This thesis presents new tools and techniques developed to address the challenging problem of high-fidelity aerostructural optimization with respect to large numbers of design variables. A new mesh-movement scheme is developed that is both computationally efficient and sufficiently robust to accommodate large geometric design changes and aerostructural deformations. A fully coupled Newton-Krylov method is presented that accelerates the convergence of aerostructural systems
and provides a 20% performance improvement over the traditional nonlinear block Gauss-Seidel approach and can handle more flexible
structures. A coupled adjoint method is used that efficiently computes derivatives for a gradient-based optimization algorithm. The
implementation uses only machine accurate derivative techniques and is
verified to yield fully consistent derivatives by comparing against
the complex step method. The fully-coupled large-scale coupled adjoint solution method is shown to have 30% better performance than
the segregated approach. The parallel scalability of the coupled
adjoint technique is demonstrated on an Euler Computational Fluid Dynamics (CFD) model with more than 80 million state variables coupled
to a detailed structural finite-element model of the wing with more than 1 million degrees of freedom.
Multi-point high-fidelity aerostructural optimizations of a long-range wide-body, transonic transport aircraft configuration are performed using the developed techniques. The aerostructural analysis employs Euler CFD with a 2 million cell mesh and a structural finite element model with 300000 DOF. Two design optimization problems are solved:
one where takeoff gross weight is minimized, and another where fuel burn is minimized. Each optimization uses a multi-point formulation with 5 cruise conditions and 2 maneuver conditions. The optimization problems have 476 design variables are optimal results are obtained within 36 hours of wall time using 435 processors. The TOGW minimization results in a 4.2% reduction in TOGW with a 6.6% fuel burn reduction, while the fuel burn optimization resulted in a 11.2% fuel burn reduction with no change to the takeoff gross weight.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/35861 |
| Date | 08 August 2013 |
| Creators | Kenway, Gaetan Kristian Wiscombe |
| Contributors | Joaquim, Martins |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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