The concawe model for calculating the propagation of noise from open-air industrial plants

Marsh, Katy

doi:10.1016/0003-682x(82)90003-2

Cited by 24 publications

(11 citation statements)

References 8 publications

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“…the wind speed vector projected on the plane through source and receiver) is a sufficient parameter for an accurate prediction of the refraction effect. The CONCAWE [5] meteorological classification model, developed for use in relation to industrial noise sources (and thus point sources for distant receivers), treats strong oblique winds in a similar way as moderate downwind propagation, while no wind and crosswind are put in the same category [6]. Li and Wang [6] concluded, based on their analytical considerations, that the effect of wind can be ignored in the crosswind situation because the effective Mach number is zero under such circumstances.…”

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confidence: 99%

Numerical Investigation of the Effect of Crosswind on Sound Propagation Outdoors

Hornikx¹,

Renterghem²

2016

Acta Acustica united with Acustica

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The effect of wind on sound propagation in the atmosphere has been studied extensively before with an emphasize on downwind sound propagation, typically representing worst--case scenarios. However, the influence of oblique and crosswind on propagation from various types of sources raises some questions in acoustic literature. In this work, the effect of a logarithmic wind speed profile at different wind directions has been studied for sound emitted by a point source, a coherent line source and an incoherent line source. For this purpose, a full three--dimensional wave--based method was used. For the incoherent line source simulation, the Harmonoise engineering approach based on a summation of source segments was considered as well and shows to be in satisfying agreement with the latter. While for a point source and coherent line source crosswind shows to have an insignificant effect, it is important in case of an incoherent line source. Also, the stretch of the incoherent line source contributing to the noise level at a receiver close to this line differs strongly depending on the wind direction. PACS 43.20.El, 43.28.Fp, 43.28.Js 2 Introduction Sound propagation outdoors is strongly influenced by the refractive state of the atmosphere [1]. Phenomena linked to either downwind or upwind sound propagation have been studied extensively.

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confidence: 99%

Numerical Investigation of the Effect of Crosswind on Sound Propagation Outdoors

Hornikx¹,

Renterghem²

2016

Acta Acustica united with Acustica

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“…When the programme is executed for various combinations of NP, C and F, the optimal set of parameters is determined based on three factors: objective function value, number of generations to reach the optimum value and CPU time. 12,20,21 The result of running the GA for different crossover values while the other parameters were set constant according to Table 10 is shown in Figure 7. As can be seen from Figure 7, the crossover rate has considerable effect on the speed of convergence of the GA. A crossover value of 1 means that at every iteration, all the parent chromosomes will create new child chromosomes, so every iteration will contain different chromosomes to the one before.…”

Section: Evaluation Of Different Crossover Ratesmentioning

confidence: 99%

A genetic algorithm approach for optimization of machinery noise calculations

Rao

Tripathy

2019

Noise & Vibration Worldwide

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Noise produced by various noise sources in the mines is considered as a serious environmental problem. Exposure to such noise levels is considered as hazardous to workers working in such conditions. Noise assessment was exercised in a highly mechanized opencast bauxite mine, located in eastern India according to the Director General of Mines Safety Technical Circular No. 18 of 1975 and No.5 of 1990. There are numerous approaches in the literature on machinery noise prediction based on statistical models, soft computing techniques such as fuzzy inference system, artificial neural networks, support vector machines, adaptive neuro fuzzy inference system and other classification methods. The main drawback of statistical models, fuzzy inference system, artificial neural network, support vector machine and adaptive neuro fuzzy inference system is lack of interpretation for human and optimization issues. An attempt has been made to examine the applicability of a genetic algorithm, to take the advantage of genetic structures to find an optimal sound pressure level of the machinery noise taking into consideration the distance, directivity index, sound power level and other attenuation parameters under several noisy operating conditions according to ISO 9613-2:1996, ISO 6395:2008 and other related standards. Genetic algorithm used in this article has several advantages: it can be applied for low, high and dynamical system and uses a simple procedure to determine the order and the parameters with high accuracy. Genetic algorithm model is trained and tested in MATLAB to find the optimum parameters. Experimental results show that genetic algorithm is able to converge and find the optimum values faster along with acceptable computational time. By comparing the predicted values with the measured values, it proves the effectiveness of the proposed model as a useful and efficient method for machinery noise optimization problems.

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“…Manning [9,10] Thus, in a simplified form the sound pressure level at a remote point can be related to the source sound power level by the expression:…”

Section: Concawe Noise Prediction Modelmentioning

confidence: 99%

Machinery Noise Prediction in Opencast Mines Using Concawe Model: A Case Study

Nanda¹,

Tripathy

2010

Noise & Vibration Worldwide

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Noise is often regarded as a nuisance rather than as an occupational hazard in an opencast mining environment. Prolonged exposure to noise over a period of years generally causes permanent damage to the auditory nerve and/or its sensory components. To maintain the good working environment in mines, appropriate noise survey of machineries should be conducted. The measured sound pressure levels (SPL) of equipment are not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular noise prediction models e.g. CONCAWE, VDI and ENM have been applied in mining and allied industries to predict the machineries noise by considering all the attenuation factors. In this paper, authors have predicted machineries noise in an opencast mine using CONCAWE model.

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The concawe model for calculating the propagation of noise from open-air industrial plants

Cited by 24 publications

References 8 publications

Numerical Investigation of the Effect of Crosswind on Sound Propagation Outdoors

Numerical Investigation of the Effect of Crosswind on Sound Propagation Outdoors

A genetic algorithm approach for optimization of machinery noise calculations

Machinery Noise Prediction in Opencast Mines Using Concawe Model: A Case Study

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