Overview of Stability and Control Estimation Methods from NATO STO Task Group AVT-201

NATO STO Task Group AVT-201 on “Extended Assessment of Reliable Stability & Control Prediction Methods for NATO Air Vehicles” is studying various computational approaches to predict stability and control parameters for maneuvering aircraft. The ability to accurately predict both static and dynamic stability characteristics of air vehicles using computational fluid dynamics (CFD) methods could revolutionize the vehicle design process for NATO air vehicles. A validated CFD capability would significantly reduce the number of ground tests required to verify vehicle concepts and, in general, could eliminate costly vehicle ‘repair’ campaigns required to fix performance anomalies that were not adequately predicted prior to full-scale vehicle development. As a result, significant reductions in acquisition cost, schedule, and risk could be realized. The dramatic increase in computing power and the affordability of computer clusters now make it feasible for the majority of NATO nations to undertake time-accurate CFD simulations, and an assessment of the stateof- the-art has been carried out previously. The objective of the Task Group is to determine an overall strategy for creating stability and control databases for vehicle simulation at fullscale conditions, including the deflection of control surfaces, throughout the operational envelope of the vehicle. These assessments are being done on two vehicles: SACCON, a generic UCAV designed and tested for the predecessor to AVT-201 (AVT-161) and the X-31 highly maneuverable aircraft previously flight tested by the US and Germany. Extensive wind tunnel data has been (and is being) collected for these configurations, including static and dynamic cases, with and without control surfaces, and at low and high subsonic Mach numbers. The methods and approaches used in the Task Group will be overviewed, including an assessment of the state-of-the-art capability. The approaches for estimating static and dynamic stability parameters are discussed, including reduced-order model methods such as Volterra, Radial Basis, and Indicial functions. A status of these activities is provided and future work is overviewed, including future wind tunnel tests and simulation approaches.

[1]  Patrick Reisenthel,et al.  Data-based aerodynamic modeling using nonlinear indicial theory , 1999 .

[2]  Nikolay Abramov,et al.  Two-step linear regression method for identification of high incidence unsteady aerodynamic model , 2001 .

[3]  Vimal Singh,et al.  Identification Techniques , 1973, IEEE Transactions on Systems, Man and Cybernetics.

[4]  Mark B. Tischler,et al.  Aircraft and Rotorcraft System Identification: Engineering Methods with Flight-Test Examples , 2006 .

[5]  Mark S. Smith,et al.  Analysis of Wind Tunnel Oscillatory Data of the X-31A Aircraft , 1999 .

[6]  C. Allen,et al.  Unified fluid–structure interpolation and mesh motion using radial basis functions , 2008 .

[7]  Russell M. Cummings,et al.  Reduced order modeling of X-31 wind tunnel model aerodynamic loads , 2012 .

[8]  Ken Badcock,et al.  Framework for establishing the limits of tabular aerodynamic models for flight dynamics analysis , 2011 .

[9]  R. Kvaternik,et al.  A Computational Procedure for Identifying Bilinear Representations of Nonlinear Systems Using Volterra Kernels , 2008 .

[10]  Andreas Bergmann,et al.  Ground-Based Simulation of Complex Maneuvers of a Delta-Wing Aircraft , 2008 .

[11]  C. Allen,et al.  Domain-Element Method for Aerodynamic Shape Optimization Applied to a Modern Transport Wing , 2009 .

[12]  Andreas Schütte,et al.  Numerical Simulation of Manoeuvring Aircraft by Aerodynamic and Flight-Mechanic Coupling , 2003 .

[13]  O. Amoignon.  AESOP—a numerical platform for aerodynamic shape optimization , 2010 .

[14]  David J. Lucia,et al.  Reduced-order modelling of limit-cycle oscillation for aeroelastic systems , 2004 .

[15]  Gregory M. Gatlin,et al.  The Langley 14- by 22-Foot Subsonic Tunnel: Description, Flow Characteristics, and Guide for Users , 1990 .

[16]  Neal M. Chaderjian,et al.  Automation of a Navier-Stokes S&C database generation for the Harrier in ground effect , 2002 .

[17]  Andreas-René Hübner,et al.  Influence of Wing Elasticity on Dynamic Derivatives of Transport Aircraft. , 2010 .

[18]  P. Kelly,et al.  Residual pitch oscillation (RPO) flight test and analysis on the B-2 bomber , 1998 .

[19]  Robert M. Hall,et al.  Historical Review of Uncommanded Lateral-Directional Motions at Transonic Conditions , 2004 .

[20]  Patrick Reisenthel,et al.  Development of a nonlinear indicial model using response functions generated by a neural network , 1997 .

[21]  Russell M. Cummings,et al.  Computational Investigation into the Use of Response Functions for Aerodynamic-Load Modeling , 2012 .

[22]  D. Hummel,et al.  Low Speed Wind Tunnel Experiments on a Delta Wing Oscillating in Pitch , 1998 .

[23]  Neal M. Chaderjian,et al.  Progress Toward Generation of a Navier-Stokes Database for a Harrier in Ground Effect , 2002 .

[24]  Robert T. Biedron,et al.  Computational Methods for Stability and Control (COMSAC): The Time Has Come , 2005 .

[25]  R. V. Jategaonkar,et al.  Aircraft system identification , 2006 .

[26]  Detlef Rohlf,et al.  SACCON Stability and Control Analysis Applying System Identification Techniques , 2010 .

[27]  R. L. Hardy.  Multiquadric equations of topography and other irregular surfaces , 1971 .

[28]  Vladislav Klein,et al.  Modeling of aircraft unsteady aerodynamic characteristics. Part 1: Postulated models , 1994 .

[29]  Scott A. Morton,et al.  Comparisons of CFD Solutions of Static and Maneuvering Fighter Aircraft with Flight Test Data , 2007 .

[30]  V. Klein,et al.  Parameter Estimation of a Highly Augmented Aircraft from Flight Data , 1992 .

[31]  Martin Rein,et al.  Experimental and numerical aspects of simulating unsteady flows around the X-31 configuration , 2009 .

[32]  Gary T. Chapman,et al.  Nonlinear problems in flight dynamics involving aerodynamic bifurcations , 1985 .

[33]  David J. Lucia,et al.  Reduced-Order Model Development Using Proper Orthogonal Decomposition and Volterra Theory , 2004 .

[34]  Mikhail Goman,et al.  State-Space Representation of Aerodynamic Characteristics of an Aircraft at High Angles of Attack , 1994 .

[35]  W. Silva,et al.  Discrete-time linear and nonlinear aerodynamic impulse responses for efficient cfd analyses , 1997 .

[36]  O. J. Boelens,et al.  Prediction of the flow around the X-31 aircraft using three different CFD methods , 2012 .

[37]  Arthur Rizzi,et al.  Modeling and simulating aircraft stability and control—The SimSAC project , 2011 .

[38]  Rudolph N. Yurkovich,et al.  Status of Unsteady Aerodynamic Prediction for Flutter of High-Performance Aircraft , 2003 .

[39]  W. Silva,et al.  Identification of Nonlinear Aeroelastic Systems Based on the Volterra Theory: Progress and Opportunities , 2005 .

[40]  Larry A. Meyn,et al.  Full-scale wind-tunnel studies of F/A-18 tail buffet , 1996 .

[41]  C. Lan,et al.  Unsteady aerodynamic models for maneuvering aircraft , 1993 .

[42]  Andreas-René Hübner,et al.  Integrated Experimental and Numerical Research on the Aerodynamics of Unsteady Moving Aircraft , 2007 .

[43]  L. B. Schiff,et al.  Mathematical modeling of the aerodynamic characteristics in flight dynamics , 1984 .

[44]  Russell M. Cummings,et al.  Unsteady Aerodynamics Modeling for Aircraft Maneuvers: a New Approach Using Time-Dependent Surrogate Modeling , 2012 .

[45]  Jens Neumann,et al.  Numerical Simulation of Maneuvering Aircraft by Aerodynamic, Flight Mechanics and Structural Mechanics Coupling , 2007 .

[46]  Stuart E. Rogers,et al.  Automated CFD Parameter Studies on Distributed Parallel Computers , 2003 .

[47]  J. R. Chambers,et al.  Accomplishments of the Abrupt-Wing-Stall Program , 2005 .

[48]  C. E. Manglano-Villamarin,et al.  Three-dimensional indicial response of finite aspect ratio yawed wings , 2007 .

[49]  Eugene A. Morelli,et al.  System IDentification Programs for AirCraft (SIDPAC) , 2002 .