommendations, multiple objective GAs can be used to add cost-related variables in the optimization because they have not yet been considered in the computation.

scholarly journals Optimizing Software Modularity with Minimum Possible Variations

2018 ◽  
Vol 29 (1) ◽  
pp. 1135-1150
Author(s):  
Amarjeet Prajapati ◽  
Jitender Kumar Chhabra
Keyword(s):  
Genetic Algorithm ◽  
Software Maintenance ◽  
Fitness Function ◽  
Optimization Technique ◽  
Quality Criteria ◽  
Optimization Techniques ◽  
Software Systems ◽  
Design Quality ◽  
Early Stages ◽  
Software Modularity

Abstract Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of quality; however, they require huge modifications in the existing modular structure to realize the suggested solution. Thus these techniques are costly and time consuming if applied at early stages of software maintenance. This paper proposes a search-based optimization technique to improve the modularity of the software system with minimum possible variation between the existing and produced modularization solution. To this contribution, a penalized fitness function, namely, penalized modularization quality, is designed in terms of modularization quality and the Move or Join Effectiveness Measure metric. Furthermore, this fitness function is used in both single-objective genetic algorithm (SGA) and multi-objective genetic algorithm (MGA) to generate the modularization. The effectiveness of the proposed remodularization approach is evaluated over five open-source and three random generated software systems. The experimentation results show that the proposed approach is able to generate modularization solutions with improved quality along with lesser perturbation compared to their non-penalty counterpart and at the same time it performs better with the MGA compared to the SGA. The proposed approach can be very useful, especially when total remodularization is not feasible/desirable due to lack of time or high cost.

Combined Effects of Forward Sweep and Tip Clearance on the Performance of Axial Flow Compressor Stage

2009 ◽  
Author(s):  
P. V. Ramakrishna ◽  
M. Govardhan
Keyword(s):  
Total Pressure ◽  
Axial Compressor ◽  
Axial Flow ◽  
Pressure Rise ◽  
Tip Clearance ◽  
Numerical Work ◽  
Low Speed ◽  
Operating Range ◽  
Axial Flow Compressor ◽  
Flow Compressor

There are a number of performance indices for a turbomachine on the basis of which its strength is evaluated. In the case of axial compressors, pressure ratio, efficiency and stall margin are few such indices which are of major concern in the design phase as well as in the evaluation of performance of the machine. In the process of improving the blade design, 3D blade stacking, where the aerofoil sections constituting the blade are moved in relation to the flow. Tilting the blade sections to the flow direction (blade sweep) would increase the operating range of an axial compressor due to modifications in the pressure and velocity fields on the suction surface. On the other hand, blade tip gap, though finite, has great influence on the performance of a turbomachine. The present work investigates the combined effect of these two factors on various flow characteristics in a low speed axial flow compressor. The objective of the present paper is thereby confined to study the collective effects of sweep and tip clearance without attempting to suggest an outright new design. In the present numerical work, the performance of Tip Chordline Sweeping (TCS) and Axial Sweeping (AXS) of low speed axial compressor rotor blades are studied. For this, 15 computational domains were modeled for five rotor sweep configurations and three different clearance levels for each rotor. Through the results, 20°AXS rotor is found to be distinctive among all the rotors with highest pressure rise, higher operating range and less tip clearance loss characteristics. TCS rotors produced improved total pressure rise at the low flow coefficients when the tip gap is increased. Hence there is a chance that an “optimum” tip gap exists for the TCS rotors in terms of total pressure coefficient and operating range, while AXS rotors are at their best with the minimum possible clearance.

Multi-Objective Optimization of Stacking Sequences for Laminated Composite Beams by Genetic Algorithm

2015 ◽  
Vol 729 ◽  
pp. 89-94
Author(s):  
Fatih Karaçam ◽  
Taner Timarci
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Optimization Technique ◽  
Orientation Angle ◽  
Laminated Composite ◽  
Beam Theory ◽  
Composite Beams ◽  
Function Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective

In this study, multi-objective optimization of stacking sequences for laminated composite composite beams is studied for simply supported boundary conditions. A unified three-degrees-of-freedom shear deformable beam theory is used for analytical solution and genetic algorithm is used as optimization technique. By use of two different parameters such as the deflection and frequency together in a pre-defined fitness function, optimization process is carried out in order to maximize the fitness function. Initially, the deflection, frequency, fitness function values and corresponding stacking sequences are presented for various number of layers and increment of fiber orientation angle. The variation of the fitness function with respect to deflection and frequency depending on the number of generations are presented.

Design of Fractional Order PID Controller Using Genetic Algorithm Optimization Technique for Nonlinear System

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Shivanky Jaiswal ◽  
Chiluka Suresh Kumar ◽  
Murali Mohan Seepana ◽  
G. Uday Bhaskar Babu
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear System ◽  
Fractional Order ◽  
Pid Controller ◽  
Fitness Function ◽  
Optimization Technique ◽  
Algorithm Optimization ◽  
Simulation Results ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

AbstractIn this paper, fractional order PID controller, as well as integer order PID controller, is designed for non-linear system to enhance the system’s performance and gain the stability. The novelty of the work is achieved by the development of a new methodology for integer order PID and fractional order PID control tuning by optimizing the parameters of controllers using the Genetic Algorithms optimization technique. The performance of any system mainly depends upon how efficiently the controller will be working and hence that’s how most crucial part of the designing of FOPID controller or any controller is the tuning of its parameters. The uniquely designed and tuned parameters of the FOPID controller which is obtained by optimizing all the five parameters by using an evolutionary algorithm optimization technique i. e. a genetic algorithm which is a very powerful search tool and carrying heuristic characteristics. This method of tuning the FOPID controller which is designed and has been applied over the conical tank (nonlinear) system. The most important step in applying genetic algorithm is the selection of the fitness function and hence Integral of time multiplied by absolute error (ITAE) have been used here as the fitness function. Each chromosome comprised of all the five parameters of FOPID controller, which have been further optimised using above mentioned fitness function. From the simulation results, it can be observed that the solutions which are obtained optimally, presents an excellent performance for the system studied, by improving the behaviour of the system satisfactorily. Simulation results also show that the proposed FOPID controller gives improved performance over classical PID controller in terms of IAE and TV.

Genetic Algorithm Based Optimization Technique for Savonius-Style Wind Turbine

2021 ◽  
Author(s):  
Diplina Paul ◽  
Abhisek Banerjee
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Flow Field ◽  
Wind Turbine ◽  
Fitness Function ◽  
Optimization Technique ◽  
Coefficient Of Performance ◽  
Design Parameters ◽  
Speed Ratio ◽  
Two Dimensional

Abstract In this article, authors have studied genetic algorithm-based optimization technique to optimize rotor profile for elliptic shaped Savonius-style wind turbine with an aim to maximize the coefficient of performance. Genetic algorithm has been used to optimize design variables having distinct values and discontinuous and nondifferentiable objective functions. Optimization procedure using genetic algorithm uses the following steps: initialization, assessment, assortment, crossover and lastly alteration. Once the genetic algorithm is initialized, then the evaluation process trails, where each parametric value is evaluated based on the fitness function stated as objective function. Then the GA operators i.e assortment, cross over and alteration are applied. At the end of GA operation procedure, a new set of values of design parameter is generated. This procedure is endlessly iterated until the convergence criteria is met. Then the optimized and non-optimized profiles are studied using numerical simulation. Initially a two-dimensional numerical model is developed and validated against experimental results. The two-dimensional analysis is conducted using k-ω shear stress transport model. Unsteady Reynold’s Averaged Navier Stoke’s equations have been solved to simulate the flow field of a Savonius-style rotor. This analysis has been executed using finite volume approach in Fluent 17.2 version. Grid independence study is performed to curtail the effect of grid size on the flow field portrayals. The optimization technique implemented on the Savonius-style wind turbine, generated design parameters that were able to yield a coefficient of performance value of 0.398. The coefficient of torque and coefficient of performance values are studied for both optimized and non-optimized profile as a function of tip speed ratio. Numerical simulation predicted a maximum gain of 41% for coefficient of performance at TSR = 1.0 over for optimized profile over the non-optimized profile.

Sign in / Sign up

Export Citation Format

Share Document