Prediction Of Noise Levels Research Articles

EXPERIMENTAL STUDY OF CARRIAGEWAY OPERATIONAL CONDITION INFLUENCE ON ACOUSTIC ROADSIDE AREA POLLUTION

Environmental noise management is an important part of the policy across the EU policy context, because transportation noise is a significant local environmental problem for most of the urban population. Increasing numbers of vehicles are associated with growing noise levels from road transport in urban areas and rising public health problems. Motor transport is considered to be a main source of noise pollution, so it is important to investigate the level of traffic noise and assess the relationship with traffic flows. The paper describes the main methods for determination of noise characteristics of traffic flows. The dependences for the prediction of the equivalent noise level and the results of field measurements of sound levels are presented. The results of field experiments and the calculated values of sound levels obtained by the analytical method are tested for homogeneity, using the Wilcoxon test. Experimental studies have established that the external sound level depends largely on the speed of vehicles, road conditions, and basic operating characteristics of highways. The analytical method associated with the use of deterministic and probabilistic models makes it possible to predict the traffic noise. But when dealing with the foregoing methods, there arise specific problems, many of which have not been resolved: there is no uniform terminology, nor is there any consensus on the use of noise characteristics of traffic flows in calculations at different stages of construction and reconstruction of highways of a certain traffic flow model under conditions of human settlements. Standardized measurement methods have been established and revised throughout the years by many renowned researchers. These methodologies have been revised in order to minimize problems that may occur and may not be foreseen by a less experienced researcher when adopting the measurement methods. Standardizing the measurement method is also useful for researchers because it becomes possible for researchers from around the world to compare their data. The joint effect of road conditions and the operational status of the roadway on the acoustic pollution of the roadside area is not fully taken into account. Therefore, applying the internationally recognized acoustical measurement standards is a good way to start any noise measurement experiment. The purpose of experimental research is to investigate the joint effect of road conditions and the operational status of the roadway on the acoustic pollution of the roadside area of settlements: identify the main characteristics of noise produced by traffic flows, consider the comparability of results of the field experiment and analytical computations. Slopes of 20…40‰ have little impact on the noise caused by the movement of passenger cars and trucks. In this case, the average acoustic emissions are identical to those used in the prediction of noise mode.

APPLYING MATHEMATICAL MODELING TO PREDICT ROAD TRAFFIC NOISE IN PHUKET PROVINCE, THAILAND

Road traffic is the most significant source of noise in an urban city and is considered not only an environmental nuisance but also a threat to public health. Therefore, this study aimed to determine road traffic noise levels in Phuket Province, including Muang Phuket, Thalang, and Kathu District; and to compare them with predicted noise levels using NMTHAI 1.2. Traffic noise level, traffic volume and speed of vehicles were measured on main roads including Yaowarat, Ratsada, Montri, Patipat, Ban Muangmai, Ban Kain, Ban Lipon, Baramee and Vichitsongkram Road. The results showed that traffic noise in Muang Phuket, Thalang and Kathu Districts were 70.0-70.9, 72.9-74.7 and 74.6-74.8 dBA, respectively. The result revealed that traffic noise levels obtained from the model were higher than measured noise at an average of 4.8±2.3 dBA. A high correlation was observed between predicted and measured traffic noise levels (R2 = 0.655, P < 0.01). Speed of vehicles and traffic volume were key variables affecting traffic noise level with a correlation coefficient of 0.752 and 0.702 at 99% confidence level, respectively. The model performed reasonably well under different traffic noise conditions and could predict traffic noise of other cities in Thailand.

Wheel squeal noise control under water-based friction modifiers based on instantaneous rolling contact mechanics

Friction modifiers have been shown to be effective in reducing wheel squeal noise but not reliable in eliminating the phenomenon. To investigate this, wheel squeal noise under water-based friction modifiers is predicted using a mathematical model taking into account assumed instantaneous traction-creep behaviour. The instantaneous lateral creep curves are determined from quasistatic measurements under water-based friction modified conditions and used to obtain analytical predictions of wheel squeal noise levels that are verified numerically and experimentally based on a two disk testrig. Similar levels of squeal are obtained in both dry and water-based friction modified conditions at higher speeds despite the measured lateral creep curve not showing ‘falling friction’. For low speed, the water-based friction modification is shown to be successful in eliminating squeal except for very high angles of attack where noise levels are still greatly reduced. Subsequently, the efficient modelling is used to identify what modifications to the creep curve provide the best squeal noise reduction and elimination capabilities.

Assessment, Prediction and Mapping of Noise Levels in Vellore City, India

AbstractPresent study focuses on measurement, prediction and mapping of noise levels in important places of Vellore city located in Tamil Nadu state, India. Weekday and weekend noise levels at six locations (S1 to S6) are recorded during morning, afternoon and evening times. Sampling locations includes educational (S1), commercial (S2 and S4), tourist and recreational (S3), and mixed (S5 and S6) zones. Assessment in Vellore city showed maximum Leq level in S4 and S5 and lowest is recorded in S3 throughout the study. Highest Leq levels are recorded in evening (57.52–78.41 dB(A)) and afternoon (58.76–76.39 dB(A)) time during weekday and weekend, respectively. Along with Leq, noise descriptors are also included and discussed in this study. Weekend has higher mean value of TNI thanWeekday. CoRTN model is employed to predict the L10 values. Results showed good performance of model with marginal difference between measured and predicted L10 levels. Further noise maps are created separately for weekday and weekend using ArcGIS software. Noise maps showed alarming noise levels near sampling sites and it is suggested to take immediate preventive measures using barriers or by providing alternative traffic routes.

Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

Underwater noise level predictions of ammunition explosions in the shallow area of Lithuanian Baltic Sea

Among the noisiest man-made activities in the seas, emitting very high acoustic energy are the underwater explosions of various objects and ship shock trials. Sound energy emitted by high explosives can be predicted or measured at sea. Sometimes, it can be convenient to apply empirical formulas and scaling laws to approximate the energy of underwater explosions. In addition, at some instances the determination of the spectral properties of the explosions is useful, i.e. when possible animal exposure to impulsive noise has to be evaluated. This paper presents an example of an application of freely available scaling laws and equations for prediction of noise levels of underwater explosions of historical ordnance in the shallow sea environments.Main findings of the study: An available scaling laws applied to model underwater explosion properties; spatial extent of explosion mapped; arising issues of modelling of underwater explosions in the shallow marine areas discussed.

Asphalt Pavement Acoustic Performance Model

Low-noise pavements are used as an effective method of traffic noise mitigation. Low-noise pavements reduce the noise that arises due to interactions between tires and road surfaces (tire/road) via the implementation of three main components: low pavement roughness, negative pavement texture, and a high pavement air-void content. The tire/road noise reduction capabilities of the wearing layer vary depending on the aggregate type, gradation, bitumen and air-void content, and density. Consequently, the demand for an accurate tire/road noise prediction model has arisen from the design of asphalt mixtures. This paper deals with how asphalt mixture components of the wearing layer influence tire/pavement noise reduction and presents a model for tire/road noise level prediction based on the asphalt mixture composition. The paper demonstrates that the noise reduction level of low-noise asphalt pavements is dependent on the composition of the asphalt mixture. Asphalt wearing layer mixture composition parameters were tested in the laboratory from cores taken from 18 road sections, where acoustic properties were measured using a close-proximity (CPX) method. The proposed linear model is based on the bitumen amount, the air-void content of the mixture and aggregate shape and involves materials that comply with the general requirements for high-quality asphalt mixtures. The model allows for the prediction of the tire/road noise level at the asphalt mixture design stage using asphalt mixture components and volumetric properties. The proposed model is the first stage in the building of a complex model with a much wider range of low-noise asphalts components, pavement profile depth and CPX-value relationships.

Estimation of shipping noise from sparse measurements via generative adversarial networks

There is growing interest in prediction of anthropogenic noise levels in the ocean. Evidence suggests that sources of ambient noise such as shipping traffic may be destructive to marine organisms that rely on acoustics for communication. Characterizing and predicting ambient noise are also critical to effective naval operations. Understanding ocean noise is constrained by the limited ability to directly measure the spatial distribution of noise levels. In this work, we present a deep-learning method to estimate the spatial distribution of ambient noise due to shipping from a small number of measurements. Noise levels are typically estimated using forward models based on statistical information about shipping routes and source levels and predictions or measurements of environmental variables including bathymetry and sound-speed profile. Inverse methods are sometimes used to estimate input parameters from in situ data. In contrast, we demonstrate the robust estimation of shipping noise using a pretrained generative adversarial network (GAN) as a prior. By using a context and prior loss, our algorithm is able to accurately predict the entire spatial distribution of noise from sparsely sampled measurements. This can yield greater accuracy than a forward model based on environmental parameters taken from archival databases or inferred in situ.

Noise Impact Assessment of Thermal Plant Facilities on Receptor Environments

Noise pollution is considered as a serious environmental nuisance in the urban areas. This study has used the VDI code 2714 to investigate the impacts of the noise from all the power generation and ancilliary facilities in the proposed Independent Power Plant, Agbara, Ogun State receptor environment. Kriging interpolation method in SurferR 8.0 software to predict noise emission levels within the fence line of the proposed Independent Power Plant. Calculations of possible noise levels at some receptors locations around the plant were carried out using the VDI code 2714. Four operation scenarios were considered in this study. The scenarios considered include: Noise emission from power generation facilities only (Scenario 1); Noise from the use of Compressors and Transformers (Scenario 2); Noise emission from the operation of power generation and ancillary equipment (Scenario 3); and Noise emission from the use of Backup Generators (Scenario 4). The maximum noise from the four scenarios were 112.34, 114.58, 116.61 and 110.01 dB(A), respectively, which is above the 8-hour 90 dB(A) limit recommended by the Federal Ministry of Environment (FMEnv). The operation of the proposed power plant will not have significant impact on the receptor environments except for Karogbaji which receives less than 12 dB(A) for the worst-case scenario (Scenario 3). The modeling results show that the predicted noise levels generated by the proposed operation would generally be within the established noise criteria at all the receptor locations under all conditions.

Effect of uncertainty in meteorological conditions on aircraft noise levels

To comply with noise regulations and to plan infrastructure around airports, there is a need to accurately predict aircraft noise levels. Even when high fidelity noise propagation models are used, the accuracy of noise level predictions can be affected by uncertainty in the input parameters. This work looks at the change in SPLs due to these uncertainties. To incorporate the effect of meteorological uncertainties, the methodology presented in Wilson et al. [JASA(2014)] is extended to the geometry of aircraft noise propagation. To allow for faster computations, a simplified version of the NORD 2000 noise propagation method is used. This work explores the effect of individual meteorological uncertainties in the propagation path (such as temperature profile and wind profile) as well as their combined effect on the SPLs for terminal area aircraft altitudes. [Work supported by the U.S. Federal Aviation Administration Office of Environment and Energy through ASCENT, the FAA Center of Excellence for Alternative Jet Fuels and the Environment, project 40 through FAA Award No. 13-C-AJFE-PSU under the supervision of Hua He. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the FAA.]

Dependency of noise level indicators on the heating power of rooftop boiler room

The paper presents results of practical measurements and results of data processing of the noise level from rooftop boiler rooms used in monolithic framed buildings. Three types of rooftop boiler rooms with power of N=0.8 MW; N=1.0 MW; and N=1.2 MW were used for data recording and analysis with further obtaining of dependencies regarding various levels of their heating efficiency. A distinctive feature of monolithic framed house building is a stiffening core formed of staircase and elevator section. The basic load-bearing capacity of the frame is provided by the joint work of metal reinforcement (metal) and elements made from concrete. During observation of operating boiler rooms noise measurements were carried out in order to collect and analyze information characterizing operation mode of rooftop gas boiler house under various climatic conditions per heating period. Based on the obtained dependencies it is possible to predict future noise levels from boiler rooms while designing further territory development. Authors supplement the existing method to evaluate noise pollution of the city districts. Result of experiment and application of the obtained dependencies is predicting of harmful physical factors occurrence at different levels of heating efficiency with further reduction of negative impacts on building structures; predicting noise levels while noise mapping of the territories under development; increasing comfort of living. The given conclusions can be applicable for further modeling of noise transmission into the environment.

Modelling of Noise Pollution Due to Heterogeneous Highway Traffic in India

Abstract 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).

Application of Multiple Linear Regression Technique to Predict Noise Pollution Levels and Their Spatial Patterns in the Tarkwa Mining Community of Ghana

Predicting and preventing intraurban noise levels in our communities are very challenging for urban planning, epidemiological studies and environmental management, especially in the developing world. Most existing noise-predicting models are limited in providing changes in noise levels during intraurban development and the corresponding noise pollution. In this study, noise levels were measured at 50 purpose-designed monitoring stations and then a land-use regression model was developed for the intraurban noise prediction applying the multiple linear regression (MLR) technique. The measured and the predicted noise levels were compared. These were further compared with noise estimates from a standard noise model, Lyons Empirical model. The results from the developed MLR model did not show any significant differences in the patterns as compared with those of the Lyons Empirical model. The model performance indicators showed a standard deviation of 1.585, high correlation (R) of 0.98, R2 of 0.961 and RMSE of 1.569. The resulting maps showed a heterogeneous distribution of the noise pollution levels in the community. This confirms the usefulness of the method for assessing the spatial pattern of noise pollution in a community. This makes it a useful tool for urban planning, epidemiological studies and environmental management.

Evaluation of Two Noise Level Prediction Models: Multiple Linear Regression and a Hybrid Approach

Noise prediction models are very useful for urban planning and environmental management. As a result researchers are always searching for methods that are practically applicable in predicting noise levels accurately. It therefore became paramount to implement special systems that could to predict noise levels accurately for an urban area. In this study two land-use regression methods, were used to formulate two noise level prediction models namely, multiple linear regression (MLR) and analytical hierarchy process (AHP)-multiple linear regression for the Tarkwa Mining Community (TMC). The performances of the two models were evaluated using statistical indicators. The MLR model performed better than that of a hybrid model of AHP-MLR with RMSE of 1.569, standard deviation of 1.585, R2 of 0.961 and R of 0.980. The performance of the hybrid AHP-MLR was also RMSE of 1.774, standard deviation of 1.758, R2 of 0.955 and R of 0.977. Plotted box-and-whisker and range plots further confirmed the performances of the two models. The resulting map from the noise prediction gave insight suggested that with the appropriate data and useful tools noise pollution levels of an urban area could be well predicted and mapped for urban planning and environmental management.

Impact of the turbulence modeling approach on the accuracy of the fan noise level prediction

The fan is the dominant source of noise for modern high bypass ratio aircraft engines. The article presents the results of investigating the influence of the approach to modeling the turbulence in modeling fan stage noise generation on the results of noise level prediction. Numerical investigation was performed for a model fan stage in test conditions in an anechoic chamber. Basic operating modes determining the acoustic performance of the fan were used. Calculation of far-field noise was performed using finite element modeling on the basis of the modal composition of generated noise. The influence of the turbulence model on the number and amplitude of acoustic modes excited in the flow on the fundamental frequency harmonics was analyzed. It is shown that modeling of the anisotropic flow turbulence in calculating non-stationary rotor-stator interaction makes it possible to obtain results close to the experimental ones.

The spatial (noise) prediction of the generator noise levels in E Mansouriya for the winter of 2015 in December and the summer of 2016 in May

The study dealt with the problem of noise pollution in most of the Iraqi cities, if not all, but the noise of generators in the center of Al Mansouriya area, which occupies an area of ​​331969 dunums and located in the northern part of the district of Khalis in Diyala province, 34.23) and between longitudes (44.39) and (45) east.This is due to the random distribution of the civil generators in the research area, as they were concentrated in the old neighborhood without any other residential neighborhood. This explains the reason for the rise in noise In the residential district and not compared to the population of the search area, reaching (19870) people for the year 2016 after it was (10338) for the year 2009. The study concluded that the maximum noise level was recorded at the birth site (14) with a noise level of (108.7db) for the summer of 2016, while the lowest noise level was at (17) with a noise level of 82.5 db for the winter of 2015, While the levels of noise increased significantly in the summer of 2016 compared to the winter of 2015. The researcher attributed this rise to the effect of temperature and speed and direction of wind in the search area, where the noise levels for the summer of 2016 ranged between (91.4db - 108.7db) , While the noise levels of the winter of 2015 ranged between (82.5db - 97.1db), exceeding the pain threshold for ear, hearing and (80.74bb) for the winter of 2015 and summer 2016. The research was based on the measurement of noise levels using the Digital sound level meter, Geostatistical Analyst In Arc map10.1, for the purpose of analyzing the spatial data of each site, and then extracting the spatial distribution map to generate the brightness of the raster generator sites, based on the measurements of each point of the eligibility generator in the Mansouriya area, With the sound The distance of 50 meters is considered as the program predicts the presence of noise in the generators and spatial scales closer to those distant from the monitoring source, indicating the chromatic gradient in the search maps

Artificial intelligence techniques for the vibration, noise, and emission characteristics of a hydrogen-enriched diesel engine

ABSTRACTThe present paper investigates the prediction of vibration, noise level, and emission characteristics of a four-stroke, four-cylinder diesel engine fueled with sunflower, canola, and corn biodiesel blends while H2 injected through inlet manifold using two different artificial intelligence methods: artificial neural network (ANN) and support vector machines (SVM). The aim of using these methods is to predict vibration, noise, carbon monoxide (CO), CO2, and NOx based on the initial experimental study by varying engine speed, blends of biodiesel, and H2 energy substitution ratio. Experimental data were gathered from the literature. For the ANN method, LevenbergMarquardt backpropagation training algorithm with logarithmic sigmoid and linear transfer function for hidden and output layers, respectively, gives the best results for prediction of vibration, noise, and emission characteristics. For SVM, a regression model is implemented with Gaussian kernel function. Results show that the ANN performs better than SVM, and the best mean average percent error and R2 for the models developed are 2.03 and 0.988 for vibration acceleration, 0.39 and 0.9615 for noise, 7.27 and 0.8549 for CO, 5.09 and 0.9398 for NOx, and 2.21 and 0.993 for CO2 values, respectively. Eventually, it is found that the ANN method is a good choice for simulation and prediction of dual fueled hydrogen sunflower, canola, and corn biodiesel blends.

The Noise Level of Total Scattering Cross Section Measurement in a Reverberation Chamber

A chamber can be used to measure the total scattering cross section (TSCS) of an object, in which the time-domain response needs to be analyzed to extract the scattering damping time. It has been observed in measurements that the noise level in extracting the scattering damping time is much higher than that in extracting the chamber decay time. The reasons behind this phenomenon are explored in this letter. Analytical distributions of the noise in TSCS measurements are derived and validated by measurements. Good agreements between analytical results and measurement results have been obtained, which gives guidelines on the prediction of noise level and the required independent sample number in the total scattering cross section measurement.

Improving Aircraft Noise Predictions ConsideringFan Rotational Speed

Accurate predictions of aircraft noise levels are desirable to enforce noise control regulations around airports and to evaluate noise abatement procedures. The current best practice noise contour prediction models assume certain default net thrust values depending on the engine type and aircraft altitude. This Paper proposes a method for calculating the engine fan settings (and, hence, the net thrust) directly from audio recordings. This method was tested with a large number of Airbus A330–300 and Boeing 777–200 flyover audio recordings. A significant correlation was found between the recorded noise levels and , explaining up to 45% of the variability in the noise levels. Using the calculated values in the noise prediction models, instead of the default values, increases the agreement with the actual recorded noise levels and explains parts of the variability. The inclusion of accurate values of and the update of the aircraft noise prediction calculations is therefore highly encouraged, for example, by increasing the dependency on of the noise levels.

Magnetic Noise Prediction and Evaluation in Tunneling Magnetoresistance Sensors.

We propose a simple model for prediction of magnetic noise level in tunneling magnetoresistance (TMR) sensors. The model reproduces experimental magnetic 1/f and white noise components, which are dependent on sensors resistance and field sensitivity. The exact character of this dependence is determined by comparing the results with experimental data using a statistical cross-validation procedure. We show that the model is able to correctly predict magnetic noise level for systems within wide range of resistance, volume and sensitivity, and that it can be used as a robust method for noise evaluation in TMR sensors based on a small number of easily measurable parameters only.