When you strike a bell, it rings at a given frequency. This frequency is called the resonant frequency and is the natural frequency at which the bell likes to ring. Just about anything that can shake, rattle, or oscillate will have a resonant frequency. Things like quartz crystals, wine glasses, and suspension bridges all have a resonant frequency. The quartz crystals oscillate at frequencies high enough for accurate timekeeping in watches, the wine glasses at audible frequencies to make boring dinners more interesting, and bridges at low enough frequencies that you can feel it when you walk. It is the resonant frequency of bridges that we decided to measure.
Yesterday we rode down Ithaca's hills in an attempt to estimate the terminal velocity of a bike rider braving the city's potholes. But estimations are easy, and we relied on a number of factors - the drag coefficient and area of the bicyclist, in particular - to get them. To see how well we did, it's time to move on to the experimental portion this exercise. Our tools? My bike (figure 1), and my beloved accelerometer (figure 2), with Google's My Tracks app installed.
The impetus for this post lies with three facts. First, I like to bike to work. Second, Cornell sits on a hill. And finally, I'm not very brave.
As a result of all of these, along with Ithaca's less-than-optimal road maintenance, my semi-daily rides home tend to produce a lot of wear on my brakes as I cruise downhill at what appears to me to be very high speeds. I began to ponder just how high this speed really is, and if I could reduce my use of the brakes or if I'm going to end up using them anyway at the bottom of the hill.
You may remember when I invited everyone to play my webform version of Colonel Blotto. Well, its still up and has been up for some time, but hasn't seen any action for a while so I thought it might be time to take a look at the results.
Colonel Blotto is an interesting game. It seems to me, that much of this interest derives from the fact that how well your strategy performs is very much a function of which strategies exist in the pool. There is not a clear cut winning strategy, you need to feel out the existing pool and adapt accordingly.
So to stir things up a little bit, in what follows I will share some data from the existing database, refraining myself from commenting too much. Basically, stay tuned for a bunch of pretty pictures which will hopefully get your gears turning. The game is still up, feel free to try to game it now that this information is out. Might be interesting to see what kind of effect releasing the leaderboard will have on the leaderboard.
Sorry about the blog hiatus. During the summer, without teaching classes, inspiration is harder to come by. But, tonight I cooked a steak. I recently got a new digital meat thermometer. My plan was to slowly cook the steak until the internal temperature got to be about 140 degrees Fahrenheit with the oven at 200 degrees, take it out, wrap in tin foil, crank the oven to 500 degrees, stick it back in, and give it a nice exterior, reaching an internal temperature of about 150 degrees which would put it at about medium.
After I put the steak into the oven though, I started to watch the temperature go up on my digital thermometer and thought, why not take data. And so I did. Here are the results.
Above you see the internal temperature of the steak as a function of time. First some comments about the graph.
This is a physics blog written by a bunch of graduate students out of Cornell.
The Virtuosi is in no way officially affiliated with Cornell University. It is the side project of some of its graduate students. The opinions expressed here do not necessarily reflect those of the university or the physics department.