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.

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.

The interference of blade wake flow can pose a threat to the safety of flexible tower structures. In order to investigate the influence of airfoil position parameters on the VIV characteristics and the vibration reduction effect of nonlinear energy sinks (NES) on the cylinder, a flexible tower under the influence of blade wake flow is used as a reference, and a dynamic simulation model of an airfoil-twin degree-of-freedom cylindrical coupled structure is established based on computational fluid dynamics (CFD), structural dynamics, and nested grid techniques. The accuracy of the numerical model established in this paper is verified by comparing it with experimental data from foreign literature. The calculation results indicate that airfoils in different positions cause changes in the amplitude and frequency ratios of the cylinder and can either delay or enlarge the frequency lock-in interval of the cylinder. Under the influence of airfoil wake, the column experiences more severe vibrations. The NES can absorb the vibrational energy of the cylinder, and setting appropriate NES parameters can reduce the resonance response between the cylinder and the shedding vortices in the wake of the airfoil. This helps to change the shedding mode of the vortex and effectively suppress the VIV of the cylinder. © 2023 Elsevier Ltd

Wind turbine blade fatigue testing is necessary to verify the reliability of the structure design. A large aerodynamic drag is experienced under flap-wise fatigue testing since the prototype blade under testing moves through the air as a bluff body. An ellipse-sectional drag reducer made of a thin-wall shell is usually installed near the blade tip to alleviate the aerodynamic drag resistance. However, the correlation between drag reducer profile shape and aerodynamic characteristics has not been studied. In addition, the mass and stiffness of the drag reducer also have an adverse influence on the fatigue testing quality. In this study, a shuttle-shaped airfoil is proposed and an optimization design framework is built by a high-efficient aero-structure integrated numerical method. Results indicate that, as compared to the elliptical airfoils, an optimized shuttle-shaped airfoil can more effectively reduce the aerodynamic drag and at the same time improve the prototype blade test reliability. © 2023 Elsevier Ltd

湍流边界层尾缘噪声是翼型及风力机气动噪声的主要来源。本文应用的壁面压力谱方法是基于Aimet噪声理论提出的一种翼型尾缘噪声预测模型。首先，分别采用Goody、Rozenberg、Kamruzzaman、Lee、Hu等五种不同的壁面压力谱方法，对NACA0012和NACA64-618翼型进行噪声预测，并与实验数据对比，分析了各攻角和雷诺数下壁面压力谱方法对翼型尾缘噪声预测的准确性。其次，在Lee翼型尾缘边界层噪声建模的基础上，结合风力机叶素-动量理论，创新性地提出了一种新的风力机气动噪声预测模型，并与Bonus Combi 300 kW风力机的气动噪声实验数据进行对比，噪声谱对比结果验证了当前模型的有效性。该研究可为相关风力机气动噪声研究提供一种新的预测方法。