Development of Prediction Model for Environmental Noise of Korean Railway

Jun-Ho Cho, Jae-Chul Kim, Hyo-In Koh

Korea Railroad Research Institute, Uiwang City, Kyonggi-Do, Korea

Abstract

Recently with the progress of relevant technology and for the efficient land usage, construction and plan of railroad are actively driven in Korea. For the management and construction of railway, the railway noise must be adequately controlled. And for this, the prediction of railway noise must be accomplished. The conventional prediction model for Korean railway which was proposed at 1993-1994 by Korean National Institute of Environmental Research was somewhat incorrect and inefficient according to its various limit conditions. Therefore, in this study noise prediction model for Korean railway which is more correct and systematic than conventional model was newly developed. Developed prediction model from this study can also consider various factor affecting noise radiation and propagation such like rail joint characteristics, wind effects, screen effects and directivity characteristics. This prediction model was applied to 2 typical sites for the validity confirmation and good agreements were showed

1. Introduction

Railway system had been well known that it has the advantage of huge transportation capability, punctuality, safety and environment friendliness. With these advantages, railway industry and its technology will be more and more progressed. But railway has weak point in noise problem. Road noise is somewhat stationary according to the time but the railway environmental noise level varies abruptly before and after train passage. It was generally well known that the railway noise prediction technology had been developed in Europe and Japan etc from 1970s.[1,3,5,6] These railway noise prediction technologies could be progressed on the basis of high theoretical background and enormous measurements. Recently these technologies have been used for Environmental Impact Assessment for validity test of high speed railway construction. Increased claims and public requirement about railway noise year by year, some projects related to railway noise had been accomplished from 1990s in Korea. The first prediction model for Korean railway which was proposed at 1993-1994 by Korean National Institute of Environmental Research was somewhat incorrect and inefficient according to its various limit conditions.[8,9] Therefore, in this study noise prediction model for Korean railway which is more correct and systematic than conventional model was newly developed. Firstly the Korean Railway environmental noise was decomposed to rolling noise component and propulsion noise component because the speed range was 100- 150km/h. The decomposition was accomplished using general point source radiation model and was fitted with measured data. The developed prediction model can consider the effect of various factors such like rail joint characteristics, wind effect, screen effect and directivity characteristics. Each correction factors was suggested by the analysis of component noise SEL(Sound Exposure Level) for the measured railway noise results. This prediction model was applied to 2 typical sites for the validity confirmation and good agreements were obtained.

2. Radiation model for railway noise and its decomposition

In general the medium speed(about 100-150km/h) train noise have cosine and dipole characteristics according to the source characteristics.[4] Sound pressure level at some point apart from the railway noise source can be calculated as follows by Rathe.[6]

θπ

ρ cos24

2r

CWccp = (1)

θπ

ρ 224

2 cosr

dCWdp = (2)

where, W is acoustic power of noise source, p is sound pressure level at receiving point distance r and subscript c and d mean cosine and dipole directivity characteristics respectively. The rolling noise can be assumed by point source at every wheel position with dipole characteristics and power component noise can be modeled by point source at every machinery source such like engine with cosine characteristics. Geometrical relation was shown in Fig. 1.[4]

Receiving point

Rail

Locomotive Carriagesθj

θi

xi

xj

d ri rj

LLo

x

Fig. 1 Noise radiation model for train including locomotive

Then sound pressure level at receiving point which is composed of rolling and power components can be calculated by the following equation.[4]

jk

j jrKci

m

i irdKP θθ cos)(cos)( ∑∑

=+

==

1

212

1

212 (3)

where, vtx = , πρ 4/cWK = . From this model the railway noise can be separated by power and rolling components. Therefore measured sound pressure level of railway noise as a whole can be decomposed by the main sources. Using this model the real composition of train can be considered in prediction stage. In this study, sound exposure level(SEL) as the noise assessment quantity was adopted for inclusion total railway sound energy at receiving point.

3. Noise measurement and component decomposition analysis

To acquire the prediction equation for Korean railway environmental noise, noise measurement was accomplished at 8 sites such like Cheon-Yui section of Kyeong-Bu line. Railway noise at 4 ~ 5 point of each site was measured according to each train type, terrain type. Conceptual diagram for this measurement was shown in Fig. 2. Measurement jig for the microphone attachment which are arranged according to the height from the rail level was manufactured. Using this jig the railway noise characteristic in perpendicular plane was identified and this measurement was shown in Fig. 3

Downward

Rail level 1.5m

4.3m 15m 10m 25m 50m

Upward

Fig. 2 Conceptual diagram for noise measurement Fig. 3 Scene of directivity measurement.

Railway noise signal was recorded during from the train advent and pass away, and the recorded signal was analyzed to sound pressure level versus time in the laboratory. These sound pressure level data was analyzed to SEL of each component using railway noise radiation model. Thus noise components of Korean train operated on conventional line such like Samaeul, Moogoongwha, KTX were analyzed and built as DB. The typical component decomposition example of KTX train operated on conventional line was shown in Fig. 4. As it can be known from the decomposed SPL graph that this train was composed of 20 cars and the contribution of power car was somewhat dominant. Correlation analysis result between measured data and analyzed data using decomposition analysis was shown in Fig. 5 and good correlation was confirmed. After this, SEL of unit component equations as a function of train velocity at the distance 25m was fitted according to the various train types and the typical example was shown in Fig. 6. The short range of train velocity the fitting equation was assumed linear. The geometrical divergence of unit component of source can be obtained from graph which shows relation between component SEL and distance from the rail such like Fig. 7.

Measured SEL(dBA)

Cal

cula

ted

SEL(

dBA)

Fig. 4 Typical train noise with its source Fig. 5 Correlation analysis result between

component (KTX 140km/h, distance 12.5m) calculated and measured noise

4. Prediction equation of environmental noise for Korean railway and its application

The standard condition of noise prediction equation for Korean train which operated on the conventional line was adopted as follows.

Speed range(V) : operating speed 100-150km/h Reference position : 25m Distance range for prediction(d) : 7.5 - 100m Rail condition : linear/level/no joint rail and concrete tie on ballast Rail and wheel condition : well grinded condition

y = 0.0984x + 67.4

50

60

70

80

90

100

110

100 110 120 130 140 150Velocity(km/h)

SEL(

dBA)

y = -4.0954Ln(x) + 86.6

Distance(m)

SEL(

dBA)

Fig. 6 Relation between SEL of unit rolling noise Fig. 7 Relation between SEL of Saemaeul unit

and velocity of Moogoongwha train locomotive noise and distance The SEL of power noise component for unit locomotive car of Saemaeul train, deSEL , and the SEL of

rolling noise component for unit carriage car of Saemaeul train, reSEL , was obtained as a function of distance d in Table 1.

Table 1 SEL of Unit noise component at observing point (d[m] distance from rail center) In above table, 'eSEL and 'rSEL for reference position can be obtained from the relation between unit SEL graph and velocity such like Fig. 6. And then using train composition ESEL and RSEL can be calculated using below equation.

)log(, endeSELESEL 10+= (4)

cCtndrSELRSEL ++= )log(, 10 (5)

where en is the total number of locomotive car, tn is the total number of car.(locomotive car included) and cC is the correction factor term for effect of rail connection. SEL for 1 train passage can be calculated as follows.

)..log( RE SELSELSEL ×+×= 1010101010 (6) In case of the number of train passed before the observing point, distance d from the rail center is n during time 1 hour(3600sec), then the equivalent sound pressure level, Leq can be calculated as follow.

bCdCgCwCSELSELn

iHeqL EE ++++−×+×

== ∑ )log()]..(log[, 36001010101010

1101 (7)

where, n is the No. of passing train during 1 hour, wC is the correction factor for wind effect, gC is

correction factor for ground effect, dC is correction factor for the effect of directivity and bC is correction foactor for screen effect such like noise barrier. In equation (7), all the correction terms can be obtained from the DB's built in this study. For the validation of the prediction equation for Korean railway, comparison between measurement and prediction was performed. The track was built in embankment in this site, and the schematic diagram of this site geometry is shown in Fig. 8.

Classification Equation for prediction

Unit power component. deSEL , (dBA) )/log(.' 2549 deSEL −

Unit rolling component reSEL , (dBA) )/log(.' 25917 drSEL −

In this site, most of all correction factor were not needed. But -2.5 dB was considered for the upward direction train due to the absorption effect of ballast inside of double track and the diffraction effect of railway noise at the edge of embankment was considered.[7]

Fig. 8 Section diagram for measurement sites (Pyeongtaek-Seonghwan section of Kyeong-Bu line)

The measured SEL and predicted SEL averaged to the number of each train were compared and shown in Fig. 9 and Fig. 10, these SEL values are found in Table 2. In the case of this site, the noise are lowest at the position of Ch. 2 which is affected by the edge of embankment and the noise level of Ch. 3 and Ch. 4 were almost the same level because of the diffraction effect and the geometrical divergence. From this result, it was found that the prediction equation suggested for the Korean railway environmental noise can be effectively used in the error range of 3 dBA.

Fig. 9 SEL comparison between measurement and Calculation

(Pyeongtaek-Seonghwan section, Up direction Saemaeul train )

Fig. 10 SEL comparison between measurement and calculation

(Pyeongtaek-Seonghwan section, Down direction Saemaeul train)

Downward Rail level -3.4m

10m 7.7m

15m

40m

Upward

Rail level 1.2m

4.5m

90m

Ch. 1

Ch. 2 Ch. 3 Ch. 4

Classification Ch1 Ch2 Ch3 Ch4

Upward direction 1.4 1.7 1.6 3.2

Downward direction 0.9 1.5 1.6 2.4

Table 2 Sound Exposure level differences between measurement and calculation (Pyeongtaek-Seonghwan section, Unit : dBA)

5. Conclusion

To acquire the prediction equation for Korean railway environmental noise, noise measurement and analysis were accomplished at 8 sites according to the train type and some terrain type. With sound radiation model and component decomposition analysis using sound exposure level(SEL), the prediction equations for Korean railway environmental noise were suggested. Various conditions such like train composition, terrain type, rail joint characteristics and wind effect can be considered in these prediction equations. Finally from the application of these equations to real site, it was found that the Korean conventional railway noise can be effectively predicted in the error range of 3 dBA.

Acknowledgments

This study is performed in the Eco -Technopia 21 Project(2001~2010) which was funded by Korea Institute of Environmental Science and Technology(KIEST).

References

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