Envelope function for two sine curves

AI Thread Summary
The discussion focuses on finding a generalized envelope function for two sinusoidal curves with varying amplitudes and periods. Two cases are presented, showing different envelope functions derived from the sine curves, with the first case yielding a satisfactory envelope. The user seeks a method to combine these conditions into one function that accurately captures the peaks across all combinations of periods. Suggestions include using trigonometric identities to reformulate the combined sine functions and considering the relationships between their frequencies. The conversation emphasizes the complexity of achieving a universal solution, particularly when the frequencies are close in value.
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Homework Statement



Hi,[/B]
I am trying to get an envelope function for two sinusoidal curves (A, B) added up.
a1,a2 are the amplitudes(metre), T1,T2 are the periods (hrs), B lags by dt from A at t=0.
case-1:
a1= 1, a2=0.5, p1=11,p2=10,dt=0
w1=2*pi/T1,w2=2*pi/T2
A = a1*cos(w1*t), B=a2*cos(w2*t+dt)
t varies from 0 to 650 hours

Homework Equations


Case-1[/B]

using a envelope function sqrt(a1^2 + a2^2+2*a1*a2*(cos(w2-w1)*t+dt))
i am getting a nice envelope as in the figure 1

env12.jpg

Case-2:

a1= 1, a2=0.5, T1=60, T2=10, dt=0
using a envelope function

a2 – a1 +2*a1*a2*(cos(w1)*t+dt))
I can manage an envelope as in the figure 2

The Attempt at a Solution



Is there a way to combine these two conditions ( or any combination of T1 and T2)
so that I can have one generalized envelope function that does not miss the peaks.


Thanks in anticipation for any help
 
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Just a reminder of a trigonometric identity: \cos(x)+\cos(y)= 2\cos(\frac{x+y}{2})\cos(\frac{x-y}{2}).
 
Thanks Svein,
Could you please elaborate little more.
From what ever I have tried out, it works only for a range and not for all combinations of T1 and T2.
I want to identify the location of extreme amplitude analytically from added up sine curves.
I believe the envelope could be represented by a set of sinusoids

sorry if it is something too simple and i am getting lost.
 
OK. We start out with A_{1}\cos(\omega_{1} t)+A_{2}\cos(\omega_{2}t+\phi). Assume A1>A2. Then we can transform it into (A_{1}-A_{2})\cos(\omega_{1} t)+A_{2}(\cos(\omega_{1} t)+\cos(\omega_{2}t+\phi))=(A_{1}-A_{2})\cos(\omega_{1} t)+2A_{2}\cos(\frac{\omega_{1}+\omega_{2}+\phi}{2})\cos(\frac{\omega_{1}-\omega_{2}-\phi}{2}).
In radio technology this shows us that adding an audio signal (ω2) to a carrier wave (ω1) gives us a carrier wave plus two side bands \frac{\omega_{1}+\omega_{2}+\phi}{2} and \frac{\omega_{1}-\omega_{2}-\phi}{2}. You can see this best when ω1>>ω2. In your case (when ω1≈ω2), the result is harder to see.
 
I don't know how you arrived at the envelope function you posted, so I may be just telling you what you already know here.
First, lose the phase constant. If the two frequencies are different then the phase constant is equivalent to a shift along the time axis, so is not interesting.
I would then apply the identity Svein mentioned, but in a more symmetric manner.

Get F(t) into the form ##C\sin(\bar \omega t)\cos(\hat \omega t)+D\cos(\bar \omega t)\sin(\hat \omega t)## where ##\bar\omega = \frac{\omega_1+\omega_2}2## and ##\hat\omega = \frac{\omega_1-\omega_2}2##. Clearly the ##\bar \omega## factors are the higher frequency of the two, so we want to find the maximum of F over a cycle of that.

If this frequency is much higher than ##\hat \omega##, we can treat the ##\hat \omega## as constant over such a cycle and find the max of F on that basis. This leads to the equation you posted.

If the original frequencies are very different then ##\hat \omega## and ##\bar \omega## will be of similar magnitude, so this method does not work. Instead, you can treat the lower frequency function as roughly constant over a cycle of the higher frequency and arrive at your other equation.

So each solution is just an approximation that works well in its own domain. The worst case, I guess, would be where the ratio of the original frequencies is the same as the ratio of the sum and difference frequencies: ##\omega_1:\omega_2=\bar\omega:\hat\omega##. Try plotting one of those to see if it gives any insight.
 
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