Numerical integration for sinsoidal wave

AI Thread Summary
For numerical integration of sinusoidal curves, the trapezoidal rule, midpoint rule, and Simpson's rule are commonly discussed methods. The Simpson's rule is noted for its accuracy, especially with smooth functions like sinusoids. Users suggest that the choice of method may depend on the specific characteristics of the data points and the desired precision. It is important to consider the trade-offs between computational efficiency and accuracy when selecting a method. Ultimately, Simpson's rule is often recommended for its superior performance with sinusoidal data.
pyroknife
Messages
611
Reaction score
4
I have a plot with a bunch of data points. I would like to perform a numerical integration on it. I was just wondering, what method of integrating do you guys think would be best suited for a sinusoidal like curve?
 
Physics news on Phys.org
Well, there is the trapezoidal rule, the midpoint rule, and the Simpson rule. I recall the Simpson rule being the best.
 
I multiplied the values first without the error limit. Got 19.38. rounded it off to 2 significant figures since the given data has 2 significant figures. So = 19. For error I used the above formula. It comes out about 1.48. Now my question is. Should I write the answer as 19±1.5 (rounding 1.48 to 2 significant figures) OR should I write it as 19±1. So in short, should the error have same number of significant figures as the mean value or should it have the same number of decimal places as...
Thread 'A cylinder connected to a hanging mass'
Let's declare that for the cylinder, mass = M = 10 kg Radius = R = 4 m For the wall and the floor, Friction coeff = ##\mu## = 0.5 For the hanging mass, mass = m = 11 kg First, we divide the force according to their respective plane (x and y thing, correct me if I'm wrong) and according to which, cylinder or the hanging mass, they're working on. Force on the hanging mass $$mg - T = ma$$ Force(Cylinder) on y $$N_f + f_w - Mg = 0$$ Force(Cylinder) on x $$T + f_f - N_w = Ma$$ There's also...
Back
Top