Abstract:A novel vortex-induced piezoelectric energy converter (VIPEC) is presented in this paper to harvest ocean kinetic energy in the underwater environment. The converter consists of a circular cylinder, a pivoted plate attached to the tail of the cylinder, several piezoelectric patches and a storage circuit. Vortex-induced pressure difference acts on the plate and drives the plate to squeeze piezo patches to convert fluid dynamic energy into electric energy. The output voltage is derived from the piezoelectric constitutive equation with fluid forces. In order to evaluate the performance of the VIPEC, two-dimensional computational fluid dynamics (CFD) simulations based on the Reynolds averaged Navier-Stokes (RANS) equation and the shear stress transport (SST) k-ω turbulence model are conducted. The CFD method is firstly verified for different grid resolutions and time steps, and then validated using simulation and experimental data. The influences of the plate length and flow velocity on the wake structure, the driving force and the performance of the VIPEC are investigated. The results reveal that different parameters reach their peaks at different plate lengths, and the converter has a maximal output voltage of 2.3 mV in a specified condition and the maximal power density reaches 0.035 µW/m 3 with a resistance load of 10 MΩ. The influence of the simulated subcritical Reynolds number on the driving force is not noticeable. The simulation results also demonstrate the feasibility of this device.

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Autonomous Underwater Helicopter (AUH) is a disk-shaped Autonomous Underwater Vehicle (AUV), and it has comparative advantage of near-bottom hovering and whole-direction turn-around ability over the traditional slender AUV. An optimization design of its irregular geometric profile is essential to improve its hydrodynamic performance. A parametric representation of its profile is proposed in this paper using Non-Uniform Rational B-spline (NURBS) curve. The parametric representation of AUH profile is described with two decision variables and several data points. Based on this parametric curve, Computational Fluid Dynamics (CFD) simulation is carried out to evaluate its hydrodynamic performance with various parameters. A predication model is established over variables’ design space using Kriging surrogate model with CFD simulation results and a Genetic Algorithm (GA) procedure is conducted to find optimal design variables, which can produce an optimum lift-drag ratio. CFD verification results confirm that AUH profile with optimized design variables can increase its lift-drag ratio by 2.11 times compared with that of non-optimized ones. It demonstrates that the parametric representation and optimization procedure of AUH profile proposed in this paper is feasible, and it has a great potential in improving AUH’s performance.