Subido por Ruben Raygosa

Sensor Arena v2

Design and initial testing of a piezoelectric sensor to quantify aeolian sand
Raygosa-Barahona, R.a , Ruı́z-Martı́nez, G.a,∗, Mariño-Tapia, I.a , Heyser-Ojeda, E.a
a Departamento
de Recursos del Mar, Centro de Investigación y de Estudios Avanzados del IPN, Mérida Yucatán, México, 97310.
This paper describes a sensor for measuring the mass flux of aeolian sand transport based on a low-cost piezo-electric
transducer. The device is able to measure time series of aeolian sand transport. Maximum fluxes of 27 mg per second
can be achieved. The design includes a sand trap, an electronic amplifier circuit and an embedded system for data
collection. A field test was performed, where the basis for signal interpretation and the corresponding measurements
of aeolian sand transport are presented. The sensor successfully measures fluxes driven by sea breezes of 10 m s−1 ,
showing the importance of this process for dune-building in the region.
Keywords: aeolian sediment transport, impact sensor, piezoelectric sensor, field instrumentation, mass sediment flux
1. Introduction
The quantification of aeolian sediment transport
is necessary to understand the patterns of erosion,
accretion and other morphodynamic phenomena
(i.e. bedform development) forced by wind action
on beaches, agriculture soils and deserts. Aeolian
sediment transport occurs when the wind blows over
a sand surface or soil and its particles are moved
by the resulting shear stress. Particles can be lifted
and transported momentarily in the air (saltating or
bouncing), or can be transported by rolling, or sliding
(reptating) in the layer of sand close to the bed. In this
study the aeolian transport measured is produced by
all the particles that move over the surface of the sand
by rolling, reptating (sliding), or saltating (bouncing).
The quantification of aeolian transport has important
implications for the development of coastal dunes,
which are natural elements that influence the stability
of the coast (Hesp, 2011). In these environments,
aeolian sediment transport is influenced by variables
such as the magnitude and direction of the wind,
topography of the beach where the wind is acting, dune
vegetation, variability in surface moisture and sediment
textural characteristics, including sorting, skewness and
kurtosis. The combination of these factors changes
with time and results in considerable spatial and
∗ Corresponding
Email address: [email protected] (Ruı́z-Martı́nez, G.)
Preprint submitted to Journal of Aeolian Research
temporal variability of aeolian sand transport in coastal
Aeolian sand scientists have implemented different
methods to measure sediment transport. One of the
methods used to quantify aeolian transport is sand traps
which capture the moving sand grains in containers
(Bagnold, 1954; Horikawa & Shen, 1960; Leatherman,
1978; Wilson & Cooke, 1980; Fryrear, 1986; Arens
& van der Lee, 1995; Jackson, 1996; Nickling &
McKenna Neuman, 1997; Sherman et al., 2014).
However, the high frequency variability of aeolian
transport needs to be resolved and for this reason,
electronic instruments have been developed to record
data continuously at sampling rates of 1 Hz or higher
(Sherman et al., 1998; van der Wal, 1998; Baas &
Sherman, 2006).
Aeolian transport instruments include acoustic,
piezoelectric, and photo electronic sensors, electronic
sand traps (weight recorders) and a combination of
the above (Namikas, 2002; Barchyn & Hugenholtz,
2010; Ellis et al., 2012). The acoustic sensors record
the sound produced when grains of sand impact a
membrane which works as a diaphragm and vibrates,
generating an electronic signal from which it infers the
number of impacts that are produced by the moving
sand grains (Spaan & van den Abeele, 1991; Ellis
et al., 2009; Yurk & Hazle, 2013; Poortinga et al.,
August 27, 2016
2015). The piezoelectric sensors detect the impacts of
the grains using a highly sensitive ceramic sensor that
generates small electrical pulses which are proportional
to the mass of the sediments that are carried by the
wind (Baas, 2004; Udo et al., 2008; Udo, 2009; Swann
& Sherman, 2013). The photo electronic sensors
detect the grains of sand as they cross a laser beam,
interrupting the signal. The characteristics of said
interruption (variation of the laser wave) provide
information on the size and number of particles passing
the sensor (Jackson & McCloskey, 1997; Mikami et al.,
2005; Redmond & Dial, 2010; Hugenholtz & Barchyn,
2011; Barchyn et al., 2014). Bauer & Namikas (1998)
presented sand traps based on an electro-mechanical
instrument which automatically derived the total
mass of the sediments that were caught in the traps.
Schönfeldt (2012) implemented an electronic device
which uses acoustic sensors and a digital web camera
to measure the mass of sediments that are moved by the
wind on a beach.
As with any other type of sensor, sand transport sensors
should be calibrated using a previously calibrated
instrument, nevertheless there is no consensus amongst
aeolian transport researchers as to which sensor
is optimal for quantifying aeolian mass transport
(Barchyn & Hugenholtz, 2010; Sherman et al., 2011);
most of the calibrations are based on acoustic or
piezoelectric methods. Regardless of which method is
used, there is still a need for calibration.
The goal of this study is to present the development of
a piezoelectric sensor to measure the rate of sand mass
transport at low costs with reasonable accuracy. The
complete electronic system amounts to approximately
$50 USD, which includes circuit boards, resistors,
diodes, microprocessors, and a piezo-electric sensor.
This could allow the sensor to be massively deployed
in networks. The purpose of using a piezoelectric
device is that it can provide a direct measurement of
the force exerted by the sand grains. The sensor has
a resolution of 2.5 x 10-4 g, when calibrated in the
laboratory with a high precision electronic weighing
scale. The article is organized as follows: section two
presents the methodology including the calculation of
mass, the calibration procedure and a case study in the
field. Section three presents the results, and four and
five present the discussions and conclusions.
when subjected to a force. The amount of charge
produced can be represented in a simplified form as
q = K1 F
where q is the electrical charge, K1 is a constant which
depends on the physical characteristics and dimensions
of the material, and F is the acting force. Because
piezoelectric materials are also capacitors, they must
follow the capacitor equation:
1 t
idt =
C 0
, also called the electrical current in
where i =
amperes, which can be approximated with the time (t)
derivative of charge (q); V is the voltage and C the
capacitance in Farads. Details on the principles of
operation and the electronics amplifier of the sensor are
presented in Appendix 1.
2.1. Mass Computation
According to equation A.2, (Appendix A.1) the signal obtained by the combined embedded-piezoelectric
system is related to the momentum of the mass of
sands grains impacting the sensor. Assuming that the
sand grains are spherical and that the d50 parameter can
represent the characteristic size (and therefore the mass)
of a sand sample, then we can assume the same fall
velocity for all grains. This assumption results in the
next equation:
m = Kc dmdt
where m is the total mass, Kc is a calibration constant
which needs to be found, and t stands for the time.
As will be shown in the next section, experimental tests
suggest that it is enough to calculate only one value of
Kc since there is no significant difference between a
Kc calculated for the D5 0 and values for individual size
2.2. Sampling rate
2. Methodology
The piezoelectric effect refers to the capability of certain materials to produce an amount of electric charge
A heuristic procedure showed that a 10 kHz sampling
rate provides a smooth measure of the signal produced
by a grain of sand of approximately 0.5 mm, which free
falls from at height of 10 cm (Figure 1). Subsequent
tests were carried out using sand samples comprising
a range of grain sizes showing that the sampling rates
also work for smaller and larger grains. The sampled signal is integrated to produce a measure of the
mass. Two versions of the prototype were developed:
a standalone version for field deployment, which saves
the cumulative data over a 10 second time interval
and a desktop version for calibrating purposes. The
desktop version sends the accumulated data every 1.5
milliseconds through a serial port. The supplied data is
received by a computer running Matlab.
The calibration of the piezoelectric was performed on
a vertical sand fall flume. The devices consist of the
following elements from top to bottom (Figure 2): (1) a
funnel with a small orifice at the bottom which acts as a
dispenser, (2) a cylindrical flume tube made of acrylic
with a diameter of 76.2 mm, (3) the sensor mounted
horizontally with the center aligned with the sand
grains stream, (4) a high precision electronic weighing
scale to collect the grains and obtain the mass. The
grains were left to free fall into the funnel by hand. The
flow rates could be controlled by adjusting the size of
the orifice at the bottom of the funnel. Readings from
both the sensor and the scale were logged to compare
the relationship between both measures. Taking a
sample of sand grains from the sieved D5 0 fraction, we
throw sand into the funnel. In the bottom the electronic
weighing scale obtains the accumulated mass of the
Figure 1: The amplified signal produced by a 0.5 mm sand grain in
free fall. Note that we could consider a 200 ms interval in which the
signal vanishes at a constant rate.
2.3. Calibration process
Using the Basset Boussinesq Oseen (B.B.O.)
equation to solve acceleration (see Appendix B) it is
possible to compute the fall velocity of a spherical
particle under the influence of gravity as it falls through
a fluid (Graf, 1984). Baas (2004) presented a correction
of the B.B.O. equation as a function of grain diameter
assuming that all grains follow a vertical trajectory in
the flux, neglecting the inter-grain effects. Figure A.4
in Appendix C shows the effects of this correction,
which shows that the final velocity of a grain falling
in the flume, after a critical fall height of 0.5 cm is a
function of grain size alone. This was done under the
assumption that the mass of a sand grain is a function
of its diameter, neglecting variation in other physical
characteristics such as density, chemical composition
or porosity of the grain. During the tests carried out
in this study the previous assumption was valid. After
recovering the sand sample from the trap, a calibration
process is needed in order to overcome the differences
in the sediment composition and properties.
A simple procedure was followed to calibrate the
piezoelectric sensor by sieving the sand to obtain the
D50 value which describes the diameter of the sand
grains that form the sediment sample.
Figure 2: The test device used to estimate the calibration constant Kc .
Figure 3 shows the linear relationship between the
mass obtained with the scale and the response of the
piezoelectric sensor. Test C1 was performed with
sieved sand released from a height of 25 cm, in test
C2, the sand was released from 50 cm, while in test
C3 the height was increased to 60 cm. There were no
significant differences in the response of the sensor,
which is in accordance with the results of Baas (2004).
Test C4 was performed with a sample of silt released
from a height of 50 cm. As we expected the counts were
reduced but the linear relationship was maintained,
reinforcing the idea that a site specific calibration is
needed. Tests C5, C6, C7, and C8 were carried out
with samples of unsieved sand, which included the silt
of test C4. Comparing the results from C1, C2, C3
(sieved sand) and C5, C6, C7, C8 (unsieved sand) we
can assure that the mass of the sample of sand grains
can be approximated considering their representative
D50 calibration curve. As depicted in Table 1, the
correlation coefficient, R2 , was approximately 0.99 in
all cases.
the sensor. Figure 5 shows a diagram of the sand trap
Figure 4: Semilog cumulative probability curve of the sand sample
used in the calibration processes (Test C1, C2 and C3). The main
values are: D50 = 0.389 mm (1.362 φ-units), moderately well sorted,
coarsely skewed, and mesokurtic.
Figure 3: Comparison between the measurements obtained from the
sensor and the total mass obtained by direct mass measurement.
Table 1: Comparative between different tests, note that in the worst
case (Test C4) the linear relation was maintained and the correlation
was approximately 0.998.
Test C1
Test C2
Test C3
Test C4
Test C5
Test C6
Test C7
Test C8
Sieved sand (D50 =0.389 mm)
Sieved sand (D50 =0.389 mm)
Sieved sand (D50 =0.389 mm)
Silts (D50 < 0.062 mm)
Unsieved sand
Unsieved sand
Unsieved sand
Unsieved sand
Falling height (cm)
Figure 5: Diagram of the sand trap used (the design was based on
Swann & Sherman (2013)).
Figure 4 presents the cumulative probability curve of
the sand sample used in the calibration processes where
the range of sediment sizes can be seen.
3. Results
3.1. Field test
Following Swann & Sherman (2013), the piezoelectric sensor was installed inside a buried sand trap below
a funnel in order to measure time series of aeolian
sand transport in the field. The sand trapped by the
system was retained to validate the mass estimated by
The funnel-sensor system was installed inside a
polyethylene container to protect the batteries and the
electronics. The trap was deployed for three days on
the beach face close to a vegetated dune in Telchac,
Yucatán, Mexico (20.936N, 89.30W), as shown in
Figure 6. This region is subject to erosion problems
and the development of dune systems is crucial for the
protection of the coast. A weather station (Davis 6152
Wireless Vantage Pro2 Weather Station) is located 150
m onshore from the sand trap, where an anemometer
is installed at a height of 10 m from the ground. The
vertical sand fall ’flume’ was aligned perpendicular
to the NE, which is the dominant wind direction in
the region (Ruiz et al., 2016). Although, since it was
leveled to the ground, sand from any direction could
potentially fall into it.
4. Discussion
Figure 6: Geographical location of Telchac (image from Google
Figure 7 shows a picture of the systems installed on
the beach. The sand trap (white container) includes a
vertical sand fall ’flume’ and the funnel-sensor system
(Figure 5). The vertical sand fall ’flume’ is the only
aperture where sand can enter the trap.
Figure 7: Sand trap deployed in Telchac, Yucatn, Mexico. a) Trap
being installed and buried in the sand, b) final view of the chimney at
ground level.
Figure 8 shows the behavior of the mass transport
of sand for the three days of the field test. Increased
aeolian sand transport tends to occur preferentially
between 16:00 and 20:00 with peaks coinciding with
the maximum wind velocity which was from the
NE-ENE quadrant. This behavior of the wind is
typical for sea breezes which predominate in the region
(Enriquez et al., 2010). Wind velocities above 7 m s−1
are necessary to accumulate sand in the trap.
In this paper, we have considered the assumptions
that the sand grains are spherical, we have also assumed
that the fall velocity calculated with the D50 diameter is
adequate to characterize the whole sample, which might
not be entirely true because the terminal fall velocity
depends on the grain size. Nevertheless, tests using
an unsieved sample showed the same linear response
as for a sieved sample with only D50 sizes. We also
show that sand grains with a smaller D50 produce a
lower response (Figure 3), however the relationship
is still linear. To overcome the variability imposed
by the sand grain variations (variations in the D50 )
calibration needs to be performed wherever the sensor
is deployed. The sensor essentially differs from the
one presented by Sherman et al. (2011) in terms of the
methodology used to process the signal provided by
the sensor. The signal is processed (modified in size
and shape) and then sampled, while in Sherman et al.
(2011) the signal is sampled without any modification
of its shape. The sensor presented does not amplify
the negative part of the oscillations produced by the
impacts, unlike other existing sensors (e.g. Swann &
Sherman (2013)). This is due to the use of an amplifier
with a single voltage source and not two sources as
commonly used in operational amplifiers. To reduce
the effect of noise, we also remove signals below 200
mV and apply an analog low pass filter prior to feeding
the signal into the analog-to-digital converter, making
the signal more robust. In other sensors ((Swann &
Sherman, 2013; sensit, 2013)) the signals are processed
digitally, requiring a large processor, which is not
always ideal for transportable embedded systems. Also
the interpretation of the calculation of the mass is
obtained based on the momentum, which is linear with
respect to the mass and velocity, presenting an important
advantage over using energy (sensit, 2013) which is
quadratic in speed. The sensor was deployed in a sand
trap (Swann & Sherman, 2013); inside the trap it was
assumed that there was no direct effect of the wind, and
the velocity used to calculate the momentum is only
related to the fall velocity (sediment size). From Figure
3, we can obtain the resolution of the sensor which is
2.5 x 10-4 gr/count. As the number of counts is updated
with a frequency of 10 kHz, we can read a sand flux at
a maximum rate of 27 mg s−1 .
5. Conclusions
A piezoelectric sensor for the estimation of aeolian
transport has been presented, which includes an elec-
Figure 8: Graph of winds recorded in Telchac and the sediment fluxes. Panel a) shows the wind direction (stars), panel b) presents the relationship
between the sediment fluxes measured by the sensor and the wind speed (circles). Grey bars highlight the peaks in sediment flux, which are
identified during the afternoon when the breezes from ENE and NE occur.
tronic circuit to amplify and condition the signal from
the sensor. The complete system includes an embedded
acquisition and data processing system including an 8bit ADC with a sampling frequency of 10 mHz and
a resolution of 2.5 x 10-4 g. The sensor showed a
linear relationship between the number of counts and
the mass of the samples. The system can be fed by
both, batteries or small solar panels. A case study is
shown where aeolian sand transport was measured at a
beach, showing the importance of NE winds in aeolian
transport for dune building. The sensor is economical (
$50 USD), which makes it attractive for integration into
large sensor networks for more complex studies.
Appendix A.
Appendix A.1. Principles of Operation of the Piezoelectric Sensor
Figure A.1 shows a simplified electrical diagram of
the sensor. In this figure, R1 is the resistance between
the piezoelectric element plates, C1 is the intrinsic
capacitance device and F1 is the acting force.
6. Acknowledges
Figure A.1: A simplified diagram for the piezoelectric device.
The authors thank to Dr. David Valds Lozano for
sharing the wind record data for Telchac. Our sincere
thanks to all the reviewers for their comments and
suggestions, which helped to improve and enrich this
manuscript. Thanks to Gemma Franklin for reviewing
the English grammar.
Once the acting force has vanished, the resultant
charge generates a voltage which decays at a logarithmic rate; however there is a time interval (T) in which
we could consider a linear rate of discharge, as shown
in Figure A.2.
Amplifier. The Operational Amplifier U3A provides
a first amplification stage for the signal coming from
the piezoelectric element. The output signal from the
amplifier U3A is passed through a low-pass filter which
consists of a capacitor (C1) and a resistor (R3), U3B is
a second amplifier which is split into the rectifiers D1
and D2. The output signal from D1 decays at a rate
determined by the capacitor C3 and the resistor R4. The
output signal from D2 is used to trigger the sand grain
impact counter.
Figure A.2: A simplified diagram for the piezoelectric device.
Using that consideration we could approximate the
acting force over the piezoelectric as
F = K2 T
where K2 , is a proportionality constant and T is the time
interval in which the signal decays to a predetermined
voltage level. Taking the integral of F over the time
interval T, and using equations (1), (2) and (A.1) results
in the moment equation:
dm v = K
where K = K2 /C, a constant of proportionality, V is
voltage, and v the velocity of the particle of mass dm,
(in our case the mass of a sand grain). If in equation
(A.2), we obtain with any other method information
about v, then we could find the value of dm or at least
approximate its behavior. This is fairly simple in our
system since v is the fall velocity of the sand grains in
the trap.
Appendix A.2. Sensor Amplifier and Signal Conditioning
Prior to any digital processing procedure, the signal
supplied by any sensor should be treated or conditioned
according to the measuring devices that will receive the
signal. For this purpose we developed the electronic
system shown in Figure A.3. The sensor amplifier
and signal conditioning are composed of a piezoelectric
disk Kepo KP2310 with a diameter of 23 mm and
a 4 kHz resonant frequency, a microcontroller Atmel
ATmega328, resistors and diodes. The amplifier circuit
is depicted in Figure A.3, where every component is
labeled, including a letter for the type of component,
and a number of the part to clarify the electronic
diagram: ”CONN” is used for connectors, ”R” for
resistors, ”D” for diodes, ”C” for capacitors, ”U” for the
Integrated Circuit and ”OPAMP” for a dual Operational
Appendix A.3. Embedded System for Mass Computation and Signal Processing
Devices used for monitoring environmental variables
often require their own power supply since in most
cases they are installed far from the main electrical
networks. Solar panels, batteries, gasoline based, or
manual power generators are frequently used to supply
the necessary energy to the instruments. Generators
and solar panels are prone to vandalism and therefore
need full-time supervision. Batteries are small and
easy to hide nevertheless the energy supply is finite.
Therefore, systems that use batteries should be energy
efficient. Embedded systems meet both specifications;
they are small enough and energy friendly. In an
embedded system greater sampling rates mean more
power consumption, so the duration of the deployment
needs to be balanced with adequate sampling rates.
The microcontroller system Atmel (ATmega328) has a
power requirement of only 60 mA. The voltage supplied
by the amplifier is sampled by the embedded system
and then compared with a predetermined voltage level.
If the voltage sampled is greater than the predefined
level, a continuous pulse train triggers an incremental
counter. This process occurs until the voltage falls
below a predetermined level. The value stored in the
counter will be a function of the momentum transferred
by the sand grain to the piezoelectric sensor. The
predetermined level should be adjusted according to the
predominant grain size. As an example, the tests carried
out in laboratory for 0.5 mm sand grains suggested an
appropriate threshold value of 200 mV (see Figure 1 in
the main document). Sensor sensitivity can vary slightly
due to capacitance differences. If during the testing
process the variance in the capacitance of the sensor is
greater than 10%, that piezoelectric sensor is regarded
as faulty.
Appendix B.
The BassetBoussinesqOseen (B.B.O.) equation for
the acceleration of a spherical particle in a fluid under
Figure A.3: Signal conditioner and amplifier circuit.
the influence of gravity is given by Graf (1984):
ρ s v̇ s =
ρv̇ −
ρ (v̇ s − v̇) − · · ·
Z t
v̇ s (t1 ) − v̇(t1 )
− 6πµa (v s − v) + √
dt1 √
+ ···
t − t1
πυ t0
(ρ s − ρ) g
where v(t) is the velocity of liquid phase (m s−1 ), v s (t)
is the velocity of solid phase (m s−1 ), ρ s , ρ are the
density of fluid and particle, respectively (kg m3 ); v̇, v̇ s
are the acceleration of fluid and particle (m s−2 ); a is
the particle radius, t0 the starting time (s); υ = µ/ρ
the kinematic viscosity (m2 s−1 ); g the gravitational
acceleration (m s−2 ).
Figure A.4: Depth-velocity trajectories for 2 different grain sizes in
flume found by Baas (2004). The graph is reproduced with permission
from the author A.C.W. Baas.
Appendix C.
Figure A.4 shows the graph for terminal velocity of
two different grain sizes in a flume; Note that for grains
with a diameter smaller or near 0.6 mm, velocity could
be considered constant.
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