An interactive FORTRAN computer comparison program designed to automatically locate regions of incongruity between two databases is described. The software, guided by user input parameters, incrementally compares the databases and generates plots of these regions in the databases which do not compare within a specified tolerance. Additionally, tools are provided within the software which enable the user to statistically reduce the number of data points in the databases compared. To facilitate the description of these tools, the procedures used to compare two aerodynamic databases for an F-18A fighter aircraft are detailed

Arbuckle, P. Douglas

Mcminn, John D.

Shaughnessy, John D.

English

NASA Technical Reports Server

NASA Technical Memorandum 100609 
A DESCRIPTION OF AN AUTOMATED 
DATABASE COMPARISON PROGRAM 
- 
f NfiSA-Tm-100609) A DESCRIPTION OF A N  
AUTOllhTED DATABASE COHPBEISON PROGEAM 
N88-2 3463 
(NASA) 11 p CSCL CIYB Unclas  
G 3 / 6 1  0145917 
JOHN D, MCMINN 
JOHN D ,  SHAUGHNESSY 
P, DOUGLAS ARBUCKLE 
c 
MAY 1988 . 
National Aeronautics and 
Space Administratlon 
Langley Research Center 
Hampton. Virginia 23665 
https://ntrs.nasa.gov/search.jsp?R=19880014079 2020-03-20T06:19:13+00:00Z
SUMMARY 
An interactive FORTRAN computer comparison program designed to 
automatically locate regions of incongruity between two databases, in 
order to reduce the amount of required analysis, is described. 
software. guided by user input parameters. incrementally compares the 
databases and generates plots of those regions in the databases which do 
not compare within a specified tolerance. Additionally. to help the 
user further reduce the number of plots. tools are provided within the 
software which enable the user to statistically reduce the number of 
data points in the databases compared. 
these tools, the procedures used to compare two aerodynamic databases 
for an F-18A fighter aircraft are detailed. 
The 
To facilitate the description of 
INTRODUCTION 
In scientific work. the situation often arises where it is 
necessary to compare data from different sources. This process can 
range from being relatively simple, as is the case when comparing two 
functions of the same independent variable. to a process involving the 
comparison of databases with dissimilar data points and a number of 
independent variables which may or may not be identical. A typical 
method of comparing two databases representative of the latter case is 
the tedious and time consuming process of plotting certain data from 
each and making comparisons manually. 
A computer program could greatly reduce the magnitude of this 
effort. This software could conduct the comparison and show the user 
where large discrepancies exist between the data. allowing the user to 
look only at those regions in the databases which do not compare within 
a specified tolerance. 
For example. a situation where such software would have been 
helpful arose during the comparison of the aerodynamic data from a real- 
time simulation (mostly wind tunnel data) to data generated by the USAF 
Digital Datcom (ref. 1) .  After a limited search of the NASA Langley 
computer software library, no software was found that performed the 
desired task: therefore. it was decided to develop such a computer 
program. 
The purpose of this report is to describe the computer program 
that was developed to automate the task of comparing two databases. The 
capabilities of the software are described and an example of the 
procedures used to make a comparison is given. A comparison of two 
aerodynamic databases for an F-18A aircraft is made where one database 
was generated by the USAF Digital Datcom computer program (ref. 1) and 
the other was obtained from wind tunnel tests. Also included in this 
report are examples of the types of data generated. 
SYMBOLS AND ABBREVIATIONS 
ADC Automated Database Comparison 
CL lift coefficient 
CD drag coefficient 
CM pitching moment coefficient 
CY sideforce coefficient 
c1 
CN 
CLA 
CMA 
CNR 
C1R 
CNP 
C1P 
CMQ 
CLQ 
NR - 
1R- 
NP - 
1P- 
MQ - 
LQ - 
RT S 
rolling moment coefficient 
yawing moment coefficient 
lift coefficient due to vertical acceleration 
pitching moment coefficient due to vertical acceleration 
yawing moment coefficient due to positive yaw rate 
rolling moment coefficient due to positive yaw rate 
yawing moment coefficent due to positive roll rate 
rolling moment coefficient due to positive r o l l  rate 
pitching moment coefficient due'to positive pitch rate 
lift coefficient due to positive pitch rate 
yawing moment coefficient due to negative yaw rate 
rolling moment coefficient due to negative yaw rate 
yawing moment coefficent due to negative roll rate 
rolling moment coefficient due to negative roll rate 
pitching moment coefficient due to negative pitch rate 
lift coefficient due to negative pitch rate 
real-time simulation 
THE COMPARISON PROGRAM 
The fundamental objective of the software is to automate the 
comparison of two databases to free the user from analyzing the large 
number of plots that could be generated during a manual comparison (for 
example. in a comparison of 10 terms such as: CMQ. CLQ. CNP. CL. CD. 
CM. CLA, CY, C1. and CMA generated from two databases where each of five 
independent variables such as: angle of attack, angle of sideslip, Mach 
number, leading and trailing edge flap angles range over 10 values: 
100,000 plots could be generated). Therefore. the software should be 
able to compare two tabular, numerical databases and indicate where the 
databases do not compare within a certain specified tolerance. By 
writing appropriate data to an output file for later plotting. the 
number of plots to be viewed is greatly reduced. Additionally, the 
program should be able to examine databases which do not necessarily 
contain like terms by bringing the databases to a common reference point 
through total coefficient reconstruction equations like those discussed 
in reference 2 (i.e.. summing items in an aerodynamic database to form 
total force or moment coefficients and perform the comparison on that 
basis). 
With those guidelines in mind, the Automated Database Comparison 
(ADC) program was developed. This FORTRAN program is interactive and 
runs on systems such as Control Data Corporation's Network Operating 
System. The inputs for ADC are: (1) parameters. from a file o r  the 
keyboard: ( 2 )  names of the independent variables: ( 3 )  the independent 
values: and ( 4 )  acceptable comparison tolerance for each term. 
Once the input parameters have been provided, the ADC begins 
searching the databases for dissimilarities that exceed the specified 
tolerance. These dissimilarities and tolerances are measured in the 
form of absolute errors between the data. Originally, the software was 
designed to compare databases on the basis of percentage error (dividing 
the absolute error by the largest value from one of the databases: 
called the reference database). but the resulting information proved to 
be misleading. In cases where the magnitude of the largest value was 
small and the curves were fairly similar, the software would indicate 
errors on the order of 100 percent. For that reason, the decision was 
made to compare on the basis of absolute error and display on the output 
2 
* 
file the largest value from the reference database. Absolute error 
comparisons can also be misleading: however. this was determined to be 
the preferred means of comparison. 
The ultimate result from using ADC is plots of the data which do 
not compare within the specified tolerance. Two tools within the 
software that help isolate these regions in the databases are the error 
summary file and the file splitting function which breaks large data 
files into smaller data files for plotting. 
The error summary file shown in figure 1 displays on the first 
line the names of the databases being compared and identification 
information from the title line of the second database. The first 50 
characters of the second line are reserved for displaying any 
information supplied by the user in response to a prompt for 
supplementary information. The other piece of information on the second 
line is the suffix for the data files that correspond to the information 
in this file (the data file names are formed by the concatenation of a 
term name and a suffix which is supplied by the user in response to a 
prompt). 
Below the supplementary information is the section of the file 
labeled "TABLE OF SCHEDULES." Here all the independent variables are 
shown along with their first 10 values. To the left of the names of the 
independent variables are two columns of numbers. The first column is 
the ordinal number associated with the independent variable (i.e.. in 
fig. 1. rudder is the seventh independent variable). The second column 
of numbers under the heading "CONTROL LOOP" show the program iteration 
loop in which the value of each variable is changed. If two or more 
independent variables have the same numbers in the "CONTROL LOOP" 
column. then they are being parametrically varied at the same time (this 
option allows the user to eliminate combinations of independent 
variables that are not desired, which further reduces the amount of data 
generated). 
At the bottom of the error summary file is the Error Table. The 
left-most column of the table shows the terms being compared and the 
next column shows the maximum value encountered for a term from the 
first database (given as a reference from which to gauge the relative 
magnitude of the absolute errors). In the remaining columns of the 
table, absolute errors of similar magnitude are grouped into ranges 
(which can be altered by the user). F o r  example. in figure 1 it can be 
seen that for drag coefficient, an absolute error of a magnitude between 
0.05 and 0.1 units o f  CD occurred 18 times. By referring to this table 
the engineer can set the tolerance €or  the terms either to better 
partition the error groups or to limit the size of the resulting data 
files: the higher the tolerance, the lower the occurrence of 
discrepancies and therefore, less information is written to the data 
files. 
The second tool in ADC is the file splitting function that reduces 
a data file into one of specific error magnitudes. This option allows 
the user to make plots o f  only the data which he considers to be in 
error. For example, if the user is working with the data in figure 1 
and desires to know the conditions causing the instances of sideforce 
error between 0.01 and 0.02 units of sideforce. then this function can 
be used to create a file containing only the data for the desired plots. 
The alternative is to plot all the data and locate the desired 47 plots 
3 
from the 144 that result: obviously this function is most useful when 
there are a large number of plots. 
Despite the flexibility evidenced by the aforementioned features, 
ADC does have some limitations. 
tabular function-based format (ref. 2 ) .  A maximum of 250 errors between 
the databases for a particular term can be plotted. The variable on the 
horizontal axis of all plots is the first independent variable given in 
the input parameter list. The plots can display the values of up to 11 
independent variables (one across the horizontal axis and 10 in a legend 
on the plot). 
source code is required to allow for the diverse variety of databases 
the software is capable of comparing, 
The databases must be in a specific 
A limited amount of user reconfiguration of the ADC 
A SAMPLE APPLICATION 
In order to further illustrate the capabilities of the ADC. the 
following example is given. 
to be compared contain aerodynamic data for the F-18A aircraft and were 
derived from the USAF Digital Datcom computer program (ref. 1) and wind 
tunnel data, respectively. Additionally, the databases have a large 
number of different independent variables and do not contain the same 
terms: therefore, direct comparison is not possible. Given this 
situation and the fact that the software allows f o r  each independent 
variable to iterate through a range of 50 values during the comparison, 
it is possible that on the order of 1050plots could be generated. 
Using more realistic numbers (see fig. 11, on the order of 100 plots 
would be required. Therefore, the following procedures are used to 
compare the databases. 
In this sample comparison the two databases 
First. the input parameters are specified. In order to make a 
thorough comparison of the databases. 11 independent variables are used 
to represent the major control surfaces and flight conditions of the 
F-18A. Those variables are: angle of attack. angle of sideslip, Mach 
number. left and right horizontal tail deflections, left and right 
aileron deflections, rLdder deflection. leading and trailing edge flap 
deflections. and altitude. The values of these independent variables 
are assigned to completely cover the ranges of the databases but limited 
to a reasonable number whenever possible without compromising the 
quality of the comparison (e.g.. using five angle-of-attack values as 
opposed to 5 0 ) .  To complete the input parameter list. the tolerances 
for the terms are entered and initially set to large values (e.g.. the 
CL tolerance is set to 10 units of CL) to quickly generate the error 
summary file. 
The second step is to use the error summary tool to perform a 
sensitivity analysis with respect to independent variables that, as 
determined by engineering judgment, might not have much influence on the 
error distribution within the databases. Mach number is the variable in 
this comparison that is tested. This is done by allowing all of the 
independent variables to vary, with the exception of Mach number. The 
output from the error summary file is then recorded and compared to the 
error summaries from subsequent runs at different Mach numbers. By 
analyzing the distribution pattern of the errors and realizing that they 
are nearly the same, it is shown that Mach number has little effect on 
the error distribution. With that confirmed. it is possible to select 
4 
one Mach number that is fairly representative of the rest, thus reducing 
the number of comparison plots to be viewed. 
The third step in the strategy is to continue use of the error 
summary file to discover and take advantage of any symmetries in the 
databases that might exist. Due to the symmetric properties of the 
aircraft. these databases should have symmetry. By verifying these 
symmetries it is possible to remove those values that cause a 
duplication of errors already encountered from the schedules of the 
independent variables, e.g.. eliminating -5' sideslip since it can be 
shown that it produces the same errors as +5O sideslip. The potential 
for duplication exists not only with sideslip but also with rudder, 
ailerons, and differential horizontal tails. Due to the number of 
independent variables that this stage applies to, there is a great 
potential for significantly reducing the amount of data to be compared. 
Once the schedules of the independent variables have been 
selected, the accumulation of results begins. ADC is run multiple 
times, each time with some independent variables allowed to range over 
their list of input values while the other independent variables are not 
varied. Engineering judgment is used to decide which independent 
variables will be allowed to vary for a given run. The resulting error 
summary files. such as the one shown in figure 1. are then analyzed to 
determine the values for the independent variables which cause the 
databases not to compare well. The tolerances for each term are then 
lowered sufficiently to have the software create data files for those 
areas when ADC is rerun. 
Next, the file splitting tool is applied. This is useful to 
quickly find the conditions causing a particular group of errors. For  
example, the error table in figure 1 shows 3 2  occurrences of CM errors 
with magnitudes from 0.1 to 0.2. The table also shows the largest 
magnitude of CM from the first database to be 0 . 1 9 4  units of CM. If it 
were desired to view only those plots exhibiting data in the error range 
of the same magnitude as the largest value of CM, the file splitting 
function could be used. The result is a data file of only those 
conditions causing CM errors between 0.1 and 0.2. 
Finally. the data files are turned into plots similar to those 
shown in figure 2 .  On each plot, the variable across the horizontal 
axis is the first independent variable from the input parameter list. 
The solid curve represents data from the first database listed in the 
key on the upper right. The dashed curve represents data from the 
second database listed. The items listed below the key are the 
remaining independent variables with the values used to generate the 
curves shown. The bottom most item in the key. ERROR, is not an 
independent variable, but rather the maximum absolute error between data 
points on the plot. 
CONCLUDING REMARKS 
The software described herein (ADC) performs an automated database 
comparison to reduce the number of plots that would have to be analyzed 
in a manual comparison. ADC can automatically compare terms generated 
from two databases by varying independent variables over a range of 
values. To reduce the number of plots. the software contains two 
special tools which isolate the dissimilar areas within the databases. 
5 
The first is an error summary file which is in tabular form and provides 
a concise. comprehensive review of the distribution of the absolute 
errors between the two database values for each term of the comparison. 
The other tool provides a capability of plotting specific error ranges 
in the data files. To facilitate the description of these tools, the 
procedures used to compare two aerodynamic databases for an F-18A 
fighter aircraft are detailed. 
REFERENCES 
1. Vukelich. S. R.: and Williams. J. E.: The USAF Stability 
and Control Digital Datcom. AFFDL-TR-79-3032. April 1979. 
2 .  Arbuckle. P. Douglas: Buttrill. Carey S.: and Zeiler. Thomas: 
A New Simulation Model Building Process for Use in Dynamic 
Systems Integration Research. AIAA Paper No. 87-2498-CP. 
August 17-19. 1987. 
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2. Government Accession No. 1. Report No. 
NASA TM- 100609 
4. Title and Subtitle 
A Description of an Automated Database Comparison 
Program 
3. Recipient's Catalog No. 
5. Repon Date 
7. Author(sJ 
John D. McMinn, John D. Shaughnessy, and 
P. Douglas Arbuckle 
9. Performing Organization Name and Address 
NASA Langley Research Center 
Hampton, VA 23665-5225 
8. Performing Organization Report No. 
10. Work Unit No. 
505-66-01-02 
11. Contract or Grant No. 
, 
2. Sponsoring Agency Name and Address 
National Aeronautics and Space Administration 
Washington, DC 20546-0001 
13. Type of Report and Period Covered 
Technical Memorandum 
14. Sponsoring Agency Code 
6. Abstract 
1. Security Classif. (of this report) 
Unclassified 
An interactive FORTRAN computer comparison program designed to automatically 
locate regions of incongruity between two databases is described. The software, 
guided by user input parameters, incrementally compares the databases and generates 
plots of those regions in the databases which do not compare within a specified 
tolerance. Additionally, tools are provided within the software which enable 
the user to statistically reduce the number of data points in the databases 
compared. To facilitate the description of these tools, the procedures 
used to compare two aerodynamic databases for an F-18A fighter aircraft are 
detailed. 
20. Security Classif. (of this page) 21. No. of Pages 22. Price 
Unclassified 9 A0 2 
. Key Words (Suggested by Author(sJJ 18. Distribution Statement 
interactive database comparison 
FORTRAN program 
Unclassified - Unlimited 
Subject Category - 61, 05 
0 
. 


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