Aircraft Aerodynamic. Design Geometry and Optimization by Andras Sobester, Alexander I J Forrester

By Andras Sobester, Alexander I J Forrester

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18, our most flexible cross-section geometry. The number of shape variables doubles here, as we have now introduced a new set for a separate cargo lobe. We also have the R variables here, which determine the relative positions of the two lobes. 10, twofold. First, the cost of the optimization process (now conducted via a genetic algorithm, followed up by a Nelder and Mead pattern search to fine tune the best solution found by the genetic algorithm) has gone up by two orders of magnitude – we are into the tens of thousands of evaluations of the objective function.

6 we see the instance of our double-lobe cross-section geometry that might produce the central wing-to-body fairing area. The question is, what longitudinal variation of the parameters would produce the transition from the circular section to this double-lobe section? 11. 65 in this region). The streamwise variation of the passenger lobe parameters features short, smooth deviations from the baseline values corresponding to the circular cross-sections of most of the fuselage. 43 near the front to yield the slightly ‘squashed’ shape of the nose section.

Stage 2: A ‘Generalized Circle’ Time to make the geometry more flexible. We might ask: what gains could we make on the cross-sectional area (a surrogate, in essence, for the pressure drag of the fuselage) at the cost of sacrificing some of the structural advantages of a circle? 17: we make the two exponents N1 and N2 free to vary, as well as adding the ‘sideways stretch’ variable C. As before, we also have the uniform scaling variable and the quasi-separable vertical adjustment variable. This is a slightly more serious optimization challenge, and we used the pattern search of Nelder and Mead (1965) to accomplish it.

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