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**Modeling hours of daylight**

Desmos has a nice student activity (on teacher.desmos.com) modeling the number of hours of daylight in Florida versus Alaska – which both produce a nice sine curve when plotted on a graph. So let’s see if this relationship also holds between Phuket and Manchester.

First we can find the daylight hours from this site, making sure to convert the times given to decimals of hours.

**Phuket**

Phuket has the following distribution of hours of daylight (taking the reading from the first of each month and setting 1 as January)

**Manchester **

Manchester has much greater variation and is as follows:

Therefore when we plot them together (Phuket in green and Manchester in blue) we get the following 2 curves:

We can see that these very closely fit sine curves, indeed we can see that the following regression lines fit the curves very closely:

**Manchester:**

**Phuket:**

For Manchester I needed to set the value of b (see what happens if you don’t do this!) Because we are working with Sine graphs, the value of d will give the equation of the axis of symmetry of the graph, which will also be the average hours of daylight over the year. We can see therefore that even though there is a huge variation between the hours of daylight in the 2 places, they both get on average the same amount of daylight across the year (12.3 hours versus 12.1 hours).

**Further investigation:**

Does the relationship still hold when looking at hours of sunshine rather than daylight? How many years would we expect our model be accurate for? It’s possible to investigate the use of sine waves to model a large amount of natural phenomena such as tide heights and musical notes – so it’s also possible to investigate in this direction as well.

**Modelling tides: What is the effect of a full moon?**

Let’s have a look at the effect of the moon on the tides in Phuket. The Phuket tide table above shows the height of the tide (meters) on given days in March, with the hours along the top. So if we choose March 1st (full moon) we get the following graph:

**Phuket tide at full moon:**

If I use the standard sine regression on Desmos I get the following:

This doesn’t look a very useful graph – but the R squared value is very close to one – so what’s gone wrong? Well, Desmos has done what we asked it to do – found a sine curve that goes through the points, it’s just that it’s chosen a b value of close to 120 – meaning that the curve has a very small period. So to prevent Desmos doing this, we need to fix the period first. If we are in radians the we use the formula period = 2pi / b. Therefore looking at the original graph we can see that this period is around 12. Therefore we have:

period = 2pi/b

12 = 2pi/b

b = 2pi/12 or pi/6.

Plotting this new graph gives something that looks a lot nicer:

**Phuket tide at new moon:**

**Analysis:**

Both graphs show a very close fit to the original data – though both under-value the tide at 2300. We can see that the full moon has indeed had an effect on the amplitude of the sine curves – with the amplitude of 1.21m for the full moon and only 1.03m for the new moon.

**Further study:**

We could then see if this relationship holds throughout the year – is there a general formula to explain the moons effect on the amplitude? We could also see how we have to modify the sine wave to capture the tidal height over an entire week or month. Can we capture it with a single equation (perhaps a damped sine wave?) or is it only possible as a piecewise function? We could also use some calculus to find the maximum and minimum points.

There is a very nice pdf which goes into more detail on the maths behind modeling tides here. There we go – a nice simple investigation which can be expanded in a number of directions.

**Circular Motion: Modelling a ferris wheel**

This is a nice simple example of how the Tracker software can be used to demonstrate the circular motion of a Ferris wheel. This is sometimes asked in IB maths exams – so it’s nice to get a visual representation of what is happening.

First I took a video from youtube of a Ferris wheel, loaded it into Tracker, and then used the program to track the position of a single carriage as it moved around the circle. I then used Tracker’s graphing capabilities to plot the height of the carriage (y) against time (t). This produces the following graph:

As we can see this is a pretty good fit for a sine curve. So let’s use the regression tool to find what curve fits this:

The pink curve with the equation:

y = -116.1sin(0.6718t+2.19)

fits reasonably well. If we had the original dimensions of the wheel we could scale this so the y scale represented the metres off the ground of the carriage.

There we go! Short and simple, but a nice starting point for an investigation on circular motion.