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首页> 外文期刊>Energies >The Influence of Intra-Array Wake Dynamics on Depth-Averaged Kinetic Tidal Turbine Energy Extraction Simulations
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The Influence of Intra-Array Wake Dynamics on Depth-Averaged Kinetic Tidal Turbine Energy Extraction Simulations

机译：阵列内尾流动力学对平均潮汐动能透平能量提取模拟的影响

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摘要

Assessing the tidal stream energy resource, its intermittency and likely environmental feedbacks due to energy extraction, relies on the ability to accurately represent kinetic losses in ocean models. Energy conversion has often been implemented in ocean models with enhanced turbine stress terms formulated using an array-averaging approach, rather than implementing extraction at device-scale. In depth-averaged models, an additional drag term in the momentum equations is usually applied. However, such array-averaging simulations neglect intra-array device wake interactions, providing unrealistic energy extraction dynamics. Any induced simulation error will increase with array size. For this study, an idealized channel is discretized at sub 10 m resolution, resolving individual device wake profiles of tidal turbines in the domain. Sensitivity analysis is conducted on the applied turbulence closure scheme, validating results against published data from empirical scaled turbine studies. We test the fine scale model performance of several mesh densities, which produce a centerline velocity wake deficit accuracy (R 2 ) of 0.58–0.69 (RMSE = 7.16–8.28%) using a k-? turbulence closure scheme. Various array configurations at device scale are simulated and compared with an equivalent array-averaging approach by analyzing channel flux differential. Parametrization of array-averaging energy extraction techniques can misrepresent simulated energy transfer and removal. The potential peak error in channel flux exceeds 0.5% when the number of turbines n TECs ≈ 25 devices. This error exceeds 2% when simulating commercial-scale turbine array farms (i.e., 100 devices).
机译：评估潮汐流的能源资源，间歇性以及由于能量提取而可能产生的环境反馈，都依赖于准确表示海洋模型中动力损失的能力。能量转换通常是在海洋模型中通过使用阵列平均方法制定的，具有增强的涡轮应力项来实现的，而不是在设备规模上进行抽取。在深度平均模型中，通常在动量方程中应用附加的阻力项。但是，这样的阵列平均模拟忽略了阵列内设备唤醒交互，从而提供了不切实际的能量提取动态。任何诱发的仿真错误都会随着阵列大小而增加。对于本研究，理想的通道以低于10 m的分辨率离散化，从而解决了该领域中潮汐涡轮机的各个设备唤醒特征。在应用的湍流闭合方案上进行了灵敏度分析，并根据来自经验规模的涡轮研究的公开数据验证了结果。我们测试了几种网格密度的精细模型性能，使用k-？可以产生0.58–0.69（RMSE = 7.16–8.28％）的中心线速度尾流赤字精度（R 2）。湍流封闭方案。在设备规模上模拟各种阵列配置，并通过分析通道通量差异，将其与等效阵列平均方法进行比较。阵列平均能量提取技术的参数化可能会误解模拟的能量转移和去除。当涡轮机的数量TECs≈25台时，通道通量中的潜在峰值误差超过0.5％。模拟商业规模的涡轮阵列场（即> 100个设备）时，此错误超过2％。

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