Numerical intergration of a set of measured data points

In summary, the conversation discusses the calculation of total charge Q for a transistor test using different methods of integration. Despite R being a constant, the two methods yield different results. The reason for this discrepancy is unclear, but it is suggested that it may be due to the numerical methods used or an error in the calculations. The speaker also mentions the possibility of the difference arising from the use of spline for approximation.
  • #1
homim4
2
0
Hi,
I have faced the following question. In our lab we perform different measurements on Transistors. We program a scope and that controls the tests. For one of our tests we would like to calculate the total charge Q. Mathematically this is given by
Q=∫ dt i(t), where i(t) is given by i(t) = v(t)/R, i(t)= current, v(t)=voltage, R= resistivity ( constant value), for a time interval [t1, t2]
We cannot measure i(t) directly but we can measure v(t). This means
Q=∫dt v(t)/R.
We have noticed the following
1. if we define Q1 = ∫dt ( v(t)/R)
2. or if we define Q2= (∫dt v(t))/R
Mathematically these two integrals should produce the same result. But we see completely different results. I wonder if this depends on the numerical methods used in integration or has another reason.

Thank you for your help.
 
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  • #2
If R is truly a constant, then the answer cannot depend on when you divide. You must be making a mistake in your calculations.
 
  • #3
You have not told us how you are evaluating the intergrals. Without that bit of information there is no way we can hope to help you.
 
  • #4
Thank you for your feedbacks,
1. R is constant and I expect that the integrals should be the same but they are not.
2. I do not know what integration method is used. This is a tester connected to a circuit we can enter the function that we want to evaluate, with limits of integration.
I have been thinking if we use spline then difference between the two way of integration may come from approximation using (v(t)/R) all the way instead of just multiplying (1/R) at the end.
 

What is numerical integration?

Numerical integration is a method used to approximate the area under a curve or the value of a function by dividing it into smaller, simpler shapes and summing their values.

Why is numerical integration important?

Numerical integration is important because it allows us to estimate the value of a function or the area under a curve when it is not possible to find an exact solution using traditional methods. It is commonly used in scientific and engineering applications.

What is the difference between numerical integration and analytical integration?

Numerical integration is an approximation method that uses numerical techniques to find an approximate value of an integral. Analytical integration, on the other hand, is a method that uses mathematical formulas and techniques to find an exact solution to an integral.

What are some common numerical integration methods?

Some common numerical integration methods include the trapezoidal rule, Simpson's rule, and Gaussian quadrature. Each method has its own advantages and is suitable for different types of functions or data sets.

What factors can affect the accuracy of numerical integration?

The accuracy of numerical integration can be affected by the number of data points, the choice of integration method, and the smoothness of the function being integrated. In addition, round-off errors and truncation errors can also impact the accuracy of the result.

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