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This review is focussed on large-scale, horizontal-axis upwind turbines. Vertical-axis turbines are not considered here as they are not sufficiently efficient to be deployed in the commercial generation of electricity. Recent developments in horizontal-axis wind turbine noise research are summarised and topics that are pertinent to the problem, but are yet to be investigated, are explored and suggestions for future research are offered. The major portion of recent and current research on wind turbine noise generation, propagation and its effects on people and animals is being undertaken by groups in Europe, UK, USA, Japan, Australia and New Zealand. Considerable progress has been made in understanding wind turbine noise generation and propagation as well as the effect of wind farm noise on people, birds and animals. However, much remains to be done to answer many of the questions for which answers are still uncertain. In addition to community concerns about the effect of wind farm noise on people and how best to regulate wind farm noise and check installed wind farms for compliance, there is considerable interest from turbine manufacturers in developing quieter rotors, with the intention of allowing wind farm installations to be closer to populated areas. The purpose of this paper is to summarise recent and current wind farm noise research work and the research questions that remain to be addressed or are in the process of being addressed. Topics that are the subject of on-going research are discussed briefly and references to recent and current work are included.

Related reviews are given of (1) international wind turbine noise regulations and standards and (2) modern experimental methods in aeroacoustic measurement with sample applications. For the latter, specific emphasis has been placed on three measurement techniques: (1) single microphone measurements, (2) phased microphone array measurements, and (3) compact microphone array measurements. Through a description and analysis of recent successful application scenarios of the above techniques, their immense potential utilizing experimental aeroacoustics is explained.

The association of wind turbine noise (WTN) with sleep and physical/mental health has not been fully investigated. To investigate the relationship of WTN with the prevalence of self-reported symptoms of sleep and health problems, a socioacoustic survey of 1079 adult residents was conducted throughout Japan (2010-2012): 747 in 34 areas surrounding wind turbine plants and 332 in 16 control areas. During face-to-face interviews, the respondents were not informed of the purpose of the survey. Questions on symptoms such as sleeplessness and physical/mental complaints were asked without specifying reasons. Insomnia was defined as having one or any combination of the following that occurs three or more times a week and bothers a respondent: Difficulty initiating sleep, difficulty maintaining sleep, premature morning awakening, and feeling of light overnight sleep. Poor health was defined as having high scores for health complaints, as determined using the Total Health Index, exceeding the criteria proposed by the authors of the index. The noise descriptor for WTN was LAeq,n outdoor, estimated from the results of actual measurement at some locations in each site. Multiple logistic analysis was applied to the LAeq,n and insomnia or poor health. The odds ratio (OR) of insomnia was significantly higher when the noise exposure level exceeded 40 dB, whereas the self-reported sensitivity to noise and visual annoyance with wind turbines were also independently associated with insomnia. OR of poor health was not significant for noise exposure, but significant for noise sensitivity and visual annoyance. The above two moderators appear to indicate the features of respondents who are sensitive to stimuli or changes in their homeostasis.

Wind noise impairs the functionality of hearing aids and hearables outdoors or during sports by interfering with communication signals. This study aims to visualize the wind noise generation patterns around the human head by validated scale-resolved flow simulations. For the first time, the three-dimensional turbulent flow field at wind speeds of 10 km/h and 20 km/h around a female, a male and an artificial head is analyzed. It is possible to extract non-accessible data even inside the body, e.g., the pressure field deep inside the ear cavity in front of the eardrum. Head-geometry-independent flow features are identified. In the temple area, large-scale vortex shedding occurs. Small-scale vortices detach at the upper edge of the pinna and across the entire ear area. At typical microphone positions of behind the ear worn hearing devices, the pressure fluctuations are more pronounced than those at the auditory canal entrance. The tragus of the pinna plays a decisive role in attenuating wind noise in front of the entrance to the auditory canal. Anatomically exact ear canals ensure that velocity fluctuations are attenuated more effectively compared to an artificial one. At 20 km/h, the A-weighted pressure levels recorded at the microphone location of a behind the ear worn hearing devices exceed 85 dB(A). The results lead to a first understanding of wind noise effects and how they increase the perception threshold for recognition. Manufacturers can use the model to facilitate the wind noise optimal placement of microphones in new products to enhance communication under windy conditions.

The propagation of aerodynamic noise from multi-wind turbines is studied. An efficient hybrid method is developed to jointly predict the aerodynamic and aeroacoustics performances of wind turbines, such as blade loading, rotor power, rotor aerodynamic noise sources, and propagation of noise. This numerical method combined the simulations of wind turbine flow, noise source and its propagation which is solved for long propagation path and under complex flow environment. The results from computational fluid dynamics (CFD) calculations not only provide wind turbine power and thrust information, but also provide detailed wake flow. The wake flow is computed with a 2D actuator disc (AD) method that is based on the axisymmetric flow assumption. The relative inflow velocity and angle of attack (AOA) of each blade element form input data to the noise source model. The noise source is also the initial condition for the wave equation that solves long distance noise propagation in frequency domain. Simulations were conducted under different atmospheric conditions which showed that wake flow is an important part that has to be included in wind turbine noise propagation.

To evaluate the quality of available evidence on the effects of environmental noise exposure on sleep a systematic review was conducted. The databases PSYCINFO, PubMed, Science Direct, Scopus, Web of Science and the TNO Repository were searched for non-laboratory studies on the effects of environmental noise on sleep with measured or predicted noise levels and published in or after the year 2000. The quality of the evidence was assessed using GRADE criteria. Seventy four studies predominately conducted between 2000 and 2015 were included in the review. A meta-analysis of surveys linking road, rail, and aircraft noise exposure to self-reports of sleep disturbance was conducted. The odds ratio for the percent highly sleep disturbed for a 10 dB increase in Lnight was significant for aircraft (1.94; 95% CI 1.61–2.3), road (2.13; 95% CI 1.82–2.48), and rail (3.06; 95% CI 2.38–3.93) noise when the question referred to noise, but non-significant for aircraft (1.17; 95% CI 0.54–2.53), road (1.09; 95% CI 0.94–1.27), and rail (1.27; 95% CI 0.89–1.81) noise when the question did not refer to noise. A pooled analysis of polysomnographic studies on the acute effects of transportation noise on sleep was also conducted and the unadjusted odds ratio for the probability of awakening for a 10 dBA increase in the indoor Lmax was significant for aircraft (1.35; 95% CI 1.22–1.50), road (1.36; 95% CI 1.19–1.55), and rail (1.35; 95% CI 1.21–1.52) noise. Due to a limited number of studies and the use of different outcome measures, a narrative review only was conducted for motility, cardiac and blood pressure outcomes, and for children’s sleep. The effect of wind turbine and hospital noise on sleep was also assessed. Based on the available evidence, transportation noise affects objectively measured sleep physiology and subjectively assessed sleep disturbance in adults. For other outcome measures and noise sources the examined evidence was conflicting or only emerging. According to GRADE criteria, the quality of the evidence was moderate for cortical awakenings and self-reported sleep disturbance (for questions that referred to noise) induced by traffic noise, low for motility measures of traffic noise induced sleep disturbance, and very low for all other noise sources and investigated sleep outcomes.

Reliable assessment of airborne infrasound emitted by natural and technical sources has been gaining importance, while technology is limited and regulations are not sufficient in the infrasound frequency range. This paper provides a comparison of measurement microphones and microbarometers used for infrasound measurements in the field near wind power plants. Simultaneous measurements with both sensor systems were conducted at multiple sites in the vicinity of a wind park. Based on these measurements, it was shown that wind-induced noise is a major concern when measuring infrasound in the field. For this reason, additional experiments were conducted in an aeroacoustic wind tunnel to further investigate the sensitivity to wind of the sensors used. A combination of field measurements and laboratory experiments was applied to determine the influence of wind-induced noise in a real-life situation and multiple strategies are discussed for establishing quality criteria and controlling the influence of wind on such measurements.

This systematic review assesses the quality of the evidence across studies on the effect of environmental noise (road traffic noise, aircraft noise, railway noise, wind-turbine noise) on quality of life, wellbeing and mental health. Quantitative studies of noise effects on children and adults published from January 2005 up to October 2015 were reviewed. A total of 29 papers were identified. 90% of the papers were of cross-sectional design, with fewer studies of longitudinal or intervention design. Outcomes included depression and anxiety, medication use and childhood emotional problems. The quality of the evidence across the studies for each individual noise source was assessed using an adaptation of the GRADE methodology. Overall, given the predominance of cross-sectional studies, most evidence was rated as very low quality, with evidence of effects only being observed for some noise sources and outcomes. These ratings reflect inconsistent findings across studies, the small number of studies and a lack of methodological robustness within some domains. Overall, there are few studies of clinically significant mental health outcomes; few studies of railway noise exposure; and studies of large samples are needed. The lack of evidence for noise effects across studies for many of the quality of life, wellbeing and mental health domains examined does not necessarily mean that there are no effects: rather, that they have not yet been studied robustly for different noise sources.

In order to confront the challenge of efficiently evaluating interior wind noise levels in passenger vehicles during the early stages of shape design, this paper proposes a methodology for predicting interior wind noise. The methodology integrates vehicle shape features with a whale optimization Xception model (WOA-Xception). A nonlinear mapping model is constructed between the vehicle shape features and the wind noise level at the driver’s right ear. This model is constructed using key exterior parameters, which are extracted from wind tunnel test data under typical operating conditions. The exterior parameters include the front windshield, A-pillar, and roof. The key hyperparameters of the Xception model are adaptively optimized using the whale optimization algorithm to improve the prediction accuracy and generalization ability of the model. The prediction results on the test set demonstrate that the WOA-Xception model attains mean absolute percentage error (MAPE) values of 9.78% and 9.46% and root mean square error (RMSE) values of 3.73 and 4.06, respectively, for sedan and Sports Utility Vehicle (SUV) samples, with prediction trends that align with the measured data. A comparative analysis with traditional Xception, WOA-LSTM, and Long Short-Term Memory (LSTM) models further validates the advantages of this model in terms of accuracy and stability, and it still maintains good generalization ability on an independent validation set (mean absolute percentage error of 9.45% and 9.68%, root mean square error of 3.77 and 4.15, respectively). The research findings provide an efficient and feasible technical approach for the rapid assessment of in-vehicle wind noise performance and offer a theoretical basis and engineering references for noise, vibration, and harshness (NVH) optimization design during the early shape phase of vehicle development.

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions.