Under the rapid development of wind turbines, the rotor size has substantially increased in recent years. To meet the key design criteria, finding a trade-off between aerodynamic, structure and noise (ASN) impact becomes a challenging problem. The blade cross-section is the basic element of the blade, its outer contour is the airfoil profile that produces aerodynamic loads as well as noise, and the inner part is the supporting composite material that provides enough stiffness to balance the loads. Modifying local blade sections can adjust the rotors’ overall performance. However, in the work of blade-combined ASN optimization, the iterative process using traditional numerical simulation methods becomes extremely heavy. In this study, a sustainable database is created based on a large number of calculations of aerodynamic, structural and noise attributes at various cross-sections. Then a platform named AFML (Airfoil machine learning) for simultaneously predicting the comprehensive performance of the cross-section is constructed by using the integrated Gradient Boosting Regression Tree algorithm. Results show that the prediction accuracy of AFML is acceptable even for unseen inflow conditions. By calling the pre-trained AFML, ASN data of the cross-section can be immediately obtained, and the blade shape and inner structure can be updated quickly. © 2024 Elsevier Ltd

The paper introduced a new concept to capture ocean energy from an offshore wind farm. The principal idea is to utilize an oscillating hydrofoil connecting the existing underwater tower of an offshore wind turbine. A proper motion of the hydrofoil is defined such that the tidal current energy from the vortical flow behind the underwater tower can be effectively captured. Based on the offshore wind resource and the existing wind farm layout, the proposed hybrid energy capturing device can effectively utilize the additional tidal current energy without requiring too much extra cost and ocean space. The physical model of the ocean energy capture system was conceptually developed. In order to track the maximum power efficiency, more comprehensive analysis was carried out by using the Lattice Boltzmann Method with Large Eddy Simulation, which includes the combined effects of different hydrofoil motion parameters such as the effect from heave amplitude, pitch angle and hydrofoil chord length. The overall efficiency of the offshore wind farm is improved with the oscillating flow generator. It is also found that the existence of the oscillating flow generator can effectively reduce the drag force on the wind turbine tower, which implies longer life time of the wind turbine system.

In order to study the dynamic design method and suppression effect of strakes’ vortex induced vibration suppression device for large marine riser, based on finite element modal calculation, computational fluid dynamics (CFD) method, structural dynamics theory and overset mesh technology, and considering the inline flow direction and cross flow direction vibration of the vortex induced vibration suppression device, the fluid structure interaction model of strakes’ vortex induced vibration suppression device is established to predict the suppression effect of strakes’ vortex induced vibration suppression device on marine riser vortex induced vibration. The accuracy of the numerical method is verified by comparing with the experimental data in reference. The numerical simulation design method of strakes’ vortex induced vibration suppression device proposed in this paper can be used for the dynamic design of large scale riser’s vortex induced vibration suppression device. Compared with the traditional hydrodynamic calculation method, it is more reliable. And compared with the fluid structure coupling calculation of full-scale vortex induced vibration suppression device, it saves calculation resources and calculation time. Using the structural parameters of the strakes in this paper for simulation, the cross flow direction amplitude suppression efficiency of the strakes is more than 94.5%, and the drag force coefficient is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Yaw control is a potential control strategy to redirect the wakes of wind turbines for the purpose of improving the wind farm performance. Because of its time-consuming nature, high fidelity numerical simulations are hard to be applied in engineering applications. Instead, a simple and efficient engineering wake model is preferable. In this paper, a novel yaw wake model is developed in the following three steps: a novel wake velocity model for non-yawed wind turbines is first developed; second, a novel wake deflection model is developed to overcome the shortcomings in existing models; and third, a novel wake model for yawed wind turbines is developed. To verify its accuracy, the results of the novel yaw wake model implemented in the in-house Analytical Wake Model Calculator (AWMC) are validated against experimental data and simulation results. Moreover, the comparisons with a few existing yaw wake models show its superiority. In terms of error, the overall accuracy of the wake deflection model is enhanced by more than 20 % compared to other models in all cases. In addition, the overall accuracy of the novel yaw wake distribution model is improved by at least 10 % in most cases as compared to state-of-the-art yaw wake models. © 2023 Elsevier Ltd

Wind turbine blades are prone to failure due to high tip speed, rain, dust and so on. A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed. On the experimental measurement data, variational mode decomposition filtering and Mel spectrogram drawing are conducted first. The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network. Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients, considering the complexity of the real environment. The surfaces of Wind turbine blades are classified into four types: standard, attachments, polishing, and serrated trailing edge. The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%. In addition to support the differentiation of trained models, utilizing proper score coefficients also permit the screening of unknown types. © 2023 The Author(s)

Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,variational mode decomposition filtering and Mel spectrogram drawing are conducted first.The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network.Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients,considering the complexity of the real environment.The surfaces of Wind turbine blades are classified into four types:standard,attachments,polishing,and serrated trailing edge.The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%.In addition to support the differentiation of trained models,utilizing proper score coefficients also permit the screening of unknown types.

科技的飞速进步与发展也带动了传统能源的快速消耗,能源危机使可再生能源进入了大众的视野。风能作为可再生能源里不可或缺的一类,在近年得到了足够的重视,受到了国家的大力推广。而风资源和土地资源有限,风力机的安装位置也逐渐向居民区靠近。尽管风能清洁、低碳环保,但是风力机在运行时,会产生一定的气动噪声,并且以中低频噪声为主,因此风电噪声会对风电场附近的居民有潜在的健康危害。目前,耦合风力机布机位置与风电场噪声传播影响的研究还不够深入,因此,如何在规划的区域内,寻找到周围对居民区噪声影响最小的风电场布局方式是本课题的研究目标。减少风电场在运行时对居民的健康危害,让风能被大众接受并成为真正意义上的清洁能源是本课题潜在的研究意义。为了实现上述研究目标,本文围绕风力机叶素动量理论、风力机尾流数值模拟、平坦地形单台风力机的噪声传播数值模拟、平坦地形风电场的噪声传播数值模拟等方面进行了如下工作:（1）介绍并研究了国内外对风电场噪声传播的进展,对噪声可能造成的健康危害进行了简单的说明。（2）结合叶素动量理论（BEM）分析了叶片的气动性能,介绍了 Jensen尾流模型,并利用MATLAB模拟分析了单台Nibe-B风力机以及两台串列Nibe-B风力机的尾流情况。（3）介绍了风力机的噪声源,阐释了气动噪声的产生机理,并利用课题组自研软件NoiseGen分析计算了 NREL 5-MW风力机在6种风速下噪声源的声功率级。（4）介绍了计算噪声传播衰减的抛物方程方法（PE方法）,并结合噪声传播公式,利用MATLAB软件求解,模拟了单台NREL5-MW风力机的噪声传播情况,并建立数据库。（5）设计了两种经典布局的风电场,使用MATLAB软件对这两种布局的风电场的噪声传播进行了模拟,并且对比分析了忽略尾流的噪声传播和考虑尾流的噪声传播两种情况。计算结果表明,改变风力机的布机位置对风电场周围接收点的噪声等级有不同程度的影响;进一步考虑风力机尾流影响,发现整个计算区域内的噪声等级都有了一定的降低,主要原因是尾流导致速度衰减,从而降低了噪声源的强度。最后,用PE方法对一实地风电场噪声传播进行模拟,模拟结果可用于检测风电场噪声或者风电场运行优化。

随着风力发电的迅猛发展,风力机朝着大型化和大规模化发展。伴随装机容量增加、叶片变长、叶尖速比变大的发展特征,风力机及风电场离居民区越来越近,噪声问题越发突出。风力机噪声包括机械噪声和气动噪声。通过先进的加工制造技术可以大幅度降低风力机的机械噪声,气动噪声则成为主要的噪声来源。近些年来研究气动噪声的方法主要有基于CFD的数值模拟和基于半经验公式的模型预测两类。但基于CFD方法的数值模拟一般需要耗费大量计算资源和时间,适用于初步设计之后的论证阶段,在实际工程应用中极少采用这种方法;半经验公式有着计算速度快,相对精度较高的优势,是目前广泛应用的风力机噪声预测手段。因此,建立风力机气动噪声预测模型,对风力机噪声实现快速准确的预测,可以帮助减噪措施的研究,包括风力机位置的布局、居民区选址及动物迁徙路线调整。本文的主要工作如下:（1）分析了国内外对风力机噪声研究的进展,以及噪声可能带来的综合影响。（2）介绍了风力机的基本结构、相关气动理论以及阐述了实际运行中的复杂工况。（3）研究了风力机噪声基本理论以及风力机机械噪声和气动噪声,详细讨论了气动噪声中的5种噪声源并阐释相关产生机理。（4）采用BPM和壁面压力谱（WPS）两种半经验公式,对翼型自噪声预测模型进行建模并结合实验数据进行了验证。（5）首次采用叶素动量理论和WPS翼型噪声预测模型,结合Lowson湍流入流噪声预测模型,使用MATLAB软件建立了包含气流的风剪切效应、塔影效应及等多种非稳态效应的风力机气动噪声预测模型。（6）新模型预测结果与以BPM为基础的风力机气动噪声预测结果及两台给定的风力机实际测量的噪声数据进行对比验证。结果表明,新模型预测结果与BPM预测结果、实验测量结果有较高的一致性,证明了本文提出的风力机噪声预测模型是有效可行的,为风力机气动噪声提供了一种快速有效的预测方法。（7）研究了新模型桨距角、风速、湍流强度、风切系数等因素对风力机气动噪声的影响。结果表明,适当地增大桨距角可以有效地降低风力机的气动噪声;风力机的噪声与马赫数呈正;湍流强度及风切系数对中低频噪声影响较大,对于高频部分产生的影响较小。（8）研究了风力机的声指向性,结果表明风力机的噪声辐射特性呈现为偶极子声源,由于叶片的扭曲也存在非对称性。因此,上风向和下风向位置,通常是风力机噪声辐射强度最高的区域,居民区的位置一般选择风轮平面的切线方向。