1. Peer Reviewed Paper Presented at AlaSim 2015
Simulation of Fluid Mediums for Suction Tube Degradation Testing
Pamela V. O’Neal, PhD, RN, Associate Professor College of Nursing, Matthew Duchock, BS Student Aerospace
Engineering, Robert Hicks, BS Student Aerospace Engineering, Jake Baldwin, BS Student Mechanical Engineering, Adam
Dziubanek. BS Student Aerospace Engineering, and Dr. Daniel Armentrout, PhD, PE, Lecturer College of Mechanical and
Aerospace Engineering
University of Alabama in Huntsville Alabama
ABSTRACT: The College of Nursing at the University of Alabama in Huntsville is working alongside the
Engineering College to develop a data acquisition system (DAQ) that simulates and records the suction loss in suction
connecting tubes as well as the degradation of said tubes over a period of use. The amount of suction at the point of
patient entry may differ from what is shown on the pressure regulator on the wall of the hospital. This can be due to
blockage in the tubes or too long of a length of tubing. The Hagen-Poiseuille equation suggests a relationship between
pressure in the tube and the length (L), diameter (d) of the tube, the viscosity (G) of the fluid. A previous study
evaluating intrinsic suction and simulated secretions has used this equation, but tubing length and suction pressure
were consistent. The proposed study will identify pressure measurements based on different suction tubing length,
different viscosities, and changes in suction pressure, which would be new information with quality and safety
implications to patients who require suctioning. When a patient is anesthetized for an operation, one of the major
dangers to the patient is fluid buildup in the lungs which can lead to pulmonary edema and ultimately to the patient’s
death. Too much suction pressure can be just as harmful as too little, and the current way to test suction patency prior
to connecting a patient to a suction device is for a nurse to put his or her finger over the tube and say “That seems
about right.” This project is designed to eliminate the guesswork by reading the pressure differential between the
patient and the wall monitor for fluids of varying viscosities as well as suction connector tubing of varying length.
1. Background
1.1 Hospital Mainline Vacuum System
Hospitals use a central vacuum system to pull vacuum
pressure from every outlet throughout the building. This
system has a constant vacuum being pulled out of the wall
outlet that is much too high for a patient to withstand. To
control and regulate this pressure, a regulator control
valve is used. This regulator displays its pressure reading
in mmHg and ranges from zero to about 350 mmHg. A
previous UAH engineering team worked on a device to
calibrate these regulators. Even though it was discovered
that the regulators could be calibrated, the pressure felt at
the patient varies greatly instead of pulling at a constant
pressure. Our customer, Dr. O’Neal, was interested in
what actually happens on the patient’s end of a hospital
grade mainline vacuum system, since no in-depth research
has been done on this subject.
1.2 System Components
The MPXV6115VC6U Differential Pressure Sensor from
Freescale Semiconductor was selected because it met
three important criteria: functionality, accuracy and cost
efficiency. Figure 1 is a picture of the sensor from the
manufacturer. The main requirement for this sensor was
that it had to be able to read vacuum pressure. This
pressure sensor could read as low as -853 mmHg, which
was acceptable since the lowest pressure the hospital
suction could produce was -500 mmHg. The pressure
sensor also needed to function in an environment created
by the testing conditions. It would need to continue to
function after possible contact with any of the simulated
fluids. It also had to have a five volt input so that an
Arduino Uno microcontroller could power and read the
sensor without the need for an external power source.
Figure 1: Pressure Sensor [1]
This pressure sensor is accurate to ±1.5% Full Scale
Output (FSO). The Arduino supplied an input voltage of
5 volts to the sensor, and the FSO was found to be 4.5
volts. An analog pin on the Arduino was used to read the
output voltage from the sensor, which was represented as
a ratio out of 1024. Because the FSS was around 4.5
volts, the sensors were actually reading zero pressure
around 926/1024. This value was multiplied by five to
find the actual output voltage. The voltage was converted
to kPa by linear interpolation of the graph provided in
Figure 2. The pressure reading could then be multiplied
by a conversion factor to convert the reading to either psi
or mmHg, depending on the user’s preference.

2. Figure 2: Vacuum Pressure KPa vs Output Voltage
[2]
This sensor is a piezoresistive transducer. A change
in pressure causes a diaphragm to deflect a relative
degree which directly alters the amount of voltage
that can flow through an internal circuit. Figure 3
shows the sensor in its final setup. The sensor was
sealed to a t-joint using heat shrink tubing. The
hospital lines could then connect to either end of the
joint.
Figure 3: Pressure Sensor Setup
Figure 4 shows the system after completion. The
team designed the main housing in Solidedge and
fabricated it with a 3D printer. The LCD gave a
quick reference during the testing procedure. Sensor
one mimicked the wall, and sensor two could be
placed anywhere in-line for measurements.
Figure 4: Differential Suction Monitor Components
1.3 Arduino Code
The code written for the Arduino to perform in this
project focused on two areas: the output of the
pressures to the LCD screen and the output of the raw
sensor data to the serial monitor. The outputting of
pressures to the LCD was much more challenging
than the output to the serial monitor. An analog read
was used to get the value directly from the sensor.
This value was outputted to the serial monitor at the
maximum frequency that the Arduino could run;
however, the LCD monitor needed the sensor value
converted to mmHg and it could not display properly
at a high sampling rate. To handle this, a conversion
factor for the sensor value was used to convert from a
10 bit number to pressure in mmHg. A timer counter
was also utilized to slow the sampling rate down for
the LCD screen to be able to properly display the
data.
1.4 MATLAB Code
Matrix Laboratory (MATLAB) is a multi-paradigm
numerical computing language that is used today by a
large portion of academia as well as engineering and
scientific communities. MATLAB was used to create
a Graphical User Interface (GUI), shown in Figure 5,
for the acquisition and analysis of the data from the
sensors. In short, the GUI script runs an interface
which allows for the user to selectively start and stop
runs for testing. After running the script the interface
pops up. The GUI has a large amount of
functionality including Start, Stop, Plot, Save, and
the ability to choose the COM port that the Arduino
is currently on. The GUI script is separated into sets
of Callback Functions and Create Functions. The
Callback functions only run when the part of the GUI
it relates to has been manipulated or affected. The
Create functions run at the time that the object is
created in the GUI. In order to pass function and
GUI status between the different functions of the
GUI an indicator was created whose text displays the
current state of the GUI and recording functions.
This function takes in the CSV file and plots the data
to a figure and saves that figure to a jpeg with the
same filename. Further analysis including signal
analysis and other methods are done in Excel if
needed.
Figure 5: The Graphical User Interface
2. Testing Methodology
2.1 Calibration

3. The pressure sensors used on this project came
calibrated from the manufacturer with a percent error
of ±1.5%. The initial calibration was done by setting
the sensors to atmospheric (zero) pressure. This was
done by using a correction factor in the Arduino code
for the LCD display and also in MATLAB for the
data recording that set the pressure equal to zero
when no external pressures were applied. Figure 6
shows the line of code in the Arduino sketch that
converted the 10 bit sensor value (a number from 1 to
1023) to pressure in mmHg. This equation was
derived from the data sheet for the sensor. The data
sheet gave a transfer equation given the voltage input
and voltage output. This equation is simply that
equation solved for pressure with the calibrated
constant for error included.
Figure 6: Arduino Conversion Code
Huntsville hospital provided a Meriam Instruments
digital monometer to calibrate the pressure sensors.
All manometers used at the hospital are calibrated
annually. A calibration certificate was obtained from
Onsite Calibration Service Inc. The monometer and
pressure sensor were connected in-line and the
readings were compared. Figure 7 below shows
pressure Sensor 1 in the calibration process. It can be
noted the pressures are not equal in the picture. This
is due to the picture being taken in the middle of the
calibration process. The correction factor in the
Arduino code was once again changed to read zero
pressure calibrated to the manometer. Sensor 2 was
calibrated in the same manor. The pressures were
also viewed at various pressures and the sensor was
once again found to be within .02 psi.
Figure 7: Calibration Process with the Monometer
2.2 Uncertainty Analysis
The data sheet for the sensor as well as the data sheet
for the Arduino UNO list values for certain
components of the total uncertainty. Equation 1,
shown below, was used to calculate the total
uncertainty of the system’s calculated pressure. The
Accuracy (accnom) of the sensor is ±1.5% VFSS or
Voltage Full Scale Span. As described in the data
sheet for the sensor this accounts for linearity
discrepancies and hysteresis. This value was then
converted to mmHg by solving the transfer equation
for the nominal 5V input and 4.6V output and then
again with 5V input and 4.534V output (1.5% of
4.4V subtracted from 4.6V). This gave a nominal
accuracy of 12.939 mmHg. This was further
supported by the 1.725 kPa or 12.939 mmHg error
limits described in the data sheet. The resolution of
the ADC is given as the full scale input of 5V divided
by the steps in the 10 bit number divided again by the
Full Scale Span and multiplied by the Full Scale
Pressure Output. This Full Scale Pressure Output
was found to be -115 kPa or -862.57 mmHg when
converted. This all in turn gave a total uncertainty of
13 mmHg. Although this value is high, the primary
goal of these tests was to prove there is a significant
difference in the pressure felt by a patient compared
to the pressure at a regulator. The uncertainty also
stayed constant. So if a pressure was initially at 140
mmHg it would return back to that value after a test
was run; therefore, the tests showed the accurate
pressure fluctuations between the two.
𝑢 𝑝 = √ 𝑎𝑐𝑐 𝑛𝑜𝑚
2 + 𝑟𝑒𝑠 𝐴𝐷𝐶
2
(1)
2.3 Testing Procedure
The fluids used were analogous to human secretions.
This allowed for testing to be unhindered by
biological hazards. The fluid mediums chosen were
syrup, applesauce, water and air. Syrup and
applesauce were chosen by Dr. O’Neal because a
nurse would be able to easily compare viscosities
from common household fluids that can be found
lying around the house. Each fluid medium was
subjected to three different levels of pressure. The
midrange pressure chosen was 100 mmHg; as this is
the pressure most likely to seen by the patient. The
lower pressure used was 40 mmHg. This was chosen
because this pressure would be used to clear low
viscous fluids as well as clearing around sensitive
tissues. The higher pressure used was 140 mmHg; as
this pressure would clear high viscos fluids. The
hospital suction tubing came in 6ft lengths, so the
lengths tested were 6ft, 12ft, and 18ft. The longer
lengths were used to test for suction degradation.
Occasionally, when a patient is moved to a different
room more hospital suction tubing would be used to
accommodate the move. Sometimes the excess
tubing would not be removed and the patient may

4. have 18ft of tubing when only 6ft would suffice. The
length tests were used to show that using too long of
a tube could have harmful repercussions on the
patient. Except for air, each test that was run used
20cc’s of the test fluid which was injected by a
syringe.
3. Data, Analysis & Results
The following figures and graphs represent some of
the data read for each type of test in the test matrix.
The results followed a similar pattern from test to
test and proved most of the initial assumptions the
group had regarding the effects of fluid viscosity on
the pressure. Each set of graphs is elaborated on
with the limited knowledge the team had. It is noted
that some of the tests actually occurred at lower
pressures than expected. This is due to the regulator
having a hysteresis effect that was unknown when
testing began and due to the fluids not completely
sealing off the tube. The regulator was initially set
at the pressure given for a test and was left that way
for the series of runs for all the fluids in the section.
This means that around 16 tests were ran per setting
of the regulator. The hysteresis effect was an
unknown factor at the time however the results are
still constant for the actual pressure shown by the
pressure sensors.
The first test grouping was at a pressure of 40mmHg
where each of the four test fluids was sucked
through a 6ft section of suction tubing. Figure 8
contains graphs of the first test grouping. Each of
the four tests has unique characteristics, but all
follow the same general path. The sensors overreact
towards the beginning of the graph when the fluid is
introduced. This is due to the syringe increasing the
pressure in the system. For air in this graph,
fluctuations are more easily noticeable because the
values are not as far apart as with the larger
pressures. For applesauce, there was a huge
increase in pressure at sensor one. This is not an
outlier as the data steadily increased and decreased
throughout the peak. The pressure then fluctuates
until it evens out to its steady state. This same
pattern in observed in the syrup and water tests.
Figure 8: First Test Grouping at 40 mmHg

5. For the next series of tests, all four fluids were tested
in the same manner but used 100mmHg of pressure.
The results became easier to read because the fluid
changed at more noticeable peaks due to its more
rapid pace. Figure 9 contains graphs of these results.
The air once again stays relatively constant with
slight changes around 1 mmHg. The applesauce has
a similar pressure loss over time as the 40mmHg test.
The largest and most interesting changes came from
the water and syrup tests. Initially the team thought
of syrup as the most difficult fluid to clear from the
tube. On the contrary, testing has proven that syrup
flows much more easily through the tube compared
to that of the applesauce. The conclusion reached is
that applesauce acts like a non-Newtonian fluid since
it has a more solid state with chunks of material
which was prone to clogging the suction tubing.
Figure 9: Second Test Grouping at 100 mmHg
One thing to note is that the syrup test ran for almost
two minutes vs the applesauce time of only 50
seconds. The reasoning behind this is the applesauce
was almost at a dead standstill during this test. The
test had to be stopped early and the pressure turned to
maximum to clear the tube. Interestingly enough this
is the same method that nurses use to clear overly
thick fluids from the tube. Figure 10 shows the effect
of clearing the fluid at full pressure with stunning
results. The pressure spike when the fluid is released
is great enough to cause damage if the regulator is
not turned down immediately, and for higher
pressures that may not be soon enough. This has led
to discussion of a phase II of the project discussed
later in the report. The syrup and water also behaved
much differently during these tests. The initial
sensor readings are once again erratic, but then
immediately fall into a steady state for both wall and
patient. This steady state then has a spike upward,
similar to the clearing of applesauce. This spike
corresponds with the majority of the fluid being
sucked out of the line which was discovered to be a
quick process once the fluid began flowing into the
waste trap. Once the fluid started going in the trap its
velocity increased at a rapid pace, thus creating the
pressure spike.

6. Figure 10: Fluid Clear at Full Pressure
In the next phase all the fluids were tested at 140
mmHg. The fluids had similar reactions as the
previous tests but with much faster clearing times.
This is shown in the graphs located in Figure 11
Once again the large spike initially in the water test
was caused by a sensor overreacting to the positive
pressure introduced by the syringe and is not an
outlier.
Figure 11: Third Test Grouping at 140 mmHg

7. Another interesting observation for the syrup and
water tests was that after the tube cleared, the
remaining fluid would separate into sections instead
of being a one solid body moving along. All of the
fluid would never completely clear unless the
pressure was turned to maximum. The syrup would
stick to the edges and have longer separate sections
moving at a quick pace, and the water would have
smaller non-sticking sections that moved at a much
more rapid pace. This was due to the viscosity,
adhesion, and cohesion differences between the two
fluids. Viscosity is the fluid’s resistance to flow,
adhesion is its ability to stick to other objects and
cohesion is the intermolecular forces keeping the
fluid held together. Because syrup is greater in all
three categories, the differences between the flow
types after clearance make sense.
The final tests recorded were conducted with air and
water at three different tube lengths all at a pressure
of 140mmHg. This experiment was used to test the
degradation of pressure throughout extended lengths
of tubing. It would make logical sense to have a
pressure drop over the extra length, but no in-depth
testing had been performed to confirm this theory.
Graphs of this test group can be found in Figure 12.
It was noticed that the pressure changed more rapidly
and followed the normal trend for 6 feet of tubing.
As the length of tubing increased, the pressure at the
patient end tended to stay closer to zero and did not
fluctuate nearly as much as the shorter lengths. More
testing with thicker fluids will be a future goal for the
project but this area was not in Dr. O’Neal’s interest
at this time.
Figure 12: Final Test Group at 6ft, 12ft, and 18ft of Suction Tubing

8. The results from the testing show that pressures in
hospital tubes at a patient can fluctuate in a
disorderly manner compared to the regulator at the
wall. Everything from fluid thickness to tube length
can have an effect on the pressure drop. It also
shows that thicker fluids have much more difficulty
travelling through the tubes which leads to the
pressure having to be increased. This can be
dangerous to the patient and the process could use
improving.
4. Conclusion
This system could directly impact the medical field
by creating a device that is useable by nursing staff
to monitor suction pressure in real time. The nursing
staff will be able to adjust the vacuum suction as
needed by the patient that is under care. The data
gathered from this device has the potential to save
lives by alerting the nursing staff when the pressure
loss is greater than it should be. The suction monitor
created is a prototype but plans are being made to
increase its functionality and eventually incorporate
it into medical practice. There is a great deal more
research to be done with this device. A more
powerful DAQ system combined with superior
sensors would enhance the capabilities of this device
and possibly allow it to be used in real-world
applications. This project was funded by students,
however with more funding could progress to the
next level of research capabilities.
5. Acknowledgements
5.1 Dr. O’Neal
A unique aspect about this project is that The
Engineering Team had a customer as opposed to
creating a project. The customer was Dr. O’Neal
from the UAH College of Nursing. She played a
pivotal role in every aspect of this project. Her
previous research and insight gave ideas and
direction for The Engineering Team to head towards.
After she presented us with the conditions for the
project to meet, she gave us access to the tools and
people necessary to complete them. This project
would not have been possible without access to the
nursing research lab and Huntsville Hospital, which
she acquired access to both. Dr. O’Neal also
supplied the hospital suction tubing and manometer
used to calibrate the sensors. On top of giving the
resources needed, she also gave more support and
positive outlook than could ever have been asked. It
was truly a wonderful experience getting to work
alongside her for the duration of the project.
5.2 Dr. Armentrout
The team would also like to thank Dr. Daniel
Armentrout for giving the opportunity to collaborate
with the nursing program. He worked along with Dr.
O’Neal to start this relationship. Dr. Armentrout also
aided in the selection of the sensors used as well as
the calibration and uncertainty analysis performed on
the device. He also granted access to the
instrumentation lab at UAH which provided the team
with the tools necessary to build the monitor. He was
a mentor and kept the team motivated and on a path
to success throughout the project.
References
[1] Digi-Key, "Freescale Semiconductor
MPXV6115VC6U," 20 April 2015. [Online].
Available: http://www.digikey.com/product-
detail/en/MPXV6115VC6U/MPXV6115VC6U-
ND/951851. [Accessed 20 April 2015]
[2] Freescale, "High Temperature Accuracy
Integrated Silicon Pressure Sensor for Measuring
Absolute Pressure, On-Chip Signal Conditioned,
Temperature Compensated and Calibrated," 1
January 2013. [Online]. Available:
http://www.freescale.com/files/sensors/doc/data_
sheet/MPXV6115V.pdf. [Accessed 20 April
2015]
Author Biographies
Jacob Baldwin is a junior at the University of
Alabama in Huntsville pursuing an undergraduate
degree in Mechanical Engineering. He is currently
working in collaboration with the College of Nursing
to develop various devices that will be used in
research applications. The suction monitor project
provided the opportunity to present at Alasim. Jacob
is expected to graduate in Fall of 2016 and his future
career aspiration is working in the defense industry
Matthew Duchock is a junior at the University of
Alabama in Huntsville pursuing an undergraduate
degree in Aerospace Engineering. This project
provided Matthew with the opportunity to present at
the AlaSim conference. This work is an
interprofessional collaboration between the College
of Nursing and the College of Engineering This
project is incorporates the lessons learned from
engineering and applies it to a real world medical
application. This is his first time presenting at the
Alasim conference. He is expected to graduate in Fall
of 2016 and he plans to pursue a carrier with NASA.

9. Adam Dziubanek is a junior at the University of
Alabama in Huntsville pursuing an undergraduate
degree in Aerospace Engineering. He worked on a
project for the College of Nursing in conjunction
with the College of Engineering. This project is
incorporates the lessons learned from engineering
and applies it to a real world medical application.
This is his first time presenting at the AlaSim
conference. He is expected to graduate in Fall of
2016 and he plans to pursue a carrier with NASA.
Dalton Hicks is a sophomore at the University of
Alabama in Huntsville pursuing an undergraduate
degree in Aerospace Engineering. This past semester
he’s worked on this inter-professional collaborative
project with the College of Nursing through his
Principles of Measurement and Instrumentation class.
He hopes to continue his education to the doctorate
level and devote his career to research in exotic
propulsion systems.
Pamela V. O'Neal, Ph.D., R.N. is an Associate
Professor and teaches in the Undergraduate and the
Doctor of Nursing Practice programs in the College
of Nursing. She has a research focus in Assessing
Suctioning Processes to Improve Patient Outcomes.
She has experience in both laboratory work related to
suctioning equipment and bedside clinical research
with adults and newborns. She is President of the
North Alabama Chapter of the American Association
of Critical Care Nurses, Chair of the Institutional
Review Board for UAH, and was recognized as the
Outstanding Faculty in the College of Nursing.
Daniel Armentrout, Ph.D., was the Interim Chair
and lecturer in the Mechanical and Materials
Engineering department at the University of Denver
(DU). He has taught a diverse mix of graduate and
undergraduate courses in mechanical engineering,
general engineering, physics and general science. In
his 14 years at DU, his research concentrated
primarily on the testing and analysis of composite
materials exposed to extreme environments. In 2011
he received the Faculty Pioneer Award for
Scholarship and Leadership at DU and in 2007 the
Best Citizen Award in the School of Engineering and
Computer Science. His employment experience also
includes the Rocky Flats Environmental Technology
Site (1990-1996) where he worked on materials
analysis and waste encapsulation techniques of
radioactive and hazardous waste. He has published
over 35 articles in international journals and 20
conference proceedings. Daniel Armentrout
graduated with a bachelor degree in physics from
Drake University in 1985. He also has MS and PhD
degrees in physics from the University of Denver. He
is a licensed professional engineer in the state of
Colorado.