scholarly journals Research on Multistage Rotor Assembly Optimization Methods for Aeroengine Based on the Genetic Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yue Chen ◽  
Jiwen Cui ◽  
Xun Sun ◽  
Shihai Cui
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Spatial Location ◽  
Optimization Methods ◽  
Optimization Method ◽  
Propagation Model ◽  
Optimization Strategy ◽  
Assembly Method ◽  
Alignment Process ◽  
Rotor Assembly

The coaxiality and unbalance are the two important indexes to evaluate the assembly quality of an aeroengine. It often needs to be tested and disassembled repeatedly to meet the double-objective requirements at the same time. Therefore, an intelligent assembly method is urgently needed to directly predict the optimal assembly orientations of the rotors at each stage to meet the double-objective requirements simultaneously. In this study, an assembly optimization method for the multistage rotor of an aeroengine is proposed based on the genetic algorithm. Firstly, a spatial location propagation model is developed to accurately predict the spatial position of each rotor after assembly. The alignment process of the assembly screw holes of the adjacent rotors is considered for the first time. Secondly, a new assembly optimization strategy is proposed to select different assembly data for the specific values of the coaxiality and unbalance, respectively. Finally, a double-objective fitness function is constructed based on the coaxiality and unbalance. The simulation and experimental results show that the assembly optimization method proposed in this study can be utilized to achieve synchronous optimization of the coaxiality and unbalance of an aeroengine during preassembly.

scholarly journals CONSTRUCTAL DESIGN AND SIMULATED ANNEALING EMPLOYED FOR GEOMETRIC OPTIMIZATION OF A Y-SHAPED CAVITY INTRUDED INTO CONDUCTIVE WALL

2015 ◽  
Vol 14 (1) ◽  
pp. 79
Author(s):  
G. V. Gonzales ◽  
E. D. Dos Santos ◽  
L. R. Emmendorfer ◽  
L. A. Isoldi ◽  
E. S. D. Estrada ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Solid Wall ◽  
Internal Heat ◽  
Optimization Methods ◽  
Optimization Method ◽  
Internal Heat Generation ◽  
Geometric Optimization ◽  
Constructal Design ◽  
Optimal Shapes

he problem study here is concerned with the geometrical evaluation of an isothermal Y-shaped cavity intruded into conducting solid wall with internal heat generation. The cavity acts as a sink of the heat generated into the solid. The main purpose here is to minimize the maximal excess of temperature (θmax) in the solid. Constructal Design, which is based on the objective and constraints principle, is employed to evaluate the geometries of Y-shaped cavity. Meanwhile, Simulated Annealing (SA) algorithm is employed as optimization method to seek for the best shapes. To validate the SA methodology, the results obtained with SA are compared with those achieved with Genetic Algorithm (GA) and Exaustive Search (ES) in recent studies of literature. The comparison between the optimization methods (SA, GA and ES) showed that Simulated Annealing is highly effective in the search for the optimal shapes of the studied case.

The Optimization Design of the Cross-Sectional Layout of an Umbilical Based on the Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Xu Yin ◽  
Zhixun Yang ◽  
Dongyan Shi ◽  
Jun Yan ◽  
Lifu Wang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Cross Sections ◽  
Optimization Design ◽  
Mechanical Performance ◽  
Fitness Function ◽  
Hybrid Genetic Algorithm ◽  
Optimization Strategy ◽  
Cross Sectional ◽  
Great Probability ◽  
The Cross

Abstract The umbilical which consists of hydraulic tubes, electrical cables and optical cables is a key equipment in the subsea production system. Each components perform different physical properties, so different cross-sections will present different geometrical characteristic, carrying capacities, the cost and the ease of manufacture. Therefore, the cross-sectional layout design of the umbilical is a typical multi-objective optimization problem. A mathematical model of the cross-sectional layout considering geometric and mechanical properties is proposed, and the genetic algorithm is introduced to copy with the optimization model in this paper. A steepest descent operator is embedded into the basic genetic algorithm, while the appropriate fitness function and the selection operator are advanced. The optimization strategy of the cross-sectional layout based on the hybrid genetic algorithm is proposed with the fast convergence and the great probability for global optimization. Finally, the cross-section of an umbilical case is performed to obtain the optimal the cross-sectional layout. The geometric and mechanical performance of results are compared with the initial design, which verify the feasibility of the proposed algorithm.

scholarly journals Genetic Algorithm for Job Scheduling with Maintenance Consideration in Semiconductor Manufacturing Process

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Seungchul Lee ◽  
Jun Ni
Keyword(s):  
Genetic Algorithm ◽  
Semiconductor Manufacturing ◽  
Job Scheduling ◽  
Optimization Methods ◽  
Optimization Method ◽  
Scheduling Problems ◽  
Simulation Based ◽  
Sequencing Problems ◽  
Maintenance Activities ◽  
Single Cluster

This paper presents wafer sequencing problems considering perceived chamber conditions and maintenance activities in a single cluster tool through the simulation-based optimization method. We develop optimization methods which would lead to the best wafer release policy in the chamber tool to maximize the overall yield of the wafers in semiconductor manufacturing system. Since chamber degradation will jeopardize wafer yields, chamber maintenance is taken into account for the wafer sequence decision-making process. Furthermore, genetic algorithm is modified for solving the scheduling problems in this paper. As results, it has been shown that job scheduling has to be managed based on the chamber degradation condition and maintenance activities to maximize overall wafer yield.

Parameter Identification of Thermal Visco-Plastic Model Considering Dynamic Recrystallization

2007 ◽  
Vol 561-565 ◽  
pp. 1869-1874
Author(s):  
Quan Lin Jin ◽  
Yan Shu Zhang
Keyword(s):  
Numerical Simulation ◽  
Genetic Algorithm ◽  
Parameter Identification ◽  
Optimization Methods ◽  
Optimization Method ◽  
Special Program ◽  
Global Optimization Method ◽  
Material Parameters ◽  
Real Coded Genetic Algorithm ◽  
Deformation Test

A hybrid global optimization method combining the Real-coded genetic algorithm and some classical local optimization methods is constructed and applied to develop a special program for parameter identification. Finally, the parameter identification for both 26Cr2Ni4MoV steel and AZ31D magnesium alloy is carried out by using the program. A comparison of deformation test and numerical simulation shows that the parameter identification and the obtained two sets of material parameters are all available.

scholarly journals Genetic Algorithm Application in Optimization of Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Ali Norouzi ◽  
A. Halim Zaim
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Fitness Function ◽  
Optimization Methods ◽  
Wireless Sensor ◽  
Network Coverage ◽  
Final Formula ◽  
Nonlinear Optimization Methods

There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs.

scholarly journals Optimizing Virtual Private Network Design Using a New Heuristic Optimization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hongbing Lian ◽  
András Faragó
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Method ◽  
Virtual Private Network ◽  
Successful Model ◽  
Search Speed ◽  
Private Network

In virtual private network (VPN) design, the goal is to implement a logical overlay network on top of a given physical network. We model the traffic loss caused by blocking not only on isolated links, but also at the network level. A successful model that captures the considered network level phenomenon is the well-known reduced load approximation. We consider here the optimization problem of maximizing the carried traffic in the VPN. This is a hard optimization problem. To deal with it, we introduce a heuristic local search technique called landscape smoothing search (LSS). This study first describes the LSS heuristic. Then we introduce an improved version called fast landscape smoothing search (FLSS) method to overcome the slow search speed when the objective function calculation is very time consuming. We apply FLSS to VPN design optimization and compare with well-known optimization methods such as simulated annealing (SA) and genetic algorithm (GA). The FLSS achieves better results for this VPN design optimization problem than simulated annealing and genetic algorithm.

scholarly journals Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 171
Author(s):  
Nihad Dib ◽  
Umar Al-Sammarraie
Keyword(s):  
Optimal Design ◽  
High Speed ◽  
Search Algorithm ◽  
Fitness Function ◽  
Logic Gate ◽  
Optimization Methods ◽  
Optimization Method ◽  
Cmos Inverter ◽  
Symbiotic Organisms Search ◽  
Symbiotic Organisms

This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods.

scholarly journals Improved Immune Genetic Algorithm to Optimize the Design of Wing Structure of Competition Aircraft

2021 ◽  
Vol 2137 (1) ◽  
pp. 012075
Author(s):  
Xi Feng ◽  
Yafeng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Aircraft Design ◽  
Flight Test ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Immune Genetic Algorithm ◽  
Maximum Deformation ◽  
Design Variables ◽  
Wing Structure

Abstract An improved immune genetic algorithm is used to design and optimize the wing structure parameters of a competition aircraft. According to the requirements of aircraft design, multi-objective optimization index is established. On this basis, the basic steps of using immune algorithm to optimize the main design parameters of aircraft wing structure are proposed, and the optimization of the wing parameters of a competition aircraft is used as an example for simulation calculation. The design variables in the optimization are the size of the wing components, and the optimization goal is to minimize the weight of the wing and the maximum deformation of the wing structure. Research shows that compared with traditional optimization methods; the improved immune genetic algorithm is a very effective optimization method. At the same time, a prototype is made to check the validity and feasibility of the design. Flight test results show that the optimization method is very effective. Although the method is proposed for competition aircraft, it is also applicable to other types of aircraft.

scholarly journals Evaluation of optimization methods for solving the receptor model for chemical mass balance

2015 ◽  
Vol 80 (2) ◽  
pp. 253-264 ◽  
Author(s):  
N. Anu ◽  
S. Rangabhashiyam ◽  
Antony Rahul ◽  
N. Selvaraju
Keyword(s):  
Genetic Algorithm ◽  
Air Quality ◽  
Optimization Methods ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Quality Analysis ◽  
Algorithm Optimization ◽  
Chi Square ◽  
Source Contribution ◽  
Source Contributions

Balance (CMB) model has been extensively used in order to determine source contribution for particulate matters (size diameters less than 10 ?m and 2.5 ?m) in the air quality analysis. A comparison of the source contribution estimated from the three CMB models (CMB 8.2, CMB-fmincon and CMB-GA) have been carried out through optimization techniques such as ?fmincon? (CMB-fmincon) and genetic algorithm (CMB-GA) using MATLAB. The proposed approach has been validated using San Joaquin Valley Air Quality Study (SJVAQS) California Fresno and Bakersfield PM10 and PM2.5 followed with Oregon PM10 data. The source contribution estimated from CMB-GA was better in source interpretation in comparison with CMB8.2 and CMB-fmincon. The performance accuracy of three CMB approaches were validated using R-square, reduced chi-square and percentage mass tests. The R-square (0.90, 0.67 and 0.81, 0.83), Chi-square (0.36, 0.66 and 0.65, 0.43) and percentage mass (67.36 %, 55.03 % and 94.24 %, 74.85 %) of CMB-GA showed high correlation for PM10, PM2.5 Fresno and Bakersfield data respectively. To make a complete decision, the proposed methodology has been bench marked with Portland, Oregon PM10 data with best fit with R2 (0.99), Chi-square (1.6) and percentage mass (94.4 %) from CMB-GA. Therefore, the study revealed that CMB with genetic algorithm optimization method holds better stability in determining the source contributions.

An Application of GA on Gas Lift Parameters Optimization

2011 ◽  
Vol 402 ◽  
pp. 654-659
Author(s):  
Yan Qiang Wu ◽  
Xiao Dong Wu ◽  
Teng Fei Sun ◽  
Jing Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Gas Injection ◽  
Injection Pressure ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Design Parameters ◽  
Practical Situation ◽  
Gas Lift ◽  
Quick Search

This paper has created a rapid optimum method to design the gas lift parameters. Optimal Containment Genetic Algorithm (OMSGA) is applied in this method to optimize the parameters such as mass flow rate(Q), volume of gas injection(Qin), injection pressure(Pin), tubing header pressure(Pt), tubing inside diameter(Dt). According to practical situation of gas lift production, the gas lift efficiency (η) is selected as the objective function, the suitable fitness function and value of operators of OMSGA are given, and reasonable convergence delay-independent conditions is set. Based on the intelligence and global quick search of GA and the convergence of OMSGA, the design parameters of gas lift can be globally optimized quickly and accurately. An example is taken to prove that the application of GA in the field of gas lift production is successful. This new optimization method based on GA can provide guide for field design.

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