ASEE Southeast Section Conference

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ASEE Southeast Section Conference     INTEGRATING
MODEL VALIDATION AND UNCERTAINTY ANALYSIS INTO AN
UNDERGRADUATE ENGINEERING LABORATORY   W. G.
Steele and J. A. Schneider   Department of
Mechanical Engineering Bagley College of
Engineering Mississippi State University Mississip
pi State, MS 39762
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• INTRODUCTION
• MS STATE LABORATORY COURSES
• METHODOLOGY
• EXAMPLE
• CONCLUSION

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• INTRODUCTION
• Laboratories introduce the student to the use of
  various measurement devices along with the
  associated experimental uncertainties
• Theoretical engineering models are used to
  compare predicted outcome with the experimental
  results
• Usually no consideration of the uncertainty
  associated with the theoretical model
  calculations
• Concept of engineering model validation using
  uncertainty analysis is extension of verification
  and validation research for CFD and other
  computational design codes

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• MS STATE LABORATORY COURSES
• Experimental Orientation
• basic measurements
• data acquisition
• concepts of uncertainty analysis
• Experimental Techniques I
• experiment design using uncertainty analysis
• experiment operation
• Experimental Techniques II
• model, plan, design, construct, operate, and
  analyze results of an experiment including
  model validation

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Consider a validation comparison
m ? value from the model r ? result from
experiment E ? comparison error E r -
m ?r - ?m
U
ri
r
m Um
E
mi
X
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Validation Comparison of Model Results with
Experimental Results
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METHODOLOGY The comparison error
has an uncertainty If is less than
UE, the level of model validation is UE. If
is greater than UE, the level of model
validation is .
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For the experimental result
the uncertainty is where br systematic
standard uncertainty sr random standard
uncertainty
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The systematic standard uncertainty of the result
is defined as   where and where Bi is the
95 confidence estimate (2bi) of the limits of
the true systematic error for variable Xi.  

The random standard uncertainty of the result is
defined as  
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For the model result the uncertainty is
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EXAMPLE
Experiment result was the measured head loss in a
pipe, ?hpr, over a range of flow rates.
Engineering model was where and
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Fluid Flow Test Facility
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Experimental Results vs. Model Predictions
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Uncertainty Estimates for Result and Model
Variables
 
 
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Comparison Error
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Uncertainty Percentage Contributions
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• CONCLUSION
• Understanding the limitations of physical models
  is key to the successful practice of
  engineering.
• The uncertainty of both the model and experiment
  results are used to assess the model validity.
• The validation process allows the identification
  of ranges where different or improved models are
  needed or shows that improved variable
  uncertainties are needed to reduce the validation
  uncertainty.