In the rapidly deploying process of the unmanned aerial vehicle with folding wings, the aerodynamic characteristics could be largely different owing to the effects of deformation rate and the aerodynamic interference. The investigation on the unsteady aerodynamics is of great significance for the stability analysis and control design. The lifting-line method and the vortex-lattice method are improved to calculate the unsteady aerodynamics in the morphing stage. It is validated that the vortex-lattice method predicts the unsteady lift coefficient more appropriately than the lifting-line method. Different tandem wing configurations with deployable wings are simulated with different deformation rates during the morphing stage by the vortex-lattice method. As results indicated, the unsteady lift coefficient and the induced drag of the fore wing rise with the deformation rate increasing, but it is reversed for the hind wing. Additionally, the unsteady lift coefficient of the tandem wing configuration performs well with a larger stagger, a larger magnitude of the gap and a larger wingspan of the fore wing; however, the total induced drag has a larger value for the configuration that the two lifting surfaces with the same wingspans are closer to each other.
In this paper, we introduce a class of new selection and routing problems, and name it as the traveling salesman problem with profits and stochastic customers (TSPPSC), which is an extension of the traveling salesman problem with profits (TSPP). The class of new problems is put forward to address how to deal with stochastic customer presence under the environment in which an associated profit is obtained once a customer is visited. It is defined on a complete graph in which profits are associated with the vertices and travel costs are associated with the edges. Each vertex (customer) has a probability of requiring a visit. The objective is the simultaneous optimization of the expected collected profits and expected travel costs. According to the way the two objectives (profits and travel costs) are addressed, TSPPSC is categorized into three subproblems. Mathematical formulations are provided for these problems and a genetic algorithm is proposed to solve one of these subproblems. Computational experiments conducted on several sets of instances show a good performance of the proposed algorithm.
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