Energy Reports (Jun 2024)
Wind energy harvesting with building-integrated ducted openings: CFD simulation and neural network optimization
Abstract
Building-integrated wind energy harvesting offers a promising local and decentralized energy generation solution, reducing transmission losses and infrastructure expenses. However, the complex urban wind patterns demand accurate aerodynamic optimization for optimal performance. This study seeks to optimize the geometric design of building-integrated ducted openings to maximize wind energy harvesting. The research aims to create a data-driven model predicting the openings' performance accurately, explore the interlinked impacts of different geometrical variables, find optimal configurations across different building heights, and understand underlying aerodynamic mechanisms. The proposed design comprises a nozzle, throat, and diffuser. To increase power density, a data-driven design optimization method is employed. The optimization process involves determining the optimum opening height from the ground, along with five geometrical variables: inlet diameter, throat diameter, outlet diameter, nozzle length, and diffuser length. A comprehensive test matrix is developed using design of experiments (DoE) to build an artificial neural network (ANN) model. This neural network is developed using computational fluid dynamics (CFD) simulations validated with experimental data. Subsequently, a gradient-based global optimization method fine-tunes the neural network and identifies optimal designs for various heights. The research concludes that nozzle and diffuser opening angles within the range of 19° ≤ ΦNoz ≤ 23°and 7.5° ≤ ΦDif ≤ 8.2°, respectively, result in the optimal converging-diverging ducted opening geometry. This optimal opening can yield a power density increase of 31.5 to 35.5 times when compared to freestream flow and 17.5 to 22.4 times in comparison to cylindrical openings, depending on the opening location along the building height.
