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2016 April 4

First week’s grading done

Filed under: Circuits course — gasstationwithoutpumps @ 22:44
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I spent all day Sunday grading the first set of lab reports.  I was expecting 24 reports of about 3 pages each, but I got 25 averaging about 5 pages each.  I think that the reports were a bit better this year than at corresponding times in previous years, but I did not get my grading done until almost midnight Sunday night, keeping me from getting much else done this weekend.

(I did manage to get my hair cut and to build a new strobe stand with room for 20 of my LED boards, which should give 1800 lumens during the flash. With a duty cycle of only 1/65, I don’t think that I need heat sinks on the boards for the strobe, as the average current should be only 40mA, though the peak current will be about 2.6A.)

In class on Monday, I gave students some group feedback on their writing, plus a couple of \LaTeX pointers, then took questions, some of which were about writing, but most were about the optimization of the fixed resistor in the voltage divider for the resistance-to-voltage converter in the thermistor lab.  I showed them how to set that up, but did not try to solve it in class.

After class, when I was making up the key (redoing all the problems—I don’t like just looking up results—refreshing my memory on how to solve the problems by resolving them is best), I ran into a little trouble doing the optimization. I used to be able to just ask Wolfram Alpha to solve the differential equation, but their newer parser seems to be much harder to convince to do anything.  I eventually gave up and used a cruder tool to just take the second derivative and solved for the resistance by hand.  That was faster than the time I wasted trying to get Wolfram Alpha to do anything useful.  (I suspect that they have deliberately crippled it, to make people pay for Mathematica.)

Monday afternoon and evening (from about 1:30 to 7:45) was spent grading the first pre-lab homework.  Again the results are a little better than previous years, but there were 9 prelabs fewer than I expected (3 students have dropped already and 6 did not do the prelab).  I hope that those who did not do the prelab were just confused about when it was due, and not starting a trend towards coming to class and lab unprepared. I also hope that no more students drop—this class is not a weed-out class, though it is a lot of work.

Back in January, Mike wanted to know where I ended up doing my grading. Sunday I did my grading in my breakfast room, with the laptop on the floor where I could get to it if I really needed to look something up, but where it was not a constant temptation to goof off.  On Monday, I worked in my office on campus, where the e-mail was a minor distraction that I checked between problems.  (For the prelabs, I graded the entire stack for problem 1, then the entire stack for problem 2, and so forth.  This makes for more consistent and faster grading than grading a student at a time, but it would be faster still if the students didn’t put their answers in random order on what they turned in.) I’ll probably continue with weekend grading in the breakfast room and prelab grading in my office until the distractions get to be too much—then I’ll look for a coffeeshop to grade in.

2015 April 8

Optimization and model fitting went well today

Filed under: Circuits course — gasstationwithoutpumps @ 21:47
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Today’s lecture in BME 101 (the Applied Electronics for Bioengineers class) went very smoothly. I started with a little light entertainment: a video about blowing up capacitors, sent to me by Jameco: This video reinforced the message that I had given them in lab last week about electrolytic capacitors.

I had two topics to cover: the optimization problem from last weekend’s homework, which only one student had managed to do, and model fitting to extract parameters for the thermistor model from the data collected in lab yesterday.

I gave the students the choice of order for the topics, and they wanted the optimization first.  The problem is fairly straightforward from an engineering standpoint: we want to maximize the sensitivity of the circuit that converts temperature to voltage at a specific operating temperature Topt. I first sketched very rough plots of what the voltage would look like as a function of temperature: monotone increasing but not particularly linear. I reminded them that sensitivity is the change in output that you get for a given change in input: dV/dT.  I coaxed out of the class the idea that to maximize the function dV/dT, we need to take its derivative and set it to 0: \frac{d^2 V }{d T^2} = 0.  We only have one free variable in our circuit to change (the load resistor R), so we need to take that equation and solve it for R, to get the value of R that maximizes sensitivity.

I reminded them of the simple model for the resistance of a thermistor that we’d had on Monday: R_{T} = R_{\infty} e^{B/T}, and they had no trouble coming up with the equation for the output voltage V = V_{in} \frac{R}{R+R_{\infty} e^{B/T}}.  I then suggested using Wolfram Alpha to solve the equation, and switched from the chalkboard to the screen to type

solve d^2 (R/(R+S e^(B/T)))/ dT^2 = 0 for R

(changing R to S, to avoid confusing Wolfram Alpha with subscripts).
Wolfram Alpha conveniently replied with two solutions: R=0 (which would result in a constant 0 output voltage, clearly a minimum sensitivity) and R= S e^{B/T} \frac{B-2 T}{B+ 2T}. I pointed out that the first part was just the resistance of the thermistor at the temperature we were optimizing for, and the second term scales that down a little.

We then moved on to fitting the model. I showed three data sets that students had sent me—one which was a little messy, but still quite usable, one that was a little better, and one that was really beautiful.  I continued with the really good data set.  I explained that gnuplot tries to minimize the error on the y-axis for a function of things on the x-axis, and the students decided that minimizing the temperature error for a given resistance was probably best here. (I suspect it doesn’t make much difference with this data, but it is good to get them to think about that as a decision that needs to be made for each modeling problem.)

On the board, I turned around the equation for the model, to get temperature in terms of R: T = \frac{B}{\ln R - \ln R_{\infty}}.

I then developed the gnuplot script for the problem live, debugging as I went.  Some of the errors to debug were ones I introduced deliberately, others were inadvertent, but all were good for teaching both the process of debugging and the notion of doing sanity checks.  My first mistake was an inadvertent one: I capitalized a variable differently in the parameter list and in the function body, and gnuplot is case-sensitive.  This was very easy to find and fix, and gave me an opportunity to tell students that gnuplot was sensitive to case, since that had not come up previously. My second mistake was a deliberate one: I typed in the model exactly as we had derived it on the board, knowing full well that the model was for °K, but the data was for °C.  I’d even reminded students of that earlier, when we were doing the optimization problem.

I then ran the fit in gnuplot and asked students if we were done, or if there was a sanity check we could do.  A couple of them asked if we could plot the model and the data on the same plot, so I did that. The fit was very obviously completely wrong. So I asked the students what was wrong and how we could fix it. I did dice-assisted cold calling to put 3 or 4 students on the spot, getting not very useful answers, then took answers from a couple of students who raised their hands. The second student pointed out the °C vs °K problem.

So I fixed the formula and ran the fit again, expecting everything to work fine.  It didn’t! So we went into debugging mode, trying to see if starting with better estimates of B and R would help. Nope. Then I realized that I had made a second inadvertent error: I’d put in 273.15 with the wrong sign! I fixed that, ran the fit again, and again things failed (producing negative values for R).  This time, though when I put in better initial estimates, everything converged to a very good fit, which gave me a teachable moment about the need to have decent estimates before fitting, so that the optimization algorithm that did the fitting could converge.

The final script was

temp(R,B,Rinf) = B / ( log(R) - log(Rinf)) - 273.15
B=3000; Rinf=1e-4
fit temp(x,B,Rinf) 'student-thermistor-data-3.txt' using 2:1 via Rinf,B
plot 'student-thermistor-data-3.txt' using 2:1, \
      temp(x,3435,10*exp(-3435/(25+273.15))), \

The second curve of the plot is using the data sheet values for the B value and for R25°C=10kΩ.
Here is the plot produced:

Notice that I did not have axis labels, title, nor echoing of the parameters in the key.  We covered that last week and I'm hoping that students can carry skills over from one week to the next.

Notice that I did not have axis labels, title, nor echoing of the parameters in the key. We covered that last week and I’m hoping that students can carry skills over from one week to the next.

The model fit is slightly better than the data sheet values, but the data sheet values were closer than I had expected based on previous years’ fits. I think that it may be that the students had a properly calibrated thermometer (I’d removed any digital thermometers that reported ice water as warmer than 0.5°C—about 1/3 of them were that far out of calibration), and that this particular pair of students had been very careful with their measurements.

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