By Andras Sobester, Alexander I J Forrester
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Additional resources for Aircraft Aerodynamic. Design Geometry and Optimization
16. 4. While this allows much freedom in shaping the cross-section, in some cases it might be desirable to add the possibility of stretching the geometry – this is a simple alteration, but one that comes at the cost of another design variable C: Y(Z) = CZ N1 (1 − Z)N2 , Z ∈ [0, 1]. 17) This is now a fairly flexible geometry, but it is missing two key tricks, which are particularly important in the design of passenger airliner-type fuselages. We next look at ways of addressing these shortcomings.
Evolutionary algorithms typically operate over a discretized version of the design space, so solutions resulting from them may need to be fine tuned via more fine-grained local search methods (occasionally these are integrated more tightly into the evolutionary algorithm through a process imitating the idea of Lamarckian learning). Surrogate Model-Based Methods The simulations required for the calculation of objective functions may take up computational resources that limit such calculations to numbers that are too low for any conventional global search technique to make much progress with.
Neither type is ‘right’ or ‘wrong’ – not being clear on which type we are using is wrong. 1 introduced some of the key features that a parametric geometry must have. Here, we shall review some desirable characteristics that it should have. Different engineers may hold subtly different mental wish-lists under this heading, but these differences are usually in the ranking, rather than in the entries themselves. Here, then, are the top three items on a list – coloured, no doubt, by the authors’ personal biases.