Inspired by topology of humpback whale flippers, a meta-model is designed for wing planform design. The net is trained based on experimental data using cascade-forward artificial neural network (ANN) to investigate effects of the amplitude and wavelength of sinusoidal leading edge configurations on the wing performance. Afterwards, the trained ANN is coupled with a genetic algorithm method towards an optimum design strategy. Finally, flow physics of the problem for an optimized rectangular planform and also a real flipper geometry planform is simulated using Lam-Bremhorst low Reynolds number turbulence model with damping wall-functions resolving to the wall. Lift and drag coefficients and also details of flow are presented along with comparisons to available experimental data. Results show that the proposed strategy can be adopted with success as a fast-estimation tool for performance prediction of wing planforms with wavy leading edge at preliminary design phase. <\/p>\r\n","references":"[1]\tB.L. Woodward, J. P. Winn and F. E. Fish, \u201cMorphological specializations of baleen whales associated with hydrodynamic performance and ecological niche\u201d, Journal of Morphology, vol.267, pp.1284\u20131294, 2006. \r\n[2]\tHumpback whale documentation, Marine Mammal Center, Fort Cronkhite, Sausalito, CA. \r\n[3]\tW. Welles, Picture Amy Whale, breaching, Stellwagen Bank National Marine Sanctuary, 2007.\r\n[4]\tF.E. Fish and J. M. Battle, \u201cHydrodynamic design of the humpback whale \ufb02ipper\u201d, Journal of Morphology, vol.225, No.1, pp.51\u201360, 1995.\r\n[5]\tJ. Potvin, J.A. 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