AME 514
Applications of
Combustion
Lecture 13A: Common-sense primer
for successful research
Designing and building an experiment
 Do the simplest thing first, then build on experience, e.g.
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Bic lighter and thermometer
Bunsen flame and thermocouple
Counterflowing jet burner, thermocouple and Labview
Coherent Anti-Stokes Raman Spectroscopy (CARS) and gas turbine
combustor
make your mistakes quickly and cheaply and safely!
 Any measurement consists of
 Transducer
 Data acquisition
 Algorithm for data processing
and all 3 must be valid, otherwise your measurement is invalid
 Noise and shielding
 Differential input
 Twisted wires
 Average multiple readings with computerized DAS
 First test of any instrument must be a reality check, e.g.
 Temperature measurement - check ice water and boiling water
 Pressure measurement - check atmospheric and vacuum
 Know how your control programs (e.g. LabView) work - you can't rely
on something written many years ago!
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Conducting an experiment
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NEVER TRUST ANY INSTRUMENT
Turn only one knob at a time
Skip around
Choose conditions wisely - plot as you go
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Take more data where the action is
Re-check suspicious points
Take data at x = 1, 1.5, 2.3, 3.3, 5, NOT 1, 2, 3, 4, 5
Turn the knobs as far to the left and the right as you safely can
 Use all your information
 Never
base a conclusion on one data point!
3.5
3
1
Measured quantity
Measured quantity
1.1
2.5
2
1.5
1
Condition 1
Condition 2
0
1
2
3
4
5
0.8
0.7
Physical behavior
Measured data (equal interval data taking)
Measured data (geometric interval data taking)
0.6
0.5
0
0.9
6
Adjusted quantity
7
8
0.5
0.1
0.2
0.3
0.4
0.5
Adjusted quantity
0.6
0.7
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Conducting an experiment (continued)
 Don't base conclusions on polynomial fits to small data sets
 Check repeatability - what is random and what is real?
 What happens if I do exactly the same test 10 times? What is the standard
deviation as a percent of the mean value?
 What happens if I repeat some of the points on the curve? Do I get the
same trend?
 What happens if I turn off the instrument? Does my signal change?
 Know what your units are (volts is not a unit of pressure or
temperature!)
 Use
video but put a caption and a scale in every video clip!
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First data set
Second data set
Erroneous use of polynomial fit
Data and true functional form
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Measured quantity
Measured quantity
1
6
4
2
0
0
1
2
3
Adjusted quantity
4
5
0.8
0.6
0.4
0.2
0
0
2
4
6
Adjusted quantity
8
4
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Scrutinizing your analysis
 First level - smoke test - do the units work? (Pv = R/T doesn't)
 Anything added must have the same units
 Anything inside an exp, ln, sin, etc. must be dimensionless
 Anything units inside a square root must be a square (e.g. m2/s2)
 Second level - function test - do the results make physical sense?
 Is the sign reasonable? (Pv = -RT isn't)
 Is it reasonable that as x increases, y decreases? (Pv = R/T isn't)
 Take the limit as x  ∞ or x  0
 Third level - performance test - how accurate is the result?
 Pv = 7RT passes smoke and function test, but not performance test
 Need to compare prediction to previous analysis, experiment, detailed
numerical computation, etc. that you trust
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Scrutinizing your computation
 Any colorful computer generated 3D orthographic projection
of results with shading from the northwest looks correct
 First level - smoke test - are mass, momentum, energy,
species, etc. conserved?
 Goal of most computational methods is to conserve these
quantities at every cell, but as a first check, is it conserved
globally?
 Second level - performance test
 Compare your result to a known analytical solution in a
simplified geometry with simplified (e.g. constant) property
relations
 Compare to previous computation that you trust
 Third level - function test - how accurate is the result?
 Similar to function test for analyses
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Communicating with others
 Use electronic format, not hard copies
 Name files with something more descriptive than
results.dat!!!
 Significant figures - usually 1.1 or 1.13, not 1, not
1.34098753987
 Make a xerox of hard-copy equipment manuals, or email an
electronic copy
 Meetings must have
 An agenda - what will be discussed
 Minutes - what was said and done
 Action items - what will be done differently as a result of the
meeting?
otherwise, what was the purpose of the meeting?
 Make a backup of everything!!! What would happen if your
hard disk crashed right now? What would happen if the lab
burned down right now??? Is your electronic and hard- 7
Making a decent figure
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Text is too small to read
Scales are weird, not 1, 2, 3, …
No units on vertical scale
Legend means nothing to audience
(what does Test 117 mean?)
Some data sets have connecting lines,
others not - why?
Too much white space
Too many grid lines
Plot symbols are too small to read
Jagged connecting lines look clumsy use smoothed line
Need vertical log scale since data spans
> 10x range
Tick marks inside and outside, too thin,
no distinction between major and minor
tick marks
 Lower plot: better figure (same data)
Rise time (ms)
 Upper plot: lousy figure, many
problems
arc at center
arc at end plate
corona
corona+brush
100
10
0.6
0.7
0.8
0.9
Equivalence ratio
1
1.1
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Oral presentations - preparing
 Golden rule: ask yourself, “if I were seeing this presentation for the first
time, would I understand it?”
 Format
 Introduction
» What is your topic and why is it important?
» Complain about what's lacking in the current knowledge
 Objectives - what are you trying to measure or predict or prove that is
better than what has been done before?
 Approach to experiment, computation or analysis
 Results - what did you learn and how sure are you?
 Conclusions - what did you measure or predict or prove? What is your
MESSAGE?
 No “bonus” text or figures - if it doesn't add to your message, leave it
out!
 A picture is worth 1000 words, and a video is worth 1000 pictures
 Every picture has a length scale, every movie has a length and time
scale
 Print a hard copy on 8.5” x 11” paper, put pages on the floor, can you
read it standing up? If not, the text/figures are too small!
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Oral presentations - doing
 Test your computer in advance
 Don't start out by reading your title!
 Face and address the audience: “This plot shows you the
effect of ABC on DEF…”
 Don't read equations, e.g. E = mc2
 Say “this equation shows that the energy contained by a
substance (point to E) is equal to its mass (point to m) times
the speed of light squared (point to c)
 DON'T say “this equation shows that eee equals emm cee
squared” (the audience already sees that)
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Written papers
 Golden rule: ask yourself, “if I were reading this paper for
the first time, would I understand it?”
 Format similar to oral presentation, plus abstract (before),
acknowledgments & references (after)
 Every figure
 Is mentioned in the text and labeled in the order it is first
mentioned
 Has a caption with all relevant conditions stated
 Has all symbols and lines defined in the caption
 References are numbered in the order they are first cited
in the text (unless the Harvard system, e.g. Smith and
Jones, 1972)
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Why was my paper rejected?
 Acceptable papers
 Have a clear, consistent message –
all information helps convey the
message
 State what is different from & better
than prior work
 State modeling assumptions &
identify empirical constants
 Have a minimum # of pictures,
scatter plots, extensive equations &
derivations - focus on quantitative
results & their relation to the
message
 Respond to the reviewers' comments
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White paper
 Part of your HW #5 assignment will be to prepare a “white
paper” research pre-proposal (≈ 2 pages + figures) on an
original topic
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State what your topic is and why it is important
State what is known about the subject
Complain about what is lacking in the current state of knowledge
Explain what you would do that would improve the state of knowledge
(i.e. specifically what computer simulation or experiment or analysis you
would perform)
 Describe how you would analyze or interpret the data
 Speculate as to what results you might obtain
 State how the results advance the state of knowledge of the field
 Verify that what you propose hasn’t already been done; e.g. check the
ISI Web of Science
AME 514 - Spring 2013 - Lecture 13
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