Javascript must be enabled to continue!
Rear Fuselage Structural Optimization Using Genetic Algorithm
View through CrossRef
Structural optimization is one of the most important tasks during the airplane structural design and yet there is a lack of well established procedures to determine the optimum design of aerospace structures. This paper represents development of structural optimization code based on genetic algorithm, and results for light aircraft aft fuselage optimal design using the optimization code. We have developed a MATLAB code according to the genetic algorithm and FE model for the composite light aircraft aft fuselage using MSC PATRAN to generate NASTRAN input file; the MATLAB code was coupled with MSC NASTRAN which used to perform linear static and buckling analysis. we obtained the minimum weight of the aft fuselage with a linear static and buckling constraints; the minimum weight is 21.6 kg; it has been obtained after 43 iterations; the margin of safety is of the optimum design is 1.22 and the buckling factor is 1.24 and hence the structure is free of static failure and buckling. The code is efficient in the conceptual and preliminary structural design phases to obtain the optimal aft fuselage weight.
Title: Rear Fuselage Structural Optimization Using Genetic Algorithm
Description:
Structural optimization is one of the most important tasks during the airplane structural design and yet there is a lack of well established procedures to determine the optimum design of aerospace structures.
This paper represents development of structural optimization code based on genetic algorithm, and results for light aircraft aft fuselage optimal design using the optimization code.
We have developed a MATLAB code according to the genetic algorithm and FE model for the composite light aircraft aft fuselage using MSC PATRAN to generate NASTRAN input file; the MATLAB code was coupled with MSC NASTRAN which used to perform linear static and buckling analysis.
we obtained the minimum weight of the aft fuselage with a linear static and buckling constraints; the minimum weight is 21.
6 kg; it has been obtained after 43 iterations; the margin of safety is of the optimum design is 1.
22 and the buckling factor is 1.
24 and hence the structure is free of static failure and buckling.
The code is efficient in the conceptual and preliminary structural design phases to obtain the optimal aft fuselage weight.
Related Results
The Aerodynamics Analysis on Cambered Fuselage Model
The Aerodynamics Analysis on Cambered Fuselage Model
There various factors gives influence in determining the fuselage shapes, such as the payload, cockpit, wing and tail placements or in manner up and down loading the payload for a ...
Form Follows Force: A theoretical framework for Structural Morphology, and Form-Finding research on shell structures
Form Follows Force: A theoretical framework for Structural Morphology, and Form-Finding research on shell structures
The springing up of freeform architecture and structures introduces many challenges to structural engineers. The main challenge is to generate structural forms with high structural...
Design and Aerodynamic Analysis of Fixed-Wing Vertical Take-Off Landing (FW-VTOL) UAV
Design and Aerodynamic Analysis of Fixed-Wing Vertical Take-Off Landing (FW-VTOL) UAV
This study aims to produce a Fixed Wing-Vertical Takeoff Landing (FW-VTOL) design that has good aerodynamic characteristics on its wing and fuselage. The design process begins with...
Experimental and CFD Investigation of Directional Stability of a Box-Wing Aircraft Concept
Experimental and CFD Investigation of Directional Stability of a Box-Wing Aircraft Concept
This study aimed to explore the directional stability issues of a previously studied light box-wing aircraft model with a pusher propeller engine in the fuselage aft section. Earli...
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm ...
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction
The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm ...
Shape optimization for beam structural design
Shape optimization for beam structural design
"Optimization is concerned with achieving the best outcome of a given objective while satisfying certain restrictions. The central purpose of structural analysis is to predict the ...
A NEW MULTI-OBJECTIVE ARITHMETIC OPTIMIZATION ALGORITHM
A NEW MULTI-OBJECTIVE ARITHMETIC OPTIMIZATION ALGORITHM
Today, as engineering problems become more complex in terms of the effective variables in these problems and the range of their changes and their multidimensionality (in terms of n...

