Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

The multidisciplinary design optimization (MDO) is an important concept that focuses on bringing together disciplines involved with machine design. It is possible to use MDO in any stage of an aircraft design, that is in the conceptual, preliminary, or detailed design, as long as the numerical models are fitted to each of these stages. This work describes the development of a multidisciplinary design optimization applied to flexible aircraft wings, with respect to structural and aeroelastic characteristics. As tools for the aircraft designer, the numerical models must be fairly accurate and fast. Therefore, metamodels for the critical flutter speed prediction of aircraft wings were considered, thereby reducing significantly the computational cost of the optimization. For this purpose, artificial neural networks (NN) metamodeling is evaluated, based on their inherent properties in dealing with complex mappings. The NN metamodel is prepared using an aeroelastic code based on finite-element model coupled with linear potential aerodynamics. Results of the metamodel performance are presented, from where one can note that the NN is well suited for flutter prediction. Multiobjective optimization (MOO) using the genetic algorithm (GA) based on non-dominance approach is considered. The objectives were the maximization of critical flutter speed and minimization of structural mass. One case study is presented to evaluate the performance of the MOO, revealing that overall optimization process actually achieves the search for the Pareto frontier.

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

[2]  Dewey H. Hodges,et al.  Introduction to Structural Dynamics and Aeroelasticity: Contents , 2002 .

[3]  Ramana V. Grandhi,et al.  Multidisciplinary optimization of an aircraft wing/tip store configuration in the transonic regime , 2004 .

[4]  Murray B. Anderson,et al.  Using Pareto genetic algorithms for preliminary subsonic wing design , 1996 .

[5]  Achille Messac,et al.  Metamodeling using extended radial basis functions: a comparative approach , 2006, Engineering with Computers.

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

[7]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .

[8]  Dewey H. Hodges,et al.  Introduction to Structural Dynamics and Aeroelasticity , 2002 .

[9]  Peter Horst,et al.  INFLUENCE OF AEROELASTIC EFFECTS ON PRELIMINARY AIRCRAFT DESIGN , 2000 .

[10]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[11]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[12]  Bento Silva de Mattos,et al.  Artificial neural networks to predict aerodynamic coefficients of transport airplanes , 2017 .

[13]  Rakesh K. Kapania,et al.  Preliminary Design of a Structural Wing Box Under a Twist Constraint Part I , 2004 .

[14]  Daniel Raymer,et al.  Enhancing Aircraft Conceptual Design using Multidisciplinary Optimization , 2002 .

[15]  Ilan Kroo,et al.  Framework for Aircraft Conceptual Design and Environmental Performance Studies , 2005 .

[16]  Martin T. Hagan,et al.  Neural network design , 1995 .

[17]  Teng Long,et al.  Comprehensive Study of Typical Metamodel Methods Applied in Aircraft Multidisciplinary Design Optimization , 2011 .

[18]  R. Haftka,et al.  Estimating training data boundaries in surrogate-based modeling , 2010 .

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

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

[21]  Luca Cavagna,et al.  NeoCASS: An integrated tool for structural sizing, aeroelastic analysis and MDO at conceptual design level , 2010 .

[22]  Valder Steffen,et al.  Optimization of aircraft structural components by using nature-inspired algorithms and multi-fidelity approximations , 2009, J. Glob. Optim..

[23]  De Baets,et al.  A methodology for aeroelastic constraint analysis in a conceptual design environment , 2004 .

[24]  Caixeta,et al.  Neural network metamodel-based MDO for wing design considering aeroelastic constraints , 2010 .

[25]  Alfred G. Striz,et al.  Influence of Model Complexity and Aeroelastic Constraints on Multidisciplinary Optimization of Wings , 1998 .

[26]  Jenn-Long Liu,et al.  Intelligent Genetic Algorithm and Its Application to Aerodynamic Optimization of Airplanes , 2005 .

[27]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[28]  Franco Mastroddi,et al.  On the use of geometry design variables in the MDO analysis of wing structures with aeroelastic constraints on stability and response , 2011 .