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8/9/2019 11exp Dat Pres
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Experiments, Tests, and Data
Massachusetts Institute of Technology, Subject 2.017

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Purpose of Experiments and Tests
Prove or Support a Hypothesis The Earths diameter is 6500km.
Multiple propellers on a single shaft can reduce cavitation (Turbinia).
The archaea prokaryotes in the ocean fix carbon and consume other organisms,
and the balance has profound impact on ocean uptake of CO2. (Ed Delong, AnnPearson, etc.)
Outriggers provide better roll stability than does a single hull in random beamseas, when wavelength is much larger than the beam.
Prove a Capability, Support Design Manned flight to the upper atmosphere can be achieved biweekly with a
specialized aircraft (XPrize).
Characteristic of lift force as a function of elevator aspect ratio and inflow angle.
Delay calculation in pulsed 20kHz acoustic signals is possible with the TattleTaleModel 8, and the performance obtained is XX.
Massachusetts Institute of Technology, Subject 2.017

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Does the work
stand up to
scrutiny?
Use of controls
Calibration
Data qualityData processing
Documentation and
recordkeeping!
Massachusetts Institute of Technology, Subject 2.017

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Controls
Did you really measure what you thought?
Rat Maze: Is the maze acoustically navigable?(R. Feynman)
Mass Spectroscopy: When you put in a sampleof known composition, are the other bins clean?
When measuring electrical resistance, touch theprobes together. Check a precision resistor too.
Resonance in load measurement rigs?
When measuring hull resistance, does zero speed give zero force?
Massachusetts Institute of Technology, Subject 2.017

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Calibration
More time can be spent on calibration than the rest of the experiment!
Sensors should be calibrated and rechecked using independent references, such as:
Manufacturers specifications
Another sensor with very wellknown calibration A tape measure, protractor, calipers, weights & balance, stopwatch, etc..
Calibration range should include the expected range in the experiment. Some statistics of the calibration:
Precision of fit (rvalue orV)
Linearity (if applicable)
Understand special properties of the sensor, e.g., drift, PWM
Massachusetts Institute of Technology, Subject 2.017

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Data and Sensor Quality
SignaltoNoise Ratio
(SNR): compares Vto the signal you want
Repeatability/Precision: If
we run the same test again,
how close is the answer?
Accuracy: Take the average
of a large number of tests
is it the right value?
Massachusetts Institute of Technology, Subject 2.017

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Time and Frequency Domain
Fourier series/transforms establish an exactcorrespondence between these domains, e.g.,
X = VTcos( 2Sm t / T ) z(t) dt * 2 / T, m = 0,1,2,m 0Y = V
Tsin( 2Sm t / T ) z(t) dt * 2 / T
m 0
z(t) = X0 / 2 + 6Xm cos( 2Sm t / T ) + 6Ym sin( 2Sm t / T )
Massachusetts Institute of Technology, Subject 2.017

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Time Resolution in
Sampled Systems
The Sampling Theorom shows that the highestfrequency that can be detected by sampling at frequencyZ = 2S't is the Nyquist rate: ZN = Zs / 2.s
Higher frequencies than this are aliased to the range
below the Nyquist rate, through frequency folding. Thisincludes sensor noise!
The required rate for visual analysis of the signal, andphase and magnitude calculation is much higher, say tensamples per cycle.
Massachusetts Institute of Technology, Subject 2.017

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Sample Statistics
Sample mean m:
Sample standard dev. V:V= sqrt [ ( (x1m)2 + (x2m)2 + + (xnm)2 ) / (n1) ]
Error budgets for multiplication and addition
(VA is standard deviation of A):
(A + VA)(B + VB) ~ AB + AVB + BVA
Example: (1.0 + V0.2)(3.0 + V0.3) ~ 3.0 + V0.9
(A + VA) + (B + VB) = A + B + V(A+B)
Example: (1.0 + s0.2) + (3.0 + s0.3) = 4.0 + V0.5
Massachusetts Institute of Technology, Subject 2.017

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Gaussian (Normal) Distribution
Probability Density Function f(x) ~ Histogramf(x) = exp [  (xm)2 / 2V2 ] / sqrt(2S) / V
This is the most common
distribution encountered in sensors and systems.
+/ 1Vcovers 68.3% +/ 2Vcovers 95.4%+/ 3Vcovers 99.7%Area under f(x) is 1!
Massachusetts Institute of Technology, Subject 2.017

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Filtering of Signals
Filterx xf
Use good judgement!
filtering brings out trends, reduces noise
filtering obscures dynamic responseCausal filtering: xf(t) depends only on past
measurements appropriate for realtime implementation
Example: xf(t) = (1H)xf(t1) + Hx(t1)Acausal filtering: xf(t) depends on all
measurements appropriate for postprocessing
Example: xf(t) = [ x(t+1) + x(t) + x(t1) ] / 3
Massachusetts Institute of Technology, Subject 2.017

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A firstorder filter transferfunction in the freq. domain:
xf(jZ) / x(jZ) = O/ (jw + O)
At low Z, this is approximately1 (OO)
At highZ
, this goes to 0magnitude, with 90 degreesphase lag (O/jZ= jOZ)Time domain equivalent:
dxf/dt = O(x xf)In discrete time, try
xf(k) = (1O't) xf(k1) +O't x(k1)
Massachusetts Institute of Technology, Subject 2.017

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BUT linear filters will not handle outliers very well!
First defense against outliers: find out their originand eliminate them at the beginning!
Detection: Exceeding a known, fixed bound, or animpossible deviation from previous values. Example:vehicle speed >> the possible value given thrust leveland prior tests.
Second defense: set data to NaN (or equivalent), soit wont be used in calculations.
Third defense: try to fill in.
Example:
ifabs(x(k)
x(k
1))
>MX,
x(k)=x(k1);end;
Limited usefulness!Massachusetts Institute of Technology, Subject 2.017

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Presentations: Written
and Spoken
or
People will pay more attention to you if you communicate well!
Massachusetts Institute of Technology, Subject 2.017

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Sources and Ethics
Somebody has almost certainly thought aboutwhat you are doing, and parts of it have almostcertainly been solved.
For specific items, you must give an original
source and cite it properly. Refereed publications vs. flashy Internet
postings.
Plagiarism: Consider it ILLEGAL.If there is any question about whether a phrase(or even a particular word) should be cited,protect yourself! and the associated noise is
systematically coupled to the
underlying process [13].
Massachusetts Institute of Technology, Subject 2.017

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Linearity
Start at the beginning
and go to the end!
Antithesis: Michael
Ondaatji (The
English Patient)
Flowchart or detailed
outline may help
Omit needless
words*.
* Strunk, Jr., W. and E.B. White, 1972.The elements of style. Allyn and
Bacon: Boston.
Massachusetts Institute of Technology, Subject 2.017
Introduction:
Approach:
Discussion:
Bring reader from general to specific
State hypothesis or objective
Indicate why work is important
Review prior work that applies
etc
How the experiment or test was designed
Details of the apparatus or system
Accuracy and precision issues etc
Results:
Major Result A, with figures and description
Major Result B
etc
Do results support hypothesis?
Impact of findings
Future work
etc

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Pointers on Speaking
The audience is here to see YOU, not
just your materials.
Smile and engage them!
Write out your talk so it is
clean from start to end.
Dont lose anyone!
Practice your talk so you
are confident up there.
Get feedback on your talk,
because it will help.
distribution of expertise
level in the audience
layman expert
cumulative
average YOU average YOU
Prepare for questions.student (student) professional (professional)
90% of the talk accessible to
90% of the audience
Massachusetts Institute of Technology, Subject 2.017

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A GOOD FIGURE > 1000 WORDSA bad figure is worth a few bad words
Massachusetts Institute of Technology, Subject 2.017

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Vehicle trajectory: one
hidden independent
variable; five dependent
variables
Wind speed and direction as a
function of time. Top two plots
are combined into the bottom
plot: one independent
variable, three dependentvariables.
Massachusetts Institute of Technology, Subject 2.017

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Figure from Principles of Naval Architecture, E. Lewis, ed.,
SNAME: New York, 1988. Original reference: Vossers, G., and
W.A. Swaan 1960. Some seakeeping tests with a Victory model.
Int. Shipbuilding Progress.
Image removed for copyright reasons.Figure and its caption from abovementioned source.
Shows two independent
and one dependent
variable. Style shows the
effects of varying phase and period.
Caption injured, and yaxis label missing; gives
three independent variables (length ratio, Froude
number, and heading to waves) and one
dependent variable (added power coefficient).Massachusetts Institute of Technology, Subject 2.017