Uncertainty Propagation in Fractional Expressions

AI Thread Summary
The discussion revolves around the propagation of uncertainty in a fractional expression defined as f = (u*v)/(u+v). Participants explore how to derive the maximum error, e_f, and its relationship to the errors in u and v, denoted as e_u and e_v. The main challenge is reconciling the derived expression for e_f with the expected result, particularly regarding the sign of a specific term. Clarification on the use of partial derivatives is provided, emphasizing their role in evaluating the uncertainties. Ultimately, the conversation leads to a resolution on how to correctly factor and evaluate the uncertainties involved.
Oerg
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Homework Statement


Given that f=\frac{\bar u \bar v}{\bar u +\bar v}

show that

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

where e refers to the error. ok so I added up the fractional uncertainties

Homework Equations





The Attempt at a Solution


and I got this

\frac{e_f}{f}=\frac{e_u}{u}+\frac{e_v}{v}+\frac{e_u+e_v}{u+v}

after some simplifying, I got to this,

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

and then I realized that I could never get the answer, however, if this term was negative,
\frac{e_u+e_v}{uv}, 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.

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

unfortunately, it evaluaes out to be

f \times (\frac{v}{u(u+v)})

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 f \times (\frac{v}{u(u+v)}) for the coefficient of e_u when it should have been
\frac{f}{u^2}
 
  • #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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