scholarly journals Multidisciplinary design optimization of the belt drive system considering both structure and vibration characteristics based on improved genetic algorithm

AIP Advances ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 055115 ◽  
Author(s):  
Yongliang Yuan ◽  
Xueguan Song ◽  
Wei Sun ◽  
Xiaobang Wang
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Vibration Characteristics ◽  
Drive System ◽  
Multidisciplinary Design ◽  
Belt Drive ◽  
Improved Genetic Algorithm

scholarly journals An improved particle swarm optimization algorithm for dynamic analysis of chain drive based on multidisciplinary design optimization

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401982961
Author(s):  
Mengjiang Chai ◽  
Yongliang Yuan ◽  
Wenjuan Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Method ◽  
Drive System ◽  
Multidisciplinary Design ◽  
Chain Drive ◽  
Swarm Optimization

Chain drive is one of the most commonly used mechanical devices in the main equipment transmission system. In the past decade, scholars focused on basic performance research, but ignore its best performance. In this study, due to the large vibration of the chain drive in the transmission system, the vibration performance and optimization parameters are also considered as a new method to design the chain drive system to obtain the best performance of the chain drive system. This article proposes a new method and takes a chain drive design as a case based on the multidisciplinary design optimization. The system optimization objective and sub-systems are established by the multidisciplinary design optimization method. To obtain the best performance for the chain, the chain drive is executed by an improved particle swarm optimization algorithm. Dynamic characteristics of the chain drive system are simulated based on the multidisciplinary design optimization results. The impact force of the chain links, vibration displacement, and the vibration frequency are analyzed. The results show that the kinematics principle of the chain drive and the optimal parameter value are obtained based on the multidisciplinary design optimization method.

Study on Multidisciplinary Design Optimization of 3-RRS Parallel Robot

2012 ◽  
Vol 214 ◽  
pp. 919-923
Author(s):  
Jing Zhang ◽  
Bai Lin Li
Keyword(s):  
Genetic Algorithm ◽  
Quadratic Programming ◽  
Design Optimization ◽  
Sequential Quadratic Programming ◽  
Multidisciplinary Design Optimization ◽  
Main Idea ◽  
Parallel Robot ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Robot System

The paper aims to apply the idea of multidisciplinary design optimization to the design of robot system. The main idea of collaborative optimization is introduced. The collaborative optimization frame of 3-RRS parallel robot is analyzed. With the method of genetic algorithm and Sequential Quadratic Programming, the investigation is made on the executing collaborative optimization of working stroke, driving performance and hydraulic components. The numerical results indicate that the collaborative optimization can be successfully applied to dealing with the complex robot system, and lay a foundation to solve more complex mechanical system.

Method of Genetic Algorithm Optimization Design of Permanent Magnet Retarder

2012 ◽  
Vol 215-216 ◽  
pp. 362-367
Author(s):  
Yi Qi Huang ◽  
Gan Wei Cai ◽  
Yu Jiang ◽  
Zhao Yu Luo
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Permanent Magnet ◽  
Multidisciplinary Design Optimization ◽  
Optimization Design ◽  
Basic Structure ◽  
Multidisciplinary Design ◽  
Design Parameters ◽  
Magnetic Flux Density ◽  
Algorithm Optimization

This paper introduced the method of multidisciplinary design optimization based on genetic algorithm. The basic structure and new auxiliary braking mechanism of permanent magnet retarder was analyzed. The influences of magnetic field parameters, structural design parameters, rotor parameters and permanent magnet temperature parameters on the behaviors performance of the permanent magnet retarder were discussed. The conceptual model of permanent magnet retarder was developed to maximize the brake torque of the permanent magnet retarder. The design variables included the radial width and the axis length of permanent magnet, the number of permanent magnet, the radius of rotor, the thickness of rotor, and the air gas. The constraint conditions included permitting temperature of rotor, saturation magnetic flux density of magnet material, and relation of structural geometry. The results of design optimization variables were obtained by applying genetic algorithm. The multidisciplinary design optimization in this paper is an effective method for the global design optimization of the permanent magnet retarder.

scholarly journals Multidiscilinary design optimization for aircraft wing using response surface method, genetic algorithm, and simulated annealing

2020 ◽  
Vol 14 (1) ◽  
pp. 28-41
Author(s):  
Xuan-Binh Lam
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
Multidisciplinary Design Optimization ◽  
Global Optimum ◽  
Multidisciplinary Design ◽  
Aircraft Wing ◽  
Surface Method

Multidisciplinary Design Optimization (MDO) has received a considerable attention in aerospace industry. The article develops a novel framework for Multidisciplinary Design Optimization of aircraft wing. Practically, the study implements a high-fidelity fluid/structure analyses and accurate optimization codes to obtain the wing with best performance. The Computational Fluid Dynamics (CFD) grid is automatically generated using Gridgen (Pointwise) and Catia. The fluid flow analysis is carried out with Ansys Fluent. The Computational Structural Mechanics (CSM) mesh is automatically created by Patran Command Language. The structural analysis is done by Nastran. Aerodynamic pressure is transferred to finite element analysis model using Volume Spline Interpolation. In terms of optimization algorithms, Response Surface Method, Genetic Algorithm, and Simulated Annealing are utilized to get global optimum. The optimization objective functions are minimizing weight and maximizing lift/drag. The design variables are aspect ratio, tapper ratio, sweepback angle. The optimization results demonstrate successful and desiable construction of MDO framework. Keywords: Multidisciplinary Design Optimization; fluid/structure analyses; global optimum; Genetic Algorithm; Response Surface Method.

Multidisciplinary Design Optimization of Small Canister-Launched Space Launch Vehicle Using Genetic Algorithm

2013 ◽  
Vol 302 ◽  
pp. 583-588 ◽  
Author(s):  
Fredy M. Villanueva ◽  
Lin Shu He ◽  
Da Jun Xu
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Method ◽  
Launch Vehicle ◽  
Low Earth Orbit ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Design Variables

A multidisciplinary design optimization approach of a three stage solid propellant canister-launched launch vehicle is considered. A genetic algorithm (GA) optimization method has been used. The optimized launch vehicle (LV) is capable of delivering a microsatellite of 60 kg. to a low earth orbit (LEO) of 600 km. altitude. The LV design variables and the trajectory profile variables were optimized simultaneously, while a depleted shutdown condition was considered for every stage, avoiding the necessity of a thrust termination device, resulting in reduced gross launch mass of the LV. 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.

A Game Theoretic Based Scheme for Multidisciplinary Optimization With Uncertainty

2004 ◽  
Author(s):  
Amit Gupta ◽  
K. Krishnamurthy
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Processing Time ◽  
Internal Combustion ◽  
Multidisciplinary Optimization ◽  
Optimization Process ◽  
Multidisciplinary Design ◽  
Ic Engine ◽  
Game Theoretic

A game theoretic based scheme is considered in this study for multidisciplinary design optimization under uncertain conditions. The methodology developed is illustrated by considering the example of an internal combustion (IC) engine. Various game protocols are used to model the optimization process and the results obtained are compared with each other. A genetic algorithm (GA) is used as an optimization and constraining tool. Convergence, constraint handling and processing time are considered to evaluate the efficacy of the methodology developed.

Improved Multidisciplinary Design Optimization Based on Genetic Algorithm and Artificial Neural Networks and Its Application

2016 ◽  
Vol 13 (10) ◽  
pp. 6501-6508
Author(s):  
Yi Su ◽  
Fa-Yin Wang ◽  
Jian-Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Computational Cost ◽  
System Level ◽  
Multidisciplinary Design ◽  
Local Optimum ◽  
Artificial Neural

Multidisciplinary Design Optimization (MDO) is an algorithm widely used in the engineering field currently. However, traditional MDO often leads to the failure of convergence or local optimum problems caused by convergence. In such cases, a multidisciplinary design optimization based on genetic algorithm (GA) and artificial neural networks (ANN) (GA-ANN-MDO) is presented in the paper. Under the thought of parallel distribution of traditional MDO, the real sub-disciplinary model is replaced by a highly precise ANN model dependent on the Latin Hypercube experimental design method in the GA-ANN-MDO, so as to reduce the computational cost and smooth the value noise. The GA optimization system level is applied to decline the possibility of partial solution involved in the optimization. As shown from the optimization results of two classic mathematical examples, GA-ANN-MDO is presented good robustness, which could quickly and effectively converge to the global optimal solution. In addition, a project example was employed finally to verify the feasibility of GA-ANN-MDO in the engineering.

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