Bearing-Rotor Coupled System Stability Optimization Design Based on Genetic Algorithm

2011 ◽  
Vol 199-200 ◽  
pp. 1130-1133
Author(s):  
Yu Feng Ding ◽  
Lin Gan ◽  
Jun Bo Zhou ◽  
Bu Yun Sheng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Mutual Coupling ◽  
Coupling Effect ◽  
Coupled System ◽  
System Stability ◽  
Design Parameters ◽  
Sliding Bearing ◽  
Design Variables ◽  
Algorithm Theory

The structure parameter of sliding bearing has significant influence on entire shafting. The structural optimization of elliptical sliding bearing based on the genetic algorithm theory is studied in the paper. Shafting logarithmic decrement is taken as the objective function, the bearing width-diameter ratio, relative clearance and main design parameters such as ellipticity is taken as design variables. Mutual coupling effect between sliding bearing and rotor is considered in the optimization model. An elliptical sliding bearing of shafting has been optimized, and the solution result shows that the shafting stability has been increased, it indicates this method is feasible.

Structure Optimization Design for the Bed of Gear Hobbing Machine

2013 ◽  
Vol 765-767 ◽  
pp. 176-180
Author(s):  
Rong Chuang Zhang ◽  
Ao Xiang Liu ◽  
Jun Wang ◽  
Wan Shan Wang
Keyword(s):  
Optimization Design ◽  
Optimal Solution ◽  
Element Model ◽  
Weighting Coefficient ◽  
Design Parameters ◽  
Analytic Hierarchy ◽  
Design Variables ◽  
Gear Hobbing ◽  
Vibration Modal ◽  
Hierarchy Process

In the optimization design of the gear hobbing machine bed, the finite element model is build and the static analysis and vibration modal analysis are performed. Then sensitivity analysis is used to gain the main design parameters which influence the bed property most. Furthermore, the multi-objective optimization design of the bed is performed in ANSYS Workbench with these design parameters as the design variables. At last, after all optimum proposals are showed up, Analytic Hierarchy Process is used to determine the weighting coefficient, and the most optimal solution is found out. As a result, the dynamic and static performances of the machine bed are improved under control of the machine bed mass.

The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

2014 ◽  
Vol 721 ◽  
pp. 464-467
Author(s):  
Tao Fu ◽  
Qin Zhong Gong ◽  
Da Zhen Wang
Keyword(s):  
Robust Optimization ◽  
Objective Function ◽  
Robust Design ◽  
Optimization Design ◽  
Optimization Methods ◽  
Optimization Method ◽  
Design Parameters ◽  
Hybrid Particle Swarm Optimization ◽  
Design Variables ◽  
Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

Optimization Design for Air Heat Exchanger of Large Fast Reactor Based on Genetic Algorithm

2013 ◽  
Author(s):  
Huajin Yu ◽  
Lina Zhu ◽  
Zhenxing Zhang ◽  
Ziyu Liao
Keyword(s):  
Genetic Algorithm ◽  
Heat Exchanger ◽  
Fast Reactor ◽  
Optimization Design ◽  
Heat Removal ◽  
Tube Length ◽  
Design Parameters ◽  
Shell Side ◽  
Air Resistance ◽  
Fin Tube

The passive design for decay heat removal system of future fast reactor will put forward higher requirement for air heat exchanger (AHX), which is directly relevant to the structure and anti-seismic design of stack. Under considering the heat exchanger ability and the structure compactness comprehensively, a strategy for the optimization design of AHX based on genetic algorithm was developed in this paper. The air resistance in shell side of vertical fin tube AHX was chosen as the objective function, and the effect of design parameters including fin pitch, number of tube rows, tube pitch and tube length on the air resistance was discussed. The results of the study show that the method for the optimization design of AHX based on genetic algorithm can effectively optimize the structure of AHX and improve the resistance characteristic of the shell side evidently, which leads to design the fast reactor plant, stack structure and seismic resistance simply.

Genetic algorithm-based optimization design method of the Formula SAE racing car’s rear wing

2017 ◽  
Vol 232 (7) ◽  
pp. 1255-1269 ◽  
Author(s):  
Hui Wang ◽  
Qiuyang Bai ◽  
Xufei Hao ◽  
Lin Hua ◽  
Zhenghua Meng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Formula Sae ◽  
Design Variables ◽  
Rear Wing ◽  
Sample Points ◽  
Lift And Drag

The aerodynamic devices play an important role on the performance of the Formula SAE racing car. The rear wing is the most significant and popular element, which offers primary down force and optimizes the wake. In traditional rear wing optimization, the optimization variables are first selected, and separately enumerated according to the analyzing experience of the racing car’s external flow field, and thus the optimal design is chosen by comparison. This method is complicated, and even might lose some key sample points. In this paper, the attack angle of the rear wing and the relative position parameters are set as design variables; then the design variables’ combination is determined by the DOE experimental design method. The aerodynamic lift and drag of the racing car for these variables’ combinations are obtained by the computational fluid dynamics method. With these sample points, the approximation model is produced by the response surface method. For the sake of gaining the best lift to drag ( FL/ FD) ratio, i.e. maximum down force and the minimum drag force, the optimal solution is found by the genetic algorithm. The result shows that the established optimization procedure can optimize the rear wing’s aerodynamic characteristic on the racing car effectively and have application values in the practical engineering.

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.

Parameter Optimization Design of Soil Nail Supporting to Deep Foundation Pits

2013 ◽  
Vol 671-674 ◽  
pp. 235-239
Author(s):  
Wei Xu ◽  
Jian Sheng Feng
Keyword(s):  
Genetic Algorithm ◽  
Parameter Optimization ◽  
Optimization Design ◽  
Hybrid Genetic Algorithm ◽  
Design Parameters ◽  
Deep Foundation ◽  
Soil Nail ◽  
Soil Nail Wall ◽  
Parameter Optimization Design ◽  
The Cost

In order to reduce the cost of soil nail supporting to deep foundation pits and ensure safety as well as stability, a model is established to optimize the design parameters for soil nail wall. This paper is concerned with the analysis of soil nail channel number, diameter, length, horizontal spacing, vertical spacing and dip angle. For parameter optimization design of soil nail wall is a complex problem, the traditional method is easy to fall into local optima. The genetic algorithm is a global optimization method. To some extent it has the drawback of appearing premature convergence and oscillation so as to slow iterative process. Therefore, a forward and backward search algorithm is proposed, which is combined with genetic algorithm. Furthermore some improvement measures are put forward by means of improved hybrid genetic algorithm. As a result engineering studies arrive at an optimum design. It shows that the optimized design results of IHGA not only ensure the stability of foundation, but greatly reduce the cost of engineering materials.

Multi-Objective Optimization Design of Tanker Ships via a Genetic Algorithm

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Marcelo Ramos Martins ◽  
Diego F. Sarzosa Burgos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Ship Design ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Total Cost ◽  
Multi Objective ◽  
The Cost

The cost of a new ship design heavily depends on the principal dimensions of the ship; however, dimensions minimization often conflicts with the minimum oil outflow (in the event of an accidental spill). This study demonstrates one rational methodology for selecting the optimal dimensions and coefficients of form of tankers via the use of a genetic algorithm. Therein, a multi-objective optimization problem was formulated by using two objective attributes in the evaluation of each design, specifically, total cost and mean oil outflow. In addition, a procedure that can be used to balance the designs in terms of weight and useful space is proposed. A genetic algorithm was implemented to search for optimal design parameters and to identify the nondominated Pareto frontier. At the end of this study, three real ships are used as case studies.

scholarly journals Multi-Objective Optimisation of an Aerostatic Pad: Design of Position, Number and Diameter of the Supply Holes

2020 ◽  
Vol 36 (3) ◽  
pp. 347-360
Author(s):  
F. Colombo ◽  
F. Della Santa ◽  
S. Pieraccini
Keyword(s):  
Genetic Algorithm ◽  
Load Capacity ◽  
Design Parameters ◽  
Objective Functions ◽  
Aerostatic Bearing ◽  
Multiobjective Optimisation ◽  
Damping Coefficients ◽  
Supply Pressure ◽  
Design Variables ◽  
Stiffness And Damping

ABSTRACTIn this paper, a rectangular aerostatic bearing with multiple supply holes is optimised with a multiobjective optimisation approach. The design variables taken into account are the supply holes position, their number and diameter, the supply pressure, while the objective functions are the load capacity, the air consumption and the stiffness and damping coefficients. A genetic algorithm is applied in order to find the Pareto set of solutions. The novelty with respect to other optimisations which can be found in literature is that number and location of the supply holes is completely free and not associated to a pre-defined scheme. A vector x associated with the supply holes location is introduced in the design parameters and given in input to the optimizer.

Optimization Design of the Geared Rotor System With Critical Speed Constraints Using the Enhanced Genetic Algorithm

2008 ◽  
Author(s):  
T. N. Shiau ◽  
C. H. Kang ◽  
D. S. Liu ◽  
E. K. Lee ◽  
W. C. Hsu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Critical Speed ◽  
Hybrid Genetic Algorithm ◽  
Transmission Error ◽  
Rotor System ◽  
Gear Mesh ◽  
Design Variables ◽  
Bearing Stiffness ◽  
Gear Mesh Stiffness

This paper presents an efficient enhanced genetic algorithm to minimize the shaft weight, the unbalance response and the response due to the transmission error simultaneously. The minimization plays an important role in designing the geared rotor system under critical speed constraints. In the process of optimization, the design variables consist of shaft inner radii, bearing stiffness and the gear mesh stiffness. The enhanced genetic algorithm of optimization comprises the Hybrid Genetic Algorithm (HGA) and the Interval Genetic Algorithm (IGA). The HGA deals with this optimal design problem and the IGA accomplishes the interval optimization design. The results show that the presented enhanced genetic algorithm can not only effectively reduce the shaft weight and the transmission error response, but also precisely determine the interval ranges of design variables with feasible corresponding objective error.

The Dynamic Analysis of SFD-Sliding Bearing Flexible Rotor System Based on Optimization Design

2012 ◽  
Vol 159 ◽  
pp. 355-360
Author(s):  
Ji Yan Wang ◽  
Rong Chun Guo ◽  
Xu Fei Si
Keyword(s):  
High Speed ◽  
Optimization Design ◽  
Critical Speed ◽  
Structural Parameters ◽  
Rotor System ◽  
Flexible Rotor ◽  
Optimization Analysis ◽  
Sliding Bearing ◽  
Critical Speeds ◽  
Design Variables

The paper establishes the mechanical model of SFD-sliding bearing flexible rotor system, adopting Runge-Kutta method to solve nonlinear differential equation, thus acquiring the unbalanced response curve and then gaining the first two critical speeds of the system. Meanwhile, the paper analyzes the sensitivity of the system on the first two critical speeds towards structural parameters, offering design variables to optimization analysis. Based on sensitivity analysis, genetic algorithm is employed to give an optimization analysis on critical speed, which aims to remove critical speed from working speed as much as possible. The critical speed ameliorates after the optimization which supplies theoretical basis as well as theoretical analysis towards the dynamic stability of high-speed rotor system and provides reference for the design of such rotor system.

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