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Aerodynamic noise emitted by small wind turbines is a concern due to their proximity to urban environments. Broadband airfoil self-noise has been found to be the major source, and several studies have discussed techniques to reduce airfoil leading-edge and trailing-edge noises. Reduction mechanisms inspired by owl wings and their airfoil sections were found to be most effective. However, their effect/s on the tip vortex noise remain underexplored. Therefore, this paper investigates the effects of implementing an owl airfoil design on the tip vortex noise generated by the National Renewable Energy Laboratory (NREL) Phase VI wind turbine to gain an understanding of the relationship, if any, between airfoil design and the tip vortex noise mechanism. Numerical prediction of aeroacoustics is employed using the Ansys Fluent Broadband Noise Sources function for airfoil self-noise radiation. Detailed comparisons and evaluations of the generated acoustic power levels (APLs) for two distinguished inlet velocities were made with no loss in torque. Although the owl airfoil design increased the maximum generated APL by the baseline model from 105 dB to 110 dB at the lower inlet velocity, it significantly reduced the surface area generating the noise, and reduced the maximum APL generated by the baseline model by 4 dB as the inlet velocity increased. The ability of the owl airfoil to mitigate the velocity effects along the span of the blade was found to be its main noise reduction mechanism. [ABSTRACT FROM AUTHOR]

Construction noise is an occupational noise that is potentially harmful, and it usually originates from machinery in construction sites. The impact of construction noise on the health and safety of construction workers is one of the main concerns in the industry. Prolonged noise exposure duration will cause physiological, physical, psychological damage, and negatively affect the working performance of the workers as well. Furthermore, the adverse impacts arising from construction noise may jeopardize public welfare, particularly for those who live nearby the construction site. Therefore, this research aims to develop a Stochastic Deep Learning (SDL) noise prediction model by integrating stochastic modelling and deep learning techniques. Stochastic modelling is applied in this study to generate a set of simulated randomized data based on several parameters as the input for the deep learning model. The deep learning model is trained with the input from stochastic modelling to predict the noise levels emitted from the construction site. The predictive performance of the deep learning model will be assessed with several statistical measures such as absolute difference, mean absolute difference and root mean square deviation. Ten case studies are conducted to validate the reliability and accuracy of the SDL noise prediction model. The SDL model showed high accuracy of prediction results with an average absolute difference of less than 1.2 A-weighted decibels (dBA) among the case studies as compared to the measurement. The reliability of the results from the prediction model is high. In conclusion, the SDL model is established and provided a promising outcome with satisfactory predictive performance. Finally, further development of the model would be worthwhile to fully exploit the potential of the SDL noise prediction model in construction industries as a planning, managerial and monitoring tool.

The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping.

The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping. [ABSTRACT FROM AUTHOR]

Cavity flows are a prevalent phenomenon in aerospace engineering, known for their intricate structures and substantial pressure fluctuations arising from interactions among vortices. The primary objective of this research is to predict noise levels in high-speed cavity flows at Mach 4 for a rectangular cavity characterized by an aspect ratio of L/D = 7. Moreover, this study delves into the influence of the plasma actuator on noise control within the cavity flow regime. To comprehensively analyze acoustic characteristics and explore effective noise reduction strategies, a computational fluid dynamics technique with the combination of a delayed detached eddy simulation (DDES) and plasma phenomenological model is established. Remarkably, the calculated overall sound pressure level (OASPL) and plasma-induced velocity closely align with the experimental data, validating the reliability of the proposed approach. The results show that the dielectric barrier discharge (DBD) plasma actuator changes the movement range of a dominating vortex in the cavity to affect the OASPL at the point with the maximum noise level. The control of excitation voltage can reduce the cavity noise by 2.27 dB at most, while control of the excitation frequency can only reduce the cavity noise by 0.336 dB at most. Additionally, the increase in excitation frequency may result in high-frequency sound pressure, but the influence is weakened with the increase in the excitation frequency. The findings highlight the potential of the plasma actuator in reducing high-Mach-number cavity noise. [ABSTRACT FROM AUTHOR]

Aerodynamic noise emitted by small wind turbines is a concern due to their proximity to urban environments. Broadband airfoil self-noise has been found to be the major source, and several studies have discussed techniques to reduce airfoil leading-edge and trailing-edge noises. Reduction mechanisms inspired by owl wings and their airfoil sections were found to be most effective. However, their effect/s on the tip vortex noise remain underexplored. Therefore, this paper investigates the effects of implementing an owl airfoil design on the tip vortex noise generated by the National Renewable Energy Laboratory (NREL) Phase VI wind turbine to gain an understanding of the relationship, if any, between airfoil design and the tip vortex noise mechanism. Numerical prediction of aeroacoustics is employed using the Ansys Fluent Broadband Noise Sources function for airfoil self-noise radiation. Detailed comparisons and evaluations of the generated acoustic power levels (APLs) for two distinguished inlet velocities were made with no loss in torque. Although the owl airfoil design increased the maximum generated APL by the baseline model from 105 dB to 110 dB at the lower inlet velocity, it significantly reduced the surface area generating the noise, and reduced the maximum APL generated by the baseline model by 4 dB as the inlet velocity increased. The ability of the owl airfoil to mitigate the velocity effects along the span of the blade was found to be its main noise reduction mechanism.

As the speed of the new generation of high-speed electric multiple units (EMU) increases, the requirements for vibration and noise reduction in traction gear trains are becoming higher and higher. Although most researchers have focused on the vibration mechanics analysis of gears, the actual noise has the most direct impact on passenger experience and safety. To address this problem, a new type of curved cylindrical gear is proposed to analyze the dynamic characteristics of the gear pair and predict its radiated noise based on the acoustic-vibration coupling theory using the finite element-boundary element method. Parametric modeling of the gear pair using CREO and assembly motion analysis were performed. ANSYS was used to analyze the stress distribution, inherent frequency, and inherent vibration pattern of the gear pair, and harmonic response analysis was performed using the modal superposition method to solve the displacement frequency response curve and vibration characteristics. ACTRAN was used to construct the free-field model, create acoustic excitation based on the acoustic-vibration coupling equation, set the field points, and predict radiated noise. The research results show that the noise is mainly concentrated in the tooth meshing area, and the root mean square RMS range of its sound pressure level value is 91–100 dB. Its dynamic characteristics and noise values are in line with the traction requirements of high-speed EMU, providing a new idea for improving the noise prediction of traction gears for new generation high-speed EMU, which in turn strongly support the noise control of high-speed EMU stock and thus improve the passenger experience and driving environment. [ABSTRACT FROM AUTHOR]

Determination of health hazards of noise pollution is a challenge for any developing city intersection. The people working at roadside open-air shops or near the congested roads of any intersection face intense noise pollution. It becomes very difficult to efficiently determine the hazards of noise on the health of people living near the intersection. An attempt was made to determine the noise-induced health hazards of the developing city of Bahadurpur, UP, India. The noise levels were monitored over 17 station points of the intersection for three months at different times of the day. Equivalent noise level (Leq) maps were determined within an accuracy of ±4dB. Areas adjacent to intersections indicated noise exposure levels close to 100 dB. Health hazards for the people of the intersection were determined through the testing of auditory and non-auditory health parameters for 100 people. A total of 75–92% of the people who work/live near the noisy intersection were found to be suffering from hearing impairment, tinnitus, sleep disturbance, cardiovascular diseases, hypertension, etc. Whether the recorded health hazards were indeed related to noise exposure was confirmed by testing the health parameters of people from the nearby and less noisy area of Pure Ganga. The nearby site reported mild hazards to the health of the population. An alarming level of hearing impairment was prevalent in the noisy Bahadurpur intersection (79–95%) compared to the same in Pure Ganga (13–30%). The estimated noise-induced health hazards were also compared for noisy and less-noisy study sites using ANOVA statistics. The results suggested that the health hazards reported in the two sites are not similar. Further, the severe hazards to people’s health at the underdeveloped intersection were found to be primarily caused by the intense exposure to noise.

The goal of this project was to integrate NASA’s Aircraft Noise Prediction Program (ANOPP2) into the RCAS2WOPWOP code suite. The implementation was made to be generalized for an arbitrary number of rotors, requiring changes to the previous ANOPP2 procedure to allow for multiple ANOPP2 runs in succession. Individual rotor noise predictions are combined into a total noise prediction. Another second goal was to use the updated RCAS2WOPWOP code suite to predict noise for a propeller-wing case, focusing specifically on the influence of interactional aerodynamics on the predicted noise. A case with an isolated propeller was run, then a propeller-wing case, and the results were compared to highlight where interactional aerodynamics had altered the noise produced. [ABSTRACT FROM AUTHOR]

The objective of this work is to assess the ability to predict noise generated by incident turbulence on aerodynamic forces. New batery technology has made multi rotor vehicle designs feasible that have disparate sound signatures compared to conventional rotorcrati (i.e., helicopters). A greater interest in rotorcrati broadband noise followed but is not complete. Leading edge noise created by incident turbulence is still required for a comprehensive understanding of rotorcraft noise. This research uses high-fidelity computational fluid dynamics (CFD) simulation to evaluate the turbulence near the leading edge of rotor blades. High fidelity grids are required to resolve the blade tip vortices and the surrounding turbulence in the blade wakes. From the CFD solutions, the turbulence kinetic energy is extracted and the turbulence intensity and turbulence integral length scale are calculated. The broadband noise is then calculated from the turbulence quantities using Amiet’s leading edge noise formula. [ABSTRACT FROM AUTHOR]

In a numerical study, a shorter span extent than experiment is often used to save the computational resource. The predicted sound pressure level should be corrected before compared with the experimental results. This study concerns the extended aeroacoustic spanwise correction method for the noise prediction radiated from large-span objects. Four new types of spanwise correction models are derived based on the assumption of the spanwise coherence function taking the form of a rectangular function, a trigonometric function, a Laplacian function and a Gaussian function, respectively. The large eddy simulation (LES) combined with the acoustic analogy theory is used for the aerodynamic noise prediction. The predicted far-field sound levels are then corrected by the proposed spanwise correction methods for the large-span objects. Far-field acoustic measurements and near-field hot-wire measurements are also performed in an anechoic wind tunnel for validation purpose. The predicted aerodynamic and aeroacoustic results are found in good agreement with the experiments with the proposed spanwise correction method. The present models based on the Laplacian function and Gaussian function are unified models taking the advantage of that there is no need to compare the relative extent of the numerical length, experimental length, and coherence length. The results also indicate that although there is no significant difference between the various functions, corrections based on the Gaussian profile seem to perform better compared with other functions. [ABSTRACT FROM AUTHOR]

Determination of health hazards of noise pollution is a challenge for any developing city intersection. The people working at roadside open-air shops or near the congested roads of any intersection face intense noise pollution. It becomes very difficult to efficiently determine the hazards of noise on the health of people living near the intersection. An attempt was made to determine the noise-induced health hazards of the developing city of Bahadurpur, UP, India. The noise levels were monitored over 17 station points of the intersection for three months at different times of the day. Equivalent noise level (Leq) maps were determined within an accuracy of ±4dB. Areas adjacent to intersections indicated noise exposure levels close to 100 dB. Health hazards for the people of the intersection were determined through the testing of auditory and non-auditory health parameters for 100 people. A total of 75–92% of the people who work/live near the noisy intersection were found to be suffering from hearing impairment, tinnitus, sleep disturbance, cardiovascular diseases, hypertension, etc. Whether the recorded health hazards were indeed related to noise exposure was confirmed by testing the health parameters of people from the nearby and less noisy area of Pure Ganga. The nearby site reported mild hazards to the health of the population. An alarming level of hearing impairment was prevalent in the noisy Bahadurpur intersection (79–95%) compared to the same in Pure Ganga (13–30%). The estimated noise-induced health hazards were also compared for noisy and less-noisy study sites using ANOVA statistics. The results suggested that the health hazards reported in the two sites are not similar. Further, the severe hazards to people's health at the underdeveloped intersection were found to be primarily caused by the intense exposure to noise. [ABSTRACT FROM AUTHOR]

Noise pollution is a challenging environmental issue in densely built urban areas and requires a holistic understanding of its sources and alleviation processes. Taking Isfahan City in Iran as a typical case, this study developed a combined GIS-artificial neural network (ANN) model to predict the spatio-temporal contribution of low-width parks to poise pollution mitigation. The 30-min equivalent sound level was measured at 100 stations in six urban parks (with a total area of 55.84 ha) under stable and controlled winter and summer conditions. The noise level predicting variables were hypothesized to be the area of vegetation cover; NDVI-based vegetation density and standard deviation (std); vegetation height; and road coverage measured within 100-, 200-, and 300-m radius buffer rings drown around each noise sampling station. These predictors were introduced to a multi-layer perceptron ANN model to identify and compare the most important noise alleviation variables among the selected predictors. The mean noise levels ranged from 67.23 to 70.57 dB. The number of vehicles showed an insignificant temporal difference, indicating that the noise source was relatively constant between the seasons. The ANN model performed satisfactorily in both seasons with SSE values of < 0.03. The Mann–Whitney U test showed a significant difference in the predicted noise levels between summer and winter. This study highlighted the efficiency of the combined GIS-ANN model in predicting distant-dependent urban processes, especially noise pollution whose levels and variability are essential in formulating urban land-use management. [ABSTRACT FROM AUTHOR]

Aerofoil noise as self-noise is detrimental to system performance, in this paper NACA 0012 optimization parameters are presented for reduction in noise. Designing an aerofoil with little noise is a fundamental objective of designing an aircraft that physically and functionally meets the requirements. Aerofoil self-noise is the noise created by aerofoils interacting with their boundary layers. Using neural networks, the suggested method predicts aerofoil self-noise. For parameter optimization, the quasi-Newtonian method is utilised. The input variables, such as angle of attack and chord length, are used as training parameters for neural networks. The output of a neural network is the sound pressure level, and the Quasi Newton method further optimises these parameters. When compared to the results of regression analysis, the values produced after training a neural network are enhanced. [ABSTRACT FROM AUTHOR]

Cavity flows are a prevalent phenomenon in aerospace engineering, known for their intricate structures and substantial pressure fluctuations arising from interactions among vortices. The primary objective of this research is to predict noise levels in high-speed cavity flows at Mach 4 for a rectangular cavity characterized by an aspect ratio of L/D = 7. Moreover, this study delves into the influence of the plasma actuator on noise control within the cavity flow regime. To comprehensively analyze acoustic characteristics and explore effective noise reduction strategies, a computational fluid dynamics technique with the combination of a delayed detached eddy simulation (DDES) and plasma phenomenological model is established. Remarkably, the calculated overall sound pressure level (OASPL) and plasma-induced velocity closely align with the experimental data, validating the reliability of the proposed approach. The results show that the dielectric barrier discharge (DBD) plasma actuator changes the movement range of a dominating vortex in the cavity to affect the OASPL at the point with the maximum noise level. The control of excitation voltage can reduce the cavity noise by 2.27 dB at most, while control of the excitation frequency can only reduce the cavity noise by 0.336 dB at most. Additionally, the increase in excitation frequency may result in high-frequency sound pressure, but the influence is weakened with the increase in the excitation frequency. The findings highlight the potential of the plasma actuator in reducing high-Mach-number cavity noise.

It is still difficult to conduct numerical calculation of the aerodynamic noise of full-scale, long-marshalling, high-speed trains. Based on the Lighthill acoustic analogy theory, the aerodynamic sound source of the high-speed train is equivalent to countless micro-vibrating sound sources. An acoustic radiation model of the dipole sound source of high-speed trains is established, and a method to predict the aerodynamic noise in the far field of long-marshalling high-speed trains is proposed. By this method, combined with numerical simulation technology, the flow field, noise source, and far-field noise characteristics of high-speed trains with different marshalling numbers are studied. The improved delayed detached eddy simulation method is used for flow field calculation, to obtain aerodynamic noise source information regarding the surface of high-speed trains. The numerical calculation method is verified by wind tunnel testing. The results show that the flow field and noise source characteristics of high-speed trains with different marshalling numbers are similar. The greater the length of the train body, the longer the trailing distance of the train wake, and the stronger of a surface noise source the tail car becomes. The spatial distribution characteristics of aerodynamic noise in the far field of high-speed trains do not change significantly with the length of the train body, but the magnitude of the sound pressure level will increase with the increase in length of the train body. The middle car body parts of high-speed trains with different marshalling numbers have similar noise distributions and sound pressure levels. Based on the noise calculation results of the 3-marshalling high-speed train, the far-field noise of the 5-marshalling and 8-marshalling train models is predicted and found to be in good agreement with the far-field noise of the actual train model. The differences in average sound pressure level are 1.01 dBA and 1.74 dBA, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult to accurately predict the radiated noise of rotary vane compressor under multi-source excitation, in this paper, the rigid body dynamics model and the dynamics model of airflow pulse excitation of the suction-compression-exhaust process of the rotary vane compressor were established, and the mechanism of multi-body contact excitation and airflow pulsation excitation for rotating vane compressor is expounded. Meanwhile, a flexible multibody finite element mesh model including a rotor, blades, cylinder, end cover and shell was established, and mechanical excitation and airflow pulse excitation were taken as mechanical boundary conditions to calculate the vibration response characteristics of the rotary vane compressor. To estimate the radiated noise of the rotary vane compressor, a compressor acoustic boundary element model was established, and a noise test bench system was built in a semi-anechoic chamber to compare and analyze the calculated noise value. At constant speed and acceleration, the rear sound pressure value is larger, followed by the right part, which is close to the exhaust port, and the front sound pressure is obviously lower than other points. The maximum relative error of sound pressure levels at four field points is 4.8%, and the average relative error of all measured points is 2.4%. The measured values are in good agreement with the calculated values, which verifies the accuracy of the radiated noise prediction model of rotary vane compressor. [ABSTRACT FROM AUTHOR]

Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue's sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire. [ABSTRACT FROM AUTHOR]

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Environmental noise can lead to adverse health outcomes. Understanding the spatial variability of environmental noise is crucial for mitigating potential health risks and developing influential urban strategies for reducing noise levels. This study aimed to measure noise levels and develop a land use regression (LUR) model to determine the spatial variability of environmental noise in Shiraz, Iran. A grid-based technique was used to establish 191 noise measurement sites (summer) across the city to generate the LUR model based on two noise metrics: L den and L night. Leave-one-out cross-validation (LOOCV) and 38 additional measurement sites (winter) were used for the LUR model assessment. The mean values of L den and L night during summer were 68.20 (±8.05) and 58.95 (±9.55), respectively, while during winter, the corresponding values were 69.46 (±5.46) and 58.81 (±6.79). The LUR models explained 67% and 65% of the spatial variability in L den and L night , respectively. LOOCV analysis demonstrated R2 values of 0.64 and 0.61. Moreover, findings indicated mean absolute error (MAE) values of 3.96 dB(A) for L den and 4.74 dB(A) for L night. Validation based on an additional set of 38 measurement sites revealed R2 values of 0.62 for both L den and L night , with MAE of 2.78 and 3.31, respectively. In addition, the adjusted R2 values were 0.54 and 0.53. The results indicated no significant temporal variations between summer and winter. The results revealed that road-related variables significantly influenced noise levels. Moreover, the results indicated that L den and L night levels were higher than the World Health Organization recommendations for exposure to road traffic noise. The results of our study showed that the LUR modeling approach based on geographical predictors is an effective tool for assessing changes in ambient noise levels in other cities in Iran and around the globe. [Display omitted] • LUR models were validated using the LOOCV method and 38 additional sites. • The models explained 67% and 65% of the spatial variability in L den and L night. • Road-related variables significantly influenced noise levels. • During the day, 235 km2 of the city experienced noise levels <73 dB(A). • During nighttime, 208 km2 of the city experienced noise levels <63 dB(A). [ABSTRACT FROM AUTHOR]

• Revealed the acoustic modal components of the twin-rotor noise with rotor-to-rotor interactions. • Experimental verification of the twin-rotor noise prediction method based on acoustic modal analysis. • Theoretical analysis of the modulation effect of initial azimuth angle on global rotor noise. • The mechanism of rotor-to-rotor interactions significantly increases the axial noise of rotors was revealed through acoustic modal analysis method. Existing research mainly investigated the influence of rotor-to-rotor interactions on the aerodynamics performance and noise level, but did not reveal the mechanism by which loading fluctuation significantly increases the noise below the rotor. In this study, first, an acoustic modal analysis method was established by mapping the source modal domain to acoustic modal domain, to investigate the global noise generated by a twin rotor considering the rotor-to-rotor interactions. Then, it was concluded from the simulation that the rotor-to-rotor interactions have little influence on the distribution of the sound sources, while they obvious change the noise in region at high elevation angle. Next, the method proposed was verified through experiments in an anechoic chamber, and the prediction error of overall sound pressure level (OASPL) was less than 7%. Finally, acoustic modal analysis reveals that the mechanism is attributed to the high radiation efficiency of the higher-order source modal components and dominant vertical directivity pattern of new acoustic modal components. Moreover, the initial azimuth angle of the rotors has an amplitude modulation effect on those acoustic modal components. When the difference in initial azimuth angle is π / L for rotors with L blades, the new modal components generated by different rotors cancel out each other for the odd-order blade passing frequency (BPF) noises. [ABSTRACT FROM AUTHOR]

• A mesh refinement technique is utilised to resolve tip-vortex cavitation, essential in predicting the noise. • The proposed technique reduces the computational efforts by adapting both the mesh and the refinement criteria. • The broadband hump in spectra shifts towards the low-frequency as the propeller load increases and hence cavitation. Underwater radiated noise (URN) of a marine propeller has received significant interest in recent decades due to its implications on marine fauna. Therefore, an accurate prediction of URN at an early stage of the propeller design is becoming imperative. This study presents a numerical investigation into the noise prediction of a marine propeller, including cavitation and a comparison with experimental test results obtained from the URN database from the King's College D (KCD) standard propeller series. Amongst the propellers tested in the series, the member KCD-193 was chosen to scrutinise in this study due to the significant variance of the cavitation types experienced by this propeller member and consequent characteristic variations observed in its URN spectral response. Numerical URN predictions of different flow conditions, represented by the advance coefficient and cavitation number, were conducted to investigate their effects on the noise spectrum. These predictions were compared with the experimental results to enable interpretation of the impact of various aspects of the simulation on URN prediction accuracy. In this investigation, one of the most prominent noise sources, tip-vortex cavitation (TVC), was identified as a critical aspect that needs to be captured by the numerical simulations for accurate URN predictions using CFD simulations. The influence of TVC on the spectrum was observed to be significant. The inception and stable presence of TVC dominated the frequency response of the broadband hump. In order to address this, a systematic adaptive mesh refinement strategy was implemented based on the vortex criterion to solve the flow characteristics in the propeller slipstream accurately. To further complement this task, a correlation between the cavitation bubble growth and collapse phenomenon by the sensitivity of the broadband hump on the spectrum was established based on the experimental results. The central frequency of the broadband hump was observed to vary with the advance coefficient and cavitation number. The reduction in the cavitation number resulted in a shift of this hump towards lower frequencies. The URN level of the hump decreased slightly in the high frequency by the reduction in the advance coefficient and the developing cavitation, demonstrating the cushioning effect on the spectrum. An accurate assessment of the noise spectrum, as far as numerical predictions are concerned, particularly on the broadband hump frequency bandwidth, was directly associated with the resolution of the TVC. [ABSTRACT FROM AUTHOR]

As the speed of the new generation of high-speed electric multiple units (EMU) increases, the requirements for vibration and noise reduction in traction gear trains are becoming higher and higher. Although most researchers have focused on the vibration mechanics analysis of gears, the actual noise has the most direct impact on passenger experience and safety. To address this problem, a new type of curved cylindrical gear is proposed to analyze the dynamic characteristics of the gear pair and predict its radiated noise based on the acoustic-vibration coupling theory using the finite element-boundary element method. Parametric modeling of the gear pair using CREO and assembly motion analysis were performed. ANSYS was used to analyze the stress distribution, inherent frequency, and inherent vibration pattern of the gear pair, and harmonic response analysis was performed using the modal superposition method to solve the displacement frequency response curve and vibration characteristics. ACTRAN was used to construct the free-field model, create acoustic excitation based on the acoustic-vibration coupling equation, set the field points, and predict radiated noise. The research results show that the noise is mainly concentrated in the tooth meshing area, and the root mean square RMS range of its sound pressure level value is 91–100 dB. Its dynamic characteristics and noise values are in line with the traction requirements of high-speed EMU, providing a new idea for improving the noise prediction of traction gears for new generation high-speed EMU, which in turn strongly support the noise control of high-speed EMU stock and thus improve the passenger experience and driving environment.

Noise is considered one of the most critical environmental issues because it endangers the health of living organisms. For this reason, up-to-date knowledge seeks to find the causes of noise in various industries and thus prevent it as much as possible. Considering the development of railway lines in underdeveloped countries, identifying and modeling the causes of vibrations and noise of rail transportation is of particular importance. The evaluation of railway performance cannot be imagined without measuring and managing noise. This study tried to model the maximum A-weighted noise pressure level with the information obtained from field measurements by Emotional artificial neural network (EANN) models and compare the results with linear and logarithmic regression models. The results showed the high efficiency of EANN models in noise prediction so that the prediction accuracy of 95.6% was reported. The results also showed that in noise prediction based on the neural network-based model, the independent variables of train speed and distance from the center of the route are essential in predicting.

There are several noise modeling software packages in order to predict noise level, each of them has different accuracy in different country. The present study was undertaken to analyze Traffic Noise Model software package (TNM) for two large highway in Tehran township. Firstly, field measurement for equivalent noise level (Leq) was carried out by sound level meter(SLM), then Leq prediction by the software was done. Finally, the results of the two previous steps were compared. the mean deviation of the results for sound levels below 80 dB was 0.33 dB, but for values further than 80 dB, the deviation was high. due to the obsolescence of a large number of the trucks inside Iran, new trucks instead of using software-defined trucks, were defined. With this change, the modeling and measurement results were more coordinated. The coefficient of determination (R2) increased from 0.4915 to 0.7312.

Abstract With the gradual transformation of chemical industry park to digital and intelligent, various types of environmental data in the park are extremely rich. It has high application value to provide safe production environment by deeply mining environmental data law and providing data support for industrial safety and workers’ health in the park through prediction means. This paper takes the noise data of the chemical industry park as the main research object, and innovatively applies the 3σ principle to the zero-value processing of the noise data, and builds an LSTM model that integrates multivariate information based on the characteristics of the wind direction classification noise data combined with the wind speed and vehicle flow information. The Prophet model integrating multi-site noise information was adopted, and the Multi-PL model was constructed by fitting the above two models to predict the noise. This paper designs and implements a comparative experiment with Kalman filter, BP neural network, Prophet, LSTM, Prophet + LSTM weighted combination prediction model. R 2 was used to evaluate the fitting effect of single model in Multi-PL, RMSE and MAE that were used to evaluate the prediction effect of Multi-PL on noise time series. The experimental results show that the RMSE and MAE of the data processed by the 3σ principle are reduced by 32.2% and 23.3% in the multi-station ordered Prophet method, respectively. Compared with the above comparison models, the Multi-PL model prediction method is more stable and accurate. Therefore, the Multi-PL method proposed in this paper can provide a new idea for noise prediction in digital chemical parks.

The vibration and noise radiation from underwater structures can be harmful for aquatic ecosystems, especially for endangered species which are sensitive to particle motion and sound pressure. In this study, a water supply pipeline was chosen to investigate the flow-induced vibration and underwater noise radiation. A finite element model was developed to predict the vibration of the pipeline-tunnel-soil coupling system using fluid–structure interaction analysis. Next, a three-demission boundary element acoustic model was developed to simulate underwater noise radiation and propagation. Parametric analysis was conducted to investigate the influence of scouring depth on vibration and acoustic radiation. The results showed the flowing fluid–induced vibration produced broad band noise radiation, with dominant frequency range from 3 to 25 Hz. The sound pressure radiated from the model with once-in-a-century scouring depth was about 3 dB larger than the model with normal depth due to thinner sediment. The sourcing depth has significant influence on the noise distribution and radiation directivity. The simulated sound pressure level and water particle motion can exceed the threshold of some underwater species in certain frequency range, especially for the once-in-a-century scouring depth. The proposed methodology can be used for acoustic radiation prediction in further study to reduce the influence on aquatic environment. [ABSTRACT FROM AUTHOR]

The aerodynamic noise behavior of flow passing the simplified leading car and nose car scale models of a high-speed train is investigated through the vortex sound theory and acoustic analogy approach. The unsteady flow developed around the geometries is solved numerically and the data are applied to study the near-field quadrupole sound source and calculate the far-field noise radiated. It is found that the turbulent flow developed around the leading car is characterized by multi-scale vortices separated from the geometries. The intensity of volume dipole source is much larger than that of volume quadrupole source and the volume dipole source becomes the predominate source of the near-field quadrupole noise. The flow is separated noticeably in the regions of the nose, bogies, bogie cavities, and train tail of the leading car where the pressure fluctuations are generated largely upon the solid surfaces and correspondingly a dipole noise of high level is produced. By comparison, the noise contribution from the leading bogie and bogie cavity is larger than that from the other components. Moreover, the numerical and experimental results of train nose car model demonstrate that the flow around the bogie region is the dominant aerodynamic sound source. Therefore, the flow-induced noise generated from the leading cars may be reduced efficiently within a certain frequency range and specific direction by mitigating the flow interactions around the areas of leading bogie and bogie cavity. [ABSTRACT FROM AUTHOR]

Electromagnetic interference (EMI) produced by series arc fault has long been a concern in dc distribution systems. As EMI fault may lead to serious safety risks, it is important to detect and predict arc disturbance for EMI management purposes. This article proposes a deep-learning-based approach for series dc arc diagnosis and circuit behavior prediction. Using time–frequency slices generated from power supply-side signals as a reference input, the proposed method is capable of extracting supply-side circuit features with a time–frequency feature extractor. The two-step feature extractor adopts convolutional neural network to extract static features on each time–frequency slice, and combines a long short-term memory network to capture dynamic time-varying signatures of time–frequency slice sequence. Fully connected network layers are lined up behind to implement mapping relation between extracted features and expected output. To further evaluate the performance of the proposed method, an experimental platform is established for series dc arc simulation and circuit data collection. Experimental data under normal switching operation and various arcing states are captured and employed to train the deep learning model. In this article, trained models show an overall accuracy of 98.43% in arc fault diagnosis, and give a time-domain prediction result resembling the actual signal. [ABSTRACT FROM AUTHOR]

The absence of excitation characteristic parameters significantly hinders the efficacy and precision of underwater noise predictions, especially under multiple excitations. Addressing this issue, a novel noise prediction reconstructed source method has been proposed for the noise analysis of a stiffened cylindrical shell. This method diverts attention from the actual sources and reconstructs the source utilizing the shell's vibrational response and transfer functions. The structural vibration response induced by the reconstructed source remains congruent with the actual behavior. The issue of non-unique solutions inherent in the reconstructed source is resolved through the application of the least squares principle. With the reconstructed sources and the stability of the transfer functions, underwater noise prediction becomes readily attainable. The method has been substantiated via an experiment from a stiffened cylindrical shell. The results indicate that the noise predictions derived from the reconstructed source method exhibit excellent agreement with experimental data, the overall level error of noise prediction is up to 0.7 dB. Furthermore, in comparison to the BEM, this method significantly enhances the efficiency under identical computational settings, with the prediction duration constituting a mere 1% of that required by the BEM. • The reconstructed source method has been proposed to achieve prediction of noise from stiffened cylindrical shells. • The least squares theory is used to reconstruct the source and the actual sources are not considered in this method. • An experiment on a stiffened cylindrical shell was conducted, and a experimental system has been established. • The validity and efficiency of the method are verified by experiments and BEM calculations, respectively. [ABSTRACT FROM AUTHOR]

Construction noise is an occupational noise that is potentially harmful, and it usually originates from machinery in construction sites. The impact of construction noise on the health and safety of construction workers is one of the main concerns in the industry. Prolonged noise exposure duration will cause physiological, physical, psychological damage, and negatively affect the working performance of the workers as well. Furthermore, the adverse impacts arising from construction noise may jeopardize public welfare, particularly for those who live nearby the construction site. Therefore, this research aims to develop a Stochastic Deep Learning (SDL) noise prediction model by integrating stochastic modelling and deep learning techniques. Stochastic modelling is applied in this study to generate a set of simulated randomized data based on several parameters as the input for the deep learning model. The deep learning model is trained with the input from stochastic modelling to predict the noise levels emitted from the construction site. The predictive performance of the deep learning model will be assessed with several statistical measures such as absolute difference, mean absolute difference and root mean square deviation. Ten case studies are conducted to validate the reliability and accuracy of the SDL noise prediction model. The SDL model showed high accuracy of prediction results with an average absolute difference of less than 1.2 A-weighted decibels (dBA) among the case studies as compared to the measurement. The reliability of the results from the prediction model is high. In conclusion, the SDL model is established and provided a promising outcome with satisfactory predictive performance. Finally, further development of the model would be worthwhile to fully exploit the potential of the SDL noise prediction model in construction industries as a planning, managerial and monitoring tool. [ABSTRACT FROM AUTHOR]

With the gradual transformation of chemical industry park to digital and intelligent, various types of environmental data in the park are extremely rich. It has high application value to provide safe production environment by deeply mining environmental data law and providing data support for industrial safety and workers' health in the park through prediction means. This paper takes the noise data of the chemical industry park as the main research object, and innovatively applies the 3σ principle to the zero-value processing of the noise data, and builds an LSTM model that integrates multivariate information based on the characteristics of the wind direction classification noise data combined with the wind speed and vehicle flow information. The Prophet model integrating multi-site noise information was adopted, and the Multi-PL model was constructed by fitting the above two models to predict the noise. This paper designs and implements a comparative experiment with Kalman filter, BP neural network, Prophet, LSTM, Prophet + LSTM weighted combination prediction model. R2 was used to evaluate the fitting effect of single model in Multi-PL, RMSE and MAE that were used to evaluate the prediction effect of Multi-PL on noise time series. The experimental results show that the RMSE and MAE of the data processed by the 3σ principle are reduced by 32.2% and 23.3% in the multi-station ordered Prophet method, respectively. Compared with the above comparison models, the Multi-PL model prediction method is more stable and accurate. Therefore, the Multi-PL method proposed in this paper can provide a new idea for noise prediction in digital chemical parks. [ABSTRACT FROM AUTHOR]

The existing traffic noise prediction models in road intersections relate mainly to the typical solutions of intersection geometry and traffic organisation. There are no models for large and more complex intersections such as signalised roundabouts. This paper presents the results of studies on the development of a traffic noise prediction model for this type of intersection. The model was developed using a multiple regression method based on the results of field measurements of traffic parameters and noise levels in the vicinity of signalised roundabouts in Poland. The obtained model consists of two groups of variables affecting noise levels at the intersection. The first group determines in detail the influence of traffic and geometry of the closest entry. The second group shows the influence of more distant noise sources (traffic at the three remaining entries of the intersection) and the influence of the dimensions of the entire intersection. The developed model was verified through additional field measurements, as well as compared to the results of two methods of traffic noise prediction: the French ‘NMPB-Routes-2008’ and the German ‘RLS-90’. The obtained results confirmed a higher accuracy of calculations performed using the developed model in the range of: −1.2 dB ÷ +1.0 dB, while the ‘NMPB-Routes-2008’ and ‘RLS-90’ calculate precision were respectively: −2.8 dB ÷ +1.3 dB, and +0.8 dB ÷ +5.2 dB. Therefore, the developed model allows for a more accurate prediction of noise levels in the vicinity of signalised roundabouts in a flat terrain without buildings and noise barriers.

In the quest for the reduction of noise pollution, novel hybrid-electric or fully-electric power-trains promise to provide a substantial contribution. Especially closer to airfields, where acceptability issues tend to limit air operations with conventional fuel-burning engines, such novel power-trains allow to fly terminal maneuvers with a dramatically reduced impact on pollution. Considering the General Aviation (GA) field, where such new types of propulsion are more likely to gain a significant market share thanks to their favorable characteristics for this weight category, the reduction of the noise impact on ground may increase the infrastructural value of smaller airfields, often located in densely populated areas. This in turn would help in making novel power-train technologies economically advantageous at a system level. Despite these evident advantages, a methodology to quantify noise emissions of a novel type of power-train has not been identified yet – a fundamental step towards the assessment of the potential contribution of hybrid-electric or fully-electric aircraft to the global scenario of future aviation. This work introduces and discusses a possible procedure to provide such estimation. While mainly focused on the field of propeller-driven GA aircraft, the procedure presented herein can be easily scaled to cope with the specific features of heavier categories.

Aerodynamic is a branch of fluid dynamics that evaluates the behavior of airflow and its interaction with moving objects. The most important application of aerodynamic is in aerospace engineering, designing and construction of flying objects. Reduction of noise emitted by aerodynamic objects is one of the most important challenges in this area and many efforts have been to reduce its negative effects. The prediction of noise emitted from these aerodynamic objects is a low-cost and fast approach that can partially replace the "fabrication and testing" phase. One of the most common and successful tools in prediction procedures is data mining technology. In this paper, the performance of different data mining algorithms such as Random Forest, J48, RBF Network, SVM, MLP, Logistic, and Bagging is evaluated in predicting the amount of noise emitted from aerodynamic objects. The experiments are conducted on a dataset collected by NASA, which is called "Airfoil Self-Noise". The obtained results illustrate that the proposed hybrid model derived from the combination of Random Forest and Bagging algorithms has better performance compared to other methods with an accuracy of 77.6% and mean absolute error of 0.2279.

Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mapping in order to speed decomposition rate and realize stable estimate the prediction system. Then, we analyze and compute the cause of the noise from multiple views including computing the similarity of regions and the correlation between noise categories by kernel distance, which improves the credibility to infer the noise situation and the categories of regions. Finally, we devise a prediction algorithm based on the kernel-matrix tensor factorization model. We evaluate our method with a real dataset, and the experiments to verify the advantages of our method compared with other existing baselines. [ABSTRACT FROM AUTHOR]

The variation of the noise metrics of a group of sound sources due to its movement at a constant velocity along a straight track is discussed in the paper. When sound sources move, frequency and amplitude modulation is observed in the radiated sound field. The frequency and amplitude increase when sound sources are approaching and decrease when going away. We found that when frequency and amplitude modulation were considered, the calculated noise levels LAmax, LASmax, LAeq,T became larger than the calculated levels without their consideration. The difference of the calculated levels between the cases increased with respect to the sound source velocity. This difference was independent of distance between the track and the receiving point. Approximating discrete sound sources in line by a finite line source would underestimate the noise level. The level of this underestimation would increase by decreasing source division. The underestimated level was calculated for sources with reference noise spectrum for rolling stocks with a constant sound power level as we focused on the influence of frequency and amplitude modulation on the radiated sound field. The underestimated level was large for LAmax and LASmax compared with LAeq,T. The difference of the calculated levels between the cases decreased monotonically with respect to the source frequency.

Compared to homogeneous traffic flow, traffic speed variation is drastic with the involvement of heterogeneity. With an intent of studying the negative upshot of fluctuating speeds of heterogeneous traffic on the environment, the current paper is the outcome of the research done on various highways located in the states of Andhra Pradesh and Telangana in India, with an objective of developing a comprehensive noise prediction model by taking into account the traffic and roadway factors. Quantified noise levels [Leq (dBA) and L10 (dBA)] revealed that for the traffic speed variation of 10 to 95 kmph, the traffic noise levels were significantly affected by the variations in the proportion of the vehicle. On a specific note, the proposed model can be effectively used for the highway traffic noise prediction especially for the heterogeneous traffic, as the difference between the measured and predicted noise levels are within 1 to 10 dB (A).

Noise prediction is important for crew comfort in an offshore platform such as oil drilling rig. A deep neural network learning on the oil drilling rig is not widely studied. In this paper, a deep belief network (DBN) with the last layer initialized with trained DBN (named DBN-DNN) is used to model the sound pressure level (SPL) in the compartments of the oil drilling rig. The method finds an optimal number of the hidden neurons in restricted Boltzmann machine by using a normalized Euclidean distance from the worst possible error for each hidden layer progressively. The dataset used for experimental results is obtained via vibroacoustics simulation software such as VA-One and actual site measurements. The results show that output parameters such as spatial SPL, average spatial SPL, structure-borne SPL and airborne SPL improve the testing root mean square error to around 20% as compared to randomly assigning the number of neurons for each hidden layer. The testing RMSE in the output parameters has improved when compared with a multi-layer perceptron, sparse autoencoder, Softmax, self-taught learning and extreme learning machine. [ABSTRACT FROM AUTHOR]

Introduction: In open-cast mines, noise pollution has become a serious concern due to the extreme use of heavy earth moving machinery (HEMM). Materials and Methods: This study is focused to measure and assess the effects of the existing noise levels of major operational mines in the Keonjhar, Sundergadh, and Mayurbhanj districts of Odisha, India. The transportation noise levels were also considered in this study, which was predicted using the modified Federal Highway Administration (FHWA) model. Result and Discussion: It was observed that noise induced by HEMM such as rock breakers, jackhammers, dumpers, and excavators, blasting noise in the mining terrain, as well as associated transportation noise became a major source of annoyance to the habitants living in proximity to the mines. The noise produced by mechanized mining operations was observed between 74.3 and 115.2 dB(A), and its impact on residential areas was observed between 49.4 and 58.9 dB(A). In addition, the noise contour maps of sound level dispersion were demonstrated with the utilization of advanced noise prediction software tools for better understanding. Conclusion: Finally, the predicted values at residential zone and traffic noise are correlated with observed values, and the coefficient of determination, R2, was calculated to be 0.6891 and 0.5967, respectively.

Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq ) by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 ) and the Mean Squared Error (MSE). Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.

The noise prediction using machine learning is a special study that has recently received increased attention. This is particularly true in workplaces with noise pollution, which increases noise exposure for general laborers. This study attempts to analyze the noise equivalent level (Leq) at the National Synchrotron Radiation Research Center (NSRRC) facility and establish a machine learning model for noise prediction. This study utilized the gradient boosting model (GBM) as the learning model in which past noise measurement records and many other features are integrated as the proposed model makes a prediction. This study analyzed the time duration and frequency of the collected Leq and also investigated the impact of training data selection. The results presented in this paper indicate that the proposed prediction model works well in almost noise sensors and frequencies. Moreover, the model performed especially well in sensor 8 (125 Hz), which was determined to be a serious noise zone in the past noise measurements. The results also show that the root-mean-square-error (RMSE) of the predicted harmful noise was less than 1 dBA and the coefficient of determination (R2) value was greater than 0.7. That is, the working field showed a favorable noise prediction performance using the proposed method. This positive result shows the ability of the proposed approach in noise prediction, thus providing a notification to the laborer to prevent long-term exposure. In addition, the proposed model accurately predicts noise future pollution, which is essential for laborers in high-noise environments. This would keep employees healthy in avoiding noise harmful positions to prevent people from working in that environment. [ABSTRACT FROM AUTHOR]