Week 4

This week was spent modeling and setting up the statistical framework for the research.

The statistical backbone of my analysis is in R, a data manipulation language. R is a PBV (Pass By Value) language; once a parameter is called up, any subsequent changes or operation do not alter it in its stored form, only its referenced form. This makes R very useful for manipulating data without integrity risk. The central object of R is the data frame, an arbitrarily large/dimensional matrix that can be referred to but not called on, allowing the user to call down an array by using its coordinate.

My physical model for the radiometer is that of a constant power, in-viscid flow, such that P=L-bv^2. L, the net thermal input term, can be measured from the surface temperature of the system at equilibrium as 65W, negating the need to know the environmental input. This setup makes use of the handy identity of v dp=p dv, to integrate subsequent terms. Ignoring thermal creep, this gives us a resting velocity of v= sqrt(P/b).

Week 3

The better part of this week was spent designing, assembling and perfecting a wooden U-mount to serve as a holder for the tachometer's emitter and diode, as pictured below. Despite slight warping in the wood and refraction by the bulb, the array is able to detect the minute interruptions in the laser perfectly.

From this setup, I received the following time series, with the anticipated results in magenta, and the actual results in blue. Although it fit the general prediction well, there is a great deal of temporal aliasing , as well as an unexplained dip near the 32 second mark. The temporal aliasing is a result of a sample rate smaller that that of the signal, leading to discretized, 'jumpy' data. It can be minimized by aggressive smoothing algorithms, which run the risk of being too reductive.

Week 2

Effects of heating: Short demo vid

Week 1

This week efforts were divided among three fronts: preliminary research, qualitative testing, and instrument drafting.
My preliminary research focused on different models of dynamic driven heat engines, to get a general grasp on how and why certain changes would affect the system. This was followed by qualitative testing to determine what parameter changes were worthy of consideration. I discovered that by heating the apparatus up lightly, thereby increasing the internal pressure, I could increase the steady state frequency slightly, until it dipped at higher temperatures.
I also researched and tested three separate frequency measurement systems: stroboscope, laser tachometer, and video analysis. First, by shining a strobe light on the instrument until it appeared stationary, I was able to find one of the modes of its frequency. This will be confirmed by slowed down video analysis of the light mill's motion. Lastly, I'm attempting to build a laser tachometer to measure the time between passes at steady state using a laser pointer and a photogate.
Special thanks to Dr. Haar and Dr. Hoffman for their amazing help

Intro

I'm Alex Poelstra, a senior at BASIS Tucson North. My project attempts to model the turbulent flow in Crookes Radiometers to better predict their steady state, with the eventual goal of a large scale, energy harnessing model.
Crookes Radiometers, or Light Mills, are turbines that run off radiative heating. They consist of a spindle with a four-vaned rotor in a partially evacuated bulb, which rotates due to the diffusion of gas particles from the cold to hot edges.
Because the chamber is in a partial vacuum, it has a high Knudsen number, meaning particles particles will travel relatively far between collisions. This means the analysis must rely on statistical methods to simulate the system's motion rather than continuum mechanics.
My research is taking place at the U of A Physics and Atmospheric Science building, in Dr Manne's lab.The entirety of my proposal is available here.