Direct and Surrogate-Based Optimization of Dual-Rotor Wind Turbines

摘要

Dual-rotor wind turbines (DRWT) may offer better energy efficiency over their single-rotor counterparts. The design and analysis of DRWT requires, among other, the use of computational fluid dynamics models. These models can be, depending on their formulation, computationally heavy. Numerous simulations are then required during the design process, and this may render the overall computational cost to be prohibitive. This paper investigates and compares several optimization techniques for the design of DRWTs. In particular, we solve the DRWT fluid flow using the Reynolds-Averaged Navier-Stokes equations with a two-equation turbulence model on an axisymmetric mesh, and consider three design approaches: the traditional parametric sweep where the design variables are varied and the responses examined, direct optimization with a derivative-free algorithm, and surrogate-based optimization (SBO) using data-driven surrogates. The approaches are applied to test cases involving two and three design variables. The results show that the same optimized designs are obtained with all the approaches. However, going from the two parameter case to the three parameter one, the effort of setting up, running, and analyzing the results increases significantly with the parametric sweep approach. The optimization techniques are much easier to use and deliver the results with lower computational cost, where the SBO algorithm outperforms the direct approach.