Class:  MAE 2381  EXPERIMENTAL METHODS AND MEASUREMENTS 
Subject:  Mechanical and Aerospace Engineering 
University:  University of Texas  Arlington 
Term:  Fall 2014 
An exponential trendline is a curved line that is most useful when data values rise or fall at increasingly higher rates. You cannot create an exponential trendline if your data contains zero or negative values.
Linear Trendline  A linear trendline is a bestfit straight line that is used with simple linear data sets. A linear trendline usually shows that something is increasing or decreasing at a steady rate. 
Polynomial Trendline  A polynomial trendline 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 trendline 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 Trendline  power trendline 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 onesecond intervals. You cannot create a power trendline if your data contains zero or negative values. 
Trendline Exponential 
An exponential trendline is a curved line that is most useful when data values rise or fall at increasingly higher rates. You cannot create an exponential trendline if your data contains zero or negative values. 
Why is Plagiarism Bad? 

Unintentional Plagiarism 

intentional Plag. 

Infamous Plagiarism Scandals 

When in Doubt  Cite teh source 
cheating 

To avoid cheating allegations: 

What Does Not Constitute Cheating ? 

Measurement 

Measurement system  (1) sensor–transducer stage, (2) signalconditioning stage, (3) output stage, and (4) feedbackcontrol 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: Sensor  A physical element that employs a natural phenomenon to sense’ the variable being measured. 
Stage 2:Transducer 

Stage 3: Signal Conditioner 

Stage 4: Output Stage  
Feedback Control 

Experimental Test Plan  Variables and parameters What question needs to be answered? What needs to be measured What variables affect the results? Often the most troublesome question 
Variables 

Parameters  Functional groups’ of variables.Parameter = combination of properties Contrast 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 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 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 

Repetition  repeat the same measurement during a single test Helps to see if there is any variation in the variable being measured 
Concomitant Methods 

Calibration definition 

Static Calibration  Values are held constant and you make a calibration curve 
Dynamic Calibration 

Static sensitivity: 

Resolution: 

Accuracy 

Error 

Uncertainty: 
We should usually say “the uncertainty is...” instead of “the error 
Types of Errors & Causes 
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 

Linearity Error  A linear static calibration curve has the equation 
Zero error: 

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 input

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 

Signal Amplification 

Aliasing 
Nyquist rate and frequency  
Folding 

Practical issues  
Types of Waveforms 

Non deterministic 

Front 
Back 


Linear Trendline  A linear trendline is a bestfit straight line that is used with simple linear data sets. A linear trendline usually shows that something is increasing or decreasing at a steady rate.  
Polynomial Trendline  A polynomial trendline 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 trendline 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 Trendline  power trendline 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 onesecond intervals. You cannot create a power trendline if your data contains zero or negative values.  
Trendline Exponential 
An exponential trendline is a curved line that is most useful when data values rise or fall at increasingly higher rates. You cannot create an exponential trendline if your data contains zero or negative values.  
Why is Plagiarism Bad? 
 
Unintentional Plagiarism 
 
intentional Plag. 
 
Infamous Plagiarism Scandals 
 
When in Doubt  Cite teh source  
cheating 
 
To avoid cheating allegations: 
 
What Does Not Constitute Cheating ? 
 
Measurement 
 
Measurement system  (1) sensor–transducer stage, (2) signalconditioning stage, (3) output stage, and (4) feedbackcontrol 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: Sensor  A physical element that employs a natural phenomenon to sense’ the variable being measured.  
Stage 2:Transducer 
 
Stage 3: Signal Conditioner 
 
Stage 4: Output Stage  
Feedback Control 
 
Experimental Test Plan  Variables and parameters What question needs to be answered? What needs to be measured What variables affect the results? Often the most troublesome question  
Variables 
 
Parameters  Functional groups’ of variables.Parameter = combination of properties Contrast 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 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 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 
 
Repetition  repeat the same measurement during a single test Helps to see if there is any variation in the variable being measured  
Concomitant Methods 
 
Calibration definition 
 
Static Calibration  Values are held constant and you make a calibration curve  
Dynamic Calibration 
 
Static sensitivity: 
 
Resolution: 
 
Accuracy 
 
Error 
 
Uncertainty: 
We should usually say “the uncertainty is...” instead of “the error  
Types of Errors & Causes 
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 
 
Linearity Error  A linear static calibration curve has the equation  
Zero error: 
 
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 input
 
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 
 
Signal Amplification 
 
Aliasing  
Nyquist rate and frequency  
Folding 
 
Practical issues  
Types of Waveforms 
 
Non deterministic 

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