11exp Dat Pres

8/9/2019 11exp Dat Pres

1/20

Experiments, Tests, and Data

Massachusetts Institute of Technology, Subject 2.017


2/20

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 bi-weekly with a

specialized aircraft (X-Prize).

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.



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Does the work

stand up to

scrutiny?

Use of controls

Calibration

Data qualityData processing

Documentation and

record-keeping!



4/20

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?



5/20

Calibration

More time can be spent on calibration than the rest of the experiment!

Sensors should be calibrated and re-checked using independent references, such as:

Manufacturers specifications

Another sensor with very well-known 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 (r-value orV)

Linearity (if applicable)

Understand special properties of the sensor, e.g., drift, PWM



6/20

Data and Sensor Quality

Signal-to-Noise 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?



7/20

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 )



8/20

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.



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Sample Statistics

Sample mean m:

Sample standard dev. V:V= sqrt [ ( (x1-m)2 + (x2-m)2 + + (xn-m)2 ) / (n-1) ]

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



10/20

Gaussian (Normal) Distribution

Probability Density Function f(x) ~ Histogramf(x) = exp [ - (x-m)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!



11/20

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 real-time implementation

Example: xf(t) = (1-H)xf(t-1) + Hx(t-1)Acausal filtering: xf(t) depends on all

measurements appropriate for post-processing

Example: xf(t) = [ x(t+1) + x(t) + x(t-1) ] / 3



12/20

A first-order 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) = (1-O't) xf(k-1) +O't x(k-1)



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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


14/20

Presentations: Written

and Spoken

or

People will pay more attention to you if you communicate well!



15/20

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].



16/20

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.


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


17/20

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



18/20

A GOOD FIGURE > 1000 WORDSA bad figure is worth a few bad words



19/20

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.



20/20

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 above-mentioned source.

Shows two independent

and one dependent

variable. Style shows the

effects of varying phase and period.

Caption injured, and y-axis 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

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