Using Multiobjective Evolutionary Algorithms and Data-Mining Methods to Optimize Ornithopters' Kinematics

摘要

The aim of this work is to present a method to find and analyze maximum propulsive efficiency kinematics for anbirdlike flapping-wing unmanned aerial vehicle using multiobjective evolutionary optimization and data-miningntools. For the sake of clarity and simplicity, simple geometry (rectangularwingswith the same profile along the span)nand simple kinematics (symmetrical harmonic dihedralmotion) are used. In addition, it is assumed that the birdlikenaerial vehicle (for which the span and surface area are, respectively, 1m and 0:15 m2n) is in horizontal motion at lowncruise speed (6 m=s). The aerodynamic performances of the flapping-wing vehicle are evaluated with a semi-nempirical flight physics model and the problem is solved using an efficient multiobjective evolutionary algorithmncalled u0001-MOEA. Groups of attractive solutions are defined on the Pareto surface, and the most efficient solutionsnwithin these groups are characterized. Given the high dimensionality of the Pareto surface in the kinematicnparameters space, data-mining techniques are used to conduct the study. First, it is shown that these groups can benqualified versus the whole Pareto surface by accurate mathematical relations on the kinematic parameters. Second,nthe inner structure of each group is studied and highly accurate mathematical relations are found on the optimizednparameters describing the most efficient solutions.