Why is the approximation of small angles valid for cosine but not for sine?

In summary: Therefore, I don't think this approximation is valid. In summary, the person is questioning the validity of an approximation for sin(a+n) and cos(a+n) when n is small. They point out that the expansion for sin(a+n) should also include a linear term in n, but the person providing the approximation has only included the constant term. The person also suggests that this discussion should be in the math section, not physics.
  • #1
mathfilip
8
0
Hi! I have a question about approximation of functions with small angles. I was looking through some notes from my teacher and didnt understand why the following approximation is valid. We have a system which is at equlibrium at an angle, say a. Now we wanted to se what happens with the equilibrim if we deviate from a, with a small angle say, n. All the calculations isn't necessary here so i cut them out.

He chooses to write cos(a+n) = cosa * cos n - sina * sin n, just by expanding.

However for sin(a + n) he just writes sin(a+n) = sin(a)

Can you please validate this and tell me why this is ok to do? I mean why we don't expand sin(a+n) in the same manner?
 
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  • #2
If you check it and expand it yourself you get:

sin(a+n)=sin(a)cos(n)+sin(n)cos(a)

if N is small, Sin(n) is insignificant. let's just say close enough to 0. and using the same principle, if N is small, Cos(n) is close enough to 1. Think of the sin and cos curves for small values. This leaves you with;

sin(a+n)=sin(a)1+0cos(a) which therefore approximates to Sin(a) as stated.

All he has done is missed out the calculation because he knows something about the values of N for sin and cos when they are small.

Btw this should be in the maths section not the physics.

Hope this helped.

Si
 
  • #3
I'm not sure this expansion is correct. If you want to expand for small n you have to keep all term that are linear in n. In the case of the sine this would be
[tex]\sin(a+n)=\sin(a)\cos(n)+\sin(n)\cos(a)\approx \sin(a) + n \cos(a)+ \mathcal{O}(n^2)[/tex]

for the cosine it would be
[tex]\cos(a+n)=\cos(a)\cos(n)-\sin(a)\sin(n) \approx \cos(a) - n \sin(a)+ \mathcal{O}(n^2)[/tex]

It is inconsistent to keep the linear term for the epxansion of the cosine but not the sine.
 

1. What is the definition of "approximation of small angles"?

The approximation of small angles is a mathematical technique used to simplify complex calculations involving angles that are close to 0. It assumes that for small angles, the sine of the angle is equal to the angle itself in radians, and the cosine of the angle is equal to 1. This allows for easier and more accurate calculations.

2. Why is the approximation of small angles important in science?

The approximation of small angles is important in science because it allows for more efficient and accurate calculations in various fields such as physics, engineering, and astronomy. It is also used in the development of scientific instruments and in the analysis of experimental data.

3. How is the approximation of small angles used in real-life applications?

The approximation of small angles is used in various real-life applications such as navigation systems, satellite communication, and in the design of structures and machines. It is also used in the study of celestial bodies and their movements, as well as in the analysis of oscillatory systems.

4. What are the limitations of the approximation of small angles?

The approximation of small angles is only accurate for angles that are close to 0. As the angle increases, the accuracy of the approximation decreases. It also does not take into account other factors such as friction and air resistance, which can affect the motion of objects.

5. How does the approximation of small angles differ from the exact calculation of angles?

The approximation of small angles is a simplified approach, while the exact calculation of angles uses the trigonometric functions sine, cosine, and tangent to determine the relationship between the sides and angles of a triangle. The approximation method is quicker and easier to use, but can result in slightly different values compared to the exact calculation method.

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