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Hydrokinetic turbine array characteristics for river applications and spatially restricted flows

机译:适用于河流和空间受限流量的流体动力学涡轮机阵列特性

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Multiple hydrokinetic turbines in three array configurations were characterized computationally by employing Reynolds Averaged Navier-Stokes equations. The simulations were conducted for pre-existing turbines operating at their optimum power coefficient of 0.43 which was obtained by design and optimization process. Mechanical power for two adjacent units was predicted for various lateral separation distances. An additional two-by-two turbine array was studied, mimicking a hydro-farm. Numerical simulations were performed using actual physical turbines in the field rather than using low fidelity models such as actuator disk theory. Steady state simulations were conducted using both Coupled and SIMPLE pressure-velocity solvers. Steady three dimensional flow structures were calculated using the k-omega Shear Stress Transport (SST) turbulence model. At a lateral separation distance of 0.5D(t), the turbines produced an average 86% of the peak power a single turbine producing. Interaction effects at lateral separation distances greater than 2.5D(t) were negligible. The wake interaction behind the upstream turbines causes a significant performance reduction for downstream turbines within 6D(t) longitudinal spacing. Downstream turbines employed for the present study performed around 20% or less of a single unit turbine performance for the same operating conditions. Downstream turbines yielded comparable reductions in power to that of experimental results. (C) 2016 Elsevier Ltd. All rights reserved.
机译:通过采用雷诺平均Navier-Stokes方程对三个阵列构型的多台流体动力学涡轮机进行了计算表征。对通过设计和优化过程获得的最佳功率系数为0.43的现有涡轮机进行了仿真。预测了两个相邻单元在各种横向间隔距离下的机械功率。研究了另外一个二乘二的涡轮机阵列,模仿了水力发电场。数值模拟是在现场使用实际的物理涡轮机进行的,而不是使用诸如执行器盘理论之类的低保真度模型进行的。使用耦合和简单压力速度求解器进行稳态仿真。使用k-ω剪应力传输(SST)湍流模型计算了稳定的三维流动结构。在横向间隔距离为0.5D(t)时,涡轮机产生的平均功率是单个涡轮机产生的峰值功率的86%。横向分离距离大于2.5D(t)时的相互作用影响可忽略不计。上游涡轮机后面的尾流相互作用导致6D(t)纵向间隔内的下游涡轮机的性能显着降低。在相同的运行条件下,本研究采用的下游涡轮机的单机性能约占20%或更少。下游涡轮机的功率降低与实验结果相当。 (C)2016 Elsevier Ltd.保留所有权利。

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