# Mid-Term - Flashcards

## Flashcard Deck Information

 Class: MAE 2381 - EXPERIMENTAL METHODS AND MEASUREMENTS Subject: Mechanical and Aerospace Engineering University: University of Texas - Arlington Term: Fall 2014
- of -
- INCORRECT     - CORRECT     - SKIPPED
Hide Keyboard shortcuts
Next card
Previous card
Mark correct
Mark incorrect
Flip card
Start Over
Shuffle
Mode:         ? pages
 Linear Trend-line A linear trend-line is a best-fit straight line that is used with simple linear data sets. A linear trend-line usually shows that something is increasing or decreasing at a steady rate. Polynomial Trend-line A polynomial trend-line is a curved line that is used when data fluctuates. It is useful, for example, for analyzing gains and losses over a large data set. The order of the polynomial can be determined by the number of fluctuations in the data or by how many bends (hills and valleys) appear in the curve. An Order 2 polynomial trend-line generally has only one hill or valley. Order 3 generally has one or two hills or valleys. Order 4 generally has up to three. Power Trend-line power trend-line is a curved line that is best used with data sets that compare measurements that increase at a specific rate — for example, the acceleration of a race car at one-second intervals. You cannot create a power trend-line if your data contains zero or negative values. Trend-line Exponential An exponential trend-line is a curved line that is most useful when data values rise or fall at increasingly higher rates. You cannot create an exponential trend-line if your data contains zero or negative values.
Generated by Koofers.com
 Why is Plagiarism Bad? Constitutes Academic Theft and Fraudbad study and homework habitsMisrepresentation of abilities Unintentional Plagiarism Paraphrasing without giving citationSummarizing without citationGiving inappropriate, improper, or incorrect information about the source improper citation of a source intentional Plag. Copying words, sentences, or paragraphs from previously published articlesQuoting only a part of a quotation and claiming the rest as your ownpaste of material from sources without giving full citation,quotation, or attributionBuying ready made material from internet vendors Infamous Plagiarism Scandals Joe Biden: Plagiarism in Law School Plagiarized substantial portions of a law review article in Law SchoolFailed course, later caused substantial damage to presidential bid
Generated by Koofers.com
 When in Doubt Cite teh source cheating PlagiarismCollusion: Giving work to or taking it from another student for the purposes of helping one of them improve their work To avoid cheating allegations: Summarize, paraphrase, and citeAsk for help if you need itDo your own work What Does Not Constitute Cheating ? Two or more students working together at the same time Must each produce their own workGetting assistance with proofreading or editing
Generated by Koofers.com
 Measurement the act of assigning a specific value to a specific variable Measurement system (1) sensor–transducer stage, (2) signal-conditioningstage, (3) output stage, and (4) feedback-control stage. These stages form the bridge between theinput to the measurement system and the system output, a quantity that is used to infer the value ofthe physical variable measured Stage 1: Sensor A physical element that employs a natural phenomenon to sense’ the variable being measured. Stage 2:Transducer Converts sensed information into a detectable signalThis can be a change in voltage, resistance, current, etc. or a mechanical signal like the mercury level in thermometer
Generated by Koofers.com
 Stage 3: Signal Conditioner Takes transducer signal and modifies it to a desired magnitude range.A portion of the signal may be removed with a filter (to be explained later). Stage 4: Output Stage Feedback Control Measurement tool coupled to another device that can affect the value of the measured parameter. Experimental Test Plan Variables and parametersWhat question needs to be answered?What needs to be measuredWhat variables affect the results? Often the most troublesome question
Generated by Koofers.com
 Variables Independent variable:Can be changed independently of other variablesDependent variable: Affected by changes in other variables “y = y(x)”Extraneous variables: Can’t be controlled in the experiment  and Differences in results with same conditions Parameters Functional groups’ of variables.Parameter = combination of propertiesContrast to “property”: physical characteristic of system 2) Tolerance Design Plan How can measurements be made? Highly accurate/precise experiments can become expensive. Noise and Interference Two types of extraneous variables: Noise and InterferenceTwo types of extraneous variablesNoise: Random variable in the measured signal. Increases scatter of data but it can be accounted for with statistics (filtering, averaging, etc.) Interference: Imposes a trend on the signal which can be more difficult to account for
Generated by Koofers.com
 3) Data Reduction Plan How will the data collected answer the question?What trend is being looked for?If there are anticipated results, how will they be analyzed? Replication repeat measurements in another test with similar operating conditions Repetition repeat the same measurement during a single test Helps to see if there is any variation in the variable being measured Concomitant Methods Get two or more estimates of a measurement based on different methods that can be comparedHelps to validate results so people believe them
Generated by Koofers.com
 Calibration definition Apply a known input value to a measurement system to observe the outputKnown input value’ is very important and must be accurate It is called a standard: best estimate of the ‘true value’Weights, voltage, force profile, etc. Static Calibration Values are held constant and you make a calibration curve Dynamic Calibration Variables that change in time and space have magnitude and frequency Static sensitivity: slope of calibration curve between input and output Constant for a linear relationshipMost devices that can be purchased are linear for simplicity
Generated by Koofers.com
 Resolution: Smallest increment of the value that can be measured (i.e., tick marks on a ruler or dial gauge) Accuracy Accuracy: closeness of agreement between measured value and true value Do we ever know the true value?  (Hint: no, almost never) Error Quantitative measurementDifference between measured value and true value Uncertainty: if the true value is not known, then this is an estimate of the interval around the measurement where the true value isWe should usually say “the uncertainty is...” instead of “the error
Generated by Koofers.com
 Types of Errors & Causes Precision errors: not repeatable Scatter about a mean Quoted as mean and an indication of deviation, Experimental ‘noise’ is a big factor in precision errors Finite quantized values in measurement system can’t be 100% precise Bias (or systematic) errors (accuracy)These are repeatable and may be due to multiple factors Bias in instrument, Bias in instrument,  In many instances, systematic errors can be removed by calibrating the instrument This is only true if you know you have a systematic error Hysteresis Error The output of the system is dependent on the value that was previously measured Linearity Error A linear static calibration curve has the equation
Generated by Koofers.com
 Zero error: Drift in output for the condition of zero input May occur over time in some devices Amplitude: magnitude of the input Frequency how the input changes with time order of a system (zero, first, second) Describes behavior of system response to inputInformation can consist of multiple magnitudes with associated frequenciesit is common to view data as amplitude vs. time,Time domain. Amplitude vs. frequency is just as important. Frequency domain
Generated by Koofers.com
 A/D Conversion where M is the number of bits of the register2 M is number of quantized values available that the converted signal can have in the range. Called “counts”The converter compares the analog signal and finds theclosest digital value of voltage to itTruncates: always rounds down Waveform Classifications Analog: continuous in time (most things in nature)Discrete time: only information at certain points in time (but quantity measured still varies continuously)Discrete time: only information at certain points in time but quantity measured still varies continuously) Signal Amplification Amplification involves an increase in the overall magnitude of a signal; usually voltage Aliasing
Generated by Koofers.com
 Nyquist rate and frequency Folding Frequencies in the original signal above, fN are folded back into the sampled signal as lower frequencies below, fN Practical issues Types of Waveforms varies in a way that is predictableSimple periodic: only one frequencyComplex periodic: more than 1 frequencyA periodic: does not repeat at regular intervals
Generated by Koofers.com
 Non deterministic cannot be predicted, must be described by statistical characteristics: Noise, turbulence
Generated by Koofers.com

## List View: Terms & Definitions

Front
Back
Linear Trend-lineA linear trend-line is a best-fit straight line that is used with simple linear data sets. A linear trend-line usually shows that something is increasing or decreasing at a steady rate.
Polynomial Trend-lineA polynomial trend-line is a curved line that is used when data fluctuates. It is useful, for example, for analyzing gains and losses over a large data set. The order of the polynomial can be determined by the number of fluctuations in the data or by how many bends (hills and valleys) appear in the curve. An Order 2 polynomial trend-line generally has only one hill or valley. Order 3 generally has one or two hills or valleys. Order 4 generally has up to three.
Power Trend-linepower trend-line is a curved line that is best used with data sets that compare measurements that increase at a specific rate — for example, the acceleration of a race car at one-second intervals. You cannot create a power trend-line if your data contains zero or negative values.
Trend-line Exponential

An exponential trend-line is a curved line that is most useful when data values rise or fall at increasingly higher rates. You cannot create an exponential trend-line if your data contains zero or negative values.

1. Constitutes Academic Theft and Fraud
2. bad study and homework habits
3. Misrepresentation of abilities
Unintentional Plagiarism

1. Paraphrasing without giving citation
2. Summarizing without citation
3. Giving inappropriate, improper, or incorrect information about the source
4. improper citation of a source
intentional Plag.
1. Copying words, sentences, or paragraphs from previously published articles
2. Quoting only a part of a quotation and claiming the rest as your own
3. paste of material from sources without giving full citation,quotation, or attribution
Infamous Plagiarism Scandals
1. Joe Biden: Plagiarism in Law School
2. Plagiarized substantial portions of a law review article in Law School
3. Failed course, later caused substantial damage to presidential bid
When in DoubtCite teh source
cheating
1. Plagiarism
2. Collusion:
1. Giving work to or taking it from another student for the purposes of helping one of them improve their work
To avoid cheating allegations:
1. Summarize, paraphrase, and cite
2. Ask for help if you need it
What Does Not Constitute Cheating ?
1. Two or more students working together at the same time Must each produce their own work
2. Getting assistance with proofreading or editing
Measurement
1. the act of assigning a specific value to a specific variable
Measurement system(1) sensor–transducer stage, (2) signal-conditioning
stage, (3) output stage, and (4) feedback-control stage. These stages form the bridge between the
input to the measurement system and the system output, a quantity that is used to infer the value of
the physical variable measured

Stage 1: SensorA physical element that employs a natural phenomenon to sense’ the variable being measured.
Stage 2:Transducer
1. Converts sensed information into a detectable signal
1. This can be a change in voltage, resistance, current, etc. or a mechanical signal like the mercury level in thermometer
Stage 3: Signal Conditioner
1. Takes transducer signal and modifies it to a desired magnitude range.
2. A portion of the signal may be removed with a filter (to be explained later).
Stage 4: Output Stage
Feedback Control
1. Measurement tool coupled to another device that can affect the value of the measured parameter.
Experimental Test PlanVariables and parameters
What question needs to be answered?
What needs to be measured
What variables affect the results? Often the most troublesome question
Variables
1. Independent variable:Can be changed independently of other variables
2. Dependent variable: Affected by changes in other variables “y = y(x)”
3. Extraneous variables: Can’t be controlled in the experiment  and Differences in results with same conditions
ParametersFunctional groups’ of variables.Parameter = combination of properties
Contrast to “property”: physical characteristic of system
2) Tolerance Design PlanHow can measurements be made?
Highly accurate/precise experiments can become expensive.

Noise and InterferenceTwo types of extraneous variables: Noise and Interference
Two types of extraneous variables
Noise: Random variable in the measured signal. Increases scatter of data but it can be accounted for with statistics (filtering, averaging, etc.) Interference: Imposes a trend on the signal which can be more difficult to account for
3) Data Reduction PlanHow will the data collected answer the question?
What trend is being looked for?
If there are anticipated results, how will they be analyzed?
Replication
1. repeat measurements in another test with similar operating conditions
Repetitionrepeat the same measurement during a single test Helps to see if there is any variation in the variable being measured
Concomitant Methods
1. Get two or more estimates of a measurement based on different methods that can be compared
2. Helps to validate results so people believe them
Calibration definition
1. Apply a known input value to a measurement system to observe the output
2. Known input value’ is very important and must be accurate It is called a standard: best estimate of the ‘true value’Weights, voltage, force profile, etc.
Static CalibrationValues are held constant and you make a calibration curve
Dynamic Calibration
1. Variables that change in time and space have magnitude and frequency
Static sensitivity:
1. slope of calibration curve between input and output
2. Constant for a linear relationship
3. Most devices that can be purchased are linear for simplicity
Resolution:
1. Smallest increment of the value that can be measured (i.e., tick marks on a ruler or dial gauge)
Accuracy
1. Accuracy: closeness of agreement between measured value and true value Do we ever know the true value?  (Hint: no, almost never)
Error
1. Quantitative measurement
2. Difference between measured value and true value
Uncertainty:
1. if the true value is not known, then this is an estimate of the interval around the measurement where the true value is
We should usually say “the uncertainty is...” instead of “the error
Types of Errors & Causes
1. Precision errors: not repeatable Scatter about a mean Quoted as mean and an indication of deviation,
Experimental ‘noise’ is a big factor in precision errors
Finite quantized values in measurement system can’t be 100% precise
Bias(or systematic) errors (accuracy)
These are repeatable and may be due to multiple factors
Bias in instrument, Bias in instrument,  In many instances, systematic errors can be removed by calibrating the instrument This is only true if you know you have a systematic error
Hysteresis Error
1. The output of the system is dependent on the value that was previously measured
Linearity ErrorA linear static calibration curve has the equation
Zero error:
1. Drift in output for the condition of zero input May occur over time in some devices
Amplitude:magnitude of the input
Frequencyhow the input changes with time
order of a system (zero, first, second)Describes behavior of system response to input
1. Information can consist of multiple magnitudes with associated frequencies
2. it is common to view data as amplitude vs. time,Time domain. Amplitude vs. frequency is just as important. Frequency domain
A/D Conversion
where M is the number of bits of the register
2 M is number of quantized values available that the converted signal can have in the range. Called “counts”
The converter compares the analog signal and finds the
closest digital value of voltage to it
Truncates: always rounds down
Waveform Classifications
1. Analog: continuous in time (most things in nature)
2. Discrete time: only information at certain points in time (but quantity measured still varies continuously)
3. Discrete time: only information at certain points in time but quantity measured still varies continuously)
Signal Amplification
1. Amplification involves an increase in the overall magnitude of a signal; usually voltage
Aliasing

Nyquist rate and frequency
Folding
1. Frequencies in the original signal above, fN are folded back into the sampled signal as lower frequencies below, fN
Practical issues
Types of Waveforms
1. varies in a way that is predictable
2. Simple periodic: only one frequency
3. Complex periodic: more than 1 frequency
4. A periodic: does not repeat at regular intervals
Non deterministic
1. cannot be predicted, must be
2. described by statistical characteristics: Noise, turbulence