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Aeroacoustic sources of motorcycle helmet noise

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The prevalence of noise in the riding of motorcycles has been a source of concern to both riders and researchers in recent times. Detailed flow field information will allow insight into the flow mechanisms responsible for the production of sound within motorcycle helmets. Flow field surveys of this nature are not found in the available literature which has tended to focus on sound pressure levels at ear as these are of interest for noise exposure legislation. A detailed flow survey of a commercial motorcycle helmet has been carried out in combination with surface pressure measurements and at ear acoustics. Three potential noise source regions are investigated, namely, the helmet wake, the surface boundary layer and the cavity under the helmet at the chin bar. Extensive information is provided on the structure of the helmet wake including its frequency content. While the wake and boundary layer flows showed negligible contributions to at-ear sound the cavity region around the chin bar was identified as a key noise source. The contribution of the cavity region was investigated as a function of flow speed and helmet angle both of which are shown to be key factors governing the sound produced by this region.
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Kennedy, J., Adetifa, O., Carley, M., Holt, N. and Walker, I., 2011.
Aeroacoustic sources of motorcycle helmet noise. The Journal
of the Acoustical Society of America, 130 (3), pp. 1164-1172.
Link to official URL (if available):
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Aeroacoustic sources of motorcycle helmet noise
J. Kennedy,
a)
O. Adetifa, and M. Carley
Department of Mechanical Engineering, University of Bath, Bath, BA2 7AY, United Kingdom
N. Holt
School of Science, Society and Management, Bath Spa University, Newton Park, Newton Street Loe,
Bath, BA2 9BN, United Kingdom
I. Walker
Department of Psychology, University of Bath, Bath, BA2 7AY, United Kingdom
(Received 8 February 2011; revised 27 June 2011; accepted 5 July 2011)
The prevalence of noise in the riding of motorcycles has been a source of concern to both riders
and researchers in recent times. Detailed flow field information will allow insight into the flow
mechanisms responsible for the production of sound within motorcycle helmets. Flow field surveys
of this nature are not found in the available literature which has tended to focus on sound pressure
levels at ear as these are of interest for noise exposure legislation. A detailed flow survey of a com-
mercial motorcycle helmet has been carried out in combination with surface pressure measurements
and at ear acoustics. Three potential noise source regions are investigated, namely, the helmet
wake, the surface boundary layer and the cavity under the helmet at the chin bar. Extensive infor-
mation is provided on the structure of the helmet wake including its frequency content. While the
wake and boundary layer flows showed negligible contributions to at-ear sound the cavity region
around the chin bar was identified as a key noise source. The contribution of the cavity region was
investigated as a function of flow speed and helmet angle both of which are shown to be key factors
governing the sound produced by this region. V
C2011 Acoustical Society of America.
[DOI: 10.1121/1.3621097]
PACS number(s): 43.28.Ra, 43.50.Nm, 43.50.Lj [AH] Pages: 1164–1172
I. INTRODUCTION
Noise inside motorcycle helmets has been recognized as
a hearing hazard for just over 20 years, since the first exten-
sive studies were conducted by police forces in the Nether-
lands
14
and the United Kingdom.
5,6
Since then it has been
understood that noise in helmets is dominated by the aerody-
namic sources caused by flow over the helmet, and there have
been a number of published measurements of noise levels
inside helmets
7
(for example), reflecting the desire to educate
motorcyclists in the need to protect their hearing.
8
Despite the work which has been done on measuring noise
inside helmets, there are relatively few data in the open litera-
ture on the mechanisms of noise generation or on the nature of
the noise. The flow on, and around, the helmet is the noise
source responsible for hearing damage. As part of a study on
noise inside helmets a flow survey of a commercial motorcycle
helmet has recently been conducted. This survey had the aim
of establishing the main features of the flow which are, or
might be, responsible for noise generation. Other factors which
must be considered are propagation of noise from the source
through the helmet and head of the rider, but without a knowl-
edge of the source, little can be done to control the noise.
To gain some insight into the features which might be
expected in a flow survey of a motorcycle helmet we consider
flow around spherical bodies. Data for flow around a sphere
at Reynolds numbers similar to those in our tests have been
published by Taneda
9
and by Achenbach.
10,11
These are use-
ful in establishing broad parameters for the flow over isolated
helmets, although it may be that a better approximation for
the geometry of a rider is a form such as an ellipsoid, but
there are few data on the aerodynamic processes which give
rise to noise in and around the surface of bodies comparable
to helmets.
An issue which is not considered in this paper is the
effect of the rider’s body and of the motorcycle structure, in
particular the fairing or windscreen. We have studied these
effects elsewhere, however, and have found that while the
windscreen does have an effect on noise in the helmet,
depending on head position,
12
wind-tunnel measurements on
an isolated head model do accurately reproduce the noise
measured on a real rider on the road.
13
In this other work,
studies were conducted on two different helmets, including
that used in an earlier on-road study,
14
and it was found that
the noise measured in both cases was very similar, even
though the helmets were quite different in their external fea-
tures. We conclude that the details of the helmet make little
difference to the noise and assume that our results are general.
From published data, however, some general conclusions
can be drawn. The Reynolds number for a sphere of diameter
D¼300 mm at free stream velocity of 80 km/h
(Um¼22 m=s) is Re ¼UmD= 4:5105. Boundary layer
separation on a smooth sphere at this Reynolds number occurs
at /120from the inflow axis while the transition to turbu-
lent flow has been shown
10
to occur at /95.Thesame
a)
Author to whom correspondence should be addressed. Electronic mail:
kennedj@tcd.ie.
1164 J. Acoust. Soc. Am. 130 (3), September 2011 0001-4966/2011/130(3)/1164/9/$30.00 V
C2011 Acoustical Society of America
Author's complimentary copy
work also reports a characteristic Strouhal number St ¼fD=Um
¼0:18 at this Reynolds number. Given that a motorcycle hel-
met is not a smooth sphere in a uniform flow, we would expect
both separation and transition to occur earlier but these figures
are useful indications of what to expect. On the downstream
side of a sphere, flow visualization studies at the relevant Reyn-
olds numbers
9
show evidence of a vortex sheet shed from the
back of the sphere to generate a vortex pair. For Re >5105,
this feature oscillates randomly through less than 180.We
might expect to see such a feature in these tests, though prob-
ably at lower Reynolds number.
II. EXPERIMENTAL FACILITIES AND
INSTRUMENTATION
The large wind tunnel facility at the University of Bath
was used for all tests. This closed loop facility has a
2m1:5m3 m test section and can provide flow veloc-
ities up to 25 m/s with a freestream turbulence intensity of
0.1%. In order to compare the results of wind tunnel tests
with future on-road data three flow speeds were chosen
which are representative of driving conditions, namely, 11,
16.5, and 22 m/s equivalent to road speeds of 40, 60, and 80
km/h. The motorcycle helmet was mounted on the structur-
ally isolated arig, a system which varies the incidence angle
of the helmet relative to the free stream. This provides
dynamic control of helmet position while isolating the hel-
met from any wind tunnel vibrations. The blockage ratio
caused by the helmet and arig was 16%. Figure 1shows the
setup within the wind tunnel.
The helmet used in this investigation is one of a number
of helmets provided by manufacturers for noise investigations.
As such, the make and model are covered by a confidentiality
agreement. It is a commercially available extra large (XL)
motorcycle helmet and was mounted using an expanded poly-
styrene mannequin head the dimensions of which are given in
Table I. The majority of the mannequin head measurements
correspond to the 50th percentile for adult males according to
the NASA Man-Systems Integration Standards.
15
The helmet
dimensions were 26 cm 25 cm 36 cm. The helmet fea-
tured numerous air vents and an aerodynamic wing on the
back surface commonly found on many high end motorcycle
helmets. This paper investigates the importance of three
potential sound producing regions of the flow, namely, the
helmet wake, the surface boundary layer and the cavity region
beneath the helmet rim.
The flow measurements were acquired using a calibrated
Dantec 55P11 hot wire probe and a DISA type 56C01 CTA
unit with a DISA type 56C16 CTA bridge. The probe was
mounted in a three axis traverse system controlled by dedi-
cated Labview software. The wake flow was investigated
using two separate test grids. The first grid assessed flow
symmetry and consisted of 5 horizontal traverses of the hot
wire as shown in Fig. 2. Due to the complexity of the flow
region to be measured and the symmetric shape of the helmet
a second detailed grid extending from the free stream to 1 cm
past the midpoint of the helmet was also used. The second
grid consisted of 451 points and was designed with varying
spatial resolution for a smooth transition from the free stream
to the turbulent wake of the helmet. Measurements were
acquired using this grid at four locations of increasing dis-
tance from the back surface of the helmet. These locations
were chosen based on the helmet length as
Z=L¼0;0:25;0:5;and 1:0. Figure 3shows the grid point
locations relative to the helmet. Boundary layer measure-
ments were made at the helmet surface adjacent to the right
ear and at a position at the top of the helmet.
Further measurements were acquired using 1=4 inch
130D20 PCB Piezotronics microphones connected to a PCB
442B117 signal conditioner. These were calibrated using a
Larson Davis CAL200 microphone calibration unit. Surface
pressure fluctuations were acquired using two flush mounted
FIG. 1. (Color online) Wind tunnel facility.
TABLE I. Mannequin head dimensions.
Dimension
Face breadth 14.0 cm
Head length 19.5 cm
Face length 11.5 cm
Head breadth 16.5 cm
Head circumference 29.0 cm
FIG. 2. Grid 1 hot wire measurement locations.
J. Acoust. Soc. Am., Vol. 130, No. 3, September 2011 Kennedy et al.: Motorcycle helmet noise 1165
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microphones in the helmet visor. The microphones used
were located 12 cm from the visor center at eye level. The
sound produced by the cavity under the helmet rim was
investigated using a microphone located at the helmet chin
bar. Sound pressure levels and spectra at ear were acquired
using two microphones mounted within the mannequin head.
In order to quantify and remove the effects of tunnel
noise a sixth 1=4 inch PCB microphone was mounted
upstream of the arig within the wind tunnel. The hot wire and
PCB microphone data were acquired using a 16 channel NI
DAQ system. This system comprised a PC with a NI-PCI-
MIO-16E-1 acquisition card and BNC-2090 connector box.
III. SIGNAL CONDITIONING
The wind tunnel used has no acoustic treatment meaning
that there is a risk of signal contamination by spurious back-
ground noise. Also, we wish to compare wind tunnel measure-
ments to data taken on a motorcycle where there is a
contribution to the in-helmet noise from the motorcycle engine
and from environmental sources. In order to extract a “helmet-
only” spectrum, one which contains only the noise due to flow
over the helmet, we apply a signal conditioning procedure
which has been used in a number of applications
16,17
to condi-
tionally remove unwanted contributions to the output signal.
A model for the system is shown in Fig. 4. The output
signal pðtÞis composed of a sum of inputs giðtÞ,i¼1;2;….
If we consider a two input problem, where g1ðtÞis a back-
ground noise contribution to pðtÞand g2ðtÞis the “real” aero-
dynamically generated noise in the helmet, we wish to
remove from pðtÞthe part of the signal which is correlated
with g1ðtÞ. This is readily done using standard signal proc-
essing methods. If we wish to remove the effects of multiple
signals, however, we must take account of possible correla-
tions between them. In this case, if we wish to remove the
contributions of giðtÞ,i¼1;2, leaving the “true” signal due
to g3ðtÞ, we cannot simply remove from pðtÞthe part which
is correlated with g1ðtÞand/or g2ðtÞ. Instead, we must decor-
relate the input signals before proceeding.
The method of partial coherence is a systematic technique
for performing this decorrelation in order to rigorously assess
the contribution of different sources. If the inputs are uncorre-
lated, the coherence function of each with the output signal is
c2
ipðfÞ¼ jGip ðfÞj2
GiiðfÞGpp ðfÞ;
where GppðfÞis the autospectrum of pðtÞ,Gii ðfÞis the auto-
spectrum of giðtÞand Gip is the corresponding cross-spec-
trum. The contribution of the ith source to the output can
then be removed by subtracting the correlated part:
Gpp:i¼ð1c2
ipÞGpp :(1)
The notation Gpp:idenotes the spectrum of the signal pðtÞ
with the contribution of the ith input removed.
If the input signals are correlated, however, this proce-
dure is not valid, as it will lead to a correlated part being sub-
tracted more than once. In this case, the input signals must
be processed to make them mutually uncorrelated. This is
done by using a recursive conditioning procedure, treating
each signal in turn:
Gpp:i¼1c2
ip:ði1Þ!
hi
Gpp:ði1Þ!:(2)
Here, Gpp:ði1Þ!is the power spectrum of pðtÞwith the corre-
lated part of all inputs up to i1 removed and the partial co-
herence c2
ip:ði1Þ!given by
c2
ip:ði1Þ!¼jGip:ði1Þ!j
Gii:ði1Þ!Gpp:ði1Þ!
;(3)
where Gip:ði1Þ!is the cross-spectrum with the correlated part
of inputs up to i1 removed. The residual autospectra and
cross spectra are given by
Gjk:r!¼Gjk:ðr1Þ!LrjGjr :ðr1Þ!;(4)
with Lrj the conditioned frequency response function
Lrj ¼Grj:ðr1Þ!
Grr:ðr1Þ!
:(5)
FIG. 3. Grid 2 hot wire measurement
locations.
FIG. 4. System model for partial coherence processing.
1166 J. Acoust. Soc. Am., Vol. 130, No. 3, September 2011 Kennedy et al.: Motorcycle helmet noise
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For example, in a three input, single output system, Table
II shows the conditioning sequence. At step 1, the auto- and
cross-spectra for the inputs and output are generated. At step 2,
the spectra for i2 and for the output have subtracted from
them the part which is correlated with the first input, generat-
ing spectra Gij:1. At step 3, the same procedure is applied for
i3, using Eq. (2) and (3) and the spectra from step 2. This
generates spectra Gij:2!, i.e., spectra which have had removed
from them the part which is correlated with inputs 1 and 2.
Finally, at step 4, the procedure is applied to the third input
and to the output, generating the final spectra Gij:3!which have
had removed the effects of all three inputs. The right hand col-
umn of Table II contains a set of output autospectra which
have had successively removed the contribution from each of
the inputs. To return to the concrete example, if the output sig-
nal is an at-ear noise recording and inputs 1 and 2 are measures
of background noise, Gpp:2!is the spectrum of the at-ear noise
with the background noise removed, in other words, an esti-
mate of the “true” aerodynamic noise.
IV. WAKE FLOW FIELD
The flow statistics available from these experiments are
the mean velocity magnitude and turbulence intensity. While
the 55P11 hot wire probe used in this investigation is not
directionally sensitive the orientation of the probe was such
that the results presented here can be taken as the velocity in
the free stream direction. A single hot wire probe, such as the
one used in this investigation, is also poorly suited to meas-
urements within a recirculation region. The absence of a
recirculation region within the measurement planes is demon-
strated by the clear transition of the mean velocity magni-
tudes and turbulence intensities to the values found in the
downstream measurement planes. Figure 5shows the mean
velocity and turbulence intensity profiles measured at the
grid locations shown in Fig. 2. These profiles show a consis-
tently symmetric flow profile at the 5 heights measured. As a
result the data from the second test grid could be mirrored to
produce the full flow field behind the helmet. It should be
noted that due to the curved surface of the helmet that the dis-
tance from the helmet to the measurement planes increases
from the midpoint of the back surface of the helmet.
Figures 6(a) to 6(h) show the mean velocity magnitude
and turbulence intensity plots for the four test locations
behind the helmet. As can be seen from Figs. 6(a) and 6(b)
FIG. 5. Mean velocity
u=Umand turbulence intensity ðu02Þ1=2=Umon grid 1.
TABLE II. Conditioning sequence for a three-input system.
Step Spectra
1G11 G12 G13 G1pG22 G23 G2pG33 G3pGpp
2G22:1G23:1G2p:1G33:1G3p:1Gpp:1
3G23:2!G2p:2!G33:2!G3p:2!Gpp:2!
4G3p:3!Gpp:3!
FIG. 6. (Color online) Mean velocity u=Umand turbulence intensity
ðu02Þ1=2=Um.
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the helmet shape and aerodynamic wing have succeeded in
preventing separation of the free stream flow over the top
surface of the helmet. High turbulence intensities are only
found at or below the aerodynamic wing at the back of the
helmet. A stagnation area can be found on the upper half of
the helmet surface below the wing. The flow coming around
the rider’s neck and from the base of the helmet produces a
region of high turbulence intensity close to the helmet
surface.
A quarter helmet length downstream Figs. 6(c) and 6(d)
show how this region of high turbulence from the lower half
of the helmet increases in intensity as it mixes with the tur-
bulence produced by the collapse of the stagnation region
from the upper half of the helmet.
Figures 6(e) and 6(h) show how the entrainment of the
surrounding fluid leads to lower turbulence intensities and
higher mean velocities as the measurement planes move fur-
ther from the helmet surface.
V. WAKE SPECTRAL RESULTS
In order to gain an understanding of possible noise sour-
ces the frequency content of the wake turbulence was inves-
tigated. Of particular interest is the nature of the turbulence
produced by the various air vents and surface features of the
helmet. The data were acquired at a sample rate of 44.0 kHz
for 10 s and spectra were calculated at each point in the mea-
surement grid using a window size of 8192 points providing
a frequency resolution of 5.4Hz. This provides extensive
spectral data for the helmet wake. Using the data from the
Z=L¼0 measurement plane it is possible to relate the peaks
seen in the spectra to the helmet features that produced them
and so the data from that measurement plane is what is pre-
sented here. In order to present this information in a concise
form the spectra from the 16 y-axis grid positions are pre-
sented as a single surface plot of 28 x-axis measurements.
Each of these surface plots is labeled to correspond to the
FIG. 7. Wake spectral content 0–50 Hz.
1168 J. Acoust. Soc. Am., Vol. 130, No. 3, September 2011 Kennedy et al.: Motorcycle helmet noise
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measurement planes shown in Fig. 3. This information is
presented over several frequency ranges.
Figure 7shows the 0–50 Hz range which covers the nat-
ural vortex shedding frequency of the overall helmet struc-
ture. Figure 8shows the 100–200 Hz range where the
majority of the wake energy is located and the air vents begin
to produce vortices. Figure 9shows the 500–1000 Hz range
which demonstrates the wide range over which the air vents
produce turbulence structures. At the flow velocity tested the
helmet wake turbulence was found to be an order of magni-
tude lower than the free stream turbulence above 5 kHz.
As can be seen from Figs. 7(a) to 7(e) the helmet wake
does not begin until below the aerodynamic wing structure.
Figures 7(f) to 7(j) contain the region of strong low fre-
quency vortex shedding from the side of the helmet. This is
also the region containing the stagnation area below the aer-
odynamic wing as can be seen from the low amplitude spec-
tra within this stagnation region. Figures 7(j) to 7(l) show the
continuation of strong vortex shedding from the sides of the
helmet combined with turbulence being generated from the
center of the helmet base. Figures 7(m) to 7(p) shows the
distributed wake being generated by the rider’s neck and hel-
met base with an absence of strong vortex shedding from the
helmet sides.
The 100–200 Hz range shown in Fig. 8contains the
same starting point of the wake. Figure 8(e) clearly shows
the start of the turbulence being generated from the helmet
air vents. It is interesting to note that flow over the top of the
helmet and off the aerodynamic wing have directed the flow
downward so that the air vent turbulence is detected 2 cm
below the air vents at this location. The stagnation area
shown in Figs. 8(f) to 8(h) contains the expected low ampli-
tude turbulence spectra and no evidence of vortex shedding
for the helmet side. As can be seen from Figs. 8(j) to 8(p) the
helmet base and rider neck produce a distributed wake con-
taining high levels of turbulence in this frequency range.
FIG. 8. Wake spectral content 100–200 Hz.
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While the amplitude of the turbulence spectra contained
in the 500–1000 Hz range of Fig. 9is 2 orders of magnitude
lower than in Fig. 7it is still clearly increased significantly
from the free stream. The turbulent structures produced by
the air vents are still clear in the spectra of Fig. 9(e) and
these air vents have the potential to produce noise over a
very wide frequency range. There is also evidence of low
amplitude high frequency turbulence contained in the stag-
nation area although this is still significantly lower than the
turbulence spectra found at the helmet base.
VI. AT-EAR NOISE
The combination of the turbulence intensity and wake spec-
tral information can be used to indicate areas of potential inter-
est for noise production by the helmet wake. These locations
were taken to be at the air vents, the underside of the wing, vor-
tex shedding locations for the side of the helmet and at the base
of the helmet above the rider’s neck. Simultaneous hot wire and
at-ear microphone measurements were then acquired along a
grid line covering several areas of the wake flow and at the
remaining locations. These locations are marked in Fig. 10.Cor-
relations with at-ear sound were found to be negligible for all of
these test locations which implies that the wake flow is not a
significant source for motorcycle noise exposure.
A second likely source of at-ear sound is the boundary
layer over the helmet surface. Simultaneous hot wire and at-
ear microphone measurements were taken 1 mm from the
helmet surface at the key locations shown in Fig. 10. Corre-
lations between the hot wire and at-ear microphones were
again found to be negligible for these test locations. The lack
of any correlation between at-ear sound and the boundary
layer directly above the helmet surface is a surprising result.
This may imply that the helmet lining also acts as an acous-
tic lining or that the contribution of the boundary layer to at-
ear sound is widely distributed over the helmet surface.
FIG. 9. Wake spectral content 500–1000 Hz.
1170 J. Acoust. Soc. Am., Vol. 130, No. 3, September 2011 Kennedy et al.: Motorcycle helmet noise
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The final sound source investigated in these experiments
is the region beneath the helmet at the chin bar. A micro-
phone placed at the under side of the mannequin chin was
used to investigate this source.
In order to check that the chin and at-ear microphones
are not correlated due to being placed in the same externally
imposed acoustic field, we removed the contribution of the
background noise from both using the partial coherence
techniques outlined in Sec. III. The signal taken from the in-
tunnel microphone was used as a measurement of the exter-
nal acoustic field. This was 1m from the helmet and so
within a wavelength of the other two microphones for fre-
quencies less than approximately 340 Hz. The at-ear and
chin microphone signals remained correlated with this con-
tribution removed, leading us to believe that the at-ear noise
is largely connected to a source in the chin cavity region.
The chin cavity sound source was investigated as a func-
tion of wind speed and helmet angle. The wind speeds inves-
tigated corresponded to driving conditions of 40 km/h (11
m/s), 60 km/h (16.5 m/s) and 80 km/h (22 m/s) with a helmet
angle of 90, i.e. a fully upright riding condition. Figure 11
shows the partial coherence between the chin and at-ear
microphones for these test conditions. As can be seen from
Fig. 11 the primary effect of wind speed on the partial coher-
ence is to move the frequency of the peak coherence from
approximately 65 Hz for a speed of 40 km/h (11 m/s) to 150
Hz for a speed of 80 km/h (22 m/s). The peak amplitude of
the coherence is not strongly affected.
The effect of helmet angle was investigated at a constant
speed of 80 km/h (22 m/s) for the following angles: 90,80
,
70,60
, and 50. In contrast to the effect of speed, helmet
angle was a very significant factor in affecting the amplitude
of the coherence producing a partial coherence greater than
0.8 in the region of 100 Hz for a helmet angle of 50. The
spectral content of the at-ear noise produced by the cavity
region is affected by the helmet angle with the contribution
of lower frequencies being much more significant as helmet
angle decreases.
As expected, the peak frequency for the partial coher-
ence, Fig. 11, increases with speed, with 1:7<St <2:0 for
the peak Strouhal number over the range of speeds consid-
ered, quite different from the vortex shedding Strouhal num-
ber St ¼0:18. However when this Strouhal number is
applied to the different angle configurations shown in Fig.
12 there is no collapse of the spectral peaks. No further suc-
cess was achieved by any attempt to adjust the Strouhal
FIG. 10. Correlation measurement locations.
Diamonds, boundary layer measurement;
dots, wake measurement.
FIG. 11. Partial coherence versus speed. FIG. 12. Partial coherence versus head angle, Um¼22m=s.
J. Acoust. Soc. Am., Vol. 130, No. 3, September 2011 Kennedy et al.: Motorcycle helmet noise 1171
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number using an effective length based on the helmet angle.
Using the available data there does not appear to be a single
Strouhal number relationship for all helmet configurations.
These results indicate that the contribution of this flow
region to the at-ear sound is both significant and dependent
upon riding conditions.
VII. CONCLUSIONS
The importance of three potential motorcycle helmet
noise source regions has been investigated. The helmet
wake, while being shown to contain turbulence over a wide
frequency range, did not prove to be a significant source of
at-ear noise. An investigation of the helmet boundary layer
was conducted at several locations around the helmet sur-
face. These regions did not measurably contribute to the
at-ear noise. This was surprising as one of the boundary layer
regions investigated was directly above the ear location. The
third potential noise source investigated was the cavity under
the helmet at the chin bar. Investigations in this area were
conducted using a microphone placed at the center of the
mannequin chin. After conditionally removing the contribu-
tion of tunnel noise a high coherence was achieved between
this region and the at-ear sound between 0 and 1000 Hz.
Helmet angle and flow speed were identified as key factors
governing the production of sound from this region.
The geometry of this cavity region is highly complex
and will be unique to each rider and helmet combination. It
is clear that it is possible to control the production of sound
from this region with relatively small changes to the riding
conditions. This information supports anecdotal reports of
noise reduction from riders who use a neck shield to close
off this cavity region.
ACKNOWLEDGMENTS
Part of this work was carried out in a project funded by
the Leverhulme Trust. The authors also wish to acknowledge
the assistance of Niels Bogerd of EMPA, St. Gallen, Swit-
zerland, who supplied the helmet and to remember the con-
tribution made by the late Paul Bru¨ hwiler who managed the
COST action PROHELM, which made much of this work
possible.
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2
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3
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4
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1172 J. Acoust. Soc. Am., Vol. 130, No. 3, September 2011 Kennedy et al.: Motorcycle helmet noise
Author's complimentary copy
... In this configuration, noise reductions of up to 8dBA were achieved by improving the sealing between visor and helmet, whereas treating the base of the helmet produced no effect. Recently, Kennedy, Adetifa, Carley, Holt e Walker [10] published a detailed study of the airflow around a helmet, without a windscreen, and confirmed Lower et al. conclusions that, in a free flow, the dominant noise source appears to be located at the chin gap. They also attempted at finding correlations between the pressure fluctuations in the helmet's boundary layer and the noise inside the helmet, without success. ...
... In order to avoid or, at least, minimise this contamination, a second microphone was used outside the flow stream, but as close to the helmet as possible, to sample the background noise. In this way it is possible to remove unwanted contributions to the noise measured inside the helmet by means of the parcial coherence method [10]. Assuming that the total acoustic signal inside the helmet p t (t) is a sum of the aerodynamic noise generated by the helmet p c (t) and the background noise generated by other sources p r (t), one can obtain p c (t) by removing from p t (t) the part of the signal that is coherent with p r (t). ...
Conference Paper
Full-text available
Measurements of in-helmet aerodynamic noise were made on four models of motorcycle integral helmet. The aerodynamic measurements were performed in a wind tunnel, using a head-torso acoustic mannequin. Data were gathered for free flow conditions, typically found on a motorcycle without a windscreen; and also for a shear/turbulent flow, typically found on motorcycles with a windscreen. It was found that the noise level in all four helmets varies with velocity to the power of 4, either with a windscreen or without it. Whereas in two of the models the noise spectrum content remained essentially unaltered in both flow conditions, the other two models showed a significant change between the flow with windscreen and the flow without it. This behaviour has consequences in the classification of helmets by noise level, depending on whether the measurement is made in free flow or behind a windscreen.
... In total, 16 articles were included in this section (Harrison, 1973;Henderson, 1975;Purswell and Dorris, 1977;Van Moorhem et al., 1981;Aldman et al., 1983;McCombe et al., 1994;Kuo and Morgan, 1996;Lower et al., 1996;Lee et al., 2009;Młyński et al., 2009;Castañé-Selga and Sánchez Peña, 2010;Liu et al., 2010;Wang et al., 2010;De Rome and Senserrick, 2011;Kennedy et al., 2011;Violini et al., 2015). Motorcycle noise acts as a silent killer (Lower et al., 1996). ...
... This area showed a high coherence with an at-ear sound between 0 and 1000 Hz. Helmet angle was also a significant factor governing the production of sound originated from this region (Kennedy et al., 2011). Other sources for noise have also been studied, including aspiration ("leak") noise in the visor, flow separation, and reattachment noise, and protuberance noise in the base of the helmet and the driver's neck and shoulder. ...
Article
Background Protective helmets may reduce the risk of death and head injury in motorcycle collisions. However, there remains a large gap in knowledge regarding the effectiveness of different types of helmets in preventing injuries. Objective To explore and evaluate the effectiveness of different types of motorcycle helmets; that is the association between different helmet types and the incidence and severity of head, neck, and facial injuries among motorcyclists. Also, to explore the effect of different helmet types on riders. Methods A systematic search of different scientific databases was conducted from 1965 to April 2019. A scoping review was performed on the included articles. Eligible articles were included regarding defined criteria. Study characteristics, helmet types, fixation status, retention system, the prevention of injury or reduction of its severity were extracted. Results A total of 137 studies were included. There was very limited evidence for the better protection of full-face helmets from head and facial injury compared to open-face and half-coverage helmets. There was however scarce evidence for the superiority of a certain helmet type over others in terms of protection from neck injury. The retention system and the fixation status of helmets were two important factors affecting the risk of head and brain injury in motorcyclists. Helmets could also affect and limit the riders in terms of vision, hearing, and ventilation. Multiple solutions have been discussed to mitigate these effects. Conclusion Full-face helmets may protect head and face in motorcycle riders more than open-face and half-coverage helmets, but there is not enough evidence for better neck protection among these three helmet types. Helmets can affect the rider’s vision, hearing, and ventilation. When designing a helmet, all of these factors should be taken into account.
... Whether assumed as a tool exclusively devoted to protection, or in the case of specific sports (skiing, cycling, motorcycling, formula 1), where in addition to its protective function, can undoubtedly be considered an aerodynamic advantage if properly developed. Most of the sensual efforts were focused, rightly, on reducing noise aeroacoustic [11, 12, 13, 14], and the optimization of thermal comfort [15, 16, 17, 18, 19]. Studies available on experimental aerodynamics are rare. ...
... These include age and gender (Ackaar and Afukaar 2010; Hung et al. 2006;Hurt et al. 1981;Ichikawa et al. 2003;Li et al. 2008;Mangus et al. 2004;Nakahara et al. 2005;Papadakaki et al. 2013;Xuequn et al. 2011); educational attainment (Hung et al. 2008;Hurt et al. 1981;Khan et al. 2008;Ranney et al. 2010); and motorcyclists' position on the motorbike and the time of day, day of week, and location or type of road traveled on (Akaateba et al. 2014;Bachani et al. 2012;Nakahara et al. 2005;Skalkidou et al. 1999;Xuequn et al. 2011). Other factors such as travel distance (Hung et al. 2008;Kulanthayan et al. 2000;Ouellet 2011), weather condition (Gkritza 2009;Ledesma and Peltzer 2008;Ledesma et al. 2014;Skalkidou et al. 1999), history of a motorcycle accident or injury (Mangus et al. 2004;Pitaktong et al. 2004;Ranney et al. 2010;Siviroj et al. 2012), possession of a rider's license (Kulanthayan et al. 2000;Reeder et al. 1997), embarrassment (Moghisi et al. 2014a(Moghisi et al. , 2014b, as well as helmet features and rider perceptions (Bogerd et al. 2011;Hung et al. 2008;Kennedy et al. 2011;Orsi et al. 2012) have been identified to be associated with helmet use. These studies have generally concluded that helmet nonuse is significantly correlated with young riders, male riders, less educated riders, pillion riders, riding at nighttime, riding on weekends and on rural and secondary roads, traveling short distances, good weather conditions, previous involvement in motorcycle accident, unlicensed riders, and negative perceptions of helmet features and their associated discomfort. ...
Article
This study was conducted to investigate the correlates and barriers to helmet use among motorcycle riders in Wa, a motorcycle predominant town in Ghana. An additional objective was to find out the association between helmet use and riders' knowledge, attitudes and beliefs towards helmets. Cross-sectional surveys comprising both observation of helmet use and interviews were conducted among motorcycle riders at six randomly selected fuel stations and four motorcycle service centres within and outside the Central Business District of Wa. Questions covered riders' socio-demographic and riding characteristics, helmet use, reasons for use or non-use of helmets, and knowledge, attitudes and beliefs about helmets. Analyses were based on frequencies and testing of strength of association using adjusted odds ratios (with 95% confidence intervals) in Binary Logistic Regression. The prevalence of helmet use among the 271 sampled riders was 46% (95% CI: 40.2-52.0). Gender, age, marital status and occupation were significant socio-demographic correlates of helmet use in Wa. Compared to currently married riders, unmarried riders were 5times less likely to use a helmet. No significant association existed between riders' educational attainment and helmet use. Helmet use was also positively correlated with helmet ownership and license holding. The leading reasons stated for helmet non-use among non-users were, not travelling on a long distance and helmets block vision and hearing. Protection from injury, legal requirement, and coping with the police for fear of being accosted for helmet non-use were identified as common reasons for helmet use. Positive attitudes and beliefs were also significantly correlated with helmet use. Despite the existence of a legislation mandating the use of helmets on all roads as well as the high level of awareness among riders on this legislation and the benefits of helmets, the incidence of helmet use among motorists continue to be low in Wa, Ghana. This means that efforts to identify strategies to increase helmet use need to continue. The evidence provided by this study suggests that stakeholders in road safety need to put in interventions to ensure a rigorous enforcement of the helmet use legislation and improvement in helmet design. These should be combined with the development of targeted educational programs with the aim of changing unfavorable attitudes and beliefs towards helmet use.
... A number of environmental factors have also been linked to helmet use such as the road type, the weather and the time of day (Gkritza, 2009;Hung et al., 2006;Li et al., 2008;Nakahara et al., 2005;Skalkidou et al., 1999). Likewise, a number of helmet features have been shown to reduce the likelihood of using a helmet due to causing discomfort or influencing the rider's perception such as noisiness, temperature, poor ventilation, field of vision, functional and design errors (Bogerd, Rossi, & Bruhwiler, 2010;Buyan et al., 2006;Kennedy, Adetifa, Carley, Holt, & Walker, 2011;Mlynski, Kozlowski, & Zera, 2009;Orsi et al., 2012). Riders' beliefs and attitudes towards helmet use have also been shown to affect their helmet use practices with beliefs about injury prevention and safety being among the major facilitators of helmet use and beliefs about physical discomfort and limited vision and hearing, being major barriers of helmet use (Khan et al., 2008;Li et al., 2008;Ranney et al., 2010;Skalkidou et al., 1999). ...
Article
Full-text available
The current study aimed to assess the frequency of helmet use in a sample of Greek motorcycle riders as well as identify factors affecting self-reported helmet use including the riders’ motivations and various socio-demographic, environmental and trip-related characteristics.MethodA probabilistic, stratified random sampling was performed to select 405 riders aged 19–65 years from three cities of Crete. Data were collected through an easy-to-use self-administered questionnaire during face-to-face contacts with the study participants.ResultsThe overall self-reported helmet use was very low. Gender, years of education, consumption of high concentrated alcohol, and time of day when riding occurred, were significant predictors of the frequency of self-reported helmet use. High agreement with the factors of Imitation (B = 5.4, p < .001), Experience (B = 2.6, p = .001), Self-protection (B = 3.8, p < .001), Environment (B = 5.8, p < .001), and Regulation (B = 4.2, p < .001) as well as low agreement with the factors of Discomfort (B = −4.3, p < .001) and Underestimation of danger (B = −1.9, p < .013), were associated at a statistically significant level with higher frequency of self-reported helmet use.Conclusion The evidence derived from this study could be useful in understanding the priorities for future intervention. Continuous education programs and intensification of law enforcement, particularly at night hours, may be effective in increasing helmet use.
... While aerodynamic noises generated by high Reynolds number flows (Re > 10 4 ) relevant to industrial applications (aircrafts, trains, wind turbines etc.) are extensively studied (e.g. [1,2,3]), moderate Reynolds number flows studies are few. Amoung human speech sounds, for instance, so-called unvoiced fricative speech sounds such as [s] and [ f ] are interesting and relevant examples of aerodynamic noises produced by the frication (i.e. ...
Article
In the study of aerodynamic noise, a tackling challenge is to understand the underlying aeroacoustic mechanisms leading to its generation. The current paper aims at contributing to the noise characterization by focusing on moderate Reynolds number (100 ≤ Re ≤ 10000) airflow circulating through a rectangular channel containing a trapezoidal obstacle near the outlet. The outcome of Large Eddy Simulation and acoustic experiments are compared for different experimental boundary conditions at inlet and outlet, and for different apertures below the obstacle.
Article
Full-text available
Motorcyclists (n=26) average noise exposure levels (LAeq) were found substantially different for OSHA-HC (85 dBA), OSHA-PEL (78 dBA) and ACGIH/NIOSH (87 dBA) noise standards. A significant difference in LAeq, (p=.027) and engine capacity usage (p=.045) was found amongst gender. However, no observable association was found between the LAeq and motorcycle engine capacity (p= .462) and completion of a ride (p= .695). Thus, female riders were inclined to use lower motorcycle capacities, rode at lower speeds which resulted in lower noise exposure levels, in concurrence with longer ride durations. Overall, motorcyclists’ noise exposure level functions with the increasing speed (80km/h: 88 dBA). Keywords: Motorcycle noise; Dosimeter; Speed; Noise standards eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i15.2455.
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A prospective cohort study was conducted to identify risk and protective factors for crash involvement in older motorcyclists. Over a 1-year study period from August 2013 to July 2014, study participants were recruited from local community centers of five cities in Taiwan. People aged ≥60 years who rode a motorcycle at least once per week were eligible and were invited to participate in the study. Among 256 older riders who completed the baseline assessment and at least one of the four follow-up assessments, 79 (33.7%) experienced a motorcycle crash over the study period. Results of the proportional hazards model showed that after controlling for age, gender, and riding distance, older riders who had sustained hearing impairment (hazard ratio (HR)=2.58; 95% confidence interval (CI), 1.30-5.15), rode a motorcycle at speeds of ≥41km/h (HR=2.31; 95% CI, 1.26-4.23), and had experienced a motorcycle crash in the past year (HR=1.81; 95% CI, 1.06-3.09) were more likely to be involved in a crash, compared to their counterparts. Conversely, older riders who were obese (HR=0.43; 95% CI, 0.22-0.82) were less likely to be involved in a crash than those with a normal weight, while longer functional reach distances (HR=0.96; 95% CI, 0.93-0.99) and higher Tinetti balance scores (HR=0.79; 95% CI, 0.69-0.91) were associated with a reduced risk of crash involvement. Among older people riding a motorcycle as their primary source of transportation, several factors associated with the occurrence of motorcycle crashes were identified. Restrictions and modifications of these risk factors may help design effective safety interventions for reducing crash and injury risks of this increasing riding population.
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The noise source mechanisms involved in motorcycling include various aerodynamic sources and engine noise. The problem of noise source identification requires extensive data acquisition of a type and level that have not previously been applied. Data acquisition on track and on road are problematic due to rider safety constraints and the portability of appropriate instrumentation. One way to address this problem is the use of data from wind tunnel tests. The validity of these measurements for noise source identification must first be demonstrated. In order to achieve this extensive wind tunnel tests have been conducted and compared with the results from on-track measurements. Sound pressure levels as a function of speed were compared between on track and wind tunnel tests and were found to be comparable. Spectral conditioning techniques were applied to separate engine and wind tunnel noise from aerodynamic noise and showed that the aerodynamic components were equivalent in both cases. The spectral conditioning of on-track data showed that the contribution of engine noise to the overall noise is a function of speed and is more significant than had previously been thought. These procedures form a basis for accurate experimental measurements of motorcycle noise.
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Full-text available
The noise source mechanisms involved in motorcycling include various aerodynamic sources and engine noise. The problem of noise source identification requires extensive data acquisition of a type and level that have not previously been applied. Data acquisition on track and on road are problematic due to rider safety constraints and the portability of appropriate instrumentation. One way to address this problem is the use of data from wind tunnel tests. The validity of these measurements for noise source identification must first be demonstrated. In order to achieve this extensive wind tunnel tests have been conducted and compared with the results from on-track measurements. Sound pressure levels as a function of speed were compared between on track and wind tunnel tests and were found to be comparable. Spectral conditioning techniques were applied to separate engine and wind tunnel noise from aerodynamic noise and showed that the aerodynamic components were equivalent in both cases. The spectral conditioning of on-track data showed that the contribution of engine noise to the overall noise is a function of speed and is more significant than had previously been thought. These procedures form a basis for accurate experimental measurements of motorcycle noise.
Article
Full-text available
Vortex shedding from a motorcycle windscreen results in three flow regions in which the rider's helmet may be immersed. First, the helmet may be completely in the free stream. Second, it may be in the path of vortex shedding from the windscreen. Third it may be underneath the shed vortices and shielded from the free stream by the windscreen. On-track noise tests were conducted and showed a difference in sound pressure level of more than 10dB and a change in spectra content, due to changes in riding position and helmet angle. Similar tests carried out in a wind tunnel, using simultaneous microphone and flow visualization measurements, allowed the identification of the flow regions. The potential contribution of vortex shedding to the noise was assessed using wavelet analysis to identify intermittent flow structures.
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The contribution of non-linear forces to the dynamic performance of liquid squeeze films is examined as a function of film thickness and fluid viscosity. The linear and non-linear component forces are identified from experimental data for two thicknesses at viscosities of 1.0, 13.19 and 26.87 cS using procedures based on random excitation. A procedure by which terms that contribute little to the overall dynamics of the model can be eliminated is developed and applied to the experimental data. This leads to a reduction in the complexity of the model for particular systems by removing those terms that have little significance. The technique is then validated by examining the relative contributions of the non-linear forces using a numerical simulation.
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Vortex shedding from spheres has been studied in the Reynolds number range 400 < Re < 5 × 10 ⁶ . At low Reynolds numbers, i.e. up to Re = 3 × 10 ³ , the values of the Strouhal number as a function of Reynolds number measured by Möller (1938) have been confirmed using water flow. The lower critical Reynolds number, first reported by Cometta (1957), was found to be Re = 6 × 10 ³ . Here a discontinuity in the relationship between the Strouhal and Reynolds numbers is obvious. From Re = 6 × 10 ³ to Re = 3 × 10 ⁵ strong periodic fluctuations in the wake flow were observed. Beyond the upper critical Reynolds number ( Re = 3.7 × 10 ⁵ ) periodic vortex shedding could not be detected by the present measurement techniques. The hot-wire measurements indicate that the signals recorded simultaneously at different positions on the 75° circle (normal to the flow) show a phase shift. Thus it appears that the vortex separation point rotates around the sphere. An attempt is made to interpret this experimental evidence.
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The present work is concerned with the flow past spheres in the Reynolds number range 5 × 104 [less-than-or-eq, slant] Re [less-than-or-eq, slant] 6 × 106. Results are reported for the case of a smooth surface. The total drag, the local static pressure and the local skin friction distribution were measured at a turbulence level of about 0·45%. The present results are compared with other available data as far as possible. Information is obtained from the local flow parameters on the positions of boundary-layer transition from laminar to turbulent flow and of boundary-layer separation. Finally the dependence of friction forces on Reynolds number is pointed out.
Article
The wake configuration of a sphere has been determined by means of the surface oil-flow method, the smoke method and the tuft-grid method in a wind tunnel at Reynolds numbers ranging from 104 to 106. It was found that the wake performs a progressive wave motion at Reynolds numbers between 104 and 3·8 × 105, and that it forms a pair of stream wise line vortices at Reynolds numbers between 3·8 × 105 and 106.
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The effect of turbulent loading on the noise radiated by an advanced aircraft propeller is assessed using a spectral conditioning technique which allows the inclusion of the non-linear effects associated with source rotation. The method allows the steady-source noise at a microphone to be estimated by removing the effect of measured on-blade unsteady loading. The operation of the method is demonstrated by using numerically simulated data, demonstrating the effectiveness of the technique in extracting the steady-source sound. Experimental results are presented showing the effect of random loading on the acoustic field of a propeller in an axial flow and at incidence. It is shown that, for the range of conditions and microphone positions considered, the effect of turbulent loading on the radiated noise can vary from about 2–8 dB, being stronger upstream than downstream.
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A unique set of results on the acoustics of motorcycle helmets has been gathered during road tests on a rider wearing a representative modern helmet. The data were collected during a study of the noise which can cause hearing damage and, possibly, distraction in riders. They consisted of simultaneous measurements of noise at the rider's ear and unsteady pressure on the helmet surface, combined with GPS measurements of rider position and speed. These signals have been analyzed to reduce the coherent structures in the turbulent flow responsible for noise generation. The identified structures appear to be produced by a vortex street shed by the motorcycle windscreen. The internal and external pressures proved to be poorly correlated over most of the frequency range, which has been identified as a result of the insertion loss of the helmet. The implications of these findings are that the majority of the variation in helmet noise is a function of such extrinsic factors as motorcycle configuration and rider build and posture. Efforts to reduce the harmful effects of noise in motorcycling should, then, move to studying the whole system of rider, helmet, motorcycle, and external environment.
Noise levels and noise reduction under motorcycle helmets
  • M C Lower
  • D W Hurst
  • A Thomas
M. C. Lower, D. W. Hurst, and A. Thomas, "Noise levels and noise reduction under motorcycle helmets," in Proceedings of Internoise (Institute of Acoustics, St. Albans, 1996), Vol. 96, pp. 979-982.