Download Paper - Asee peer logo

AC 2008-1751: DEVELOPMENT OF EXCEL ADD-IN MODULES FOR USE IN
THERMODYNAMICS CURRICULUM: STEAM AND IDEAL GAS PROPERTIES
Jesse Huguet, University of Alabama
Keith Woodbury, University of Alabama
Robert Taylor, University of Alabama
Page 13.431.1
© American Society for Engineering Education, 2008
DEVELOPMENT OF EXCEL ADD-IN MODULES FOR USE IN
THERMODYNAMICS CURRICULUM: STEAM AND IDEAL
GAS PROPERTIES
Abstract
For engineering graduates entering the job market, experience with appropriate computational
tools and techniques is increasingly necessary. Therefore, the University of Alabama’s
Mechanical Engineering Department is introducing students to computational problem solving
earlier in their college careers by developing Microsoft Excel-based modules to be used as
teaching tools in the sophomore and junior-level thermodynamics and heat transfer courses. The
MS Excel package was chosen as a software platform for this purpose because of its ubiquitous
nature and its ability to utilize Visual Basic for Applications (VBA) macros in a spreadsheet
format.
In the sophomore-level thermodynamics course, much of the class material focuses on the
properties of steam and ideal gases. Therefore, a suite of Microsoft Excel functions to compute
steam and ideal gas properties and assist in analyzing properties of states and processes has been
developed. A number of Excel packages that pertained to the computation of steam properties
were already available in the public domain. In a companion paper1, these packages were
compared and Magnus Holmgren’s Xsteam functions were chosen as a starting point for the
current project.
This paper details the modification of Holmgren’s Xsteam functions for classroom use and the
creation of ideal gas property functions. The Xsteam functions, as packaged by Holmgren are an
excellent collection for daily use in calculating steam properties, but lack key features needed to
solve sophomore-level thermodynamics problems. A broader range of functions has been
developed to cover common combinations of specified or known properties, and particularly, to
provide specific volume relationships. A graphing function is included to allow students to plot
states on various phase-diagrams to better understand the relationship between properties and
state. The development, testing, classroom implementation, and student response to these
functions is discussed.
Introduction
Page 13.431.2
Ubiquitous Nature of Excel
An array of various computational tools has been developed to assist with thermodynamic class
work. Almost every textbook now comes with a disc of executables developed to assist with the
examples found inside. A survey of recent graduates of the University of Alabama revealed that
none utilized any of these textbook-bundled tools. They are written in various languages, run on
various platforms, and are, in some cases, useful only for certain specific examples or
procedures. For a computational tool to be useful in a thermodynamics classroom, it must be
versatile, universal, and accessible. It must be able to run on any PC with minimal setup, must
be useful in solving the wide range of problems posed by the course, and must be easily accessed
from any computer on campus and off.
Microsoft Excel was chosen to be the platform for the development of such a tool due to its
programmability, spreadsheet functionality, visual analysis tools, and wide availability. The
Microsoft Visual Basic editor provides the programming tools needed to develop problemsolving algorithms. Spreadsheets allow data to be stored and displayed in convenient order and
provide a means of applying programmed algorithms. Excel’s graphing capabilities allow data
to be displayed in different and sometimes more intuitive ways. Most importantly, the Microsoft
Office Package is a virtually universal PC application allowing the developed tools to be used in
classrooms, labs, and home offices and to move with students to the workplace.
There are two popular ways to package a set of functions in Excel for use by a broad audience.
In order to understand and compare them, one must understand how Excel handles User Defined
Functions. In the Visual Basic editor, coded routines are stored in Macros, which are applied to
one specific workbook. Once a function is coded into the Macro it can be called on any
spreadsheet within that specific workbook. One simple way to package functions for a class is to
make the workbook containing the macros available to them. This method requires that the
students use that workbook anytime they want to use the coded functions. While this is a simple
way to share functions, limiting their use to a single workbook is not preferable. Excel offers a
more widespread sharing capability through the use of Add-Ins. An Add-In is an Excel
workbook that has been saved in Excel 2003 with the .xla file extension, or .xlam in Excel 2007.
When a workbook is saved as an Add-In, the coded macros are still intact. Add-Ins can be
applied or disabled from the Excel Add-In menu, and when one is applied all of the coded
routines and functions in the macros it contains are available to any spreadsheet in any
workbook. Also, once “added-in”, the functions remain available in the user’s customized Excel
environment in subsequent Excel sessions, until such time as it is explicitly removed. The AddIn format increases the functions’ availability in the Excel environment and is thus optimal for
this project.
Selection of the Holmgren Functions and Xsteam’s Capabilities
Problems concerning water and the calculation of its states and properties are a large portion of
the sophomore level thermodynamics course. A great deal of time is spent analyzing tables of
property data pertaining to the states of water. In many problems, interpolation is required to
find data not listed explicitly. An array of functions that could be called in a spreadsheet to find
this data would simplify the process, provide more accurate answers, and reduce time spent on
repetitive tasks.
Upon searching the public domain, a number of Excel macros providing steam property
calculating functions were identified. Of these, Magnus Holmgren’s Xsteam compilation was
chosen as a starting point due to its wide range of property functions, and their logical naming
scheme. A companion paper by Chappell, et al.1, discusses this comparison and the selection of
Holmgren’s Xsteam. A list of the functions available in version 2.5 of Holmgren’s Xsteam is
provided in Table 1. Each function can be called in a spreadsheet to calculate the first property
in its name using the required input properties that follow the underscore. For example, the
function s_Tx(100, .25) will return the specific entropy of water at a temperature of 100°C with a
quality of 25%. Holmgren based his property-calculating functions on the equations of state
adopted in 1997 by the International Association for the Properties of Water and Steam.2
Page 13.431.3
"
Table 1
Functions Included in X-Steam Version 2.5
Te mpe rature , T
Tsat_p
T_ph
T_ps
T_hs
Spe cific Entropy, s
sV_p
sL_p
sV_T
sL_T
s_pT
s_ph
Density, rho
rhoV_p
rhoL_p
rhoV_T
rhoL_T
rho_pT
rho_ph
rho_ps
Spee d of Sound, w
wV_p
wL_p
wV_T
wL_T
w_pT
w_ph
w_ps
Surface Te nsion, st
st_T
st_p
Prandtl Number, pr
pr_pT
pr_ph
Specific Inte rnal
Energy, u
uV_p
uL_p
uV_T
uL_T
u_pT
Pressure , P
psat_T
p_hs
p_hrho
Specific Enthalpy, h
hV_p
hL_p
u_ph
hV_T
u_ps
hL_T
Specific Volume, v
h_pT
vV_p
h_ps
vL_p
h_px
vV_T
h_Tx
vL_T
h_prho
Thermal
v_pT
Conductivity, tc
v_ph
v_ps
tcL_p
Specific Isochoric
tcV_p
He at Capacity, Cv
tcL_T
CvV_p
tcV_T
CvL_p
tc_pT
CvV_T
tc_ph
CvL_T
tc_hs
Dynamic Viscosity, my
Cv_pT
Cv_ph
my_pT
Cv_ps
my_ph
Specific Isobaric
my_ps
He at Capacity, Cp
Vapour Fraction, x
CpV_p
x_ph
CpL_p
x_ps
Vapour Volume
CpV_T
Fraction, vx
CpL_T
Cp_pT
vx_ph
Cp_ph
vx_ps
Cp_ps
V represents saturated vapor properties.
L represents saturated liquid properties
Page 13.431.4
Additional Requirements for Thermodynamics I
While the Xsteam package provides an excellent array of property calculating functions, they
lack some key features required for practical classroom use. The units for pressure are not
consistent with the units regularly used in the classroom or the tables in the course textbook,
Thermodynamics: An Engineering Approach 6th Edition by Cengel and Boles3. The function
list allows for a number of property combinations, but has no functions utilizing specific volume
as an argument. When selecting a function from Excel’s Insert Function icon, there are no
descriptions of the functions to assist the user in finding the correct one or knowing what the
arguments should be. The package also lacks a tool for graphical representations of properties
and states.
Water is not the only substance of importance in the sophomore level thermodynamics course.
Ideal gases and problems relating to their properties are also covered extensively. The tables
containing properties of ideal gases range across fifteen pages in the current thermodynamics
text. A complete package of functions to calculate temperature-dependent specific heats,
specific enthalpy, specific entropy, and specific internal energy would make finding the
properties of ideal gases far less time-consuming and have all the other benefits of the Xsteam
functions and the Excel platform.
Software Development
Pressure Units
In the Xsteam package, the units for all pressure arguments and outputs is bar. While bar is the
standard SI unit for pressure, kPa is used in all of the tables in the Cengel and Boles3 text. As
there are 100 kPa in one bar, the conversion is not a complicated one, but due to the frequency of
the use of pressure in the functions and the large number of internal functions, a complete
conversion of every pressure value to kPa was not sensible. Instead, the conversions were made
only to the initial arguments of the public functions for which pressure was an argument and the
final outputs for functions that output pressure. For the functions for which pressure was an
argument, a line of code was added to the beginning of the function that divided the value of the
pressure supplied by the user in kPa by 100 to change it to bar. The remainder of the function
was left as written. Similarly, for the functions for which pressure was an output, a line of code
was added to the end of the function multiplying the final pressure value by 100 to change it
from bar to kPa. Using this method, a major overhaul of the whole suite of functions was not
necessary.
Additional Functions
In the sophomore thermodynamics course, specific volume is often one of the known properties
of a particular state of water, especially when dealing with constant volume, constant mass
processes. Therefore, a range of functions using specific volume as an argument is necessary for
a useful thermodynamics computational suite. With this in mind, the functions listed in Table 2
were added to the Xsteam package.
Page 13.431.5
Table 2
Additional Functions with Specific Volume Arguments
T_pv
p_Tv
h_pv
h_Tv
s_pv
s_Tv
u_pv
u_Tv
Developing some of these functions was simply a matter of using one or more existing functions.
For example, Xsteam already contained the functions T_ph and h_prho. Knowing that the
density, rho, was the inverse of the specific volume, v, and using these two pre-existing
functions, T_pv was easily established. Similarly, h_pv utilized the pre-existing h_prho, s_pv
was found with h_prho and s_ph , and u_pv required h_prho and u_ph.
Unfortunately, Xsteam did not contain a function with temperature and density similar to the
h_prho function which allowed such smooth development of the pressure-specific volume
functions. However, functions of pressure and temperature already existed for specific enthalpy,
specific entropy, and specific internal energy. Therefore, successfully developing a p_Tv
function would allow similarly simple algorithms for the rest of the functions. Since the T_pv
function was successfully implemented and tested, it could be used to find pressure by iteration
when temperature and specific volume were known. An interval halving process could be used
to check the value of T_pv over a range of pressures until it was approximately equal to the
temperature supplied as an argument. A simple interval-halving function was developed with a
single pressure range, and failed to produce results for nearly the entire range of temperatures
and specific volumes. It was quickly realized that the function T_pv could not provide results
for any arbitrary pressure-specific volume combination: the p,v pair must lie in the state space. If
an “illegal” state is supplied to T_pv, the function is programmed to return an error, which
results in a “#VALUE!” in the spreadsheet cell.
In order to work around this behavior, the error trapping ability of Visual Basic was employed.
By coding “ON ERROR RESUME NEXT” inside the new p_Tv function, the returned
“#VALUE!” error was suppressed, and corrective action could be taken by the program.
Through experience, it was learned that the error in this instance occurred because the guessed
maximum pressure in the interval halving scheme was too high. Thus, when an error return
occurred, the maximum pressure in the interval was reduced to 90% of its prior value, and the
T_pv function call was re-executed. This allowed the p_Tv function to successively shrink the
range of pressure, until a valid range was established. From that point on, the interval halving
routine could determine the correct pressure.
Page 13.431.6
Help Descriptions
Excel provides multiple avenues for applying descriptions to user-defined functions such as the
Xsteam functions; however, the only completely reliable method is through the Macros menu.
By entering the function name and selecting Options, a description for that macro can be
inserted. Descriptions were created for every function in Xsteam which define the property the
function calculates and the input arguments, along with their respective units. These descriptions
are vital for users who are unfamiliar with the range of functions provided by Xsteam. Using the
Input Function tool, they can navigate the full list of useful functions and see what each one
requires and provides.
Graphing Functions
Pressure-specific volume (P-v), temperature-specific volume (T-v), and temperature-specific
entropy (T-s) diagrams are used regularly in the thermodynamics course to plot states and
processes and to better understand the phase of a substance. These graphs include a saturation
curve that divides the graph into regions corresponding to three states of interest; compressed
liquid, saturated mixture, and superheated vapor. Individual states and phases are then plotted to
show their phases and their relationships to other states. In the classroom, these graphs are
drawn by hand with vaguely approximate curves and points. Developing an editable, electronic
version to replace these approximate, hand-sketched plots increases the reliability and visibility
of the data.
Three graphing sub-functions were created in Excel to draw P-v, T-v, and T-s diagrams. These
functions create two arrays of points from which the saturation curve is drawn. For the P-v
diagram, one array stores a range of pressure values while the other stores the values of vV_p
and vL_p. These two functions are the saturated vapor and saturated liquid specific volumes
calculated from the known pressure. The graphing function then creates an x-y scatter plot and
inputs the two arrays as the x and y values of a series. In order to clearly see the compressed
liquid portion of the diagram, the x-axis must be on a log scale. The graphing function
automatically makes this and a few other cosmetic formatting changes. The result of this
function is shown in Figure 1a. The T-v and T-s graphing functions perform the same
operations, but record an array of temperature instead of pressure and, in the case of the T-s
diagram, use sV_p and sL_p for the saturation curve. Figures 1b and 1c show the results of these
functions.
Once one of these graphs is created showing the saturation curve, users will want to plot points
on it corresponding to the states they are interested in. To allow this functionality, three sub
functions were created, one for each type of diagram. These functions pop up a set of message
boxes asking for the values of the two properties at the state the user wishes to plot and creates a
new series on the graph with these inputs as the x and y values. Figure 2 shows a P-v plot with a
point corresponding to 5000 kPa and 0.005 m3/kg.
Page 13.431.7
25000
Pressure (kPa)
20000
15000
10000
5000
0
0.001
0.01
0.1
1
Specific Volume (m^3/kg)
Figure 1a
4
3
Temperature (C)
3
2
0
5
0
5
2
0
1
5
1
0
5
0
0
0
0
0
0
0
0
0
0
2
4
6
8
1
0
Specific Entropy (kJ/kgK)
Figure 1b
Page 13.431.8
400
350
Temperature (C)
300
250
200
150
100
50
0
0.001
0.01
0.1
1
10
100
Specific Volume (m^3/kg)
Figure 1c
Figures 1a,b,c. The P-v, T-s, and T-v diagrams created by running graphing
functions programmed into the Xsteam compilation. Running these functions
automatically produces the saturations curves and formatting seen in the figures.
In thermodynamics, constant pressure and temperature processes are often discussed and plotted
on various diagrams. A function was created for each of the three previously discussed diagrams
to draw a line of constant pressure on the temperature diagrams and constant temperature on the
pressure diagram. The sub function ‘pvtemperatureline’ plots a constant temperature line on a Pv diagram by first displaying a message box requesting the temperature the user wishes to plot.
As with the saturation curve, it then creates two arrays: one fills with the pressure range values
and the other stores the value of v_pT for each of these pressure values and the temperature input
by the user. These arrays are added to the graph as a series and the data markers are removed to
show a curve. Figure 2 shows a constant temperature line corresponding to 300°C added to a Pv diagram. The functions ‘tvpressureline’ and ‘tspressureline’ follow the same logic as
‘pvtemperatureline,’ to plot constant pressure lines on a T-v or T-s diagram simply changing the
y-axis property and value range and using v_pT and s_pT to fill in the x-axis array.
Page 13.431.9
25000
Pressure (kPa)
20000
15000
10000
5000
0
0.001
0.01
0.1
1
10
Specific Volume (m^3/kg)
Figure 2. P-v diagram showing a point added with the ‘pvaddpoint’ function
corresponding to a state at 5000kpa and 0.005 m3/kg and a constant temperature
line added with the pvtemperatureline function corresponding to 300°C
Ideal Gas Functions
As with the steam, a large amount of property data is provided in tabular form in
thermodynamics texts for ideal gases. There are seven particular ideal gases that are of interest
here; air, carbon monoxide, carbon dioxide, hydrogen, water, nitrogen, and oxygen. Cengel and
Boles3 provide a useful starting place for the calculations of ideal gas properties by supplying, in
Appendix 1, Table A-2, a polynomial form of temperature-dependent isobaric specific heats.
潔拍椎 噺 欠 髪 決劇 髪 潔劇 態 髪 穴劇 戴
Page 13.431.10
The coefficients for each gas were written into a macro and the third-order polynomial was
divided by the gas’ molecular mass to provide an equation for specific heat. The table in Cengel
and Boles3 reports an applicable temperature range of 273 K to 1800 K with max errors in the
range of 0.53% to 1.19%. A similar table in Sonntag, Borgnakke, and Van Wylen4 provides
different, simple polynomial equations for temperature-dependent specific heats, but reports a
much larger applicable temperature range, 300 K to 3500 K with max errors in the range of
0.30% to 0.60%. In order to increase the range of applicability of the functions, two ifstatements were used to apply the Cengel and Boles3 equations to a temperature range of 200K to
1800K and the Sonntag et al.4 equations to the range of 1800 K to 3500 K. Though the
applicable temperature range was specified in the tables as 273 K, a true replication of the tables
required that temperatures as low as 200 K be considered. Functions implementing these
temperature ranges were written and tested. The values for the properties, specifically h, s, and
v, were compared to the table values and were found to be within the relative error required by
the project. Therefore, the use of the polynomials at the lower temperatures was justified. The
reason for not using the Sonntag et al.4 equations alone will be explained when specific enthalpy
is considered.
In addition to specific heat, specific enthalpy, specific entropy, and specific internal energy are
required to successfully duplicate the tables. From Cengel and Boles3 we know that specific
enthalpy is the integral of specific heat or in the case of the temperature dependent:
決
潔
穴
欠岫劇 伐 劇待 岻 髪 に 盤劇 態 伐 劇待 態 匪 髪 ぬ 盤劇 戴 伐 劇待 戴 匪 髪 ね 盤劇 替 伐 劇待 替 匪
潔拍椎
月 噺 豹 穴劇 髪 月待 噺
髪 月待
警
警
A similar equation was developed from the Sonntag et al.4 polynomial equations for specific heat
and both sets of equations were tested against values from the Cengel and Boles3 property tables.
It was found that at low temperatures, under 1800K, the relative errors for the Cengel and Boles3
polynomials was less than the relative errors for the Sonntag et al.4 equations. Therefore, to
improve the average relative errors of the functions, the Cengel and Boles3 equations were used
for temperatures in the range of 200K to 1800K and the Sonntag et al.4 equations for 1800K to
3500K. The only exception to this process was the equation set for air. As air is not included in
the Sonntag et al.4 table, its equations were taken completely from the Cengel and Boles3 specific
heat equation.
Cengel and Boles3 also provides a similar equation for change in entropy:
嫌 伐 嫌待 噺 嫌ソ 伐 嫌待 ソ 伐 迎 غ
鶏態
穴劇
鶏態
噺 豹 潔椎
伐 迎 غ
鶏怠
劇
鶏怠
For the purpose of duplicating the tables, only the temperature dependent part of the equation is
of interest. The temperature dependent entropy is signified by the “°” symbol and is calculated
with the integral portion of the right hand side of the above equation. Considering the
polynomial equation for cp, the formula for the temperature dependent part of the specific
entropy can be written as:
潔
穴
欠 غ岫劇 伐 劇待 岻 髪 決岫劇 伐 劇待 岻 髪 に 盤劇 態 伐 劇待 態 匪 髪 ぬ 盤劇 戴 伐 劇待 戴 匪
潔拍椎 穴劇
嫌ソ 噺 豹
髪 嫌待 ソ 噺
髪 嫌待 ソ
警 劇
警
As with specific enthalpy, a similar equation was derived for the temperature dependent part of
the specific entropy from the Sonntag et al.4 relations. If statements were used to apply these
equations to their appropriate temperature ranges.
A definition for specific internal energy is derived from the definition of enthalpy and the ideal
gas equation of state.
月 噺 憲 髪 鶏懸
憲 噺 月 伐 迎劇
Page 13.431.11
鶏懸 噺 迎劇
The equations for specific enthalpy and entropy include a reference state with the zero subscript.
In order to match the results with the tables in the text, the first non-zero data point in the tables
was used as the reference. Thus the air equations drew their reference at 200 K while the other
gases started at 220 K.
Once functions for specific isobaric heat capacity, specific enthalpy, specific entropy and
specific internal energy were written for each gas, the functions were tested. By evaluating each
function for each gas over a range of various temperatures and comparing the results to data
taken from the Cengel and Boles3 tables, the relative error could be calculated for the functions.
Table 5 shows the test chart for air. Of all the data points tested, the largest relative error came
from the specific internal energy function for oxygen at 0.618%. Consequently, that function
posted the largest average percent error at 0.286%. Every function matched the table with better
accuracy than our desired 0.5%.
Table 4
Test Values for the Air Ideal Gas Functions
T
K
200
300
400
500
600
700
800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800
2000
2200
2250
Cp
kJ/kgK
0.9900
1.0038
1.0197
1.0372
1.0561
1.0758
1.0960
1.1162
1.1361
1.1552
1.1732
1.1896
1.2040
1.2161
1.2253
1.2314
1.2338
1.2262
1.1993
1.1892
Table
h
kJ/kg
199.97
300.19
400.98
503.02
607.02
713.27
821.95
932.93
1046
1161.1
1277.8
1396
1515.4
1636
1757.6
1880.1
2003.3
2252.1
2503.2
2566.4
AVG
Calculated
h
kJ/kg
199.9700
299.6384
400.7937
503.6248
608.2802
714.8673
823.4531
934.0635
1046.6841
1161.2595
1277.6938
1395.8501
1515.5511
1636.5785
1758.6734
1881.5362
2004.8267
2251.1255
2494.0327
2553.7531
% diff
0.0000
0.1838
0.0465
0.1202
0.2076
0.2239
0.1829
0.1215
0.0616
0.0163
0.0075
0.0086
0.0087
0.0372
0.0628
0.0764
0.0762
0.0433
0.3662
0.4928
0.103
Table
u
kJ/kg
142.56
214.07
286.16
359.49
434.78
512.33
592.30
674.58
758.94
845.33
933.33
1022.8
1113.5
1205.4
1298.30
1392.7
1487.2
1678.7
1872.4
1921.3
Calculated
u
kJ/kg
142.5694
213.5375
285.9926
360.1235
436.0785
513.9654
593.8508
675.7610
759.6813
845.5565
933.2905
1022.7465
1113.7472
1206.0743
1299.4690
1393.6316
1488.2217
1677.1200
1862.6267
1907.9969
0.0000
0.2487
0.0585
0.1762
0.2987
0.3192
0.2618
0.1751
0.0977
0.0268
0.0042
0.0072
0.0204
0.0551
0.0900
0.0669
0.0687
0.0941
0.5220
0.6924
Table Calculated
s0
s0
% diff
kJ/kgK
kJ/kg
1.29559 1.2956 0.0000
1.70203 1.6995 0.1473
1.99194 1.9904 0.0764
2.21952 2.2198 0.0129
2.40902 2.4106 0.0641
2.57277 2.5748 0.0801
2.71787 2.7198 0.0709
2.84856 2.8501 0.0524
2.96770 2.9687 0.0334
3.07732 3.0779 0.0182
3.17888 3.1792 0.0094
3.27345 3.2737 0.0090
3.36200 3.3624 0.0133
3.44516 3.4459 0.0227
3.52364 3.5247 0.0312
3.5979
3.5992 0.0367
3.6684
3.6697 0.0352
3.7994
3.7994 0.0013
3.9191
3.9152 0.0988
3.9474
3.9421 0.1350
0.144
0.045
% diff
Implementation
Page 13.431.12
As stated in the introduction, one of the key requirements of a classroom software package is that
it be accessible. Three key factors contribute to the accessibility of this style of computational
course package: in-class training, individual experience, and external availability. In order for
the software to be considered useful in the classroom, it must be discussed there. Simply
providing the software and expecting the students to utilize it on their own is not practical.
While such in-class training is imperative for introducing the student to the software, this
introduction is not sufficient for thorough familiarization. The best way to promote the usage of
such a tool is to force the students to utilize it on an individual basis. As class time is limited,
this individual use is most practically gained through assigned homework problems. It follows
then that the software must be made available outside the classroom.
For a student, understanding how the property functions are useful requires an understanding of
how the tables in the text are used. Therefore, the theory of the tables and their use was
introduced in classroom lectures. Once this theory was taught, the Excel functions were
introduced through in-class examples. The advantage of using Excel is not only its
programmability, but also the organization and data manipulation provided by its spreadsheet
platform. Students were taught how to utilize these spreadsheet tools to make their work
organized and explicit. Students were expected to define the problem, provide a schematic, and
state their variables and answers using a four column system. The spreadsheet from an in-class
example problem utilizing these formatting guidelines is shown in Figure 3. The first column
states the variable name, the second contains the value of the variable, the third displays its units,
and the fourth either cites the source of the variable’s value or displays the equation used to
calculate it.
These formatting tips were not only introduced in the in-class examples, but following them was
also required in the assigned homework. Requiring the use of Excel and the property functions
in the homework met three important goals. First, the students gained the experience with the
Xsteam and ideal gas functions necessary to understand their use. Secondly, they learned how to
organize an Excel spreadsheet so that it was more than random numbers on a page. Finally, they
gained that introduction into computational problem solving that was the purpose of this project.
To address the availability issue, textbook-bundled software packages provide a hard copy that
can be installed or accessed on any computer, but this requires the students to have the storage
disc available when they wish to use it. The unparalleled availability of the internet makes
online posting a far more accessible means of storage. Posting the functions on a website as
Excel Add-Ins allowed them to be downloaded and applied to Excel on any computer. A
website5 was designed for the project and hosted on a University server. Each Add-In was
posted on a page for the course it was written for and a description of its contents was given. A
tutorials page was developed to post PowerPoint files in which the use of each Add-In and other
general Excel tips were explained.
Student Response
Page 13.431.13
Descriptive data was gathered in order to gage the opinion of students being taught the Exceldriven thermodynamics curriculum through two avenues; online surveys and focus group
discussion. Survey questions were designed through discussions with the project’s educational
consultant to sample student opinions regarding their experiences. Two web-based surveys were
distributed to the thirty students in the class. One was given approximately 80% into the course,
the “Midterm” survey, which received nineteen student responses. A sample of these questions
Figure 3. Example problem woorked in classs showing good
g
formattting
practiices and use of the XSteam functionns.
Page 13.431.14
and the distribution
d
of
o their answ
wers are show
wn in Figuree 4. When asked about thheir current level
l
of experttise in using Microsoft Excel
E
for genneral purposees, the vast majority
m
repoorted adequaate to
good, witth only one respondent
r
s
selecting
minnimal. Mostt students also felt that thhey knew thhe
steps neccessary to solve a thermoodynamics problem.
p
Thhe vast majority of studeents agreed or
o
strongly agreed that student
s
achieevement in thermodynam
t
mics is direcctly related too their teachher’s
effectivenness in the teeaching of appropriate
a
e
engineering
c
computation
nal tools. Whhen asked iff they
felt that it
i was difficu
ult to explainn how to usee an engineerring computtational tool in solving a
problem in thermody
ynamics, a quuestion desiggned to meaasure the leveel of comforrt students haad
Figure 4. Selected results from the Midterm survey.
Page 13.431.15
with the tool, most were unsure, but a few more agreed rather than disagreed. The vast majority
agreed or strongly agreed that Microsoft Excel is useful in solving engineering problems. When
asked whether using Excel to solve engineering problems required more effort than benefit
gained, about a third (31.6%) of students strongly agreed or agreed, but the majority (52.7%)
disagreed or strongly disagreed with that sentiment.
A second survey was conducted 110% into the course, the students being invited via e-mail to
participate in the web survey after the final exam. This “final” survey received a lower response
rate (14/30) than the Midterm survey. A selection of questions from the survey, along with the
distribution of responses, is seen in Figure 5. In this survey, all students rated their expertise
with excel as very good or adequate. 92.9% agreed or strongly agreed that Excel could be a
useful computational engineering tool, and, not seen in Figure 5, 85.7% agreed or strongly
agreed that it could be used as an effective organizational tool. The vast majority (78.6%) agreed
or strongly agreed that they knew the steps necessary to solve a thermodynamics problem. When
asked if they felt the use of Excel inhibited their ability to learn material in the Thermodynamics
I course, two out of three (64.3%) disagreed, and only one respondent (7.1%) agreed.
Interestingly, the majority (57.1%) agreed that use of Excel expanded the range of
thermodynamics problems they can solve, although 28.6% disagreed. Not shown in Figure 5,
92.8% of students agreed or strongly agreed that using Excel as an organizational workbook for a
collection of engineering problems was a good idea, and 85.7% agreed or strongly agreed that
they learned a lot about Excel in Thermodynamics I. 82.8% of students agreed that Excel was
useful in solving engineering problems. When asked whether using Excel to solve engineering
problems required more effort than benefit gained, the majority (60%) disagreed or strongly
disagreed, although a significant minority (28.5%) agreed.
A focus group was recruited to gather more in-depth and personalized responses than those
resulting from the online surveys. Four students were chosen to join the group by a graduate
student who had no previous classroom exposure to them. The students were chosen to represent
a range of grades earned in the course. That graduate student also moderated the discussion,
beginning with a question about the students’ level of expertise coming into the class. The
students had a range of responses from one student with no experience at all to one who had used
Excel extensively at his job. The other two students were comfortable with simple tasks in
Excel, but not extensive use. The consensus was that their classroom experience increased their
expertise with specific mention given to the organization skills taught in formatting
requirements. Even the student who had used Excel frequently at work expressed wishes that he
had known some of the practices taught in the class when he was preparing his reports. The
moderator asked about their experiences with the property calculating functions specifically.
The students agreed that becoming familiar with them was arduous, but after that familiarization
was complete, the functions made property calculation far more efficient. They also made it
abundantly clear that removing the need to interpolate was appealing. When questioned about
the Add-In format and its usability, they agreed that being able to use the functions on any
spreadsheet is ideal. They did not like the idea of having one macro-enabled workbook housing
the functions. The moderator turned the discussion to the in-class examples, and received two
unanimously voiced complaints. First, students expressed a desire for more full length examples.
Then they discussed some of the difficulty in following and reproducing a computational
example on their own console. These stemmed mostly from their inexperience in comparison to
the professor. If the professor moved too fast for them to follow, or if they made an error in their
Page 13.431.16
Page 13.431.17
Figure 5. Selected results from the Final survey.
typing on a specific equation, they could get caught up in trying to fix their spreadsheets to
match the example and miss out on the next steps. They insisted that posted in-class examples
on the course website were invaluable resources and helped assuage these issues.
Utilizing this data for hypothesis generation is not a straightforward process. The lack of data
from a control group and from the class at the beginning of the course prohibits quantifying the
program’s success or failure. However, there are some generalities that can be expressed.
Responses from the final survey and focus group imply that the introduction to computational
problem solving, the main intention of the project, was successful. Though the majority of
respondents do not think the use of Excel is “more trouble than it’s worth”, there are some that
disagree. Finally the vast majority of students approved of Excel as a tool for solving and
presenting the results of engineering problems.
Conclusion
Toward the goal of familiarizing students with computational problem solving earlier in their
college career, a suite of Excel functions pertinent to use in a sophomore thermodynamics course
has been developed. The functions written for steam and ideal gas properties can successfully
duplicate the textbook property tables with better than 0.5% relative error. They provide a wide
range of useful property calculation and graphing tools to better understand property-state
relationships.
Students were introduced to these functions in the classroom through example problems and
were required to use them on their own in assigned homework problems. Using these functions
and the Excel spreadsheet platform students were able to produce well formatted solutions to
thermodynamics problems. Making the functions available to them through website posting
allowed easy accessibility from any computer with an internet connection.
Student opinions concerning the introduction to Excel, the use of it with in-class examples, and
their experiences with it in the homework were recorded.
Future Work
More work is required to develop a completely comprehensive set of computational tools for the
thermodynamics courses. As the project continues, similar property functions will be written for
HFC-134A. Further development of the graphical analysis tools will eventually result in a more
user-friendly menu of functions including the plotting of commonly encountered processes and
cycles. Development of more sophisticated polling methods and data collection will make the
process of rating the success and feasibility of this project more reliable.
Acknowledgement
Page 13.431.18
This material is based upon work supported by the National Science Foundation under
Grant No. DUE-0633330. The authors gratefully acknowledge support from this NSF
award.
Disclaimer
Any opinions, findings, and conclusions or recommendations expressed in this material arethose
of the author(s) and do not necessarily reflect the views of the National ScienceFoundation.
References
1.
2.
3.
4.
5.
Chappell, Joseph and Keith Woodbury. Introducing Excel Based Steam Table Calculations into
Thermodynamics Curriculum, Proceedings of the 2008 ASEE Annual Conference & Exposition. June 2008,
Pittsburgh, PA.
Wagner, W, J. R. Cooper, A. Dittmann, J. Kijima, H.-J. Kretzschmar, A.Kruse, R. Mares, K. Oguchi, H. Sato, I.
Stocker, O. Sifner, Y. Takaishi, I. Tanishita, J. Trubenbach, Th. Willkommen. The IAPWS Industrial
Formulation 1997 for the Thermodynamic Properties of Water and Steam, Transactions of the ASME, Vol. 122,
p. 150-182. ASME, January 2000.
Cengel, Yunus A. and Michael A. Boles. Thermodynamics: An Engineering Approach, 6th Edition. New York,
NY: McGraw-Hill Companies, Inc , 2008.
Sonntag, Richard E., Claus Borgnakke, and Gordon J. Van Wylen. Fundamentals of Thermodynamics, 5th
Edition. New York, NY: John Wiley & Sons, Inc, 1998.
Excel in M. E. Project Website. 2007. University of Alabama, 17 January 2008 <http://www.me.ua.edu/excel>
Page 13.431.19