A Design Method of an Automotive Wheel-Bearing Unit With Discrete Design Variables Using Genetic Algorithms

2000 ◽  
Vol 123 (1) ◽  
pp. 181-187 ◽  
Author(s):  
Dong-Hoon Choi ◽  
Ki-Chan Yoon
Keyword(s):  
Genetic Algorithm ◽  
Design Problem ◽  
Design Method ◽  
Bearing Unit ◽  
Design Variables ◽  
Rolling Element ◽  
Discrete Design Variables ◽  
Ball Diameter ◽  
System Life ◽  
Wheel Bearing

In order to improve the efficiency of the design process and the quality of the resulting design, this study proposes a design method for determining design variables of an automotive wheel-bearing unit of double-row angular-contact ball bearing type by using a genetic algorithm. The desired performance of the wheel-bearing unit is to maximize system life while satisfying geometrical and operational constraints without enlarging mounting space. The design variables selected are number of balls, initial contact angle, standard ball diameter, pitch circle diameter, preload, distance between ball centers, and wheel offset, which must be selected at the preliminary design stage. The use of gradient-based optimization methods for the design of the unit is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding and dynamic mutation rate is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. A computer program is developed and applied to the design of a real wheel-bearing unit model to evaluate the proposed design method. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the system life of an optimally designed wheel-bearing unit is enhanced in comparison with that of the current design without any constraint violations. It is believed that the proposed methodology can be applied to other rolling element bearing design applications.

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.

Elastohydrodynamic Ball Bearing Optimization Using Genetic Algorithm and Heuristic Gradient Projection

2011 ◽  
Author(s):  
Mohamed H. Abbas ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithm ◽  
Film Thickness ◽  
Friction Torque ◽  
Gradient Projection ◽  
Rolling Element Bearings ◽  
Multi Objective ◽  
Rolling Element ◽  
Mean Diameter ◽  
Minimum Film Thickness ◽  
Ball Diameter

Rolling element bearings operation depends on some variables contributing to the machine element performance. The present work attempts to improve the performance of rolling element bearings through the increase of fatigue life and the reduction of bearing wear. The formulation is based on Elastohydrodynamic to maximize the realistically evaluated minimum film thickness without significant increase in viscous friction torque. The multiobjective problem can then be stated as maximization of minimum film thickness and minimization of total friction torque. Design vectors are reduced in the present study relative to previous studies as some variables are considered as dependent variables. A new important parameter is introduced in this study as a design variable, which is the viscosity of lubricant (η0 (Pa.s)). Lubricant viscosity contributes drastically in either increasing minimum film thickness separation or increasing the frictional torque arising in bearing. A multi-objective optimization using Genetic Algorithm is used in order to evaluate Pareto optimal solutions. Another multi-objective problem has been formulated such as a two objective problem involving maximizing minimum film thickness, and minimizing the bearing elements size (i.e.: ball diameter and mean diameter) through subjecting the bearing to the maximum allowable compressive stress of the elements. Heuristic gradient projection method is used in solving such a problem, as it can efficiently seek the optimum point in less than five iterations. In such a case, the design variables are reduced to two variables, which are the ball diameter and mean diameter. Full design vector consideration is also performed.

Optimum design of rolling element bearings using a genetic algorithm—differential evolution (GA—DE) hybrid algorithm

2010 ◽  
Vol 225 (3) ◽  
pp. 714-721 ◽  
Author(s):  
W Y Lin
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimum Design ◽  
Dynamic Loads ◽  
Representative Case ◽  
De Algorithm ◽  
Design Variables ◽  
Deep Groove ◽  
Rolling Element

Binary-code genetic algorithms (BGA) have been used to obtain the optimum design for deep groove ball bearings, based on maximum fatigue life as an objective function. The problem has ten design variables and 20 constraint conditions. This method can find better basic dynamic loads rating than those listed in standard catalogues. However, the BGA algorithm requires a tremendous number of evaluations of the objective function per case to achieve convergence (e.g. about 5 200 000 for a representative case). To overcome this difficulty, a hybrid evolutionary algorithm by combining real-valued genetic algorithm (GA) with differential evolution (DE) is used together with the proper handling of constraints for this optimum design task. Findings show that the GA—DE algorithm can successfully find the better dynamic loads rating, about 1.3—11.1 per cent higher than those obtained using the traditional BGA. Moreover, the mean number of evaluations of the objective function required to achieve convergence is about 3011, using the GA—DE algorithm, as opposed to about 5 200 000 for a representative case using the BGA. Comparison shows the GA—DE algorithm to be much more effective and efficient than the BGA.

scholarly journals An Improved Genetic Algorithm for the Optimization of Composite Structures

2006 ◽  
Author(s):  
Vladimir Gantovnik ◽  
Georges Fadel ◽  
Zafer Gu¨rdal
Keyword(s):  
Genetic Algorithm ◽  
Composite Structures ◽  
Optimization Problems ◽  
Multivariate Approximation ◽  
Approximation Procedure ◽  
Continuous Variables ◽  
Improved Genetic Algorithm ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Proposed Modification

This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed modification improves the efficiency of the memory constructed in terms of the continuous variables. The work presents the algorithmic implementation of the proposed memory scheme and demonstrates the efficiency of the proposed multivariate approximation procedure for the weight optimization of a segmented open cross section composite beam subjected to axial tension load. Results are generated to demonstrate the advantages of the proposed improvements to a standard genetic algorithm.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

Multi-Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multilevel Genetic Algorithm

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Özhan Öksüz ◽  
İbrahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Navier Stokes ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for a multi-objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size, and cost of the gas turbine engine. A 3D steady Reynolds averaged Navier–Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well-known test cases. The blade geometry is modeled by 36 design variables plus the number of blade variables in a row. Fine and coarse grid solutions are respected as high- and low-fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid multi-objective genetic algorithm successfully accelerates the optimization and prevents the convergence with local optimums.

Successive Optimal Design Procedure Applied on Conventional Fuel Injection Equipment

1996 ◽  
Vol 118 (4) ◽  
pp. 490-493 ◽  
Author(s):  
B. Kegl
Keyword(s):  
High Pressure ◽  
Optimal Design ◽  
Design Problem ◽  
Fuel Injection ◽  
Design Parameters ◽  
Optimal Design Problem ◽  
Injection Equipment ◽  
Design Variables ◽  
Discrete Design Variables ◽  
Conventional Fuel

The paper describes a procedure of solving an optimal design problem with continuous/discrete design variables. The procedure is applied to a set of design parameters of a conventional fuel injection equipment for a diesel engine. The design parameters concern the design of the cam, high pressure pump, delivery valve, snubber valve, high pressure tube and injector. By the proposed procedure the continuous/discrete optimal design problem is replaced by a finite sequence of auxiliary problems where all design variables are treated as continuous. After solving each auxiliary problem one of the discrete design variables is set equal to the closest available discrete value and eliminated from the set of design variables. This process does not guarantee that an optimal solution to the continuous/integer programming problem is located; however it does produce improved near optimal designs for conventional fuel injection equipment. The proposed procedure is illustrated with a numerical example.

An Integrated Optimization Design Method Based on Surrogate Modeling Applied to Diverging Duct Design

2016 ◽  
Vol 33 (4) ◽  
Author(s):  
Lu Hanan ◽  
Li Qiushi ◽  
Li Shaobin
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Optimization Design ◽  
Design Method ◽  
Uniform Design ◽  
Qualitative Variable ◽  
Integrated Optimization ◽  
Design Variables ◽  
Integrated Optimization Design

AbstractThis paper presents an integrated optimization design method in which uniform design, response surface methodology and genetic algorithm are used in combination. In detail, uniform design is used to select the experimental sampling points in the experimental domain and the system performance is evaluated by means of computational fluid dynamics to construct a database. After that, response surface methodology is employed to generate a surrogate mathematical model relating the optimization objective and the design variables. Subsequently, genetic algorithm is adopted and applied to the surrogate model to acquire the optimal solution in the case of satisfying some constraints. The method has been applied to the optimization design of an axisymmetric diverging duct, dealing with three design variables including one qualitative variable and two quantitative variables. The method of modeling and optimization design performs well in improving the duct aerodynamic performance and can be also applied to wider fields of mechanical design and seen as a useful tool for engineering designers, by reducing the design time and computation consumption.

scholarly journals Affordance based interactive genetic algorithm (ABIGA)

2018 ◽  
Vol 4 ◽  
Author(s):  
Ivan Mata ◽  
Georges Fadel ◽  
Anthony Garland ◽  
Winfried Zanker
Keyword(s):  
Genetic Algorithm ◽  
Design Process ◽  
Web Application ◽  
Design Method ◽  
Physical Characteristics ◽  
Steering Wheel ◽  
Design Concepts ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Parameter Values

Designers can involve users in the design process. The challenge lies in reaching multiple users and finding the best way to use their input in the design process. Affordance based design (ABD) is a design method that focuses in part on the perceived or existing interactions between the user and the artifact. The shape and physical characteristics of the product enable the user to perceive some of its affordances. The goal of this research is to use ABD, along with an optimization tool, to evolve the shape of products toward better perceived solutions using the input from users. A web application has been developed that evolves design concepts using an interactive multi-objective genetic algorithm (IGA) relying on the user assessment of product affordances. As a proof of concept, a steering wheel is designed using the application by having users rate specific affordances of solutions presented to them. The results show that the design concepts evolve toward better perceived solutions, allowing designers to explore more solutions that reflect the preferences of end users. Relationships between affordances and product design variables are also explored, revealing that specific affordances can be targeted with changes in design parameter values and highlighting the tie between physical characteristics and affordances.

Design Optimization of the Flexspline in Harmonic Drive

2012 ◽  
Vol 215-216 ◽  
pp. 59-63 ◽  
Author(s):  
Juan Dai ◽  
Li Zhi Chen ◽  
Xiao Bing Pang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Design Optimization ◽  
Optimization Model ◽  
Design Solution ◽  
Harmonic Drive ◽  
Discrete Variables ◽  
Geometrical Constraint ◽  
Design Variables ◽  
Discrete Design Variables

In order to reduce the weight of harmonic drive (HD), the total volume of flexspline and circular spline was formulated and used as an objection function. Under the constraints including the condition on the strength of flexspline, the condition on averting the tooth top interference, the condition on the transmission ratio of HD and the geometrical constraint conditions of flexspline, a design optimization model with mixed discrete variables was established. For directly applying the optimal design solution of flexspline to manufacture, a manufacture-oriented method for dealing with mixed discrete design variables was used and the established model was solved by using an improved compound genetic algorithm. An optimal design example of flexspline was given and it shows that the proposed method is practical and effective.

Sign in / Sign up

Export Citation Format

Share Document