High-fidelity aerodynamic shape optimization using efficient orthogonal modal design variables with a constrained global optimizer

58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

2017

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Construction of the aerodynamic optimization problem is considered within the context of robustness. The most common aerodynamic optimization problem considered is a liftconstrained drag minimization problem (also subject to geometric constraints), however, point-design at transonic flow conditions can produce shock-free solutions and therefore the result is highly localised, where the gains obtained at the design point are outweighed by the losses at off-design conditions. As such, a range optimization problem subject to a constraint on fixed non-dimensional lift with a varying design point is considered to mitigate this issue. It is shown, first from an analytical treatment of the problem, and second from inviscid optimizations, that more robust solutions are obtainable when considering range optimization against drag minimization. Furthermore, to effectively capture the trade-offs that exist in three-dimensional aircraft design between range, lift, drag and speed, it is shown that an induced drag factor is required and this is sufficient to produce optimal solutions exhibiting shocks.

Section: Va Single-point and Multi-point Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

Objective Function and Constraints for Robust Transonic Aerofoil Optimization

58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

2017

“…While global optimization can often be more expensive than performing gradient-based optimization, it is more likely to locate a globally optimal solution in a multimodal design space. The optimization framework has been developed by the authors, and shown to be effective an optimizing benchmark analytical problems [42] as well as transonic aerofoil problems [1].…”

Section: Iiic Optimizermentioning

confidence: 99%

“…However, to investigate the mutimodality of the ADODG multimodal benchmark problem, a state-of-the-art constrained global optimization framework [42] is employed here. The framework, which uses a parallel decomposition of the search agent population for efficient computation, has previously been used for drag minimization of aerofoils [1] and is used here for the wing optimization.…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of Wing Aerodynamic Optimization Case Exhibiting Multimodality

2018

Journal of Aircraft

An investigation into an aerodynamic optimization benchmark case that exhibits multimodality is presented using a global optimization approach. The recently suggested case 6 of the AIAA Aerodynamic Design Optimization Discussion Group involves the drag minimization of a rectangular NACA0012 wing subject to lift and root bending moment, as well as other geometric constraints. In this paper, optimization of that case using a state-of-the-art constrained population-based global optimization framework is presented.Shape control is achieved via a hierarchical application of the radial basis function domain element approach. Three different fidelity of optimization cases are performed to investigate the multimodality of various aspects of the full problem, with four independent runs of each case being performed. When considering chord optimization, the optimizer converges to two independently identifiable minima with very similar performance. When adding dihedral and sweep variation, further multimodality is introduced, though this multimodality is reasonably well defined, with minima including forward and rearward sweep, and an upward and downward winglet. Introducing thickness variables leads to a highly multimodal problem due to the coupling between the chord and thickness variables. The theoretical minimum drag for this case is 24.7 counts, which a number of the runs get close to achieving.

“…The aerodynamic model (normally a computational fluid dynamics (CFD) flow solver) is used to evaluate some metric against which to optimize called the objective, which in the case of ASO is an aerodynamic quantity, such as drag [1] or range [2], subject to a set of constraints which are usually aerodynamic or geometric. Along with the fluid flow model, the ASO framework requires a surface parameterization scheme which mathematically describes the aerodynamic shape being optimized by a series of design variables; changes in the design variables, which are made by a numerical optimization algorithm, result in changes in the aerodynamic surface.…”

Section: Introductionmentioning

confidence: 99%

Global Optimization of Multimodal Aerodynamic Optimization Benchmark Case

35th AIAA Applied Aerodynamics Conference

2017

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. An investigation into a multimodal aerodynamic optimization benchmark case using an approximate global optimization approach is presented. A new benchmark case of the AIAA Aerodynamic Design Optimization Discussion Group that involves the drag minimization of a rectangular NACA0012 wing subject to lift and root bending moment, as well as other geometric constraints, that is currently believed to be multimodal has been suggested. In this paper, optimization of that case using a constrained populationbased global optimization framework is presented, with three independent runs of the optimizer (each with different starting locations) having been performed. The runs are all coarsely-converged to give approximately converged solutions such that rapid run-times can be achieved and used to demonstrate any pitfalls that may be encountered when using a gradient-based optimizer. All three of the runs converged onto drag values close to the theoretical optimum, however with substantially different final shapes. This likely indicates the presence of a flat optimum region within the design space, where a substantial number of wing shapes are able to obtain the theoretical optimum span loading.