Uncertainty Propagation in Fractional Expressions

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Homework Help Overview

The discussion revolves around uncertainty propagation in a fractional expression involving two variables, \( u \) and \( v \). The original poster attempts to derive an expression for the maximum error \( e_f \) in terms of the errors \( e_u \) and \( e_v \) associated with \( u \) and \( v \) respectively.

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss the addition of fractional uncertainties and explore the implications of negative terms in the error expression. There are attempts to differentiate the function and evaluate partial derivatives, with some questioning the correctness of their differentiation steps.

Discussion Status

Some participants have provided guidance on using partial derivatives and evaluating the expression, while others express confusion about the relationship between their calculations and the expected results. There is acknowledgment of the need to apply similar reasoning to both variables involved.

Contextual Notes

Participants note the challenge of achieving the desired form of the error expression and the potential for misunderstanding in the application of differentiation techniques. The discussion reflects a collaborative effort to clarify the mathematical reasoning behind uncertainty propagation.

Oerg
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Homework Statement


Given that [tex]f=\frac{\bar u \bar v}{\bar u +\bar v}[/tex]

show that

[tex]e_f=f^2({\frac{e_u}{\bar u^2} + \frac{e_v}{\bar v^2})[/tex]

where [tex]e[/tex] refers to the error. ok so I added up the fractional uncertainties

Homework Equations





The Attempt at a Solution


and I got this

[tex]\frac{e_f}{f}=\frac{e_u}{u}+\frac{e_v}{v}+\frac{e_u+e_v}{u+v}[/tex]

after some simplifying, I got to this,

[tex]e_f=f^2(\frac{e_u(u+v)}{u^2v}+\frac{e_v(u+v)}{v^2u}+\frac{e_u+e_v}{uv})[/tex]

and then I realized that I could never get the answer, however, if this term was negative,
[tex]\frac{e_u+e_v}{uv}[/tex], i would get the answer perfectly, but how can it be negative? Problem is even in division, shouldn't the fractional uncertianties add up??
 
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any help?
 
e_f is the maximum error, I have no idea why the term should be negative
 
f(u,v)

df=(df/du)du+(df/dv)dv

I didn't notate it properly, but the derivatives are partial derivatives. Treat df as ef, du as eu, dv as ev
 
Im sorry, I don't understand your post. how is my working affected by this equation?
 
I don't follow your working at all, so I'm not sure if it's a right way that I don't know about.

Are you familiar with differentiation?
 
yeh but not with partial derivatives,

What I did was to add up the fractional uncertainties.

I tried evaluating your equation, but I arrived at an ugly equation that doesn't fit the final bill.
 
Oerg said:
yeh but not with partial derivatives,

What I did was to add up the fractional uncertainties.

I tried evaluating your equation, but I arrived at an ugly equation that doesn't fit the final bill.

OK, that's good. Partial differentiation is exactly like ordinary differentiation. So for examle df(u,v)/du just means normal differentiation of f with respect to u, holding v constant. It may look ugly, but it should work out.

So first evaluate df(u,v)/du.
 
ok, I tried working out the coefficient of e_u according to your equation. Which is the partial derivative of f wrt u.

[tex]\frac{df}{du}=\frac{-uv}{(u+v)^2}+\frac{v}{u+v}[/tex]

unfortunately, it evaluaes out to be

[tex]f \times (\frac{v}{u(u+v)})[/tex]

which isn't what the question asked for. Have I differentiated wrongly?
 
  • #10
Try a different rearrangement, maybe take the common denominator to be (u+v)2
 
  • #11
I have already combined the two terms in my previous post, but i got [tex]f \times (\frac{v}{u(u+v)})[/tex] for the coefficient of [tex]e_u[/tex] when it should have been
[tex]\frac{f}{u^2}[/tex]
 
  • #12
ok sorry, i got the answer, i have to factorise another f out again. THANKS FOR YOUR TIME, IM VERY VERY GRATEFUL!
 
  • #13
Wait - you have to do it for df(u,v)/dv also!
 
  • #14
yeh it's the same for the other term, it should evaluate out to be the same.
 
  • #15
And you understand what I meant in #4?
 
  • #16
yeh i do,

df=partial derivative of f wrt u times du + partial derivative of f wrt v times dv

\

from df du and dv to ef eu and ev is an approximation for a small change.
 
  • #17
OK, great (assuming what I said is correct :rolleyes:) :smile:
 

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