Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.

A Low Cost Oscillating Membrane for Underwater Applications at Low Reynolds Numbers

Active surface morphing is a nonintrusive flow control technique that can delay separation in laminar and turbulent boundary layers. Most of the experimental studies of such control strategy have been carried out in wind tunnels at low Reynolds numbers with costly actuators. In contrast, the implementation of such a control strategy at low cost for an underwater environment remains vastly unexplored. This paper explores active surface morphing at low cost and at low Reynolds for underwater applications. We do this with a 3D printed foil submerged in a water tunnel. The suction surface of the foil is covered with a magnetoelastic membrane. The membrane is actuated via two electromagnets that are positioned inside of the foil. Three actuation frequencies (slow, intermediate, fast) are tested and the deformation of the membrane is measured with an optosensor. We show that lift increases by 1%, whilst drag decreases by 6% at a Strouhal number of 0.3, i.e., at the fast actuation case. We demonstrate that surface actuation is applicable to the marine environment through an off the shelf approach, and that this method is more economical than existing active surface morphing technologies. Since the actuation mechanism is not energy intensive, it is envisioned that it could be applied to marine energy devices, boat appendages, and autonomous underwater vehicles.