1. science
2. engineering

Session F2C-4
HOW TO REDUCE THE COST OF TEACHING PHYSICAL
EXPERIMENTATION
Jerry K. Keska
Department of Mechanical Engineering
University of Louisiana-Lafayette
Lafayette, LA 70506
[email protected]
Abstract
Although theoretical and computational tools are inevitable in the teaching of engineering
processes, it is generally accepted that experimental approaches, even though they are
significantly more costly and time consuming, are far superior. In professional activities,
experiments are invaluable necessities when it comes to proving a hypothesis and turning it into
a theory or proof-of-concept for new products or technology under development. However, a
very important question is how to reduce cost of this hands-on approach to teaching physical
experimentation classes. The paper reports the details of the development and implementation of
a solution to the problem of how to reduce some of the barriers, especially costs in laboratory
classes with hands-on physical experimentation. The solution was used in a two-semester
undergraduate class in Instrumentation and Measurements. Application of miniature hardware,
construction of electronic measurement systems on prototyping boards, a combination of
instructed experiments and open-ended projects resulted in a significant cost reduction.
Introduction
In the undergraduate teaching process, only solving simple textbook problems that require little,
if any, imaginative thinking, diminishes the overall efficiency of the students’ learning. These
problems are usually significantly simplified when compared to real-life situations, and more
often than not, they have very limited connections to real world problems. In order to increase
student interest and the student’s own creative, hands-on, problem solving skills, a physical
experimentation class has been developed, which promotes students’ creativity by utilizing openended projects that formulate and investigate realistic, inventive, and complex problems. This
approach not only boosts student enthusiasm, it also aligns classroom topics more closely with
contemporary standard industrial environments and practices.
The most common hurdle in this process is the development of a laboratory and shop base,
which is necessary for the constant process of troubleshooting. The difficulty is that this
development creates a large financial expense as well as a tremendous increase in the teacher’s
responsibilities and time involvement when compared to the demands of a standard lecture or
virtual laboratory class. Oftentimes, obstacles like these force engineering educators to make
compromises and replace laboratory physical experiments with virtual experiments, which are
sometimes performed as blackboard exercises in a lecture classroom. One way to reduce some
of the financial burden is by implementing physical experiments on miniature mechanical
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
systems with prototyping sensors and measurement systems as a part of the laboratory class. All
of these approaches are extremely attractive in today’s “lean” approach to engineering education.
The course development presented here was designed for an undergraduate junior level class that
took place over two semesters for four credits and was done in conjunction with a one-hour
classroom lecture in mechanical engineering. A slightly modified version of this approach,
however, could easily be tailored to all levels of the mechanical engineering technology, or other
engineering based curricula.
This paper reports the details of an implemented solution to the problem of how to reduce some
of the barriers—especially costs—in laboratory classes that include hands-on physical
experimentation. The solution was implemented in a two-semester undergraduate class in
Instrumentation and Measurements. Application of miniature hardware, construction of
electronic measurement systems on prototyping boards, and a combination of instructed
experiments and open-ended projects resulted in a significant cost reduction as well as an
improvement in teaching quality.
Physical Experimentation
Although theoretical and computational tools (including virtual tools) are useful in the teaching
of engineering processes, it is generally accepted that experimental approaches are far superior,
even though they are oftentimes more costly and time consuming. In many cases, experiments
are invaluable necessities when attempting to prove a hypothesis and turn it into a theory.
Experiments are also necessary when trying to implement a proof-of-concept process or during
live tests for a new product or technology. Consequently, it is important for students to conduct
physical experiments so that they have hands-on experience with the types of tools used in
instrumentation and measurements. By doing these activities, students can gain knowledge about
issues such as what sensors and measurements to use, how to develop a feasibility study
program, how to conduct computer-based data acquisition and analysis processes, how to
analyze and validate experimental data for both deterministic and random processes, how to
design experiments, and how to disseminate results. Based on the trend presented in Table 1, the
key issue now in physical experimentation is to expose the students to hands-on approaches to
acquiring dynamic signals. Dynamic signals are a combination of random and deterministic
phenomena that students analyze using computer-aided systems. Students learn to disseminate
results, understand and apply the right tools, and implement their knowledge to solve a problem
in a cost effective way [1, 2, 4]. In particular, the student needs to understand:
a) Why physical experimentation, testing and measurements are crucial in all
engineering disciplines.
b) How physical experimentation, testing and measurements are related to engineering
analysis, design, and product development.
c) How to use basic mechanical, electrical, thermal, and fluid measurements for static
and dynamic processes of deterministic and random phenomena. by using a hands-on
approach.
d) How to gather and analyze experimental data of deterministic and random dynamic
processes using different levels of mathematical and computational tools.
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
e) How to build and use experimentation tools conducting over ten fully-instructed
laboratory experiments, where the lab instruction allows the student to build
computer-aided sensors and measurement systems from scratch on a prototyping
board or using professional systems. The sequence of experiments gradually
develops the student’s knowledge and experience in physical experimentation, testing
and measurements.
f) How to validate experimental results and, based on defined criteria, allows student to
judge the quality of the gathered data and the gathering process.
g) How the measurement process error is an inescapable part of the experimentation
activities. Students also learn to explain what is acceptable and how to control the
limits of acceptability.
h) How to incorporate the computer as a universal tool in all processes: design of
physical experiments, measurement and data gathering, data analysis, and process of
result dissemination (report writing and presentation).
i) How to design and build a working prototype of a completed physical system starting
with the proposal and progressing through the entire process including the feasibility
study and the final presentation of the open-ended project.
j) How to generate the basic laboratory and project communication forms.
k) How to use the basic capabilities and applications of Computer-Aided Tools
including MatLab.
To assist students in the effective study of the subject and to provide guided applications, the two
volume laboratory manuals were published and made available to students [4]. Due to the
importance and necessity of conducting physical experiments, engineering students should
become familiar with physical experimentations as early as possible. This early exposure will
build up clear connections between theory and experiment, resulting in an understanding of the
applied aspects of engineering. In today’s “lean” approach to engineering education and
instruction, administrations probably need to increase the willingness to recognize the
importance of physical experimentation and the costs, the necessary technical base, and the
increasing instructor teaching load required. Also, in physical experimentation, the closed loop
between a cause and result is real not imaginary, and a difference must always be tested and
corrected, which in many cases requires a significant amount of additional work and effort in
comparisons to a standard lecture approach.
Experimentation Systems
Because the key issue in the physical experimentation is to give students hands-on experience in
working with acquired dynamic signals, analyzing and disseminating the results, understanding
and applying the correct tools and implementing the knowledge to solve a problem in a cost
effective way, the first step is to find how to effectively generate such signals. The second step
is to choose the correct approach and to keep the cost as low as possible. The solution is to build
a physical generator of signals that are a combination of random and deterministic components
(see Figs. 1 - 3). To accomplish this solution, the phenomena of two-phase flow are
implemented [3 to 8], where the measured parameter is the spatial concentrations of the twophase flow of air-water mixture in a vertical column using both capacitive and resistive
computer-aided measurement systems. This type of physical generator is easily constructed in a
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
laboratory using a vertical column of water with an air source attached to the base.
Concentration is changed by regulating the amount of air flowing into the column. In this
experiment, a resistive and capacitive sensor is used to collect data and as a concomitant system
for data validation. The data collected are analyzed using root mean square (RMS), probability
Table 1. Trend in Experimentation
Period
Signals
1970s - 80s
deterministic
static
1980s - 90s
deterministic
dynamic
1990s – 2000s
deterministic
dynamic
Tools
calculators
computers
Instrumentation
self contained
Sensors
Presentation
readout and
electronic
output
overhead
Documentation
mostly manual
Reports
typewriter and
dictaphone
self contained
and rack
readout and
electronic
output
overhead computer
manualelectronic
word
processing
computer-aided
systems
self contained
and rack
electronic
output for DAS
2000s and up
composition of
deterministic and
random dynamic
components
computer-aided
systems
mostly rack
computer-aided
electronic
output with
DAS
computer-aided
electronic
electronic only
word
processing
word
processing only
distribution function (PDF), and power density spectrum (PSD) (see Fig. 3). One of the most
important parameters in controlling two-phase flow is the void fraction or its complementary
parameter, spatial concentration. Started in the 1940’s and continuing up until today, two-phase
flow has remained a challenging phenomenon to predict and control because of its random
nature. One aspect of the random nature of the signal is the noise component. The noise can be
filtered by different types of low-pass filters such a physical filter built with a 741 op-amp, and
also by a digital low-pass filter. The acquisition, comparison and calibration of dynamic signals
will show the importance of the experimental approach. This process results in information that
needs to be validated, analyzed and encourages students to be precise in their studies and
experimentation.
The experiments can be conducted by four different kinds of two-phase flow systems. A
comparison of system parameters for all four two-phase flow experimental systems (Full
Research System, Reduced Educational System, Bench Educational System and OEP need to
define OEP System) are show in Table 2. Because all four systems are computer-aided systems
(CAS), their computer cost was not included in the Table 2. Implementing the low cost criteria,
two systems were chosen for completion: Bench Educational System for instructed experiments
and the OEP System. This solution does not compromise quality of physical laboratory
experiments but significantly reduces cost by miniaturizing full-size systems to benchtop
experimental systems and by building electronic systems from “scratch” on prototyping boards.
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
This approach offers the ability to conduct full physical experiments including experimental
feedback, understanding of measurement process and data validation, and reduction of necessary
shop hardware support.
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
Figure 1: View of Experimental Systems. Top - Reduced Educational System. Middle - Bench
Educational System. Bottom – OEP System.
Figure 2: First OEP Hardware Project as a Miniature Version of the Bench Vertical Column
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
c v vs. Time
Voltage vs. Time
3.6
1
c v [-]
V [V]
3.4
3.2
0.5
3
2.8
0
0.5
1
t [s]
1.5
0
2
0
CPSD vs. Frequency
0.5
1
1.5
t [s]
CPDF vs. c v
2
Frequency [%]
CPSD [-]
1
0.4
0.2
0
0
50
f [Hz]
100
0.5
0
0
0.5
c v [-]
1
Figure 3: Example of Generated Signals Generated
.
Table 2: Comparison of Experimental Systems
System Type
Full
Research
System
Reduced
Educational
System
Bench
Educational
System
OEP System
Column
diameter [mm]
50
3.5 - 10
50
3.5 - 10
Instrumentation
Off shelf
Off shelf
Board built by
students
Board built by
students
Sensors
System
hardware
Off shelf
Shop build
Off shelf
Shop build
Student build
Shop build
Student build
Shop build
Column
orientation
Vertical
only
From horizontal
to vertical
gradually
Vertical only
System cost $
28,000
23,000
2,000
From
horizontal to
vertical
gradually
100
OEP Experimentation System
After seven instructed experiments, students write a proposal for an OEP, and they also develop
and build an apparatus that consists of a 6.35 mm square cross section of clear plastic tube flow
channel that serves as both a mechanical and measurement device[4]. The flow channel is
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
mounted on the support wall, which allows it to be rotated about a point from zero to ninety
degree. The air-water mixture is then circulated where water is in a closed loop and the air is
supplied using a compressor (Bubble Box). After the air passes the flow channel it is released
into the atmosphere. The air flow rate is controlled by controlling the voltage powering the
compressor. A combination of resistive, capacitive and optical sensors is mounted in the flow
channel (Fig 2, 4 and 5).
Figure 4: OEP Experimentation System
(a)
(b)
(c)
Figure 5: Details of OEP Experimentation System, (a) Flow Channel Inclined at 60° (b) CrossCorrelation Sensors (c) Slug Flow in Flow Channel
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
The second component of the OEP experimentation system is the electronic measurement and
signal processing systems with the block diagram shown on Fig. 6.
Signal Processing
Capacitive
Signal
AC Bridge
Low Pass
Filter
Flow Channel
DC Bridge
Resistive
Signal
DC Bridge
Data
Acquisition
System
Optical
Signal
Computer Aided
Experimentation
System
Cross Correlation
System
Air Supply
Figure 6: Block Diagram of the Experimentation Measurement System
Data were collected from three different sensors at the same time, i.e. resistive, capacitive and
optical. Calibration values were recorded for air and water only in order to convert voltage signal
to concentration signals (cv), as shown in Fig. 6. After that, the cv signals vs. time were used to
obtain cumulative power spectrum density (CPSD) and cumulative probability density function
(CPDF) plots. An example is shown in Fig. 7.
c v vs. Time
Voltage vs. Time
3.6
1
c v [-]
V [V]
3.4
3.2
0.5
3
2.8
0
0.5
1
t [s]
1.5
0
2
0
CPSD vs. Frequency
0.5
1
1.5
t [s]
CPDF vs. c v
2
Frequency [%]
CPSD [-]
1
0.4
0.2
0
0
50
f [Hz]
100
0.5
0
0
0.5
c v [-]
1
Figure 7: An Example of Signals Obtained and Analyzed from the OEP Experimentation System
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
The entire OEP experimentation setup is developed and built on a prototyping board by students
using simple electronic and mechanical components, as listed in Table 3. The cost of $ 151
represents the most expensive project of the 12 built by students in Fall semester 2010; all 12
projects are shown in Fig. 8. The individual costs ranged from $51 to $ 151.
Table 3: List of All Components Used to Build Discussed OEP System.
Item
½ x 2 x 4 Birch Plywood
Stanley Double Wide Corner Brace
Painter’s Cup
BubbleBox Air Pump
6’ Aquatic Air Tube
Modular IC BreadBoard Socket
Universal Soderless Breadboard
100-Piece ¼ Watt Fixed Carbon-Film Resistors
100K-Ohm Linear-Taper Potentiometer
Cds Photoresistors (5Pack)
Set of 100 Disk Capacitors
LM741CN Operational Amplifier (8-Pin Dip)
Lambro Industries 4” Galvanized Worm Gear Clamp
Real Organized 24” White Double Track Standard
Stainless Steel Wood Screws
½” Aluminum Post (2 per)
GE 2.8 oz Waterproof Silicone
Check Valve
Quarter Turn Ball Valve
Loctite .85 oz. epoxy
Quantity
1
4
1
1
2
2
1
1
1
1
1
1
1
1
1
2
1
1
1
1
Total
Price [$]
12.87
15.08
0.98
15.34
7.18
17.98
19.99
6.99
2.99
2.99
5.49
0.99
1.37
6.74
1.67
1.78
3.94
1.99
7.61
5.18
150.98
Figure 8: View of QEP Experimentation Systems Built by Students in Fall Semester 2010
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
Experimental Results
The voltage signals collected from the resistive and capacitive sensors in the calibration process
were converted to concentration values and then were analyzed in both frequency and amplitude
domains (Fig. 7). Both amplitude domain and frequency domain plots showed differences
impacted by different flow conditions (e.g., Figs. 8 to 10).
0.5
CPSD [-]
0.4
0.3
0.2
90
60
30
0
0.1
0
0
10
20
30
40
50
f [Hz]
60
70
80
90
100
Figure 8: CPSD Plots for va of 80 cm/s and Indicated Channel Inclination.
1
0.9
0.8
Probability [-]
0.7
0.6
0.5
0.4
0.3
42.35 cm/s
79.95 cm/s
125.32 cm/s
162.84 cm/s
0.2
0.1
0
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
c v [-]
Figure 9:
CPDF Characteristics for Flow in Channel Inclined of 90° and Indicated Air Velocities.
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
1
0.9
0.8
Probability [-]
0.7
0.6
0.5
0.4
0.3
42.46 cm/s
80.14 cm/s
125.17 cm/s
162.88 cm/s
0.2
0.1
0
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
c v [-]
Figure 10: CPDF Characteristics for Flow in Horizontal Channel and Indicated Air Velocities.
Conclusions
Based on experience in the development and teaching of physical experimentation classes, the
following conclusions can be derived:
1. Both developed and built experimental bench type systems are characterized by easy
operation and calibration processes and for a very low cost it can be build from “scratch”
by undergraduate students.
2. These systems generate effectively dynamic signals combining deterministic and random
phenomena, validation of data, and are applicable for interfacing into computer-aided
physical experimentation systems.
3. The combination of instructed experiments and OEP approaches, using bench type
systems, gives students a good opportunity for hands-on learning and applying physical
experimentation concepts and tools, how to deal with dynamic data, and how to validate
those experimental data.
4. Building and using this OEP system built from “scratch,” students conduct
measurements, collect and analyze data and gain hands on experience in the following:
calibration process; design and trouble shooting of mechanical and measurement
systems; sensor and transducer constructions; experimental data gathering and analysis
of deterministic and random signals; measurement of concentration, temperature,
displacement, RPMs, pressure, flow rates and velocities, resistance, capacitance, angle,
voltage and current; data analysis and validation using concomitant systems; working in
teams; report writing and presentation.
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education
5. This approach to miniaturization and student involvement in hands-on experience allows
a significant reduction in hardware and shop expenses in the application of physical
experimentation in the undergraduate teaching process.
6. The combination of instructed experiments and OEP approaches, using bench type
systems, gives students a good opportunity for hands-on learning and applying physical
experimentation concepts and tools, how to deal with dynamic data, and how to validate
those experimental data.
References
1. Patrick F. Dunn, Measurement and Data Analysis for Engineering and Science, 2005, McGrawHill.
2. J.P. Holman, Experimental Methods for Engineers, McGraw-Hill, 2001.
3. Keska, J.K. and Wang G., “Mathematical Model for Pressure Gradient Calculation for Air-Water
Heterogeneous Mixture Flow in a Small Square Horizontal Channel Based on the In-Situ
Parameters and Flow Pattern Coefficient”, International Journal of Experimental Thermal and
Fluid Science, ETF 6736, 2005.
4. Keska, J. K., “Physical Experimentation, Instrumentation and Measurements. Laboratory
Manual” Vol. I and II, Lulu Press, 2010.
5. Keska, J. K. and A Chuck Miller, “Experimental Results for Application of Two-Phase Flow in
Micro-Heat Exchangers,” Proceedings of FEDSM99 3rd ASME/JSME Joint Fluid Engineering
Conference & 1999 ASME Fluids Engineering Division Summer Meeting, pp 1-8.
6. Keska, J. K. and B. E. Williams, "Experimental Comparison of Flow Pattern Detection
Techniques for Air-Water Mixture Flow," International Journal of Experimental Heat Transfer,
Thermodynamics, and Fluid Mechanics, Vol. 19, pp. 1-12, 1999.
7. Keska, J. K., M.D. Smith, and B. E. Williams, "Comparison Study of a Cluster of Four Dynamic
Flow Pattern Detection Techniques," Flow Measurement and Instrumentation, Vol. 10, pp. 6577, 1999.
8. Keska, J. K. and R. D. Fernando, "Average Physical Parameters in an Air-Water Two-Phase
Flow in a Small Square-Sectioned Channel," Journal of Fluids Engineering, Vol. 116, pp. 247254, 1994.
Proceedings of the 2011 ASEE Gulf-Southwest Annual Conference
University of Houston
Copyright © 2011, American Society for Engineering Education