scholarly journals Kick Solid Rocket Motor Multidisciplinary Design Optimization Using Genetic Algorithm

2013 ◽  
Vol 5 (3) ◽  
pp. 293-304 ◽  
Author(s):  
Fredy Marcell Villanueva ◽  
He Linshu ◽  
Xu Dajun
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Rocket Motor ◽  
Multidisciplinary Design ◽  
Solid Rocket Motor ◽  
Solid Rocket

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.

Optimizing a solid rocket motor boosted ramjet powered missile using a genetic algorithm

2006 ◽  
Vol 181 (2) ◽  
pp. 1720-1736 ◽  
Author(s):  
Roy J. Hartfield ◽  
Rhonald M. Jenkins ◽  
John E. Burkhalter
Keyword(s):  
Genetic Algorithm ◽  
Rocket Motor ◽  
Solid Rocket Motor ◽  
Solid Rocket

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.

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