High aspect ratiowing design: Optimal aerostructural tradeoffs for the next generation of materials

Current and future composite material technologies have the potential to greatly improve the performance of large transport aircraft. However, the coupling between aerodynamics and structures makes it challenging to design optimal flexible wings, and the transonic flight regime requires high fidelity computational models. We address these challenges by solving a series of mediumand highfidelity aerostructural optimization problems that explore the design space for the wing of a large transport aircraft. We consider three different materials: aluminum, carbon-fiber reinforced composites and an hypothetical composite based on carbon nanotubes. The design variables consist of both aerodynamic shape (including span), and structural sizing, as well as ply angle fractions in the case of composites. Pareto fronts with respect to takeoff weight and fuel burn are generated. The wing performance in each case is optimized subject to stress and buckling constraints. We found that composite wings consistently resulted in lower fuel burn and lower structural weight, and that the carbon nanotube composite did not yield the increase in performance one would expect from a material with such outstanding properties. This was in part due to the minimum structural thickness constraint. For all materials, the minimum fuel burn wings were found to be longer, heavier, thinner, more flexible, and more lightly loaded than their minimum TOGW counterparts.

[1]  E. J. Hopkins Charts for predicting turbulent skin friction from the Van Driest method (2) , 1972 .

[2]  W. J. Stroud,et al.  Minimum-Mass Design of Filamentary Composite Panels under Combined Loads: Design Procedure Based on Simplified Buckling Equations. , 1976 .

[3]  V. B. Venkayya,et al.  Structural optimization: A review and some recommendations , 1978 .

[4]  Y. Saad,et al.  GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems , 1986 .

[5]  A Jameson,et al.  Computational Aerodynamics for Aircraft Design , 1989, Science.

[6]  Masoud Rais-Rohani,et al.  Integrated aerodynamic-structural design of a transport wing , 1989 .

[7]  Brett Malone,et al.  Multidisciplinary optimization in aircraft design using analytic technology models , 1991 .

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

[9]  William Gropp,et al.  Efficient Management of Parallelism in Object-Oriented Numerical Software Libraries , 1997, SciTools.

[10]  S. Brown,et al.  Displacement extrapolations for CFD+CSM aeroelastic analysis , 1997 .

[11]  Ilan Kroo,et al.  Aircraft Design: Synthesis and Analysis , 1999 .

[12]  Ilan Kroo,et al.  DRAG DUE TO LIFT: Concepts for Prediction and Reduction , 2001 .

[13]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2002, SIAM J. Optim..

[14]  Joaquim R. R. A. Martins,et al.  High-Fidelity Aerostructural Design Optimization of a Supersonic Business Jet , 2002 .

[15]  Raphael T. Haftka,et al.  Structural optimization complexity: what has Moore’s law done for us? , 2004 .

[16]  J. Alonso,et al.  A Coupled-Adjoint Sensitivity Analysis Method for High-Fidelity Aero-Structural Design , 2005 .

[17]  J. Alonso,et al.  ADjoint: An Approach for the Rapid Development of Discrete Adjoint Solvers , 2006 .

[18]  Georgi Kalitzin,et al.  Unsteady turbomachinery computations using massively parallel platforms , 2006 .

[19]  Joaquim R. R. A. Martins,et al.  An adaptive approach to constraint aggregation using adjoint sensitivity analysis , 2007 .

[20]  Joaquim R. R. A. Martins,et al.  An asymmetric suboptimization approach to aerostructural optimization , 2009 .

[21]  Joaquim R. R. A. Martins,et al.  Parallel Solution Methods for Aerostructural Analysis and Design Optimization , 2010 .

[22]  John C. Vassberg,et al.  A Unified Baseline Grid about the Common Research Model Wing-Body for the Fifth AIAA CFD Drag Prediction Workshop , 2011 .

[23]  Joaquim R. R. A. Martins,et al.  Structural and Multidisciplinary Optimization Manuscript No. Pyopt: a Python-based Object-oriented Framework for Nonlinear Constrained Optimization , 2022 .

[24]  Joaquim R. R. A. Martins,et al.  A Comparison of Metallic and Composite Aircraft Wings Using Aerostructural Design Optimization , 2012 .

[25]  Cody A. Paige,et al.  Automatic Differentiation Adjoint of the Reynolds-Averaged Navier-Stokes Equations with a Turbulence Model , 2013 .

[26]  C. Mader,et al.  Stability-Constrained Aerodynamic Shape Optimization of Flying Wings , 2013 .

[27]  John T. Hwang,et al.  Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models , 2013 .

[28]  Joaquim R. R. A. Martins,et al.  Multidisciplinary design optimization: A survey of architectures , 2013 .

[29]  Joaquim R. R. A. Martins,et al.  A laminate parametrization technique for discrete ply-angle problems with manufacturing constraints , 2013 .

[30]  Carol D. Wieseman,et al.  Aeroelastic Tailoring of the NASA Common Research Model via Novel Material and Structural Configurations , 2014 .

[31]  Joaquim R. R. A. Martins,et al.  Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration , 2014 .

[32]  Joaquim R. R. A. Martins,et al.  Aerodynamic Design Optimization Studies of a Blended-Wing-Body Aircraft , 2014 .

[33]  Graeme J. Kennedy,et al.  Scalable Parallel Approach for High-Fidelity Steady-State Aeroelastic Analysis and Adjoint Derivative Computations , 2014 .

[34]  Leonie Moench,et al.  Low Speed Aerodynamics , 2016 .

[35]  Tim Schmitz,et al.  Mechanics Of Composite Materials , 2016 .