Matrix-free aerostructural optimization of aircraft wings

• Published in: Structural and multidisciplinary optimization Vol. 53; no. 3; pp. 589 - 603
• Main Authors: Lambe, Andrew B., Martins, Joaquim R. R. A.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2016; Springer Nature B.V
• Subjects: Agglomeration; Aircraft; Aircraft design; Computation; Computational Mathematics and Numerical Analysis; Design optimization; Engineering; Engineering Design; Iterative methods; Quadratic programming; Research Paper; Structural design; Theoretical and Applied Mechanics; Wings (aircraft); Large-scale optimization; Matrix-free; Structural optimization; Constraint aggregation; Multidisciplinary design optimization
• ISSN: 1615-147X; 1615-1488
• DOI: 10.1007/s00158-015-1349-2

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.