A core Course on Modeling

# A core Course on Modeling

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## A core Course on Modeling

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1. A core Course on Modeling Introductionto Modeling 0LAB0 0LBB0 0LCB0 0LDB0 c.w.a.m.v.overveld@tue.nl v.a.j.borghuis@tue.nl P.13

2. Investigating the behavior of a functional model: • The analysis tools in ACCEL. • The tools on the analysis-tab • Conditionnumbers, sensitivityand error propagation • An example

3. Investigating the behavior of a functional model: • The tools on the analysis tab.

4. Investigating the behavior of a functional model: • The tools on the analysis tab. Select a quantitythatshould serve as argument from the list of quantities Select a quantitythatshould serve as resultfrom the list of quantities Ifnecessary, scrollthrough the list of quantities in the model Ifnecessary, scrollthrough the list of quantities in the model Ifnecessary, adjustlowerandupperbounds of the argument Ifnecessary, adjustlowerandupperbounds of the result The dependency is graphicallyplotted

5. Investigating the behavior of a functional model: • The tools on the analysis tab. Request a sensitivity analysis byclickingthis button. Choosebetween absolute or relative(=percentual) analysis. By default, the stepsizeusedfor the numericalapproximation is 1(%). Ifnecessary, adjustthisvalue. One column foreach cat.-II quantitiy , giving the conditionnumber or the absolute uncertainty. The top rowholds the (abs. or perc.) standard deviation of this cat.-II quantity. The conditionnumber or uncertainty.

6. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • Consider the functional model • y = f(x1, x2)= x1*x2. • Ifeach of the cat.-I quantities (x1, x2) has anuncertainty of 1%, what is the relativeuncertainty in cat.-II quantity y? •  conditionnumbers

7. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • Relativeuncertainty = y/y. • y = f(x1, x2) = x1*x2. • y = f/x1 x1+ f/x2x2

8. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • Relativeuncertainty = y/y. • y = f(x1, x2) = x1*x2. • y = f/x1 x1+ f/x2x2

9. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • Relativeuncertainty = y/y. • y = f(x1, x2) = x1*x2. • y = f/x1 x1+ f/x2x2 • = x2 x1 + x1 x2 • y/y= ( x2 x1+ x1 x2 )/ x1x2

10. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • Relativeuncertainty = y/y. • y = f(x1, x2) = x1*x2. • y = f/x1 x1+ f/x2x2 • = x2 x1 + x1 x2 • y/y = ( x2 x1+ x1 x2 )/ x1x2 = x1/x1 + x2/x2 • bothconditionnumbers are 1. • y/y = rel. uncertainty in x1 + rel. uncertainty in x2 ... • ... iftheywouldsimultaneouslyassumetheirextremes.

11. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • Soy/y = x1/x1+ x2/x2 (bothconditionnumbers =1) • = rel. uncertainty in x1 + rel. uncertainty in x2... • ... iftheywouldsimultaneouslyassumetheirextremes. • If x1and x2 are uncorrelated, however, we use • (y/y)2= (x1/x1)2+ (x2/x2)2 – or in general • (y/y)2 = c12(x1/x1)2+ c22(x2/x2)2. • For c1=c2=1, we get with x1/x1=1%,x2/x2=1%thaty/y=1.41 %, • and rel. uncertainties of x1and x2 have equal impact.

12. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • Comparethis with • y = f(x1, x2)= x1*x22; • then • c1 = f/x1* x1/y = x22 * x1/(x1x22) = 1 • c2= f/ x2* x2/y = 2x1x2* x2/(x1x22) = 2 • Soifboth x1and x2 have equalrelativeuncertainty, the uncertaintyin x2 has twice as much impact.

13. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • In general: formodels of the form (socalledmultiplicativeforms) • y = f(x1, x2, x3, ...) = f(xi) = iximi, the |conditionnumber| is |mi|. • In otherwords: formultiplicativemodels, • the higher the |exponent| of xi, the larger the impact of relativeuncertaintyin xi . • this impact is constant throughout the entire domain (=does not depend on the values of the cat.-I or cat.-III quantities) • Remember the detergent problem, the chimneysweepersproblemandmanyothers: suchmodelsoccurveryoften.

14. Investigating the behavior of a functional model: • Sensitivity, conditionnumbersand error propagation. • For additivemodels: • y=f(xi) = i ai xi • y = y/x x = aix. • In otherwords: foradditivemodels, • the higherthe |factor| for x1, the larger the impact of absoluteuncertainty in x1. • this impact is constant throughout the entire domain (=does not depend on the values of the cat.-I or cat.-III quantities)

15. Investigating the behavior of a functional model: • An example: the chimneysweepers’ case. nr. chimneysweepers in Eindhoven decreases as a function of the family size

16. Investigating the behavior of a functional model: • An example: the chimneysweepers’ case. rel. uncertainty in family size: 2% (goodstatistics)

17. Investigating the behavior of a functional model: • An example: the chimneysweepers’ case. rel. uncertainty in nr. families per chimney: 20% (poorstatistics)

18. Investigating the behavior of a functional model: • An example: the chimneysweepers’ case. rel. uncertainty in sweeping time per chimney per chimney: 30% (poorstatistics)

19. Investigating the behavior of a functional model: • An example: the chimneysweepers’ case. rel. uncertainty in annualworking time per sweeper: 10% (goodstatistics)

20. Investigating the behavior of a functional model: • An example: the chimneysweepers’ case. resulting rel. uncertainty in number of sweepers in Eindhoven: 26%

21. Investigating the behavior of a functional model: • An example: the chimneysweepers’ case. The details: The chimneysweepers’model is a multiplicative model. Allconditionnumbers are 1; there are 7 quantities. Expected rel. uncertaintyforall 1% uncertainties is therefore7 = 2.64. ACCEL gives 2.4 instead; this is becausenrFamPCh is anall-integer slider; thereforeconditionnumber is not computed.

22. Summary: • A functional model is allaboutdependencies. • Graphplottinghelpsascertainthatdependencies are plausible. • Input quantities (cat.-I, III) have uncertainties. These propagateintoanuncertainty in the answer. • Absolute uncertainties: y; • Relativeuncertainties: y/y; • ACCEL numericallyestimates error propagation • and the expecteduncertainty in the cat.-II quantities.