Magnetic Bearing Design Using Genetic Algorithms

1999 ◽  
Author(s):  
Yong Teng ◽  
Susan Carlson-Skalak ◽  
Eric Maslen
Keyword(s):  
Genetic Algorithm ◽  
Solution Space ◽  
Optimization Method ◽  
Time Frame ◽  
Magnetic Bearing ◽  
Simultaneous Optimization ◽  
Design Parameters ◽  
Dimensional System ◽  
Reasonable Time Frame ◽  
The Relationship

Abstract A magnetic bearing system is a coupled, nonlinear, high-dimensional system. The relationship among the design parameters, design constraints and the optimization goals is not obvious. Solving this type of design problem within a reasonable time frame is a challenge for any optimization method. This research investigated the simultaneous optimization of the magnetic bearing configuration and bearing locations. A multistage genetic algorithm was developed to search through a discrete and non-convex solution space. Because the genetic algorithm can search through a much larger solution space than any engineer can do, innovative designs different from those using traditional methods can be found.

A Vacuum-Compatible Air Bearing: Design Analysis and Optimization

2007 ◽  
Vol 339 ◽  
pp. 37-44 ◽  
Author(s):  
G.H. Khim ◽  
Chun Hong Park ◽  
H.S. Lee ◽  
S.W. Kim
Keyword(s):  
Genetic Algorithm ◽  
Exhaust System ◽  
Optimization Method ◽  
Vacuum Pump ◽  
Chamber Pressure ◽  
Design Parameters ◽  
Air Bearing ◽  
Great Care ◽  
Spatial Design ◽  
Bearing Design

This paper describes the vacuum-compatible air bearing designed with a cascaded exhaust scheme to minimize the leakage of air in a vacuum environment. The design of the air bearing, including the differential exhaust system, required great care because several design parameters, such as the number of exhaust stages, diameter and length of the exhaust tube, pumping speed and ultimate pressure of the vacuum pump, and seal length and gap greatly influenced the leakage of air and thus the degree of vacuum. A leakage analysis was performed to estimate the chamber pressure and an optimization method based on the genetic algorithm was proposed under several constraint conditions. The results showed that the degree of vacuum improved dramatically compared to the initial design, and that the distribution of the spatial design parameters and technical limit of the pumping speed were well achieved.

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.

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.

Arrangement Optimization of Instruments Based on Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3622-3626
Author(s):  
Sheng Yuan Yan ◽  
Kun Yu ◽  
Zhi Jian Zhang ◽  
Min Jun Peng
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Optimization Method ◽  
Human Machine Interaction ◽  
System A ◽  
Frequency Of Use ◽  
Search Speed ◽  
Machine Interface ◽  
Encoding Method

The instruments arrangement of human-machine interface can directly influence the operation and efficiency of human-machine interaction in system. A novel instruments arrangement optimization method based on genetic algorithm was proposed. The biology heredity and evolution mode of genetic algorithm was used to search for the optimal or satisfying arrangement solution. Fitness function was constructed based on the principles of importance, frequency of use, relevance and operational sequence. Literal permutation encoding method was applied to represent the instruments arrangement. The optimization preserving strategy was used to enhance the search speed and to enlarge the width and depth of solution space. Finally, a case of instruments arrangement optimization proves that the arrangement optimization method is effective.

scholarly journals An application of genetic algorithm to optimize the 3-Joint carangiform fish robot’ s links to get the desired straight velocity

2015 ◽  
Vol 18 (1) ◽  
pp. 27-36
Author(s):  
Phu Duc Huynh ◽  
Tuong Quan Vo
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Optimization Method ◽  
Design Parameters ◽  
Underwater Robots ◽  
Fish Robot ◽  
Lagrange Method ◽  
Snake Robot ◽  
Biomimetic Robots ◽  
Biomimetic Robot

Biomimetic robot is a new branch of researched field which is developing quickly in recent years. Some of the popular biomimetic robots are fish robot, snake robot, dog robot, dragonfly robot, etc. Among the biomimetic underwater robots, fish robot and snake robot are mostly concerned. In this paper, we study about an optimization method to find the design parameters of fish robot. First, we analyze the dynamic model of the 3-joint Carangiform fish robot by using Lagrange method. Then the Genetic Algorithm (GA) is used to find the optimal lengths’ values of fish robot’s links. The constraint of this optimization problem is that the values of fish robot’s links are chosen that they can make fish robot swim with the desired straight velocity. Finally, some simulation results are presented to prove the effectiveness of the proposed method

scholarly journals Discrete Optimization on Unsteady Pressure Fluctuation of a Centrifugal Pump Using ANN and Modified GA

2021 ◽  
Author(s):  
Wenjie Wang ◽  
Qifan Deng ◽  
Ji Pei ◽  
Jinwei Chen ◽  
Xingcheng Gan
Keyword(s):  
Genetic Algorithm ◽  
Centrifugal Pump ◽  
Pressure Fluctuation ◽  
Optimization Method ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Unsteady Pressure ◽  
Design Variables ◽  
Rotor Stator Interaction ◽  
Unsteady Simulation

Abstract Pressure fluctuation due to the rotor-stator interaction in turbomachinery is unavoidable, inducing strong vibration and even shortening the lifecycle. The investigation on optimization method of an industrial centrifugal pump was carried out to reduce the pressure fluctuation intensity. Considering the time-consuming transient calculation of unsteady pressure, a novel optimization strategy was proposed by discretizing design variables and genetic algorithm. Four highly related design parameters were chosen, and 40 transient sample cases were generated and simulated using an automatic simulation program. Furthermore, a modified discrete genetic algorithm (MDGA) was proposed to reduce the optimization cost by unsteady simulation. For the benchmark test, the proposed MDGA showed a great advantage over the original genetic algorithm in terms of searching speed and could deal with the discrete variables effectively. After optimization, an improvement in terms of the performance and stability of the inline pump was achieved.

Three-Dimensional Aerodynamic Optimization of a Multi-Stage Axial Compressor

2016 ◽  
Author(s):  
Tao Ning ◽  
Chun-wei Gu ◽  
Xiao-tang Li ◽  
Tai-qiu Liu
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Pressure Ratio ◽  
Three Dimensional ◽  
Axial Compressor ◽  
Optimization Method ◽  
Operating Point ◽  
Design Parameters ◽  
Aerodynamic Optimization ◽  
Stall Margin

An optimization method combined of a genetic algorithm, an artificial neural network, a CFD solver and a blade generator, is developed in this research and applied in the three-dimensional blading design of a newly designed highly-loaded 5-stage axial compressor. The adaptive probabilities of crossover and mutation, non-uniform mutation operator and elitism operator are employed to improve the convergence of the genetic algorithm. Considering both the optimization efficiency and effectiveness, a mixture of high-fidelity multistage CFD method and approximate surrogate model of the feed-forward ANN is used to evaluate the fitness. In particular, the database is updated dynamically and used to re-train the surrogate model of ANN for improving the accuracy for predicting. The last stator of the compressor is optimized at the near stall operating point. The tip bow with relative bow height Hb and bow angle αb are treated as design parameters. The adiabatic efficiency as well as the penalty of mass flow and total pressure ratio constitute the objective functions to be maximized. The optimum (Hb = 0.881, αb = 14.7°) obtains 0.4% adiabatic efficiency increase for the whole compressor at the optimized operating point. The detailed aerodynamic is compared between the baseline and optimized stator, and the mechanism is analyzed. The optimized version obtains 5.1% increase in stall margin and maintains the efficiency at the design point.

Optimization Method RasID-GA for Numerical Constrained Optimization Problems

2007 ◽  
Vol 11 (5) ◽  
pp. 469-477 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Random Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Optimization Method ◽  
Penalty Functions ◽  
Objective Functions ◽  
Constrained Optimization Problems

This paper proposes Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) for constrained optimization. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for unconstrained optimization problems. In general, it is very difficult to find an optimal solution for constrained optimization problems because their feasible solution space is very limited and they should consider the objective functions and constraint conditions. The conventional constrained optimization methods usually use penalty functions to solve given problems. But, it is generally recognized that the penalty function is hard to handle in terms of the balance between penalty functions and objective functions. In this paper, we propose a constrained optimization method using RasID-GA, which solves given problems without using penalty functions. The proposed method is tested and compared with Evolution Strategy with Stochastic Ranking using well-known 11 benchmark problems with constraints. From the Simulation results, RasID-GA can find an optimal solution or approximate solutions without using penalty functions.

scholarly journals Synchronization Optimization of Pipeline Layout and Pipe Diameter Selection in a Self-Pressurized Drip Irrigation Network System Based on the Genetic Algorithm

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 489 ◽  
Author(s):  
Rong-Heng Zhao ◽  
Wu-Quan He ◽  
Zong-Ke Lou ◽  
Wei-Bo Nie ◽  
Xiao-Yi Ma
Keyword(s):  
Genetic Algorithm ◽  
Drip Irrigation ◽  
Optimization Design ◽  
Optimization Method ◽  
Simultaneous Optimization ◽  
Network System ◽  
Pipe Network ◽  
Network Systems ◽  
Irrigation Network ◽  
Effective Performance

A synchronous optimization method for self-pressure drip irrigation pipe network system is proposed. We have generalized the optimization design problem of the system and have established the mathematical models for the simultaneous optimization design of pipeline layout and pipe diameters. A genetic algorithm based on the infeasibility degree of the solution was used to solve the model. A typical example is used to validate the presented method. The method exhibits effective performance in the case studied. Designers can use the results of this study to efficiently design self-pressurized drip irrigation network systems.

Solid Rocket Motor Design Optimization Using Genetic Algorithm

2014 ◽  
Vol 905 ◽  
pp. 502-506 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Rocket Motor ◽  
Design Parameters ◽  
Optimization Approach ◽  
Solid Rocket Motor ◽  
Conceptual Design Phase ◽  
Solid Rocket

A design optimization approach of a solid propellant rocket motor is considered. A genetic algorithm (GA) optimization method has been used. The optimized solid rocket motor (SRM) is intended to use as a booster of a flight vehicle, and delivering a specific payload following a predefined prescribed trajectory. Sensitivity analysis of the optimized solution has been conducted using Monte Carlo method to evaluate the effect of uncertainties in design parameters. The results show that the proposed optimization approach was able to find the convergence of the optimal solution with highly acceptable value for conceptual design phase.

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