Wing Design for a High-Speed Civil Transport Using a Design of Experiments Methodology

The presence of numerical noise inhibits gradient-based optimization and therefore limits the practicality of performing aircraft multidisciplinary design optimization (MDO). To address this issue, a procedure has been developed to create noise free algebraic models of subsonic and supersonic aerodynamic performance for use in the MDO of high-speed civil transport (HSCT) configurations. This procedure employs methods from statistical design of experiments theory to select a set of HSCT wing designs (fuselage/tail/engine geometry fixed) for which numerous detailed aerodynamic analyses are performed. Polynomial approximations (i.e., response surface models) are created from the aerodynamic data to provide analytical models relating aerodynamic quantities (e.g., wave drag and drag-due-to-lift) to the variables which define the HSCT wing configuration. A multidisciplinary design optimization of the HSCT is then performed using the response surface models in lieu of the traditional, local gradient based design methods. The use of response surface models makes possible the efficient and robust application of MDO to the design of an aircraft system. Results obtained from five variable and ten variable wing design problems presented here demonstrate the effectiveness of this response surface modeling method.

[1]  Serhat Yesilyurt,et al.  Bayesian-Validated Surrogates for Noisy Computer Simulations; Application to Random Media , 1996, SIAM J. Sci. Comput..

[2]  C. T. Kelley,et al.  An Implicit Filtering Algorithm for Optimization of Functions with Many Local Minima , 1995, SIAM J. Optim..

[3]  John E. Renaud,et al.  Response surface approximations for discipline coordination in multidisciplinary design optimization , 1996 .

[4]  L. A. Mccullers Aircraft configuration optimization including optimized flight profiles , 1984 .

[5]  Matthew Gerry Hutchison,et al.  Multidisciplinary optimization of high-speed civil transport configurations using variable-complexity modeling , 1993 .

[6]  R. Harris,et al.  An analysis and correlation of aircraft wave drag , 1964 .

[7]  Andrew J. Booker,et al.  A comparison of optimization and search methods for multidisciplinary design , 1992 .

[8]  Bernard Grossman,et al.  Mul-tidisciplinary Optimization of the High-Speed Civil Transport , 1995 .

[9]  Dimitri N. Mavris,et al.  An Application of Response Surface Methodology to the Design of Tipjet Driven Stopped Rotor/Wing Concepts , 1995 .

[10]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

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

[12]  Ilan Kroo,et al.  A role for genetic algorithms in a preliminary design environment , 1993 .

[13]  B. Grossman,et al.  Variable-complexity response surface approximations for wing structural weight in HSCT design , 1996 .

[14]  Bernard Grossman,et al.  Noisy Aerodynamic Response and Smooth Approximations in HSCT Design , 1994 .

[15]  Douglas O. Stanley,et al.  Aerodynamic configuration design using response surface methodology analysis , 1993 .

[16]  Mauro Valorani,et al.  Optimization methods for non-smooth or noisy objective functions in fluid design problems , 1995 .

[17]  M. J. Box,et al.  Factorial Designs, the |X′X| Criterion, and Some Related Matters , 1971 .

[18]  Virginia Torczon,et al.  On the Convergence of Pattern Search Algorithms , 1997, SIAM J. Optim..

[19]  Harry W. Carlson,et al.  Numerical methods and a computer program for subsonic and supersonic aerodynamic design and analysis of wings with attainable thrust considerations , 1984 .

[20]  Bernard Grossman,et al.  Variable-complexity response surface aerodynamic design of an HSCT wing , 1995 .

[21]  Bernard Grossman,et al.  A Coarse-Grained Parallel Variable-Complexity Multidisciplinary Optimization Paradigm , 1996, Int. J. High Perform. Comput. Appl..

[22]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[23]  Raphael T. Haftka,et al.  Certification of a CFD code for high-speed civil transport design optimization , 1996 .

[24]  Philip E. Gill,et al.  Practical optimization , 1981 .

[25]  J. S. Kowalik,et al.  Airplane Engine Selection by Optimization on Surface Fit Approximations , 1975 .

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

[27]  Michael J. Siclari,et al.  The design of transonic airfoil sections for an adaptive wing concept using a stochastic optimization method , 1996 .

[28]  P. Gelhausen,et al.  ACSYNT - A standards-based system for parametric, computer aided conceptual design of aircraft , 1992 .

[29]  Jaroslaw Sobieszczanski-Sobieski,et al.  Multidisciplinary aerospace design optimization - Survey of recent developments , 1996 .

[30]  Sherif Aly,et al.  Stochastic optimization applied to CFD shape design , 1995 .