How to calculate n with its uncertainty in the equation Q= kL(h^n)?

In summary, the conversation discusses the process of summing up uncertainties in a task, specifically with regards to U(E) and U(Q). The speaker mentions that they are unsure of the contributors that lead to U(Q) and notes the absence of a calculation for n in the example. They also mention uploading files for further clarification.
  • #1
Joon
85
2
Homework Statement
I was given a flow rate equation Q= kL(h^n), where k is a constant and L (width of rectangular weir) was set to 0.625 ft. A table of experimental Q values and corresponding h' and h values was provided, please refer to the picture attached. h (weir head) in the equation= h in the table. h= h' -0.3290 ft.

There are two variables in the equation, Q and h, and I need to determine the experimental value of n.

An example calculation was provided where the used equation was D=cE^n and I was told to use the same method as the one used in the example calculation. Please refer to the attached files 2-4 for the example calculation.
I get how to calculate the uncertainty of D, but I really have no idea how the uncertainty of E was calculated.
After calculating U(D) and U(E), a plot of ln (D) against ln (E) was drawn to calculate n (the gradient of the graph).

Could someone help me understand this?
Relevant Equations
Q= kL(h^n) for the task

D=cE^n for the example calculation

Hook gauge reading, weir head- vernier scale
Flow rate Q reading- venturi flow meter
I am thinking of doing the same thing (summing up all the uncertainties that contribute to U(E) in the example) for the task, so summing up all the fractional uncertainties that contribute to U(Q). But the problem is unlike energy in the example, I'm not sure what's the contributors that lead to U(Q).
 

Attachments

  • Data given.png
    Data given.png
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  • Example.png
    Example.png
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  • U(E) 1.png
    U(E) 1.png
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  • U(E) 2 and U(D).png
    U(E) 2 and U(D).png
    54.1 KB · Views: 322
Last edited:
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  • #2
I must be missing something: in the example I don't see a calculation of ##n## at all, only a (remarkable coincident) result for ##g## ? :confused:
 
  • #3
The process is a bit complicated, I am uploading everything I have, please have a look.
I'm attaching the excel file as well where the example calculation was done.
 

Attachments

  • Excel file.xlsx
    37 KB · Views: 205
  • 1st.png
    1st.png
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  • 2nd.png
    2nd.png
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  • 3rd.png
    3rd.png
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  • 4th.png
    4th.png
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  • #4
2 more files
 

Attachments

  • 5th.png
    5th.png
    89.8 KB · Views: 268
  • 6th.png
    6th.png
    56.6 KB · Views: 226

1. How do I determine the value of n in the equation Q= kL(h^n)?

The value of n can be determined by rearranging the equation to solve for n. This can be done by taking the logarithm of both sides and then rearranging the equation to isolate n. Once you have isolated n, you can use the values for Q, k, L, and h to calculate the value of n.

2. What is the uncertainty in the value of n?

The uncertainty in the value of n can be calculated using the uncertainties in the values of Q, k, L, and h. This can be done by using the partial derivatives of the equation with respect to each variable and then using the formula for calculating uncertainty in a function of multiple variables.

3. How do I calculate the uncertainty in the final result when using the equation Q= kL(h^n)?

The uncertainty in the final result can be calculated by propagating the uncertainties in the values of Q, k, L, and h through the equation. This can be done by using the formula for calculating uncertainty in a function of multiple variables.

4. Can I use this equation for any type of data?

The equation Q= kL(h^n) is commonly used in scientific research and can be applied to a variety of data sets. However, it is important to ensure that the data being used is relevant and appropriate for the equation.

5. Is there a specific method for calculating n with its uncertainty in the equation Q= kL(h^n)?

Yes, there are specific methods for calculating n with its uncertainty in this equation. These methods involve using mathematical techniques such as logarithms and partial derivatives, as well as understanding the principles of uncertainty and error propagation in scientific calculations.

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