Influence of Search Algorithms on Aerodynamic Design Optimisation of Aircraft Wings

32nd AIAA Applied Aerodynamics Conference

2014

A comparison is presented of gradient-based and agent-based optimization schemes, applied to constrained aerodynamic shape optimization in two dimensions. The optimizers developed are a feasible sequential quadratic programming (FSQP) gradient-based algorithm and a modified gravitational search algorithm (GSA), extended to include a new separation-sub-swarm (3S) constraint handling method. Lift constrained drag minimization of the NACA 0012 and RAE 2822 aerofoils at different flow conditions are presented, and results show that the constraint handling global search algorithm is able to locate better optima than the gradient-based scheme, although at the cost of more objective function evaluations. The design variables used here are derived using a mathematical decomposition, which has produced a set of orthogonal design variables that are very efficient, and as few as six are required to obtain shock-free solutions using the global search algorithm.

Section: Iib Global Optimizationmentioning

confidence: 99%

Section: Iib Global Optimizationmentioning

confidence: 99%

Comparison of Local and Global Constrained Aerodynamic Shape Optimization

32nd AIAA Applied Aerodynamics Conference

2014

“…Due to the substantial cost of using a global optimization approach for an ASO problem, and the apparent lack of multimodality in a number of ASO problems, global optimizers have had only a small use in ASO, see [37][38][39][40][41] for example. However, to investigate the mutimodality of the ADODG multimodal benchmark problem, a state-of-the-art constrained global optimization framework [42] is employed here.…”

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 application of conventional nature-inspired methods to aerodynamic optimization has been significant (see, for example [4,[39][40][41][42][43][44] ), however the use of niching methods is much less despite the clear motivation to locate multiple optima in aerodynamic problems. The main application of such approaches to date is from Obayashi et al [45] who used niching techniques to locate the pareto front of a multi-objective wing design problem.…”

Section: Introductionmentioning

confidence: 99%

Identifying Multiple Optima in Aerodynamic Design Spaces

2018 Multidisciplinary Analysis and Optimization Conference

2018

Parallel niching optimization algorithms are developed and applied to a multimodal aerodynamic optimization case to identify multiple optima in the design space. Previous work by the authors has presented niching optimization algorithms that use differential evolution with feasible selection as a basis that can identify multiple optima in constrained search spaces, which is necessary for aerodynamic optimization. In this paper, these algorithms are further developed for application to aerodynamic optimization by reformulating each to provide a parallel decomposition of the objective function evaluation at each iteration of the optimization process. These algorithms are tested on an analytical optimization problem. The parallel, constrained form of both local nearest-neighbourhood and local crowding are shown to be the best performing algorithms with a 99% confidence level. A variation on the AIAA ADODG case 6 multimodal wing optimization case is also studied. A multi-start gradient-based approach is used to show multimodality of the design space. The local nearest-neighbourhood-based algorithm is then applied to the aerodynamic optimization case and is able to successfully identify two minima in the design space, with one being close to the global minimum and one in a different part of the design space, which is a local minimum. * Senior Teaching Associate.