Optimization of Helicopter Rotor Using Polynomial and Neural Network Metamodels

This study aims to determine optimal locations of dual trailing-edge flaps and blade stiffness to achieve minimum hub vibration levels in a helicopter, with low penalty in terms of required trailing-edge flap control power. An aeroelastic analysis based on finite elements in space and time is used in conjunction with an optimal control algorithmtodeterminethe flaptimehistoryforvibrationminimization.Usingtheaeroelasticanalysis,itisfoundthat the objective functions are highly nonlinear and polynomial response surface approximations cannot describe the objectives adequately. A neural network is then used for approximating the objective functions for optimization. Pareto-optimal points minimizing both helicopter vibration and flap power are obtained using the response surface and neural network metamodels. The two metamodels give useful improved designs resulting in about 27% reduction in hub vibration and about 45% reduction in flap power. However, the design obtained using response surface is less sensitive to small perturbations in the design variables.

[1]  Uri Kirsch,et al.  3 – Approximation concepts for optimum structural design , 1985 .

[2]  Friedrich K. Straub,et al.  Rotors with Trailing Edge Flaps: Analysis and Comparison with Experimental Data , 1998 .

[3]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[4]  Shou-Jen Lee,et al.  Design optimization with back-propagation neural networks , 1991, J. Intell. Manuf..

[5]  Ranjan Ganguli,et al.  OPTIMUM DESIGN OF A HELICOPTER ROTOR FOR LOW VIBRATION USING AEROELASTIC ANALYSIS AND RESPONSE SURFACE METHODS , 2002 .

[6]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[7]  Inderjit Chopra,et al.  Aeroelastic optimization of an advanced geometry helicopter rotor , 1992 .

[8]  Kenneth F. Guinn.  Individual Blade Control Independent of a Swashplate , 1982 .

[9]  Ranjan Ganguli,et al.  Optimal placement of trailing-edge flaps for helicopter vibration reduction using response surface methods , 2007 .

[10]  Peretz P. Friedmann,et al.  New developments in vibration reduction with actively controlled trailing edge flaps , 2001 .

[11]  Prabhat Hajela,et al.  Neural networks in structural analysis and design - An overview , 1992 .

[12]  Ranjan Ganguli,et al.  Surrogate based design optimisation of composite aerofoil cross-section for helicopter vibration reduction , 2012, The Aeronautical Journal (1968).

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

[14]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[15]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[16]  Ranjan Ganguli,et al.  Aeroelastic optimization of a helicopter rotor using orthogonal array-based metamodels , 2006 .

[17]  Peretz P. Friedmann,et al.  Structural optimization for vibratory loads reduction of composite helicopter rotor blades with advanced geometry tips , 1998 .

[18]  J. G. Leishman,et al.  Indicial lift approximations for two-dimensional subsonic flow as obtained from oscillatory measurements , 1993 .

[19]  John E. Renaud,et al.  Reliability based design optimization using response surfaces in application to multidisciplinary systems , 2004 .

[20]  Li Liu,et al.  Helicopter vibration reduction throughout the entire flight envelope using surrogate-based optimization , 2007 .

[21]  Ranjan Ganguli,et al.  Survey of Recent Developments in Rotorcraft Design Optimization , 2004 .

[22]  Aditi Chattopadhyay.  VIBRATION REDUCTION IN AN ARTICULATED ROTOR BLADE USING STRUCTURAL OPTIMIZATION , 1992 .

[23]  Jongsoo Lee,et al.  Parallel Genetic Algorithm Implementation in Multidisciplinary Rotor Blade Design , 1996 .

[24]  Jianhua Zhang,et al.  Active-Passive Hybrid Optimization of Rotor Blades With Trailing Edge Flaps , 2000 .

[25]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[26]  Peretz P. Friedmann,et al.  Renaissance of Aeroelasticity and Its Future , 1999 .

[27]  Ranjan Ganguli,et al.  Aeroelastic Response of Composite Helicopter Rotor with Random Material Properties , 2008 .

[28]  T. R. Mccarthy,et al.  A multidisciplinary optimization approach for vibration reduction in helicopter rotor blades , 1993 .

[29]  H. Mhaskar,et al.  Neural networks for function approximation , 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.

[30]  Robert A. Ormiston,et al.  Hover Testing of a Small-Scale Rotor with On-Blade Elevons , 2001 .

[31]  Bryan Glaz,et al.  Surrogate based optimization of helicopter rotor blades for vibration reduction in forward flight , 2008 .

[32]  R. V. Patel,et al.  A counter-propagation neural network for function approximation , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[33]  J. -F. M. Barthelemy,et al.  Approximation concepts for optimum structural design — a review , 1993 .

[34]  Robert F. Stengel,et al.  Neural Networks for Function Approximation in Nonlinear Control , 1990, 1990 American Control Conference.

[35]  Dewey H. Hodges,et al.  Performance Enhancement of a Composite Tilt-Rotor Using Aeroelastic Tailoring , 2000 .

[36]  Satchi Venkataraman,et al.  Reliability optimization using probabilistic sufficiency factor and correction response surface , 2006 .

[37]  Bernard Widrow,et al.  The basic ideas in neural networks , 1994, CACM.

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

[39]  Martin K. Sekula,et al.  Helicopter Vibration Reduction Using Fixed-System Auxiliary Moments , 2002 .

[40]  Ranjan Ganguli,et al.  An optimization approach to vibration reduction in helicopter rotors with multiple active trailing edge flaps , 2004 .

[41]  C. E. Hammond,et al.  Wind Tunnel Results Showing Rotor Vibratory Loads Reduction Using Higher Harmonic Blade Pitch , 1980 .

[42]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[43]  Peretz P. Friedmann,et al.  A new sensitivity analysis for structural optimization of composite rotor blades , 1993 .

[44]  Peretz P. Friedmann,et al.  Structural optimization with aeroelastic constraints of rotor blades with straight and swept tips , 1990 .

[45]  Junjiro Onoda,et al.  Observability of Self-Sensing System Using Extended Kalman Filter , 2007 .

[46]  Inderjit Chopra,et al.  Aeroelastic optimization of a helicopter rotor to reduce vibration and dynamic stresses , 1996 .

[47]  Wayne Johnson,et al.  Self-Tuning Regulators for Multicyclic Control of Helicopter Vibration , 1982 .

[48]  G. Matheron.  Principles of geostatistics , 1963 .

[49]  Inderjit Chopra,et al.  Wind Tunnel Testing of a Smart Rotor Model with Trailing-Edge Flaps , 2002 .

[50]  J. G. Leishman,et al.  Unsteady aerodynamics of a flapped airfoil in subsonic flow by indicial concepts , 1995 .

[51]  Robert G. Loewy,et al.  Helicopter Vibrations: A Technological Perspective , 1984 .

[52]  Inderjit Chopra,et al.  Application of Higher Harmonic Control to Rotors Operating at High Speed and Thrust , 1990 .

[53]  Nesrin Sarigul-Klijn,et al.  Elastic-Dynamic Rotor Blade Design with Multiobjective Optimization , 2001 .