Numerical Differentiation: Difference approximation on numerical data

In summary, the given table presents data for a force (F) applied to a spring and its corresponding extension (x) and derivative (dF/dx) values. The task is to estimate the numerical value of dF/dx at x=0.16 using a difference approximation method such as backward, forward, or central difference. The attempt at a solution involved using the values around x=0.16 and calculating the slope for each method. However, this approach may not be accurate due to the variability of the data.
  • #1
Daria_Imparo
2
0

Homework Statement


I am given a table of data derived from experiment. A force (F) is applied to a spring and the extension (x) is measured and recorded. An additional column of data for the derivative (dF/dx) is also provided.

Here is the data:
x(m) F(kN) df/dx (kN/m)
0.0 0.0 5.0
0.03 0.3 10.2
0.06 0.5 4.3
0.12 0.7 5.2
0.2 1.8 8.2
0.22 1.9 1.1


The task is: using a difference approximation ( I think this is either backward, forward or central difference approximation), estimate the numerical value of the derivative dF/dx at x=0.16 based on the values provided in the table.

Homework Equations


None given


The Attempt at a Solution


None so far. Since I don't know how to go about it.

I know the solution to this must be very simple, but it just won't filter into my brain.
 
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  • #2
If you "this is either backward, forward or central difference approximation" you surely must know what those things are! Try them.
 
  • #3
HallsofIvy said:
If you "this is either backward, forward or central difference approximation" you surely must know what those things are! Try them.

OK, something has filtered: since x=0.16 is not part of the given data ( and I don't have its corresponding y value), I cannot include x=0.16 as part of the backward,forward or central difference methods.

If I work with the numbers around the 0.16 value, ie. 0.12 and 0.22:
Backward difference: slope = (0.7-0.5)/(0.12-0.06) = 3.33

Forward difference: slope = (1.9-1.8)/(0.22-0.20) = 5

Central Difference therefore: slope = (3.33+5)/2 = 4.17

But this approach cannot be correct, because the value of dF/dx between x=0.12 and x=0.2 is in the range of 5.2 and 8.2. So I would expect dF/dx for x=0.16 to be between 5.2 and 8.2. However, 4.17 obtained is not within that range.

Apparently the data is not necessarily accurate, so maybe this is correct? It will be correct if my approach is correct. And that is the question right now. Can I what I did?
 
Last edited:

1. What is numerical differentiation?

Numerical differentiation is a mathematical method used to approximate the derivative of a function at a given point using numerical data. It is commonly used in fields such as engineering, physics, and finance to estimate the rate of change of a quantity.

2. How does numerical differentiation work?

Numerical differentiation works by using difference approximations on numerical data. This means that the derivative at a point is approximated by calculating the slope of a line passing through two points on the function. The smaller the interval between the two points, the more accurate the approximation will be.

3. What are the advantages of numerical differentiation?

One advantage of numerical differentiation is that it can be used to approximate the derivative of a function at any point, even if the function is not defined in that point. It is also a useful tool for analyzing data sets and finding patterns in the data.

4. What are the limitations of numerical differentiation?

One limitation of numerical differentiation is that it can be affected by rounding errors and the choice of interval size. Using too large of an interval can result in a less accurate approximation, while using too small of an interval can lead to numerical instability. It also cannot provide an exact value for the derivative, only an approximation.

5. What are some applications of numerical differentiation?

Numerical differentiation has a wide range of applications in various fields. It is commonly used in physics to analyze motion and calculate velocities and accelerations. In engineering, it is used to optimize designs and predict behavior of systems. It is also used in finance to analyze market trends and fluctuations.

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