RipStik Acceleration

Spring 2013 I used the faculty parking lot to generate time versus distance data for an accelerating RipStik. The parking lot permitted "running out" at the end of the run, arcing back up against the slope to burn the gained speed. Getting a good curve meant accelerating hard over a distance of 1000 centimeters to generate a clear curve.

In the summer of 2013 I decided to try the new cement between the LRC and the south faculty building, using the natural slope to gain speed. I realized I could reverse my run and measure a deceleration as well. This led to a rather confusing chart as well as confusion in the minds of the students. As I laid up the data in a spreadsheet I realized I should have started with a deceleration into a turn-around and then accelerated back down the slope.

Fall 2013 I made the first attempt at a deceleration into an acceleration, using chalk marks at preset distances. Tracking the marks while riding meant looking down and spotting the marks on the fly. Concentrating on riding the RipStik while looking for chalk marks meant that at run's end I was unsure which times corresponded to the arc top, the vertex of the parabola. I did reverse engineer those, but had the sense that I needed a simpler way to track locations.

Concurrently, I had stumbled into the realization that fewer data points in laboratory three actually improved the results and understanding of the laboratory. I reverse engineered that fewer data points would work in my "arc of a RipStik" run.

After a practice run, I started from just east of the entrance to the south faculty building and accelerated hard. Shoes recommended. At one post shy of the south lot sidewalk junction I let off and began timing using the posts in lieu of chalk marks. I treated posts as being 305 cm center-on-center, even though the sidewalk junction is a 320 cm gap. The rest are 305 cm, the error is small at just under 5%. This term I have kept all measurements in centimeters so that speeds are directly comparable between the RipStik runs and laboratory data exercises.

I laid a cloth tape measure up at arc top and hoped I would make arc top for the turn. I counted posts as I rode, something that was made possible by the greater inter-mark span and the more visible posts. I did make it to the top of the slope near the LRC, but with no velocity left, so I had to swizzle gently to make the turn. Initially I opted to roll out and let the slope accelerate me, but I could feel a loss of acceleration and tried swizzling out the last couple of posts. I had little hope of decent data.

I ran a quick and dirty plot on a yellow pad post-run and was pleasantly surprised by what appeared to be potentially useful data. I assigned as homework creating a table and xy scatter graph, to be turned in via Schoology. A later analysis using Gnumeric confirmed that the run had gone better than I had expected.

The data is visually a rather good fit to a parabolic arc. Underneath there is a good deal of velocity variation away from a constant rate of change in that velocity.

I timed the passage of five posts. When counting I did not count the zeroth post, I simply started at that fifth post back from the post nearest the slope top. I then counted one, two, three, four, turn, five, six, seven... I did not time the vertex, at that point I am too busy remaining balanced at low speed while getting turned around. I split the difference between 6.78 seconds and 10.41 seconds, rounding the result. I also did not determine the vertex distance while riding, I went back and based on my memory of the turn I estimated that I had turned 60 cm beyond post four. Centering the vertex in time between post four and five probably had a positive impact on the symmetric nature of the resulting curve. Note that post five is post four - post five is post four being passed on the way back down slope.

Wednesday in class I can use this data to introduce the parabolic nature of motion with a constant acceleration. Once again the data (science) will drive the mathematics. Describing behavior in the real world drives the mathematics.

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