Accuracy of a gravitational force approximation

In summary, the conversation discusses the problem of expressing the force due to gravity on an object in terms of powers of height over radius, and using the Alternating Series Estimation Theorem to estimate the range of values for which an approximation is accurate. The expert summarizer provides a summary of the work done in solving the problem and finding the range of values.
  • #1
DivGradCurl
372
0
It seems to me that I've got part (a) right, but I'm not so sure about what I have in part (b). I just need to know whether or not I am on the right direction. Any help is highly appreciated. :smile:

Problem

The force due to gravity on an object with mass [tex]m[/tex] at a height [tex]h[/tex] above the surface of Earth is

[tex]F=\frac{mgR^2}{(R+h)^2}[/tex]

where [tex]R[/tex] is the radius of Earth and [tex]g[/tex] is the acceleration due to gravity.

(a) Express [tex]F[/tex] as a series of powers of [tex]h/r[/tex].

(b) Observe that if we approximate [tex]F[/tex] by the first term in the series, we get the expression [tex]F\approx mg[/tex] that is usually used when [tex]h[/tex] is much smaller than [tex]R[/tex]. Use the Alternating Series Estimation Theorem to estimate the range of values of [tex]h[/tex] for which the approximation [tex]F \approx mg[/tex] is accurate within 1%. (Use [tex]R = 6400 \mbox{ km}[/tex]).

My work

(a)

[tex] (R+h) ^{-2} = \frac{1}{R^2} \left( 1 + \frac{h}{R} \right) ^{-2} [/tex]
[tex] (R+h) ^{-2} = \frac{1}{R^2} \sum _{n=0} ^{\infty} \binom{-2}{n} \left( \frac{h}{R} \right) ^n [/tex]
[tex] (R+h) ^{-2} = \frac{1}{R^2} \left[ 1 + \frac{(-2)}{1!} \frac{h}{R} + \frac{(-2)(-3)}{2!} \left( \frac{h}{R} \right) ^2 + \dotsb \right] [/tex]
[tex] (R+h) ^{-2} = \frac{1}{R^2} \left[ 1 -2\frac{h}{R} + 3\left( \frac{h}{R} \right) ^2 - 4\left( \frac{h}{R} \right) ^3 + \dotsb \right] [/tex]
[tex] (R+h) ^{-2} = \frac{1}{R^2} \sum _{n=0} ^{\infty} (n+1) \left( -\frac{h}{R} \right) ^n \Longrightarrow F = mg \sum _{n=0} ^{\infty} (n+1) \left( -\frac{h}{R} \right) ^n [/tex]

(b)

The value of [tex]mg[/tex] doesn't seem to matter here. So, I consider just the terms in the series. It seems to me that the easiest way to go is to plot

[tex] \left| 1 - \sum _{n=0} ^{N} (n+1) \left( -\frac{h}{R} \right) ^n \right| [/tex]

I've tried this with:

[tex]N=1000[/tex]
Domain: [tex][0,37 \times 10^3][/tex]
Range: [tex][0,0.01][/tex].

It gives a reasonable picture to estimate that [tex]h \leq 37 \times 10^3 \mbox{ m}[/tex]. Is there a better way to find this? Am I on the right path at all?

Thank you
 
Last edited:
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  • #2
(a) looks fine.
For (b) you are required to use the Estimation Theorem for Alternating Series. For an alternating series with decreasing terms that converge to 0 for large n :

[tex]|S_{\infty} - S_n| \leq t_{n+1} [/tex]
 
  • #3
I have a question. I can't find an example in my textbook of the Alternating Series Estimation Theorem in a similar context. I know how to apply it in

[tex]e = \sum _{n=0} ^{\infty} \frac{1}{n!} \Longrightarrow t_{n+1} =\frac{1}{(n+1)!} < \left| \mbox{error} \right|[/tex]

but not really in

[tex]e^x = \sum _{n=0} ^{\infty} \frac{x^n}{n!} \Longrightarrow ? < \left| \mbox{error} \right|[/tex]

Which of the following is correct?

[tex]t_{n+1} = (n+2) \left( \frac{h}{R} \right) ^{n+1} < 10^{-2} = \left| \mbox{error} \right| \quad (1)[/tex]

[tex] t_{n+1} = (n+2) \left( \frac{1}{R} \right) ^{n+1} < 10^{-2} = \left| \mbox{error} \right| \quad (2)[/tex]

I'm just a bit confused. :smile:

Thanks
 
  • #4
This one : (the other is dimensionally incorrect)

[tex]t_{n+1} = (n+2) \left( \frac{h}{R} \right) ^{n+1} < 10^{-2} = \left| \mbox{error} \right| \quad (1)[/tex]
 
  • #5
Ok. Now, we have an inequality and two unknowns---namely, n and h. What kind of trick is then required so that I am able to estimate h properly? I think it involves plotting, but I'm not sure how---for the very same reason. :confused:
 
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  • #6
I've just seen my problem in a different way, and it seems to work.

From part (a), we have:

[tex] F = mg \left[ 1 - 2\left( \frac{h}{R} \right) + 3\left( \frac{h}{R} \right) ^2 - \dotsb \right] [/tex]

Applying the Alternating Series Estimation Theorem gives:

[tex]b_{n+1} = 2 \frac{h}{R} \leq 10 ^{-2} \Longrightarrow h \leq 32 \mbox{ km} [/tex]

which can be visually verified with the aid of a plot.
 
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Related to Accuracy of a gravitational force approximation

1. What is the gravitational force approximation?

The gravitational force approximation is a simplified mathematical model used to estimate the force of gravity between two objects. It is based on Newton's law of universal gravitation and takes into account the masses and distance between the two objects.

2. How accurate is the gravitational force approximation?

The accuracy of the gravitational force approximation depends on the specific situation and the level of precision required. In general, it is a good approximation for objects that are relatively close together and have similar masses. However, for objects that are very large or very far apart, the approximation may not be as accurate.

3. What factors affect the accuracy of the gravitational force approximation?

The accuracy of the gravitational force approximation is affected by several factors, including the masses of the objects, the distance between them, and the shape and density of the objects. Other factors, such as the presence of other nearby objects, can also impact the accuracy.

4. How can the accuracy of the gravitational force approximation be improved?

The accuracy of the gravitational force approximation can be improved by using more advanced mathematical models and techniques, such as the theory of relativity. Additionally, taking into account other factors such as the rotation and composition of the objects can also improve the accuracy of the approximation.

5. What are the limitations of the gravitational force approximation?

The gravitational force approximation is limited by its simplicity and assumptions. It does not take into account other forces, such as air resistance or electromagnetic forces, which may affect the motion of objects. Additionally, it is a classical model and does not account for the effects of quantum mechanics or relativity at very small or large scales.

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