A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes

Li, Li; Huang, Zhi; Ge, Mingwei; Zhang, Qiying

doi:10.1016/j.energy.2021.121806

Cited by 29 publications

(14 citation statements)

References 48 publications

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“…To establish the reliability of offshore wind turbines for continuous power generation, it is imperative to understand the impact of turbulent flow conditions on their aerodynamic performance and underlying fluid-structure-acoustic interaction. Traditionally, computational modeling of turbulent flows for these systems involves using various Reynolds-Averaged Navier-Stoke (RANS) equations-based models [13][14][15]19,24,[26][27][28][29], large eddy simulations (LES) [30,31], and different versions of detached eddy simulations (DES) [16][17][18]32]. Other low-order wake models [33] are also introduced along with potential flow-based methods to handle unsteady flow dynamics.…”

Section: Flow Characterization Of Offshore Wind Turbinesmentioning

confidence: 99%

“…Other low-order wake models [33] are also introduced along with potential flow-based methods to handle unsteady flow dynamics. Recently, a few studies [31,[33][34][35][36] have been carried out to incorporate the modeling of turbulent wind fields over oceans and irregular wave loads on the floating submersible structures [10]. Primary differences in physical phenomena associated with offshore wind turbines and their urban counterparts include hydrodynamic excitations of foundational platforms.…”

Section: Flow Characterization Of Offshore Wind Turbinesmentioning

confidence: 99%

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A Review of Recent Advancements in Offshore Wind Turbine Technology

et al. 2022

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Offshore wind turbines are becoming increasingly popular due to their higher wind energy harnessing capabilities and lower visual pollution. Researchers around the globe have been reporting significant scientific advancements in offshore wind turbines technology, addressing key issues, such as aerodynamic characteristics of turbine blades, dynamic response of the turbine, structural integrity of the turbine foundation, design of the mooring cables, ground scouring and cost modelling for commercial viability. These investigations range from component-level design and analysis to system-level response and optimization using a multitude of analytical, empirical and numerical techniques. With such wide-ranging studies available in the public domain, there is a need to carry out an extensive yet critical literature review on the recent advancements in offshore wind turbine technology. Offshore wind turbine blades’ aerodynamics and the structural integrity of offshore wind turbines are of particular importance, which can lead towards system’s optimal design and operation, leading to reduced maintenance costs. Thus, in this study, our focus is to highlight key knowledge gaps in the scientific investigations on offshore wind turbines’ aerodynamic and structural response. It is envisaged that this study will pave the way for future concentrated efforts in better understanding the complex behavior of these machines.

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Section: Flow Characterization Of Offshore Wind Turbinesmentioning

confidence: 99%

Section: Flow Characterization Of Offshore Wind Turbinesmentioning

confidence: 99%

A Review of Recent Advancements in Offshore Wind Turbine Technology

et al. 2022

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“…The analytical wake models based on either or both of mass and momentum conservation laws can estimate the wake growth behind a single wind turbine and evaluate the interaction of wakes of several wind turbines through superposition techniques [25][26][27][28][29][30][31] . These models can be used for wind-farm performance evaluation and layout optimization due to their low computational cost [32][33][34] . The computational fluid dynamics (CFD) techniques including Reynolds-averaged Navier-Stokes (RANS) equations and large-eddy simulation (LES) can be listed as the mid-and high-fidelity methods in wind-energy applications.…”

Section: Data-driven Modelsmentioning

confidence: 99%

Data-driven fluid mechanics of wind farms: A review

Zehtabiyan-Rezaie,

Iosifidis,

Abkar

2022

Preprint

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With the growing number of wind farms over the last decades and the availability of large datasets, research in wind-farm flow modeling -one of the key components in optimizing the design and operation of wind farms -is shifting towards data-driven techniques. However, given that most current data-driven algorithms have been developed for canonical problems, the enormous complexity of fluid flows in real wind farms poses unique challenges for data-driven flow modeling. These include the high-dimensional multiscale nature of turbulence at high Reynolds numbers, geophysical and atmospheric effects, wake-flow development, and incorporating wind-turbine characteristics and wind-farm layouts, among others. In addition, data-driven wind-farm flow models should ideally be interpretable and have some degree of generalizability. The former is important to avoid a lack of trust in the models with end-users, while the most popular strategy for the latter is to incorporate known physics into the models. This article reviews a collection of recent studies on wind-farm flow modeling covering both purely data-driven and physics-guided approaches. We provide a thorough analysis of their modeling approach, objective, and methodology, and specifically focus on the data utilized in the reviewed works.

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“…Analytical modeling can result in adequate levels of precision by adopting simple equations at ~10 6 -10 7 less CPU time per run compared to numerical models [7], such as LES [7], which makes employment of this kind of modeling suitable in commercial software [9]. Some of the studies examining wake by conducting analytical modeling as reported in literature [8,[11][12][13] (a brief description of these studies is discussed later in section literature review). It is of note that the most accurate analytical models for predicting the wake velocity are made upon the bell-shaped wind velocity distribution, and mainly rely on Gaussian distribution functions to provide an accurate wind velocity distribution of the wake (e.g., in research conducted by Ge et al [14], Bastankhah and Porté-Agel [15], Xiaoxia et al [16], Ishihara and Qian [17]).…”

Section: Introductionmentioning

confidence: 99%