A study on the wake model for floating offshore wind turbines based on CFD simulation

Hu Weicheng; Liu Zhenqing

doi:10.7638/kqdlxxb-2025.0133

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A study on the wake model for floating offshore wind turbines based on CFD simulation

更新时间：2026-07-11

- A study on the wake model for floating offshore wind turbines based on CFD simulation
- Vol. 44, Issue 6, Pages: 113-122(2026)
- 作者机构：
  
  1. 华东交通大学山区土木工程安全与韧性全国重点实验室,南昌,330013
  2. 华中科技大学土木与水利工程学院,武汉,430074
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2025.0133
  CLC： TM614
- Published：2026
- 稿件说明：
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胡伟成, 刘震卿. 基于CFD仿真的漂浮式风力机尾流模型研究[J]. 空气动力学学报, 2026, 44(6): 113−122.

Hu Weicheng, Liu Zhenqing. A study on the wake model for floating offshore wind turbines based on CFD simulation[J]. 2026, 44(6): 113-122.
胡伟成, 刘震卿. 基于CFD仿真的漂浮式风力机尾流模型研究[J]. 空气动力学学报, 2026, 44(6): 113−122. DOI： 10.7638/kqdlxxb-2025.0133

Hu Weicheng, Liu Zhenqing. A study on the wake model for floating offshore wind turbines based on CFD simulation[J]. 2026, 44(6): 113-122. DOI： 10.7638/kqdlxxb-2025.0133.

摘要

针对漂浮式风力机复杂运动导致的尾流预测难题，本文建立了基于改进致动盘模型的高精度CFD数值仿真模型。该模型采用Realizable

湍流模型，并开发了可精准模拟多运动状态下体积力的UDF（user-defined function）程序。将本文方法与其他4种湍流模型进行了系统对比，并通过风洞试验结果进行验证，结果表明，基于改进致动盘模型的Realizable

模型可精准捕捉风力机尾流速度和湍流强度分布。基于对80种单一运动工况的Sobol敏感性分析表明：来流湍流与推力系数对尾流的影响显著大于运动参数，运动周期的影响基本可忽略，纵摇、纵荡及横摇为主导运动形式。据此，本文提出一种Jensen-Gaussian尾流解析模型，在传统高斯分布框架中引入运动幅值参数，其在单一运动下平均预测误差仅为2.0%，较传统Jensen与BPA尾流模型降低了3.8%～6.6%。针对耦合运动工况，对比5种尾流叠加模型，发现最大亏损叠加模型（maximum loss

ML）对耦合运动尾流预测的精度最高，平均误差仅为1.9%，实现了多运动协同作用下的高效精准预测。本研究为深远海风电场布局优化与运行控制提供了高精度、低成本的尾流预测工具链。

Abstract

To address the challenge of wake prediction caused by complex motions of floating offshore wind turbines

this paper presents a high-precision CFD numerical model based on an improved actuator disk model. A UDF (user-defined function) program is developed to accurately compute body forces under multiple motion states. The proposed method

together with four other turbulence models

is systematically compared against

wind tunnel measurements. The simulation results are validated against wind tunnel tests. The Realizable

model with the improved actuator disk model accurately captures the velocity and turbulence intensity distributions in the turbine wake. A Sobol sensitivity analysis is conducted on 80 single-motion cases. Results show that inflow turbulence and thrust coefficient dominate the wake characteristics. Their influence is significantly greater than that of motion parameters. The motion period has a negligible effect. Pitch

surge

and roll are identified as the dominant motion forms. A Jensen-Gaussian wake model is then proposed. This model introduces motion amplitude parameters into the traditional Gaussian distribution framework. For single-motion cases

the average prediction error is only 2.0%. This error is 3.8% to 6.6% lower than that of the conventional Jensen and BPA models. For coupled motion cases

five wake superposition models are compared. The maximum loss (ML) model achieves the highest prediction accuracy. Its average error is only 1.9%. This approach enables efficient and accurate wake simulation under multi-motion synergistic effects. This study provides a high-precision

low-cost wake prediction toolchain for the layout optimization and operational control of deep-sea offshore wind farms.

关键词

Keywords

references

Hu W C, Yang Q S, Chen H P, et al. New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory[J]. Renewable Energy, 2021, 179: 2174−2186. doi: 10.1016/j.renene.2021.08.044

Bayati I, Belloli M, Ferrari D, et al. Design of a 6-DoF robotic platform for wind tunnel tests of floating wind turbines[J]. Energy Procedia, 2014, 53: 313−323. doi: 10.1016/j.egypro.2014.07.240

Hu W C, Yang Q S, Yuan Z T, et al. Wind farm layout optimization in complex terrain based on CFD and IGA-PSO[J]. Energy, 2024, 288: 129745. doi: 10.1016/j.energy.2023.129745

Fleming P A, Gebraad P M O, Lee S, et al. Evaluating techniques for redirecting turbine wakes using SOWFA[J]. Renewable Energy, 2014, 70: 211−218. doi: 10.1016/j.renene.2014.02.015

Yang Q S, Li Y, Li T, et al. Statistical extrapolation methods and empirical formulae for estimating extreme loads on operating wind turbine towers[J]. Engineering Structures, 2022, 267: 114667. doi: 10.1016/j.engstruct.2022.114667

Huang G Q, Jiang Y, Peng L L, et al. Characteristics of intense winds in mountain area based on field measurement: focusing on thunderstorm winds[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2019, 190: 166−182. doi: 10.1016/j.jweia.2019.04.020

Kyle R, Lee Y C, Früh W G. Propeller and vortex ring state for floating offshore wind turbines during surge[J]. Renewable Energy, 2020, 155: 645−657. doi: 10.1016/j.renene.2020.03.105

España G, Aubrun S, Loyer S, et al. Wind tunnel study of the wake meandering downstream of a modelled wind turbine as an effect of large scale turbulent eddies[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2012, 101: 24−33. doi: 10.1016/j.jweia.2011.10.011

Hu W C, Yang Q S, Chen H P, et al. Wind field characteristics over hilly and complex terrain in turbulent boundary layers[J]. Energy, 2021, 224: 120070. doi: 10.1016/j.energy.2021.120070

Jensen N O. A note on wind generator interaction[R]. 1983.

Feng J, Shen W Z, Li Y, et al. An optimization framework for wind farm design in complex terrain[J]. Applied Sciences, 2018, 8(11): 2053. doi: 10.3390/app8112053

Chamorro L P, Porté-agel F. Effects of thermal stability and incoming boundary-layer flow characteristics on wind-turbine wakes: a wind-tunnel study[J]. Boundary-Layer Meteorology, 2010, 136(3): 515−533. doi: 10.1007/s10546-010-9512-1

Peng Y S. The evaluation and optimization of wind farm wake effect on flat terrain[J]. International Core Journal of Engineering, 2021, 7(9): 595−598. doi: 10.6919/ICJE.202109_7(9).0083

Tang Wanhe, Yin Sihua, Zhu Chengjiu, et al. Optimization of offshore wind farm layouts based on the thrice-modified Jensen wake model[J]. Science Technology and Engineering, 2024, 24(3): 1052−1059(in Chinese). doi: 10.12404/j.issn.1671-1815.2300006

Gao X X, Li B B, Wang T Y, et al. Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements[J]. Applied Energy, 2020, 260: 114272. doi: 10.1016/j.apenergy.2019.114272

Hu W C, Liu Z Q, Li T, et al. A double Gaussian wake model for single and multiple yawed wind turbines[J]. Energy, 2025, 324: 136080. doi: 10.1016/j.energy.2025.136080

Brogna R, Feng J, Sørensen J N, et al. A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain[J]. Applied Energy, 2020, 259: 114189. doi: 10.1016/j.apenergy.2019.114189

Keane A. Advancement of an analytical double-Gaussian full wind turbine wake model[J]. Renewable Energy, 2021, 171: 687−708. doi: 10.1016/j.renene.2021.02.078

Wang J, Foley S, Nanos E M, et al. Numerical and experimental study of wake redirection techniques in a boundary layer wind tunnel[J]. Journal of Physics: Conference Series, 2017, 854(1): 012048. doi: 10.1088/1742-6596/854/1/012048

Doubrawa P, Debnath M, Moriarty P J, et al. Benchmarks for model validation based on LiDAR wake measurements[J]. Journal of Physics: Conference Series, 2019, 1256(1): 012024. doi: 10.1088/1742-6596/1256/1/012024

Sumner J, Espana G, Masson C, et al. Evaluation of RANS/actuator disk modelling of wind turbine wake flow using wind tunnel measurements[J]. International Journal of Engineering Systems Modelling and Simulation, 2013, 5(1/2/3): 147. doi: 10.1504/ijesms.2013.052382

Li Q A, Murata J, Endo M, et al. Experimental and numerical investigation of the effect of turbulent inflow on a horizontal axis wind turbine (part II: Wake characteristics)[J]. Energy, 2016, 113: 1304−1315. doi: 10.1016/j.energy.2016.08.018

Cao Jiufa, Song Quanmin, Wang Chaoqun et al. Unsteady characteristics of wake effect for multiple wind turbines under gust wind condition[J]. Acta Aerodynamica Sinica, 2022, 40(4): 247−255 (in Chinese). doi: 10.7638/kqdlxxb-2021.0309

Wang Tongguang, Tian Linlin, Zhong Wei, et al. Aerodynamic research progress in wind energy Ⅱ: Inflow and wake characteristics[J]. Acta Aerodynamica Sinica, 2022, 40(4): 22−50(in Chinese). doi: 10.7638/kqdlxxb-2021.0390

Wang T, Jin H, Wu X N. Coupled dynamic analysis of a tension leg platform floating offshore wind turbine[J]. Journal of Offshore Mechanics and Arctic Engineering, 2020, 142: 011901. doi: 10.1115/1.4044075

Shen X, Chen J G, Hu P, et al. Study of the unsteady aerodynamics of floating wind turbines[J]. Energy, 2018, 145: 793−809. doi: 10.1016/j.energy.2017.12.100

Yang J H, Liu Z Q, Hu W C, et al. A study on the wake model of floating wind turbine with swaying motions based on an improved actuator disk method[J]. Physics of Fluids, 2024, 36(8): 085114. doi: 10.1063/5.0220778

Hu Weicheng, Yang Qingshan, Zhang Jian, et al. Les study of turbulent boundary layers over three-dimensional hills[J]. Engineering Mechanics, 2019, 36(4): 72−79(in Chinese). doi: 10.6052/j.issn.1000-4750.2018.03.0138

Xu Chang, Han Xingxing, Wang Xin, et al. Study of wind turbine wake modeling based on a modified actuator disk model and extended k-ε turbulence model[J]. Proceedings of the CSEE, 2015, 35(8): 1954−1961(in Chinese). doi: 10.13334/j.0258-8013.pcsee.2015.08.015

Hu W C, Liu Z Q, Yu Z Z. Active yaw control optimization of wind turbines in wind farms considering multi-scenario wind speed and direction[J]. Physics of Fluids, 2025, 37(8): 085113. doi: 10.1063/5.0277527

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