1. Altitude-referenced Atmospheric Measurements
using a Rocket
E80 Section 1 Team 3
Siddarth Srinivasan, Emily Swindle, Michelle Lanterman, Duncan Crowley
4 May 2016
Abstract
In this paper, a method for making experimental atmospheric measurements refer-
enced with altitude using a rocket with a measurement payload is examined. Flight
path and altitude measurements from an Inertial Measurement Unit (IMU) and Abso-
lute Pressure Sensor are compared with expectations from OpenRocket simulations. In
two launches with a G79W rocket motor, we reached consistent altitudes of 480±5 and
506±5 meters which closely matched our simulated apogee heights of 510 meters. Using
this IMU, the flight path of the rocket was simulated with reasonable error, yielding a
436 ± 1000 meter apogee for that first flight. Since a consumer grade inertial measure-
ment unit was used, it was confirmed with previous literature that angular measurement
noise was the greatest contributor to rocket position uncertainty. In addition, pollution
effects of rocket launches as well as weather conditions are explored through the use of a
dust sensor and humidity sensor. Through a method of integration, more dust particles
are observed on the descent than the ascent of the rocket. Humidity is found to increase
as a function of altitude; however, no quantitative relationship is found because of poor
response time in capacitive relative humidity sensors.
Introduction and Goals
Rockets can be outfitted with sensors and
used to make useful atmospheric measure-
ments. For our experimental engineering
course, we built a rocket equipped with sen-
sors with the engineering goal of tracking the
position of a rocket in space throughout its
flight, and the scientific goal of assessing any
atmospheric impacts of a rocket launch. To
track the rocket in space, we used an inertial
measurement unit (IMU) system in combina-
tion with an absolute pressure sensor which
measured altitude as a function of pressure.
For our scientific goal of measuring the at-
mospheric impacts of a rocket launch (such
as pollution), we measured dust concentra-
tion and relative humidity as a function of
altitude before and after a great number of
rocket launches had been conducted. In this
report, we describe the sensors used, their
respective calibration techniques, our rocket
construction method, flight modeling, and
discuss the results from our rocket launches.
Sensors
We equipped our rocket with four sensors to
meet our scientific and engineering goals.
IMU
Our IMU has 4 accelerometers (X, Y, Z, High
G) to measure acceleration in the the three
spatial directions and 3 gyroscopes to mea-
sure the rocket’s rotation rate about three
axes. We used an IMU to track the trajec-
tory and altitude over the rocket’s flight.
Calibration: We used the same method of
calibration as we did in the Accel & Gyros
rotation lab. First, we calibrated a turntable
to verify that we could control it’s RPM with
precision. Then using 3 different IMU ori-
entations, we experimentally determined the
relationship between the acceleration experi-
1

2. enced by the accelerometers and the voltage
output, as well as the rotational velocity ex-
perienced by the gyroscope and its output
voltage. Table 1 shows a selection of the
calibration values we found, with the other
low-G accelerometers and the other gyros
having similar calibration constants to the
displayed X Accel and Z gyro respectively.
The slopes match the datasheet closely, al-
though the
Having determined the relationship be-
tween the voltage output of the IMU and
the acceleration and rotation it experiences,
we could place the IMU on the rocket and
numerically integrate the data to obtain the
rocket’s position and velocity over time. [1]
Sensor Literature Calibration Curve Measured Calibration Curve
X Accel V = 0.031 V/(m/s2
) + 1.5 V V = 0.03 V/(m/s2
) + 1.641 V
High G Accel V = 0.00388 V/(m/s2
) + 1.5 V V = 0.002094 V/(m/s2
) + 1.639 V
Z Gyro V = 0.1146 V/(rad/s) + 1.5 V V = 0.119 V/(m/s2
) + 1.513 V
Table 1: Representative selection of calibration data for accelerometers and gyros on IMU
Pressure Sensor
We also used a pressure sensor to measure
the altitude of the rocket through its flight.
The relationship between pressure and alti-
tude is given by the expression:
p = 101325(1 − 2.25577 · 10−5
h)5.25588
(1)
where p is the air pressure and h is the al-
titude above sea level[3]. This allows us
to convert pressure data collected from the
rocket flight to altitude. We can also com-
pare the altitude data from the pressure sen-
sor to that of the IMU to confirm accuracy
in our measurements.
Calibration: The pressure sensor outputs
voltage that varies linearly with absolute
pressure. To calibrate the pressure sensor,
we used a hand vacuum pump and a high-
accuracy pressure sensor to check the con-
stants of the linear variation. We found that
the voltage output by the pressure sensor
varied with pressure P as V = 0.044 V/kPa·
P − 0.415 with R2 = 0.999, compared to the
literature sensitivity of 0.045 V/kPa and off-
set of 0.02 V. Figure 1 shows the pressure
sensor calibration apparatus.
Figure 1: Pressure Sensor Calibration Apparatus with two sensors to compare readings.
The pressure sensor read 91.0 kPa on the ground, compared to the 91.5 ± 0.2 kPa reading
from the weather station.
2

3. Dust Sensor
After some research to identify the products
in rocket exhaust and the variety of sen-
sors available, we concluded that it would be
interesting to study how dust concentration
changed as a result of rocket emissions. Con-
ventional dust sensors use an infrared (IR)
light and phototransistor unit to measure
light scattering as a result of dust, but are
highly sensitive to vibration and were not ap-
propriate for use on our rocket. Thus, in the
interest of measuring dust in a high vibra-
tion environment, we made our own infrared
dust sensor using the same science as a con-
ventional sensor with our own low vibration
mount. Figure 2 shows a cross-sectional view
of a PVC tube through which dust travels,
with the IR diode mounted perpendicularly
to the IR photo-transistor in a black PVC
tube to minimize light reflection.
The LED used was powered from the
output of a unity-gain buffered op amp us-
ing a 100 Ω resistor to provide a consistent
50 mA current. The IR phototransistor out-
puts current as a function of light intensity
(W/m2), so we convert this into voltage us-
ing a transimpedance amplifier with an op-
amp. Transimpedance amplifiers work as an
ideal current-to-voltage converter since they
have the low loading effects and low out-
put impedance necessary for our datalogger.
Once assembled, we calibrated the resting
voltage reading to be in the 1.5 V range
when there was no dust to allow for maxi-
mal gain while still avoiding the saturation
at 3.3 V.
Calibration: To test the operation of the
dust sensor, we blew large particles of dust
through the sensor to observe the increase
in voltage corresponding to a particle pass-
ing through. As expected, we observed that
larger dust particles resulted in larger voltage
blips, and it was also observed that the tran-
simpedance amplifier added no additional
time constant to the negligible time constant
of the phototransistor on the order of 100 ns
[4].
Figure 2: Our Dust Sensor Design (left) Compared with a Conventional Miniature Dust
Sensor (right) [2]
Humidity Sensor
We use a humidity sensor to measure the Rel-
ative humidity (RH), the ratio between the
actual vapor density and the saturation va-
por density [5]. Because water is modeled as
3

4. Figure 3: Picture of humidity sensor calibration apparatus
an ideal gas, we expect the relative humidity
to change as a function of altitude because
of the temperature, pressure, and the pres-
ence of cloud or fog. When considering a
humidity sensor to use, we took into account
both the sensitivity and the cost. The sensor
we chose was the Honeywell HIH-5030-001
Humidity sensor. Although it had a slow re-
sponse time of 5s in slow moving air, it had
a near linear output voltage, an accuracy of
±3% RH, low current draw, and a low-cost
of about $8, making it the best choice given
our budget.
Calibration: To calibrate the humidity sen-
sor, we used the Crane Drop Cool Mist
Humidifier along with ann Omega humidity
probe. By recording the RH read by the hu-
midity probe and the voltage output from
the HIH-5030-001 sensor, we were able to
make a linear voltage calibration curve for
the sensor. The setup for the humidity cali-
bration is shown below in Figure ZZ. This
resulted in a linear relationship of RH =
40.35(Vsupply) − 10.12 with R2 = 0.99. The
sensor also accurately read 31.56% RH on
ground compared with the 32 ± 0.5% RH
reading from the weather station brought.
Figure 4: Our Rocket next to a yard stick for size Comparison
Hardware Design
Rocket
We built our rocket using the Aerotech Ar-
reaux rocket kit, and modified it to suit our
design requirements. The rocket body (con-
sisting of the payload tube and the body
tube) was extended so that the PC board
and motor could fit since the generic rocket
body was too short. After measuring the di-
mensions of the PC board, we constructed
the rocket body with a 38.5 cm payload sec-
4

5. tion and a 57.9 cm body tube. Both rocket
body sections were fiberglassed by rolling
with epoxied fiberglass sheet. Once the fiber-
glass had dried, the rough areas on the fiber-
glass were sanded using damp sandpaper to
create a smooth surface. The holes for the
fins and launch lugs were cut out using x-
acto knives and sandpaper. The fins and
launch lugs were attached with superglue
and then any gaps between the fin base and
body tube surface were sealed with a cement
mixture. Once all sealants and adhesives
had dried completely, the rocket was spray-
painted with multiple layers for even color-
ing. Finally, the shock cord was connected
to the motor tube within the rocket, and
the parachute was tied onto the shock cord
to complete the general assembly. Figure 4
shows an external view of our fully assembled
rocket.
Dust Sensor Apparatus
A proof-of-concept infrared dust sensor was
built using an infrared LED mounted nor-
mal to an infrared photo-diode (which gener-
ates current proportional to IR light power)
across a rigid black PVC tube. When dust
or any small particle obstructs the beam of
light from the LED, light is scattered onto
the photodiode causing a small voltage spike
proportional to the size of the dust and the
density of dust in the air. Since the orienta-
tion of the LED has a great impact upon the
reading of the photodiode, both components
were held in place with a generous coat of
hot glue to minimize position shifts as a re-
sult of rocket vibration. This tube was then
threaded through a set of semi-circular rigid
foam cut-outs that was glued to our acrylic
rocket divider. To get continuous airflow,
the dust sensor tube was snaked through our
nose-cone with holes cut on both sides to an
output hole near the bottom of the payload
tube facilitated by a bend of flexible silicone
tubing.
Humidity Sensor Apparatus
The HIH-5030-001 humidity sensor, like the
dust sensor needed to be open to air-flow.
Unlike the dust sensor however, the humidity
sensor is not sensitive to light and could be
mounted on the outside of the payload tube.
To securely attach the HIH-5030-001, the
leads of the sensor were soldered to a laser
cut PCB mount. This was then cemented
to a small, rectangular cardboard box which
allowed the hole drilled to be minimized at
3/16” for the wires to pass through the pay-
load section. The flexible wires soldered onto
the mount were threaded through this hole
and the cardboard box was cemented to the
payload section and reinforced with hot glue.
PC Board and Electronics
To take measurements while our rocket flew,
we used a measurement payload mounted
on a printed circuit (PC) board as shown in
Figure 5 which recorded each sensor’s elec-
trical signals using a low-power MuddLogg
v4 datalogger.
Figure 6 gives the circuit diagram we
built on the PCB. Note that each sensor out-
puts to a MCP6004 quad op-amp using unity
gain buffers to impedance match the sensors
with the low input impedance of the Mud-
dLogg. For power, a lithium ion 9V battery
is used to accommodate the 150 - 200 mAh
current draw of the circuit which mostly re-
sults from the two voltage regulators. For
the sensors not on the PC board, (such as
the dust sensor apparatus) flexible stranded
wires were soldered from the PC board to a
row of header pins and connectors (from the
sensors themselves) and given a coat of hot
glue to avoid shorting. When mounted in the
rocket, these connectors were given a wrap of
tape to avoid accidental disconnects. We also
attached electrical tape to the bottom of the
PC board to facilitate smooth sliding across
our acrylic rocket divider.
5

6. Figure 5: PC Board Rocket Sensor Payload
Figure 6: PC Board Rocket Sensor Payload
6

7. Modeling and Simulation
OpenRocket Simulation
OpenRocket was used to predict the flight
path and characteristics such as apogee and
burn time. Prior to the actual launch
days, simulations were run with the generic
weather inputs to give a general idea of the
launch. After the actual launches, the ac-
tual weather conditions could be used to cre-
ate a more accurate simulation of the flight.
This is used to confirm that data collected
during flight follows what is expected. Since
no meaningful data was collected on the first
launch day, we only needed to modify the
G79 simulation to compare with data col-
lected on the second launch day as shown
in Figure 7.
Pressure
On earth, air pressure results from the grav-
ity of the column of air above it. Since
the column of air above changes size as al-
titude changes, pressure is a function of al-
titude except for sections of the sky (mostly
in the stratosphere) where pressure is con-
stant. This relationship is further elaborated
by Figure 8. To make this conversion from
pressure to altitude, we then simply use a
derivative of the ideal gas law know as the
hypsometric equation:
h2 − h1 =
RdT
g
ln
p1
p2
(2)
using Rd = 287.04 J/(kg K) and g = 9.8
m/s2 (with T being temperature) which re-
lates the thickness of a layer of air to the dif-
ference in pressure across hypsometric equa-
tion. This model assumes constant tempera-
ture which we know is incorrect, but since ab-
solute temperature (from Kelvin) varies less
than 7 ◦C or 2.5% below 2000 ft, we can sim-
ply use the temperature at the mean altitude
of flight from Open Rocket simulation. This
temperature is found using the lapse rate on
the day of launch shown in Figure 9 with an
offset adjusted for the ground temperature
at the time of launch.
Figure 7: Launch simulation using OpenRocket
7

8. Figure 8: Standard Atmospheric Variation of Pressure with Altitude [6]
Figure 9: Temperature vs. altitude on launch day from 4 thermocouples [7]
8

9. Launch
Motors
The G79W, H135 and G125 motors were al-
ready mostly assembled. Our main task for
assembly was determining what our optimal
burn time was and adjusting the motor to
fit this constraint, and we found this using
OpenRocket simulations. We found the op-
timal time to apogee for the H135W, G125T,
and G79W to be 12.1, 10, and 8.69 seconds
respectively. The initial burn time for each
of the motors was 14 s, 14 s, and 10 s for
the H135W, G125T, and G79W. We were re-
stricted to subtracting motor burn time by
one second intervals. Thus we set our burn
times to be 12 seconds for the H135, 10 sec-
onds for the G125, and 8 seconds for the G79.
Video
A video camera was mounted along the side
of our rocket for the two launches on the first
day, and the final launch on the second day.
For the first two launches, the camera did
not record video, thus we spent time trou-
bleshooting the issue and concluded that we
should try out another team’s camera for the
last launch and were able to collect footage
of our rocket’s flight.
GPS Validation
On launch day, we recorded the starting and
ending points of the rocket flight to com-
pare the actual rocket path to those of our
IMU simulations. As can be seen in Fig-
ures 10 and 11, both rocket flights with the
G79W motor moved from the point at left
on the launch pad a considerable distance,
with the windy first flight resulting in a much
greater distance traveled than the calmer sec-
ond flight. This makes sense since the greater
wind should cause the rocket and parachute
to drift farther on the way down.
Figure 10: Flight 1 traveled 240 ± 2 m when flown under 10 ± 4 mph winds from the WSW
[8]
Figure 11: Flight 2 traveled 882 m when flown under 62 mph winds from the W [8]
9

10. Results
We launched our rocket a total of four times,
twice on two days. Our rocket launched and
landed safely each time. However, we only
got meaningful results on the Day 2 Flight 1.
On Day 1, our datalogger stopped recording
during the first flight because the SD card
popped out. We swapped it out a different
datalogger which used a tray to store the SD
card, and we were able to collect data for
flight 2 on Day 1. However, our humidity
sensor was not ready, our pressure sensor
made erroneous measurements because its
positioning too close to the vent hole caused
it to measure much lower pressures than
reality giving unrealistically high altitude
readings. We did obtain good measurements
on Day 2 flight 1, which is analyzed below.
Unfortunately, on Day 2 flight 2, there was
an unusual reversal of acceleration halfway
through the burn which we attribute to the
IMU sliding insiding the payload section, the
dust sensor got disconnected so it read zero
volts during the entire flight, and the humid-
ity sensor probably got damaged from the
previous flight on landing, because it did not
give any meaningful results. All our analy-
sis below is done on data obtained on Day
2 flight 1 of rocket launches, unless stated
otherwise.
Pressure Sensor
With the absolute pressure data, we con-
verted the voltages to pressures using the
calibration curves and then used the hypso-
metric equation described in the Modeling
section to convert these pressures into alti-
tudes above ground level to get the flight pro-
file. For both flights that our rocket made
with the G79W motors, the converted alti-
tude profiles are shown in Figures 12, with
both rockets reaching apogee in ∼7.4 seconds
and landing after ∼45 seconds. Though both
launches reach apogee faster than the pre-
dicted 10 seconds from OpenRocket, we find
that the predicted apogee of 510 meters is
close to the experimental apogees of 506 m
for Flight 1 and 480 m for Flight 2.
Figure 12: Flight 1 (left) traveled 240 ± 2 m when flown under 10 ± 4 mph winds from the
WSW, Flight 2 (right) traveled 88±2 m when flown under 6±2 mph winds from the W [8]
10

11. IMU
With the IMU data, we assume that the
rocket is absolutely vertical while sitting on
the launch pad since the IMU has drifted sev-
eral millivolts in the offset after calibration.
Knowing this, a noise-free second of on-the-
pad data is averaged (with standard devia-
tion) for each sensor to make new offsets for
each launch, taking into account 9.8 m/s2 of
gravity for the two downward sensors. Using
these offsets, we can easily convert voltages
to rotation rates and accelerations through-
out the flight times found from the pressure
sensor in Matlab. For the upward direc-
tion, our IMU has two accelerometers of dif-
ferent sensitivities, and we chose to use the
more sensitive one whenever the acceleration
was below the maximum (3.3V) reading of
the datalogger for more accurate readings.
Then, we rectify the data into the ground
frame by multiplying by an orientation ma-
trix C(t) made from the rotation measure-
ments, where we assume that the starting
orientation is upright, making C(0) = I or
the identity matrix. Then, we find the value
of this rotation matrix through time step by
step using C(t+t) = C(t)+C(t)∗, where we
can use the small angle approximation using
the rotation rates ωx, ωy, and ωz to make δψ
following the methods of Oliver Woodman in
An introduction to inertial navigation [11].
Essentially, we use:
Ω(t) =


0 −ωbz(t) ωby(t)
ωbz(t) 0 −ωbx(t)
−ωby(t) ωbx(t) 0

 (3)
to get the speed of transformation, and
multiply this by the time step to get the
transformation angle where δψ = δt · Ω(t).
Then, we multiply our accelerations in the
body frame by this transformation to get ac-
celeration in the ground frame, so aground =
abody · C(t) and subtract gravity (∼9.8 m/s2
at 855 m ground altitude) from the down-
ward direction. Once this is done, we check
to ensure all of the on-the-pad accelera-
tions now average about zero, since offset
errors propagate quickly through integra-
tion. Then, to get from acceleration to po-
sition, we numerically integrate the accelera-
tion matrix down the time direction using a
Simpson’s rule cumulative integration func-
tion found online, which we used because it
showed smaller random drifts than triangular
integration in testing, and is known to give
better approximations for noisy data since it
uses a quadratic method [12]. Finally, this
position data is plotted in 3D to visualize
the resulting flight path, as can be seen in
Figure 13.
To understand the error in these position
estimates, we used the methods from Ray-
mond Chow’s paper [13] to propagate the
errors for both the accelerometer and gyro-
scope caused by offset bias, scaling coeffi-
cient bias and random noise. The noise esti-
mates for the IMU sensors were taken from
the sensor datasheets, since those were most
conservative, and once calculated these po-
sition errors were added in quadrature for
each point in timeSensorDatasheets. Simi-
lar to the findings from O. J. Woodman’s
paper [11], the most significant error con-
tribution was found to be gyroscope noise,
which added up to a ∼1000 meter positional
uncertainty after only ten seconds. Using
this method, both G77W flights were ana-
lyzed, which produced a reasonable apogee
estimate of 436 ± 1000 m after 10 s of flight
for Flight 1. This flight’s IMU altitude with
error is compared with the pressure altitude
to show their reasonable agreement in Figure
14.
11

12. Figure 13: 3D plot of path taken by rocket in Flight 1 from IMU
Figure 14: Processed IMU data with Err. vs. Absolute Pressure Altitude vs. Time for
Flight 1
12

13. Filtering the position data
For the position data from the pressure sen-
sor and the IMU, it was predicted that me-
dian or mean filtering would lower noise and
have a significant impact on position esti-
mates. Nonetheless, after using 5-wide me-
dian filters which choose the moving median
of 5 points and varying the number of points
chosen greatly (up to 131-wide), very lit-
tle change in the position estimates was ob-
served (only 4 meters after the 10 second
flight). As is shown in Figure XX, this fil-
tering seemed to not be tremendously im-
pactful. Low-pass butterworth filters were
also examined with little success, showing
that the noise was actually not the most sig-
nificant contributor to flight error since the
flights were so short.
Figure 15: For both types of position data, 5-wide median filtering caused little change
Dust Sensor
After flight, we found reasonable readings
from our dust sensor corresponding to par-
ticulates observed in the air. The results
from this reading for Flight 1 can be found in
Figure 16. To further understand this data
in the spatial dimension, this data was plot-
ted against altitude as can be seen in Figure
17. One way to quantify this measurement
would be to take the area under the curve.
The presence of dust will only ever increase
the voltage from the baseline offset voltage
of 1.55 V, so the integration will always give
a positive result. The ascent gives an area
of 9.8777 Vs and the descent gives a voltage
of 15.6661 Vs. This shows us that the sen-
sor read more counts of dust on the descent
than the ascent. We believe this may have
happened for one of two reasons; either the
speed of the dust sensor on the ascent caused
the readings to be more general showing lay-
ers of fog or dust at different heights, or the
descent blew the rocket into the path of pre-
vious rocket emissions where dust was most
concentrated. Unfortunately, our clips for
connecting the dust sensor to the PC board
were not nearly reliable enough for rocket
flight and came unconnected for the second
flight giving us only this holistic comparison.
13

14. Figure 16: Dust Sensor Voltage as a function of time over entire flight (top), ascent (middle),
and descent (bottom)
Figure 17: Dust Sensor Voltage as a function of Altitude over ascent (top) and descent
(bottom)
Humidity Sensor
For the humidity data, we imported the
humidity sensor voltage values versus time
data files into Matlab and converted the
voltages into humidity values using the ex-
perimental sensor calibration described in
the Ground-Based testing section, ensur-
ing that the ground humidity readings were
within ±0.5% of the weather station read-
ings. When converting the voltages to hu-
midity readings, we had to consider that
the HIH-5030-001 sensor output is depen-
dent on the temperature such that True RH
= Sensor RH
1.0546−0.00216T [9]. The temperature as a
function of altitude was given above in Fig-
ure WW. By performing a linear regression
on this data from 0-2000m we were able to
obtain a linear relationship of T = −0.003 ·
Altitude + 17.88 with R2 = 0.999. We then
imported the altitude values versus time ob-
tained from the processing of the absolute
pressure sensor voltage readings. Taking into
account the effect of temperature on the sen-
sor reading, the humidity as a function of
both altitude and time are shown in Figure
18 for both flights that our rocket made with
the G77R motors.
When analyzing the data from the first
flight, the humidity over time increases.
However, the rocket flight had already ended
at about 50s where the humidity starts de-
creasing. We expect the humidity to start
decreasing after the rocket reaches apogee at
around 10s and starts descending because the
humidity increases as the rocket ascends. Al-
though the humidity does eventually return
to the ground humidity at about 32%RH,
this doesn’t occur until well after the rocket
has landed. This indicates a slow response
time of the sensor. Assessing the humidity
over altitude confirms this intuition. During
the ascent of the rocket, represented by the
red part of the curve, the humidity is increas-
14

15. Figure 18: Data from flight 1 shows the slow time constant of the humidity sensor
Figure 19: Data from flight 1 shows the slow time constant of the humidity sensor
ing as expected from the graph of humidity
over time. However, it continues to increase
as the rocket descends, represented by the
dark blue section of the curve. The vertical
drop in the graph at the end of the descent
when the altitude is 0m indicates that the
humidity sensor was reacting to a drop in hu-
midity even as the rocket was on the ground.
This confirms that the sensor?s slow response
time caused the late reading in the drop in
humidity which occurred during descent.
15

16. The reason for this long response time
is that the air velocity is much higher than
the sensor is sensitive to. The quoted re-
sponse time of 5s is only true in slow-moving
air, which Honeywell defines as 5m/s or less
[9]. The air velocities that the sensor is re-
ceiving during the flight are approximately
100-200m/s. We experimentally determined
an estimate of about 70s for the response
time by finding the time for the humidity
sensor to reach 90% of the actual humid-
ity at a specific time. This confirms results
found by Dooley and O’Neal when experi-
mentally determining the effect of airspeed
on RH reading in capacitive sensors. Doo-
ley and O’Neal found that for a RH sensor
that had a response time of 15s for ambient
air, the response time in air moving at 6m/s
had increased to 47s [10]. This is a 35.5%
increase in response time per m/s increase in
air velocity. If we use this relationship for
airspeeds between 100-200 m/s we expect a
response time between 160-350s for the sen-
sor to read 90% of the actual humidity. Al-
though this relationship predicts a slower re-
sponse time than we experimentally found,
it confirms the result that higher air velocity
causes an increased response time. Our find-
ings that humidity increases as altitude in-
creases confirms our expectations. Although
the decrease in temperature as a result of in-
creasing altitude would suggest that humid-
ity should decrease, low-level clouds and val-
ley fog were present the day of the launch.
This would cause the humidity to increase
as the rocket reaches altitudes where these
clouds and fog were present. The humidity
readings with respect to time for the second
flight suggest that the humidity data from
the second flight was corrupted in some way.
The humidity at time=0s shows a negative
value which is impossible especially when
the humidity reading from the weather sta-
tion brought was around 24% at the ground.
This indicates that the sensor may have been
damaged upon impact from the landing of
the first flight, since the sensor was mounted
on the exterior of the rocket.
Conclusion
In conclusion, the engineering objective of
tracking the flight path of the rocket through
space was achieved through the use of an
IMU and absolute pressure sensor. The ab-
solute pressure sensor measured an apogee of
506 ± 5 m and 480 ± 5 m for flight 1 and 2
respectively. The IMU measured an apogee
of 436 ± 1000 m and using a 6-DOF model
plotted the 3-D flight path of the rocket. The
pressure sensor was found to be a more accu-
rate method of predicting apogee when both
the IMU and pressure sensor measurements
were compared to the OpenRocket predic-
tion of apogee at 510m.
The scientific objectives of assessing the
effects of a rocket launch on dust concentra-
tions and measuring humidity against alti-
tude were achieved with less than ideal re-
sults. The dust sensor used successfully mea-
sured counts of dust on ascent and descent
but without further data points to compare
to our readings stand alone. For the humid-
ity sensor used, it was confirmed that humid-
ity rose going through low level clouds but we
also validated an increase in time constant
proportional to rocket speed, making our re-
sults not representative of the particular air
at any given altitude.
For further work, we would suggest im-
proving upon our PC board mount and con-
nections to sensors so launches would be
more reliable in producing readings. To pro-
duce more representative humidity readings,
the humidity sensor should be placed in the
still air inside the rocket to allow for optimal
operation of the capacitive humidity sensor.
Finally, the dust sensor should be tested mul-
tiple times to see if we are able to reproduce
the results, and to determine whether rocket
launches produce noticeable particulate pol-
lution.
16

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17