12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022

OPTICAL MEMS SENSORS FOR INSTANTANEOUS WALL-SHEAR STRESSMEASUREMENTS IN TURBULENT BOUNDARY-LAYER FLOWS
Nima Ebrahimzade
School of Engineering
Newcastle University
Newcastle upon Tyne, NE1 7RU, UK
Nima.ebrahimzade@ncl.ac.uk

Jose Portoles
School of Engineering
Newcastle University
Newcastle upon Tyne, NE1 7RU, UK

Peter Cumpson
Mark Wainwright Analytical Centre
University of New South Wales
Sydney, New South Wales, NSW 2052, Australia
p.cumpson@unsw.edu.au

Michael Wilkes
School of Engineering
Newcastle University
Newcastle upon Tyne, NE1 7RU, UK
Michael.wilkes@ncl.ac.uk

Richard D. Whalley
School of Engineering
Newcastle University
Newcastle upon Tyne, NE1 7RU, UK
Richard.whalley@ncl.ac.uk
ABSTRACT
Presented is a new technique based on micro-electromechanical systems (MEMS) technologies for accurately
measuring instantaneous wall-shear stress, 𝜏𝑤 , in turbulent airflows. Utilising this new technique, direct measurement of theinstantaneous wall-shear stress is performed by tracking thedisplacement of a miniature flush-mounted floating element,
which is on the order of hundreds of microns squared. In aseries of wind tunnel experiments over a Reynolds number
range of 𝑅𝑒𝜏 = 𝑢𝜏 𝛿/𝜈 = 600 to 1320 (where 𝑢𝜏 , 𝛿, and 𝜈 are
the friction velocity, boundary-layer thickness, and kinematic
viscosity, respectively), the instantaneous wall-shear stress ismeasured within the turbulent boundary-layer flow
simultaneously by the MEMS sensors and laser Doppler
velocimetry (LDV). Good agreement is observed across these
two independent measurement techniques. A micro-springand optical Moiré fringe pattern transduction are employed toenhance the sensitivity of the sensors by 120 times compared
to existing sensors on the market. The developed MEMS wallshear stress sensors have a sensitivity range of 23−740 nm/Pa,
an accuracy range of 1.4−2.4%, and a repeatability range of
0.7−1.9%.

wire probes have a high-frequency response, however, theheat conduction from the probe to the wall influences thesensor’s performance. Additionally, as the thickness of theviscous sublayer decreases with an increase in the Reynolds
number, it becomes challenging to get an adequate number ofpoints from within the viscous sublayer as the probe gets close
to the wall. Hot-film and hot-wire probes are directionallyinsensitive and hence, cannot be used to detect bi-directionalwall-shear stress. LDV can determine the directional wallshear stress by using the near-wall velocity gradient technique(Örlü and Vinuesa 2020). Direct MEMS sensors measure thedisplacement of a flush-mounted structure due to the appliedwall-shear stress. Various types of devices have beendeveloped using capacitive (Barnard et al. 2016;
Chandrasekharan et al. 2011; Mills et al. 2018), piezoresistive(Shajii et al. 1992; Barlian et al. 2007; Li et al. 2008), andoptical (Tseng et al. 2003; Horowitz et al. 2004; Chen et al.
2011; Mills et al. 2016) transduction methods. Capacitive andpiezoresistive sensors are susceptible to environmental
parameters such as temperature, humidity, andElectromagnetic Interference (EMI). Optical transduction
benefits from the absence of electronic connections to thesensor die, leading to immunity to environmental parameters.
To date MEMS wall-shear stress sensors, even though
they have been in development for more than 20 years, havenot seen routine adoption. In this study, we have developed
and characterised accurate, low-cost optical MEMS sensors
which can accurately measure the instantaneous wall-shear
stress in turbulent boundary-layer flows. Additionally, we
have developed the underpinning analytical methods that
permit the design of sensors for a range of applications.

INTRODUCTION
Obtaining instantaneous measurement of wall-shear stressin wall-turbulent flows, particularly in air flows, is notoriously
difficult and has significant fundamental and industrial
importance. Thermal sensors such as hot-film and hot-wireprobes can be calibrated to determine the wall-shear stress. For
the hot-film probes to work, the thermal conductivity of thefluid, 𝜆, should be larger than the thermal conductivity of thematerial on the wall (Alfredsson and Johansson 1988). Since
this is not the case for air flows (𝜆 = 0.024 W/mk), the wallshear stress fluctuations measurement is underestimated, andhence, hot-film probes are normally used for mean wall-shear
stress measurement and qualitative measurements in air flows(Whalley, et al. 2019). Hot-wire probes can be used tomeasure the wall-shear stress by applying the near-wallvelocity gradient technique (Hutchins and Choi 2002). Hot-SENSOR DEVELOPMENT
The MEMS wall-shear stress sensors consist of a floating
element with a width and length of 𝑊𝑒 and 𝐿𝑒 and suspendedon each side by specially designed micro-springs with a widthand length of 𝑊𝑡 and 𝐿𝑡 ; see figure 1a of an image of a
microfabricated MEMS wall-shear stress sensor. As air flows
over the MEMS sensor, the floating element is free to move,as a result of the micro-spring arrangements, in the direction

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12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022

(a)

(b)

(c)
Figure 1. (a) SEM image of a MEMS wall-shear stress sensor on a 5mm×5mm die, (b) Schematics of the rippling optoelectronics for
the MEMS wall-shear stress sensors, (c) Image of the sensor packaging and the sensor control unit.

of the fluid flow. The displacement of the devices due to theinstantaneous wall-shear stress is as low as tens of nanometersand hence, Moiré fringe transduction (Amidror, 2009) is used
to amplify the displacement of the sensors by a factor of 60 to
100 times. Moiré fringe pattern is generated during the
microfabrication process by superposition of two sets of finely
spaced gold gratings with slightly different spatial pitches, 𝑔1
and 𝑔2 . The superposition of these grating sets generates aperiodic optical pattern of light and dark regions with a larger
spatial period, 𝐺. The displacement of the Moiré fringe
pattern, Δ, is linearly linked to the mechanical displacement of𝐺
the floating element, 𝛿𝜏 𝑤 , by Δ = ( )𝛿𝜏 𝑤 , where 𝐺/𝑔1 is thefringe pattern into the photodiode. The light spots are rippled
in sequence at a frequency higher than any in the flow to
extract the sinusoidal response of the Moiré fringe pattern. As
the MEMS sensor oscillates due to the applied wall-shear
stress, the phase of the sinusoidal response of the Moiré fringe
pattern is tracked with time utilizing the fast-scanning
optoelectronics, which is calibrated to measure theinstantaneous wall-shear stress. Inspection of the
microfabricated devices using Scanning Electron Microscopy(SEM) demonstrated that the geometrical parameters of thedevices were in excellent agreement with the design
parameters; see figure 1a. Additionally, the formation of the
Moiré fringe pattern on the MEMS wall-shear stress sensors
can be observed with the distinguishable dark and light bands.

𝑔1

amplification factor. The periodic dark and light pattens on the
Moiré fringe pattern can be expressed in terms of the sum ofsinusoidal functions, where the amplitude, period, and theinitial phase are fixed, due to the microfabrication process, andthe movement of the Moiré fringe pattern only results in aphase shift (Chen et al, 2011). In the presented MEMS wallshear stress sensors, one of the gold gratings set is fixed on atransparent substrate, whilst the second set is free to movewith the sensor’s floating element, which results in the
movement of the Moiré fringe pattern. To detect the motion of
the Moiré fringe pattern, a light spot is rapidly scanned acrossthe Moiré fringe pattern. As shown in figure 1b, rippling
optoelectronics consists of an array of twelve Light Emitting
Diodes (LEDs), an array of twelve fibre optic cables, a pair ofaspheric lenses and a single photodiode is used to track thedisplacement of the Moiré fringe pattern. The light from each
LED is launched down a dedicated fiber optic cable and is thenfocused via a pair of lenses to a small spot of light, typically
10’s of microns in diameter, on the backside of the Moiré
fringe pattern. The light spot is then reflected from the Moiré

Experimental Setup
Static calibration of the packaged MEMS wall-shear stresssensors (figure 1c) was carried out in a laminar flow rig witha cross-section dimensions of 0.5×80 mm and a length of 2 m,
in which the mean wall-shear stress was determined via thepressure gradient, 𝜕𝑃/𝜕𝑥, measurement as 𝜏𝑤 = −ℎ𝜕𝑃/𝜕𝑥,
where ℎ is the channel half height. A 30 mm diameterplugging port was located 1.65 m down the flow inlet,
allowing for the MEMS wall-shear stress sensors to be
mounted directly into the channel base. The pressure gradientmeasurement was performed over a 200 mm distance with thepositive and negative ports being on the upstream anddownstream of the MEMS sensors, respectively; see figure 2a.
The inlet compressed air flow rate was controlled by adjustinga needle valve, where the flow rate was measured using aDawyer RMA-8-BV flowmeter, with an accuracy of ±4%.

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12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
Table 1. Specifications of selected MEMS wall-shear stress sensors.
DeviceMEMS 1MEMS 2MEMS 3MEMS 4MEMS 5MEMS 6

𝑊𝑒
(𝜇m)
1000
800
1000
800
650
650

𝐿𝑒
(𝜇m)
1000
800
1000
500
500
500

𝑊𝑡
(𝜇m)
7
7
10
500
500
500

𝐿𝑡
(𝜇m)
1500
900
800
12
10
10

Sensitivity(nm/Pa)
730
120
42
38
56
53

The pressure difference across the calibration rig and on twosides of the sensors was measured using a Betz PARW 79
manometer with an accuracy of ±50 𝜇m H2 O and a dynamic
range of 0-400 mmH2 O. A Voltcraft PL-125-T2USBVS
thermometer with an accuracy of ±0.15% was implemented
at the flow outlet to record the air flow temperature during thecalibration.
Dynamic characterisation of the sensors was performed
under a Zygo NewView 5000 profilometer by using the
Dynamic Metrology Module (DMM) and a stroboscopic lightsource, as illustrated in figure 2b. The devices were mounted
at the 5mm×5mm top surface of a Thorlabs PN5FC2 low
voltage shear piezoelectric stack and they were actuated by adrive signal from a Pragmatic 2416A Arbitrary Waveform
Generator (AWG).

Accuracy

Repeatability

2.1%
1.5%
1.41%
1.8%
1.76%
2.36%

0.68%
0.9%
1.1%
1.24%
0.86%
1.96%

𝒇𝟎
(kHz)
1.016
2.835
4.346
8.37
7.38
7.38

Turbulent boundary-layer measurements were conducted
1.8 m (𝑥 + = 𝑥 𝑢𝜏 /𝜈 = 28068 to 68064) downstream of a 3 mlong flat plate inside an open-loop wind tunnel with an innergeometry of 350mm×460mm (ℎ × 𝑤). The boundary-layer
was developed on a flat plate, manufactured from a 20mm
thick Formica. The flow was tripped into the turbulent
boundary-layer by using a zig-zag shape trip (Elsinga andWesterweel, 2012) that was attached to a super elliptic leadingedge as shown in figure 2c. The turbulent trip was
manufactured from a 3mm acrylic, and laser-cut into a zig-zag
shape with a pitch of 6mm, which is 11mm in width andextended across the width of the flat plate. A flap was adjusted
at the test section trailing edge to a maximum angle of 47.6°
in 3.4° increments to alter the stagnation point of the flow at
the tip of the leading edge, resulting in a zero-pressure
gradient flow along the test section.
A TSI LDV system, consisting of two 1W STM/SLM
Genesis MX series continuous-wave lasers with a wavelengthof 514 nm and 488 nm, was utilised for the turbulent
boundary-layer measurement alongside the MEMS sensors. A
TSI RV4480 optical lens beam expander was used to focus thelaser beams into the test section, generating a measurement
volume with a diameter of 50 𝜇m (𝑑 + = 𝑑 𝑢𝜏 /𝜈 = 0.78 to1.9) and a length of 250 𝜇m (𝑙 + = 𝑑 𝑢𝜏 /𝜈 = 3.96 to 9.49). A
3-axis Isel traverse system was used to traverse theintersecting laser beams in the wall-normal direction with aresolution of 5 𝜇m. A TSI receiver with a focal length of 300
mm was used in a forward scatter mode to pick up thescattered light and transmit the collected signal into an
FSA3500 processor to convert the signal to velocity. A TSI
atomizer filled with Di-Ethyl-Hexyl-Sebacat (DEHS),
introduces seeding particles of 1 𝜇m diameter into the flow.
The data from the MEMS sensors were sampled using a 16bit National Instrument NI-9215 card.

(a)

(b)

Experimental Results
Specifications of developed MEMS sensors that wereused in this study are presented in Table 1. Here, six different
types of MEMS wall-shear stress sensors with different
geometries were packaged up and tested.
MEMS wall-shear stress sensors were calibrated over awall-shear stress range of 𝜏𝑤 = 0−5.3 Pa. The output signalsof the MEMS sensors were recorded for a period of 60 seconds
at a 10 kHz sampling rate, for each wall-shear stress point. For
each device two calibration runs were performed to check forthe repeatability of the sensors’ reading. Shown in figure 3a isthe displacement of the Moiré fringe pattern’s sine curve as aresult of the phase shift due to the applied wall-shear stress inthe calibration rig, captured by the photodiode response. The
inset in figure 3a demonstrates the photodiode response froma light spot on a fixed location on the Moiré fringe pattern.
Here, it can be seen that a change in the applied wall-shear
stress results in a change in the light intensity as a consequenceof the Moiré fringe pattern light and dark bands displacement.

(c)Figure 2. (a) Schematics of the MEMS sensors calibration
setup; (b) experimental setup for the dynamic
characterisation. The MEMS sensors were mounted on the
top surface of a 2D piezoelectric shear stack; (c) an imageof the wind tunnel test section, showing (1) the ellipticalleading edge and (2) the turbulent trip.

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12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
Table 2. Turbulent flow wall-shear stress parameters measured
by MEMS sensors and LDV.

Accuracy of the MEMS sensors was obtained using thehighest deviation of the measured value with the sensors fromthe ideal value of the wall-shear stress, which can becalculated from the calibration curve (Fraden, 2004). Thisdeviation is expressed in terms of the difference between thevalue of the wall-shear stress, which has been traced backfrom the sensors reading and the actual input value of wallshear stress. The repeatability error of the MEMS sensors wasestimated using the data points from two experimental runs
(Fraden, 2004). The calibration results, figure 3b, indicate amechanical sensitivity range of 23−730 nm/Pa, an accuracy
range of 1.4−2.4%, and a repeatability range of 0.7−1.9%.

Device

𝜏𝑤
(Pa)

𝑆𝐷𝜏𝑤

𝑆𝜏𝑤

𝐾𝜏𝑤

𝑅𝑒𝜏 = 910

LDV
MEMS 1 (centre)
MEMS 2 (left)
MEMS 3 (right)

0.124
0.121
0.116
0.115

0.05640.04570.04210.04081.14251.13611.13781.1212

5.24
5.14
5.13
5.02

𝑅𝑒𝜏 = 1320

LDV
MEMS 6 (centre)
MEMS 3 (left)
MEMS 4 (right)

0.399
0.391
0.387
0.393

0.18580.15520.15440.154

1.14651.1307
1.1394
1.1426

5.22
5.16
5.18
5.21

To capture the frequency response of the devices, a quick
frequency sweep was performed for the range of 1 Hz up to 10
kHz. When the devices were in their resonant frequency
threshold, their images became blurry as the exposure time of
the image sensor on the Zygo NewView 5000 was not able toresolve high speed motion of the sensors. To resolve thesensors’ motion under the excitation, the frequency difference
between the drive signal and the strobe light were set to beequal to one, whilst the phase delay between the two signals
were set to zero. This resulted in the visualisation of thesensors’ displacement at 1 Hz, which can be resolved by theimage sensor. The video of the sensors motion at eachfrequency was recorded for a period of 10 seconds and thenconverted into the frames. An image processing algorithm was
performed on the individual frames to extract thedisplacement time response and frequency response of thedevices. Dynamic characterisation of the devices indicates aresonant frequency range of 1−8.4 kHz and a dynamic rangeof 79-109 dB: see figure 2c.
In a series of wind tunnel experiments over a flow speed
range of 𝑈∞ = 6 to 15 m/s (𝑅𝑒𝜏 = 600 to 1320), theinstantaneous wall-shear stress was measured within the
turbulent boundary-layer simultaneously by the MEMS
sensors and LDV. Prior to the wall-shear stress measurements,
canonical turbulent boundary-layer quantification was
performed in two spanwise locations, to make sure that the
canonical turbulent boundary-layer profiles are similar acrossthe span at a specific streamwise location (𝑥 + = 𝑥 𝑢𝜏 /𝜈 =28068 to 68064). This was investigated through the statisticalvalues of the turbulent flow. The first set of the canonicalturbulent boundary-layer profiles was measured at thecentreline of the test section (𝑧 + = 𝑧𝑢𝜏 /𝜈 = 0), where thecentral MEMS sensor was implemented, whilst the second
data set was captured 75mm (𝑧 + = 1166 to 2828) in the
spanwise direction and at the location of the MEMS sensor on
the right side. Figure 4a shows the time-averaged streamwise
velocity profile taken by LDV at 𝑧 + = 0, where theexperimental data are in good agreement with the Schlichting
log law (Schlichting and Gersten, 2016) in the canonicalboundary-layer.
To quantify the instantaneous wall-shear stress,
intersecting laser beams of the LDV were focused directly
above the location of the central MEMS sensor (𝑧 + = 0)
where the wall-shear stress was quantified using the near-wallvelocity gradient technique at 𝑦 + = 𝑦𝑢𝜏 /𝜈 = 4, where 𝑦 = 0 is
the wall location. Three different MEMS sensors were used tomeasure the instantaneous wall-shear stress simultaneously
across the span at each Reynolds number; see table 2. For each
Reynolds number, MEMS sensors with appropriate size,
sensitivity, and dynamic characteristics were utilised. The

(a)

(b)

(c)
Figure 3. (a) Displacement of the Moiré fringe pattern’ssine curve on a MEMS sensor (MEMS 2) due to theapplied wall-shear stress; (b) calibration curves of the
MEMS wall-shear stress sensors. The inset demonstrates
the calibration points in the lower end of the wall-shear
stress spectrum for MEMS 5; (c) frequency response of a
MEMS wall-shear stress sensor (MEMS 2). Thick blackline represents the experimental results and the dashedblue line in derived from the analytical solution.

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12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
streamwise locations of the MEMS sensors were similar,
whilst they weretime signals of the instantaneous wall-shear stress captured
simultaneously by the MEMS sensor and LDV is presented in

(a)

(b)

(c)

(d)

Figure 4. (a) Turbulent boundary-layer profile measured by LDV at 𝑧 + = 0 for 𝑅𝑒𝜏 = 600 to 1320, (b) An illustration of MEMS
and LDV configuration, (c) wall-shear stress time signals captured by a MEMS sensor and LDV, and (d) PDFs of fluctuating wallshear stress measured by the MEMS sensors and LDV. Black lines represent the measurement at 𝑅𝑒𝜏 = 600, red lines represent themeasurement at 𝑅𝑒𝜏 = 910, blue lines represent the measurement at 𝑅𝑒𝜏 = 1060, and orange lines represent the measurement at 𝑅𝑒𝜏
= 1320.
placed with a spatial pitch of 75 mm (𝑧 + = ±1380 to ±2850)
across the span, as shown in figure 4b. Up to 2× 106
streamwise velocity data points were acquired with the LDVfrom within the viscous sub-layer for each Reynolds number.
The statistical parameters of the instantaneous wall-shear
stress such as the mean wall-shear stress, 𝜏𝑤 , standard
deviation, 𝑆𝐷𝜏𝑤 , skewness, 𝑆𝜏𝑤 , and kurtosis, 𝐾𝜏𝑤 , measured
by the MEMS sensors and LDV were in very good agreement
indicating that the MEMS sensors can accurately quantify the
instantaneous wall-shear stress within the turbulent boundarylayer. To put this in context, as it is presented in table 2, at 𝑅𝑒𝜏
= 1320, the difference between the values of the mean wallshear stress measured by the central MEMS sensor (𝜏𝑤 =0.391 Pa) and LDV (𝜏𝑤 = 0.399 Pa) is ≈ 2%. This is within
the uncertainty range of the measurement techniques as theaccuracy of the MEMS sensors is quantified to be 1.4−2.4%
based on the calibration, and the accuracy of the LDV in themean velocity measurements is 1−2% (Whalley, et al. 2019).
To investigate the performance of the MEMS sensors in
capturing the wall-shear stress fluctuations, time signals of

instantaneous wall-shear stress captured simultaneously by
the MEMS sensor and LDV is presented in figure 4c, where
good agreement is observed in wall-shear stress time series.
Probability density functions (PDFs) of the fluctuatingwall-shear stress, 𝐵(𝜏𝑤 − 𝜏𝑤 ), acquired by the MEMS sensors
and LDV are illustrated in figure 4d. Here, good agreement in
the PDFs demonstrates that the MEMS sensors are accurately
capturing the wall-shear stress fluctuations within the
turbulent boundary-layer. It can be noticed that the
distribution of the wall-shear stress data measured by all thedevices are positively skewed. Moreover, the fluctuations in
the wall-shear stress are super-Gaussian, which means there isa large probability of extreme fluctuations in the wall-shear
stress. This agrees with the fluid flow physical behaviour near
the wall region, where positive velocity fluctuations, andhence forward velocity gradients will be more likely to happen
than backward velocity gradients (Gubian et al. 2019).
Normalising the wall-shear stress data sets by 𝜏𝑤,𝑅𝑀𝑆 ,collapses the data at 𝑅𝑒𝜏 = 600 to 1320. The positive tail on thenormalised wall-shear stress fluctuations demonstrates that5

12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12)
Osaka, Japan (Online), July 19-22, 2022
the MEMS sensors can capture large events in the flow
alongside with the LDV, where the values of (𝜏𝑤 −𝜏𝑤 )/𝜏𝑤,𝑅𝑀𝑆 , extend beyond 7.5 for these devices. The
normalised PDFs of the fluctuating wall-shear stressadditionally indicates that no negative wall-shear stress values
were detected by the MEMS sensors and LDV were captured.
This is not unexpected as it was shown by Örlü and Schlatter(2011) that the contribution of the negative wall-shear stressvalues is less than 0.1% of the PDF.

Diaz-Daniel, C., Laizet, S. and Vassilicos, J.C., 2017. "Wallshear stress fluctuations: Mixed scaling and their effects onvelocity fluctuations in a turbulent boundary layer. " Physics
of Fluids, 29(5), p.055102.
Elsinga, G.E. and Westerweel, J., 2012. "Tomographic-PIV
measurement of the flow around a zigzag boundary layer trip.
" Experiments in fluids, 52(4), pp.865-876.
Fraden, J., 2004. "Handbook of modern sensors: physics,
designs, and applications (Vol. 3) ". New York: Springer.
Gubian, P.A., Stoker, J., Medvescek, J., Mydlarski, L. andBaliga, B.R., 2019. "Evolution of wall shear stress with
Reynolds number in fully developed turbulent channel flow
experiments. " Physical Review Fluids, 4(7), p.074606.
Horowitz, S., Chen, T. A., Chandrasekaranm, V., Nishida,T., Cattafesta, L., Sheplak, M., and Tedjojuwono, K., 2004.
"A micromachined geometric Moiré interferometric floatingelement shear stress sensor." 42nd AIAA Aerospace Sciences
Meeting and Exhibit’. 1042.
Hutchins, N., and Choi, K., 2002. “Accurate
measurements of local skin friction coefficient using hot-wireanemometry.” Progress in Aerospace Sciences 421-446.
Li, Y., Chandrasekharan, V., Bertolucci, B., Nishida, T.,
Cattafesta, L., and Sheplak, M., 2008. "A MEMS shear stresssensor for turbulence measurements." 46th AIAA Aerospace
Sciences Meeting and Exhibit'. 269.
Mills, D. A., Patterson, W. C., Keane, C., and Sheplak,M., 2018. "Characterization of a fully-differential capacitive
wall shear stress sensor for low-speed wind tunnels. "In 2018 AIAA Aerospace Sciences Meeting (p. 0301).
Mills, D.A., Blood, D. and Sheplak, M., 2016.
"Characterization of a sapphire optical wall shear stresssensor for high-temperature applications. " In 54th AIAA
Aerospace Sciences Meeting (p. 2016).
Örlü, R., and Vinuesa, R., 2020. "Instantaneous wall-shearstress measurements: advances and application to near-wall
extreme events." Measurement Science and Technology
112001.
Örlü, R. and Schlatter, P., 2011. "On the fluctuating wallshear stress in zero pressure-gradient turbulent boundary layer
flows. " Physics of fluids, 23(2), p.021704.
Shajii, J., Ng, K.Y. and Schmidt, M.A., 1992. "Amicrofabricated floating-element shear stress sensor using
wafer-bonding technology. "Journal of Microelectromechanical Systems, 1(2), pp.89-94.
Schlichting, H. and Gersten, K., 2016. "Boundary‐Layer
Theory (pp. XXVIII–805). " Verlag Berlin Heidelberg:
Springer.
Tseng, F.G. and Lin, C.J., 2003. "Polymer MEMS-based
Fabry-Perot shear stress sensor. " IEEE Sensors Journal, 3(6),pp.812-817.
Whalley, R. D., Dennis, D. J., Graham, M. D., and Poole,
R. J., 2019. "An experimental investigation into
spatiotemporal intermittencies in turbulent channel flow close
to transition ." Experiment in Fluids 102.

Conclusion
Optical MEMS sensors have been developed to measure
the instantaneous wall-shear stress within the turbulent
boundary-layer. Sensor’s characterisation results such as thesensitivity range of 23−730 nm/Pa and the resonant frequency
range of 1−8.4 kHz, indicate that sensors can be developed
for a range of applications. The performance of the MEMS
wall-shear stress sensors has been investigated alongside LDV
within turbulent boundary-layer flows, where the wall-shear
stress data captured by these techniques were in excellent
agreement, showing that the MEMS sensors can accurately
detect the spatiotemporal wall-shear stress in turbulent
boundary-layer flows.
Acknowledgments
The authors acknowledge the support from Engineering andPhysical Sciences Research Council (EPSRC) under grant
numbers EP/T020946/1, EP/R511584/1 and EP/M5079X/1,
and from Northern Accelerator under grant number
NACCF216.
REFERENCES
Alfredsson, P. H., and Johansson, A. V., 1988. "Thefluctuating wall‐shear stress and the velocity field in theviscous sublayer." Physics of Fluids 1026-1033.
Amidror, I., 2009. "The Theory of the Moiré Phenomenon:
Volume I: Periodic Layers (Vol. 38). " Springer Science &Business Media.
Barlian, A.A., Park, S.J., Mukundan, V. and Pruitt, B.L.,
2007. "Design and characterization of microfabricatedpiezoresistive floating element-based shear stress sensors.
" Sensors and Actuators A: Physical, 134(1), pp.77-87.
Barnard, C., Meloy, J., and Sheplak, M., 2016. "An
instrumentation grade wall shear stress sensing system." IEEESENSORS. IEEE. 1-3.
Chandrasekharan, V., Sells, J., Meloy, J., Arnold, D.P. andSheplak, M., 2011. "A microscale differential capacitive direct
wall-shear-stress sensor. " Journal of microelectromechanicalsystems, 20(3), pp.622-635.
Chen, T.A., Mills, D., Chandrasekharan, V., Zmuda, H.
and Sheplak, M., 2010, January. "Optical miniaturization of a
MEMS-based floating element shear stress sensor with moiré
amplification. " In 48th AIAA Aerospace Sciences Meeting
Including the New Horizons Forum and Aerospace
Exposition (p. 498).

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