Conceptual design of UAV using Kriging based multi-objective genetic algorithm

This paper highlights unmanned aerial vehicle (UAV) conceptual design using the multi-objective genetic algorithm (MOGA). The design problem is formulated as a multidisciplinary design optimisation (MDO) problem by coupling aerodynamic and structural analysis. The UAV considered in this paper is a low speed, long endurance aircraft. The optimisation problem uses endurance maximization and wing weight minimisation as dual objective functions. In this multi-objective optimisation, aspect ratio, wing loading, taper ratio, thickness-to-chord ratio, loiter velocity and loiter altitude are considered as design variables with stall speed, maximum speed and rate of climb as constraints. The MDO system integrates the aircraft design code, RDS and an empirical relation for objective function evaluation. In this study, the optimisation problem is solved in two approaches. In the first approach, the RDS code is directly integrated in the optimisation loop. In the second approach, Kriging model is employed. The second approach is fast and efficient as the meta-model reduces the time of computation. A relatively new multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. As a result of optimisation using multi-objective genetic algorithm, several non-dominated solutions indicating number of useful Pareto optimal designs is identified.

[1]  Kazuomi Yamamoto,et al.  Efficient Optimization Design Method Using Kriging Model , 2005 .

[2]  Ricardo Martinez-Botas,et al.  Application of generic algorithms in aerodynamic optimisation design procedures , 2004, The Aeronautical Journal (1968).

[3]  Ilan Kroo,et al.  Subsonic wing planform design using multidisciplinary optimization , 1995 .

[4]  Daniel P. Raymer,et al.  Aircraft Design: A Conceptual Approach , 1989 .

[5]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[6]  P. Hajela.  Nongradient Methods in Multidisciplinary Design Optimization-Status and Potential , 1999 .

[7]  Kroo Ilan,et al.  Multidisciplinary Optimization Methods for Aircraft Preliminary Design , 1994 .

[8]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[9]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[10]  K. Srinivas,et al.  Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm , 2006, The Aeronautical Journal (1968).

[11]  Robert E. Childs,et al.  Cumulative global metamodels with uncertainty — a tool for aerospace integration , 2006, The Aeronautical Journal (1968).

[12]  Farrokh Mistree,et al.  Statistical Experimentation Methods for Achieving Affordable Concurrent Systems Design , 1997 .

[13]  Dong-Ho Lee,et al.  Application of Collaborative Optimization Using Genetic Algorithm and Response Surface Method to an Aircraft Wing Design , 2006 .

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Shigeru Obayashi,et al.  Multidisciplinary design optimization of wing shape for a small jet aircraft using kriging model , 2006 .

[16]  Luis F. Gonzalez,et al.  A Generic Framework for the Design Optimisation of Multidisciplinary UAV Intelligent Systems using Evolutionary Computing , 2006 .

[17]  Gao Zhenghong,et al.  THE INVESTIGATION OF MULTI-DISCIPLINARY AND MULTI-OBJECTIVE OPTIMIZATION METHOD FOR THE AIRCRAFT CONFIGURATION DESIGN , 2002 .

[18]  Andy J. Keane,et al.  Multidisciplinary design optimization of UAV airframes , 2006 .

[19]  Daniel Raymer,et al.  A Comparative Study of Genetic Algorithm and Orthogonal Steepest Descent for Aircraft Multidisciplinary Optimization , 2002 .

[20]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[21]  T Haftka Raphael,et al.  Multidisciplinary aerospace design optimization: survey of recent developments , 1996 .