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).