Noise Prediction Model Research Articles

An analysis of Dalaman International Airport’s (LTBS) noise prediction model and assessment of its noise impact

PurposeIn this context, this study aims to obtain information about the noise levels emitted to the environment by modeling the noise map of Dalaman Airport and correlating these noise values according to the Environmental Noise Directive (END) and World Health Organization (WHO) limits.Design/methodology/approachDalaman Airport Aeronautical Information Publication and 2022 flight data from the airport were analyzed. The noise levels exposed to schools and health institutions were determined using the Cnossos–Eu calculation method.FindingsMaximum noise levels were obtained as Lden 92.29 dB(A), Lday 85.24 dB(A), Levening 89.00 dB(A) and Lnight 85.23 dB(A) according to the noise indicators. Limit values recommended by the END and WHO according to noise indicator types and measurement results were correlated and evaluated.Originality/valueIn the noise modeling of Dalaman Airport, there has previously been no evaluation of the noise limits recommended according to END or WHO in the literature.

Structure-borne Noise Differences of Metro Vehicle Running on Different Tracks

A floating-slab track (FST) increases the interior noise and reduces the passenger's acoustic comfort. A study combining experimental measurement and numerical simulation was conducted to reveal the characteristics of internal structure-borne noise on different tracks and improve riding comfort. A field measurement was taken to analyze the essential interior noise characteristics on different tracks with different speeds. A noise prediction model was built by combining a vehicle-track coupling model, the structural finite element method and the acoustic boundary element method to predict the interior noise and study the panel contribution for noise control. The results show that the sound pressure level (SPL) of FST is 5 – 10 dB higher than the ordinary monolithic track (OMT). The curve section is 1 – 4 dB larger than the straight section. In the range of 90 – 110 Hz and 135 – 150 Hz, the SPL of FST condition is higher than that of OMT condition, with a maximum difference of 12 dB. And the SPLs at five ISO positions all have peaks at 30 Hz and 90 Hz frequencies, and the sound pressure fluctuates wildly at 100 Hz. The vibration at 90 Hz is relatively violent, which leads to more significant noise, but at 110 Hz, that is small. At different frequencies, the contribution of the panels to the sound pressure in the carriage is different, and the acoustic contribution of these panels may be the opposite.

Assessment of Heterogeneous Road Traffic Noise in Nagpur

The objective of the study is to assess the noise scenario and evaluate prediction model for heterogeneous traffic conditions. In the past few years, road traffic of Nagpur has increased significantly due to the rapid increase in the number of vehicles. Noise levels are monitored at six different squares, characterized as interrupted traffic flow due to traffic signals, high population density and heavy traffic where the major sources of noise are engines, exhausts, tires interacting with the road, horns, sound of gear boxes, breaks, etc. The A-weighted time-average sound levels (LAeq;T) are measured at the different time of day during peak and off-peak traffic hours. To assess the traffic noise more precisely, the noise descriptors such as L10, L50, L90, LAeq;T, TNI (Traffic Noise Index), NPL (Noise Pollution Level) and NC (Noise Climate) are used. In the present study, the Federal Highway Administration (FHWA) noise prediction model is used for prediction of noise levels and it is observed that one-hour duration measured LAeq;T ranged from 71 to 76 dB(A) and 71.6 to 76.3 dB(A) during peak and off peak hours respectively. Due to the heavy traffic the peak hour Sound Exposure Levels (LAE) at all locations are exceeding permissible limit of 70 dB(A) prescribed by the World Health Organization (W.H.O). Off-peak traffic hour noise levels are within permissible limit except at two locations, Jagnade and HB town square. Significant correlation was obtained when best fit lines generated between measured and predicted values gives R2 of 0.455 for all time intervals. Chi-Square test (χ2) was also computed to investigate the noise levels at different squares. The results show that the inhabitants of Nagpur city are exposed to high transportation noise during daytime.

Impact evaluation of different network planning concepts on traffic noise

The purpose of this paper is to study the impact of road structure on traffic noise from an urban network planning perspective, to provide solutions to control and prevent traffic noise at source. The process is divided into three parts. Firstly, the stochastic user equilibrium model for traffic flow allocation is improved to be more accurate. Secondly, a global traffic noise simulation method is proposed for the network based on the noise prediction model, and the noise data is visualized. Finally, a noise evaluation system is established to study the traffic noise evolution patterns under different road structures. The results show that with the increase of network density, the traffic noise value of the network decreases. The layout of one-way roads will reduce the regional traffic noise value. The impact of road grade adjustments on traffic noise is positively correlated with the rate of change in road capacity. The study can provide an effective basis for control traffic noise from the network planning stage.

Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation

Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) and derive discrete graph structures as the condition for reverse generative processes. We present a specialized hybrid graph noise prediction model that extracts the global context and the local node-edge dependency from intermediate graph states. We further utilize ordinary differential equation (ODE) solvers for efficient graph sampling, based on the semi-linear structure of the probability flow ODE. We also combine the solvers with gradient guidance from the molecule property predictor for similarity-constrained molecule optimization. Experiments on diverse datasets validate the effectiveness of our framework. Particularly, the proposed method still generates high-quality molecular graphs in a limited number of steps.

Improvement of high-power thyristor trigger circuit with switching-mode power supply for differential-mode noise reduction

This paper focuses on the differential-mode (DM) noise of high-power thyristor (HPT) trigger circuit with switching-mode power supply (SMPS) in capacitive pulsed power supply (CPPS). Parasitic parameter models of components are discussed and analyzed. Based on the parasitic parameter models of components, a DM noise prediction model of HPT trigger circuit with SMPS is developed. The generation mechanism and propagation paths of the DM noise in HPT trigger circuit with SMPS are investigated. To reduce the DM noise, a capacitive SMPS technique is adopted in the HPT trigger circuit with SMPS. Simulation results validate the theoretical analysis.

Review on Metrics and Prediction Methods of Civil Aviation Noise

Civil aviation noise is one of the main factors hindering the growth of the civil aviation industry. With the increase in global air traffic demand, the problem of aviation noise pollution will become more and more serious. It is of great significance to carry out research in aviation noise. First, by summarizing the characteristics of aviation noise metrics, this paper divides them into three categories: single event noise metrics, cumulative exposure metrics, and daily metrics. Representative metrics of each category are selected for explanation and in-depth analysis. Second, according to the principles of aviation noise prediction models, this paper classifies these existing models into three categories: best practice models, scientific models, and machine learning models. Relevant academic research results are summarized. The best practice model regards the aircraft as noise point source, and its specialty is to predict noise under complex air traffic conditions. The scientific model considers the noise from the level of aircraft components and reflects the underlying physical effects. Based on data, the machine learning model uses algorithms to mine the hidden relationship between various factors and noise to achieve the purpose of noise prediction. Then, this paper introduces two kinds of aviation noise simulation software based on the best practice and scientific models, and lists their access addresses. Finally, challenges and prospects are presented from three aspects: metrics, prediction models and simulation software.

Impact Evaluation of Network Structure Differentiation on Traffic Noise during Road Network Design

To evaluate the characteristics of traffic noise under different network structures, an evaluation method is established to clarify the mechanism of traffic noise impacted by variational network structures. First, a stochastic user equilibrium (SUE) model considering intersection delays is developed to allocate traffic flows of different network structures, and a flow-based univariate noise prediction model is used to simulate network noise distribution. Secondly, differentiation indexes including the coefficient of inhomogeneity (COI) and the concept of Lane Area Ratio (LAR) are set to quantify the network structure differences. Finally, a structural equation model (SEM) is developed to investigate the influence mechanism of network structure differentiation on traffic noise. The following conclusions are obtained: (1) The impact of network density differentiation on traffic noise is mainly reflected in the changes of road traffic flow and speed. Traffic noise decreases as the network density increases. In this case, when network density increases by 1 km/km2, traffic noise decreases by 1.6 dB. As network density increases, which means a dispensation of traffic flow, traffic noise is diminished by a reduction in traffic flow and speed. (2) The impact of road spatial location differentiation on traffic noise mainly depends on the number of noise sources. Traffic noise increases with the dense distribution of roads. In this case, when the COI increases by 1, the traffic noise increases by 3.0 dB. A higher COI indicates that the region will be exposed to more noise sources, which leads to traffic noise raises. The study can provide an effective basis for traffic noise control at the initial stage of network planning.

Construction and Simulation Analysis of A New Transformer Noise Prediction Model

In recent years, more and more attention has been paid to the environmental impact of substation operation noise. Therefore, a suitable noise prediction model is needed to evaluate the noise impact. The noise in the substation mainly comes from the main transformer. The predictions of the main transformer noise model in the existing technical specifications often do not correspond to the actual situation. Therefore, based on the theory of planar Nearfield Acoustical Holography (NAH) equivalent source approach, a new transformer noise prediction model called the main transformer noise model of the directional dot-matrix equivalent source is established. Using this model, a typical 500 kV substation is simulated in Cadna/A, and the results are compared with the simulation values and measured data of the existing standard main trans-former noise model. The comparison shows that the new prediction model can accurately predict the noise value of the substation, and the error is less than 2 dB(A).

Rocket noise models for the US Department of Defense

Accurate rocket noise prediction models are critical to support growing space defense programs. Potential deficiencies in the NASA SP-8072 model developed in the 1970s have been identified, but their impacts on accurate predictions are currently unknown. Cross-validationcomparisons to full-scale rocket measurements are needed to identify and quantify errors and to improve accuracy. This paper will review the DoD programs and policies that motivate rocket noise modeling requirements, summarize the current models in use, discuss some of the potential deficiencies of the models, and propose a roadmap for establishing confidence in models for official DoD use.

Use of artificial neural networks to assess train horn noise at a railway level crossing in India

Urban environment noise is a complex mixture of transportation, industrial, household, and recreational noise, which is identified as an emerging environmental threat. Present study monitors and evaluates a noise pollution hotspot: a railway level crossing, where several activities related to transportation noise were involved. Train honking, train movement, road vehicles, and pedestrians contribute to the noise level at a railway level crossing. Train horns are generally performed as train approach railway level crossings and they are mandatorily used to alert road users. However, the train horns are regarded as nuisance to the nearby residents. A detailed evaluation of train horn effectiveness is very much essential in the current contemporary environment. Thus, the main objective of this study is to measure noise levels emanating from train horns at a level crossing with due consideration to train types and climatic conditions. A comprehensive noise monitoring survey was conducted at an access-controlled level crossing. Furthermore, an artificial neural network (ANN)-based railway noise prediction model was developed to forecast maximum ([Formula: see text]) and equivalent (Leq) noise levels. Results revealed that train horn produced impulsive sound signals which fall under high frequency one-third octave bands causing severe irritation to trackside inhabitants. The proposed ANN models produced accurate results for [Formula: see text] and Leq noise levels and this model is identified as a vital tool for railway noise abatement. The results from this study are helpful to the urban planning and development authorities to implement strategic laws and policies to eradicate the urban environment noise.

An investigation into the effect of rubber design parameters of a resilient wheel on wheel-rail noise

Resilient wheels composed of a steel rim, a steel wheel core, a rubber layer and a press-fit ring are widely used on trams due to their ability to reduce noise and vibration. To make the press-fit process easier, the rubber layer in a resilient wheel is normally not continuous but instead, it is formed by rubber blocks which are uniformly separated in the circumferential direction. The gaps between the rubber blocks allow the rubber blocks to deform freely in the circumferential direction and therefore, generate stiffness which is lower than that when no such gaps are present. However, the question of what are the effects of the gaps on the vibration and sound radiation behaviours of the wheel and on wheel/rail noise has received little attentions. The aim of this paper is to answer this question adequately by developing a wheel/rail noise prediction model and carrying out a parametric study on key design parameters including rubber material parameters, the number of rubber blocks and the gap-rubber ratio.

Modelling the uncertainties of wind farm noise predictions

Representative predictions of wind turbine noise require to accurately model the main mechanisms and characteristics of acoustic emission (i.e., extended sound source with aeroacoustic noise generation) and acoustic propagation in outdoor environment (i.e., ground effects and atmospheric properties). As these phenomena fluctuate over time and space, it leads to great uncertainty on Sound Pressure Level (SPL) estimated at local resident bulidings/facades. Such uncertainty is not yet properly quantified by engineering noise prediction models. Thus, this paper presents a modeling tool developed in the framework of the French project PIBE, which aims at quantifying the SPL uncertainty involved in wind farm noise predictions. Ultimately, this modeling tool will be freely available online and will help to better understand the risk of noise pollution at each stage of a wind farm's life, in order to guarantee compliance with the regulatory requirements concerning the exposure of local populations.

Assessing the influence of street canyon shape on aircraft noise: results from measurements in courtyards near Amsterdam Schiphol Airport.

Aircraft noise prediction models traditionally omit buildings to optimize speed. Buildings and vertical surfaces scatter and reflect incident sound, affecting sound levels around buildings and within streets. Previous studies showed the impact of buildings on aircraft noise, based on a small number of measurements. Based on additional numerical models, it was found that the shape of buildings, i.e. a slanted or overhanging roof, lead to lower sound levels compared to streets comprised of vertical and flat surfaces. To examine these findings, a full-scale field lab was built near Amsterdam Schiphol Airport, comprised of shipping containers. The experiment consists of three courtyards, in which ten microphones measure sound levels from aircraft flyovers, near facades facing towards and away from the sound source (airplanes). Measurements are matched with meteorological and radar data, which gives information about the position of aircraft and the local weather conditions. The measurements show substantial differences depending on the position, ranging between 11dB(A) for a courtyard with straight facades, up to 14dB(A) for facades in a courtyard with a slanted roof and a roof overhang. Results can be used to rethink the way areas near airports are designed.

Porous top layer optimization of cement concrete slabs for tyre/road noise reduction

The I-STREET CUD-SF project aims at developing an urban pavement made of cement concrete slabs that can be easily removed and replaced for maintenance purposes. These slabs are made of a dense cement concrete body and a functionalized porous top layer ensuring water drainage and sound absorption. In order to optimize the porous layer for tyre/road noise reduction, several mix recipes with different porosity and maximum aggregate size were considered. Recipes with the most interesting absorption properties were preliminary selected based on absorption coefficient measurements performed on cylindrical test specimens with an impedance tube. A second phase was dedicated to evaluation of texture induced coast-by rolling noise levels with the tyre/road noise prediction model HyRoNE from 3D texture scans performed on rectangular laboratory samples. Finally, based on a porous medium model and a propagation model considering the tyre/road noise source as an omnidirectional point source over an impedance plane surface, the porous layer thickness of the selected mix was adjusted to minimize the predicted noise at roadside for different reference rolling speeds. This paper gives an overview of the results obtained at the different stages of the optimization process, prior in-situ assessment of the industrial solution in a near future.

Wind turbine noise modeling including aeroacoustic sources and propagation effects: comparison against field measurements

The study of wind turbine noise and its impact is of growing importance with the increase in the demand for green and clean energy. As it is known that wind turbine noise can be a cause of annoyance in the vicinity of wind farms it is beneficial to predict with certain accuracy the generated noise in the design phase itself. A crucial step is the validation of prediction models against field measurements (in-situ). This article presents a wind turbine noise prediction model that combines Amiet's theory to calculate trailing edge noise and turbulence interaction noise in free field with a wide-angle parabolic equation valid in moving media to account for the long-range acoustic propagation effects. The model considers the wind turbine as an extended noise source and the rotation effects (such as the convective amplification and Doppler effect) are taken into account. The predicted noise levels are compared to those obtained from a measurement campaign where acoustic, meteorological and ground impedance data have been recorded simultaneously. First, the sound source model is validated close to the wind turbines for different wind speeds and directions. Then, noise predictions are compared to SPL measurements at various distances from the sound source, between 350 and 1300meters.

Automating the assessment of sound power levels of running vehicles using information extracted from a static video

Noise has become a ubiquitous pollutant in big cities, especially Road Traffic Noise compared with other means of transportation. Several countries/regions have developed road noise prediction models based on local measurements, adjusting their requirements and goals for evaluating this pollutant. Differently than most developed countries, industrializing countries have different characteristics of noise generation due to implied differences, such as degree of maintenance (both vehicle and road conditions) and the driving behavior, for instance. The data acquisition needed for evaluating the vehicle fleet's sound power emission is expensive and time-consuming. This research proposes using a video camera close to the microphone to automate the data gathering and analysis. Using a Python script, this system extracts the sound pressure, estimates the running speed, assesses the distance from the receiver point, and classifies the vehicle under investigation. In addition, to discard inaccurate data, the expected trajectory and sound pressure are evaluated. The proposed system's performance was compared against manual measurements. The resulting sound levels differ less than 0.2 dB for most cases. Thus, acquired 3.4 times the amount of data in the same time interval. After this verification, measurements in different conditions and vehicle fleet were tested in Hokkaido, Oita, and São Paulo cities.

Prediction of rattle noise in steering gear system and analysis of contributing factors through neural networks using design and manufacturing data

In a vehicle, rattle noise may occur when driving on bumpy roads due to clearance between parts of steering gear systems. The rattle noise is perceived by the drivers' ears and it can be the cause of a repair campaign. In order to reduce rattle noise, the dimensions of clearance between parts are optimally designed. Also, in the development stage, general auto parts companies manage rattle noise below a certain level. However, it is impossible to measure noise for all products at the production stage due to cost and time issues. In this paper, the noise prediction model using the neural network model for all products based on design factors and the data of manufacturing execution system was introduced. In addition, the main factors contributing to rattle noise were analyzed in the process of creating the model. By referring to the main factors contributing to noise, it helps in the design of the steering gear to reduce the rattle noise value.

Study on regional road network planning based on time and noise integrated resistance function

To rationally reduce traffic noise emission from road network from the perspective of planning, a regional traffic flow distribution method combining time and noise integrated resistance function is proposed, and the method is applied in a typical region to verify its reasonability and practicability. Firstly, according to Greenshields model, the relationship among three traffic parameters (velocity, density and flow) is described, and combined with a line-source emission model of traffic noise, a univariate noise prediction model based on flow is established. Then, with the establishing of the traffic noise resistance function, an integrated resistance model considering traffic noise impact and travel time impact is proposed, which is used for traffic flow distribution while in the road network planning process. Finally, the network planning is realized utilizing the proposed method considering traffic noise. A network planning case in a typical region shows that the method can effectively reduce the noise emission of the regional network, which has practical value in traffic environment control.

차량 내부 소음 예측을 위한 멀티 모달 자기 지도 학습 네트워크

Predicting in-vehicle noise levels is an important issue in automobile industry. In most previous studies, various supervised learning methods that use both the input and output(labeled) variables are used to predict automobile noise levels. However, collecting labeled data for in-vehicle noise prediction is time consuming and expensive. In this study, we propose a multimodal self-supervised learning framework that can predict in-vehicle noise levels with only a small amount of labeled data, so that resources required to collect labeled data can be saved. In our framework, both original acceleration signals and spectrograms converted from the original data are used as the input to predict in-vehicle noise levels. In the first stage, we pretrain the features of the input data based on the relationship between the signal and spectrogram data using only unlabeled data, which is much easier to collect than labeled data. In the second stage, we use a small amount of data to construct the in-vehicle noise prediction model with the pretrained feature extractor. The effectiveness and applicability of the proposed framework are demonstrated using the actual acceleration signal data collected from various locations of electric power steering vehicle noise levels. The proposed framework outperforms the existing supervised learning method especially when a few labeled data are available.