Back in Winter Storm Warning, we estimated that the total accumulation (net change) of snow over the hours was the sum of a bunch of terms that all looked like , as these were the amount of snow that fell over a short time period if we assumed a constant rate (and computed it as ). Adding these up gave us an approximation of the overall change over the hours. The notation for this summation is , where the “’s” just mean that we take a sequence of times over the same size interval (i.e., the ’s were the left-hand endpoints of each interval. We will be not concerning ourselves with this “” notation anymore here- just remember that what we are plugging a sequence of ’s into while the change in (length of each interval) remains the same).

We also saw that if the length of the intervals () gets very small, our approximation gets closer and closer to the exact value (why?), which we wrote as , where is the interval of time we were interested in. Thus, “turns into” as gets very small. And they were also approximating (and eventually exactamating) the area between the graph of and the -axis.

Well, it turns out that if we replace by something else (with the goal of doing a different sort of measurement) and the measurement involves adding up the products of (“that something else”) and the estimation becomes more and more exact as gets closer to zero, then it turns out that the sum “turns into” . These types of sums are called “Riemann Sums”, named after a famous th century mathematician named Bernhard Riemann. The biggest problem in most of these applications is defining the “something”.

The following list of activities contain just a couple of a few applications of Riemann sums in which we use integrals to do more than just measure area: See the activities Stuck in the Middle , It Slices and It Dices , SoSo , and It’s a Long Way to Tipperary! .

2024-10-10 13:52:52