期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：240
期号：4
页码：1-8
DOI：10.1088/1755-1315/240/4/042020
出版社：IOP Publishing
摘要：A performance of Darrieus-type hydroturbine is strongly influenced by the channel and flow condition. These flow conditions are different from place to place and also dependent upon the seasons, therefore it is difficult to study these influences only by experiments. On the other hand, numerical simulation can be adopted for various flow conditions. However, calculation costs are very expensive since fully unsteady simulation taking account of free surface of water should be conducted for this turbine as a cross-flow type. Then, in this paper, a simple numerical model is developed. In this model, instead of solving the complex flow field around the turbine, it is modeled by an actuator disk which imposes the total pressure difference consumed by the rotating turbine. Our previous study suggested that the head coefficient defined as the total pressure difference across the runner normalized by the dynamic pressure with area averaged flow velocity into the turbine seemed to well represent the specific performance of Darrieus-type hydroturbine. In this paper, the specific performance is determined from the experiment in one channel, and the corresponding total pressure change is locally applied to the actuator disk as a function of local inflow velocity. The predicted overall head coefficient, which is defined as the total pressure difference between far upstream and downstream normalized by area averaged velocity downstream of the turbine, is compared with experiment. As a result, when the flow velocity or depth decreases, the overall head coefficient increases. The proposed model can qualitatively reflect this influence of flow velocity and depth on the turbine performance in most cases, while quantitatively the predicted overall head coefficient is different from that in the experiments, indicating the necessity of further modification of the model for quantitative prediction.
