Saturday, February 7, 2015

Thoughts on Beginning Magnetism

It has been a while since I have been able to get magnetism into the Honors Physics course, but the lack of fourteen million snow days this year has certainly helped. I'm putting it at the end of the third term, which is the term in which we studied gravity and circular motion, so it fits in pretty well - a non-contact, field-based phenomenon like gravity, which causes circular motion.

Since it has been several years, I thought that I'd completely revamp my treatment of magnetism. Here are some notes on the first day, and a brief outline of the plan of the rest. It's mostly bullet points, and at least as much for me remembering the thought process as for anything.

My favorite bit is that it's a whole 95-minute day that fleshes out the idea of field relatively well, giving students some concrete experience puzzling them out and creating representations based on their own investigations. Hopefully, this will give them a better mental picture of fields in space.

General ideas: 
- Magnetism is similar to gravity, in that it's a non-contact force - it's "invisible.'
- It's all about "fields," and we're going to need to figure out what the heck a field is at some point. That's pretty much the goal for the day.

Operational def’ns: We're going to use an operational def'n of the field (at least the B field direction) today. What’s our op. def. of temperature? It's what a thermometer measures! For B field directions, it’s going to be “where a compass points."

Let’s explore that a little:
- Map of the field in the room. Need better compasses, or maybe use phones? (yes, phones worked much better on the second day) They draw their vectors on board, "complete" them to form field lines.
- Let’s look at another field together: field of wire apparatus (through table), with compasses. Combining little arrows into loops, change i direction to see the opposite direction loop
- They investigate with bar magnet, horseshoe magnet, current loop (?), solenoid - your goal is to draw a good diagram of your object's B field on the WB.

Sharing whiteboards (unfortunately, didn't get any pics here of the boards):
- Bar first - what dir. are the field lines? (N to S) What does that tell us about Earth? (The north geographic pole is near a south magnetic pole!). 
- Horseshoe: what’s the orientation of the (unlabeled) poles?
What’s in common so far? 
- loops (are they closed? Yes - we couldn't see the part inside the magnet for these, but they're there)
- from N to S
- distance-dependent strength (how can we tell this from the diagram? Density of field lines!)
- opposites/likes
Now the solenoid: puzzling out the shape (if they didn't figure it out - one class did)

RHR - wire current

Current loop, using RHR1 - generates RHR2… let’s check it for the solenoid (go to solenoid)

Looping back around (see what I did there?): what is a field?
- has a value (magnitude, direction) at every point in space
- affects objects that are in it

Which field have we dealt with? Gravitational field, though we haven't called it that, really.
- Planet g field shape; where’s it strongest? same deal; not closed loops, though - that’s a difference.
- What objects create g fields? (masses) what objects does it affect? (masses)

For B fields, what creates them? perm. mag., currents -> moving charges (spin, domains, etc.), and it’ll affect moving charges, too! We'll look at the effects next time!

Future:
- Using this applet to examine the effects of B fields on charges - helps to figure out that the direction of the force on the charge is always perp to v, dependence on q, etc.
- Lorentz force 
- Applying that to a current; forces on wires
- Quantifying the fields of wires, solenoids

That's pretty much what we'll have time for before exams!

Thursday, December 4, 2014

Independent Friction Labs

At the end of the first term, I give my honors physics students a couple of days to design, implement, and present an independent investigation involving friction. That's about all that I specify, other than the size of the poster and a few details about requiring equations set with software, citations, etc.

This year's crop was great!

This group investigated the "friction" effects of oobleck on a block, dragged through it at constant speed. They determined that the relationship could be modeled in a friction-like way, but only if the "coefficient" was a function of speed.

This group tested the idea that the mass shouldn't affect the acceleration due to friction; three kids wore the same clothes and slid across the floor, using video analysis to determine the acceleration.

This group tested and modeled the friction between interleaved pages of books. They first modeled the friction on a single page, under some number of pages above, and then did a summation to predict the total possible static friction force between the books.

This group tested the classic physics approximation of ice being frictionless. They made pucks out of ice and dry ice, and determined friction coefficients for each.

This group tried to find the optimum pulling angle for breaking the static friction on an object, both experimentally and theoretically.

This group determined the coefficient of static friction between two blocks, then predicted the hanging mass necessary in a half Atwood machine to cause the top block to slip against the bottom block (which is attached to the cart in the half Atwood).

Another half Atwood exploration - they set up a vertical surface on a cart and increased the hanging mass until an eraser would accelerate along with the cart, instead of slipping down. 

This group dragged a boat through water at different speeds, trying to determine whether they could model fluid drag as a friction force. They showed that the "coefficient" would be velocity-dependent, so that drag is not really a friction force.

Circular Motion Simulation Follow-up

I last posted about a new circular motion applet that I was planning on using with my classes as the quantitative part of their UCM paradigm lab. Some reflections:

  • When students came up with a list of variables that might affect the size of the centripetal acceleration, the list was: speed, mass, radius (always in that order). The visual accelerometer on a rotating table showed the qualitative effect of speed nicely, and the thought experiment about driving a car around a corner (tight or wide) addressed radius. We couldn't do mass with the given stuff, so I told them to check that out in the applet. A few seconds' work with the slider showed that it's irrelevant.
  • The applet is framed in terms of string length (radius) and rotational frequency - instead of speed. This means that students had to confront (and figure out) the relationship between rotational frequency (or period) and speed just to get their data for the acceleration's dependence on speed. I like that.
  • The other way that they have to confront it is to control speed while investigating acceleration's dependence on radius - changing the radius but not the frequency would change the speed. The students have the figure out the proper frequency for each new value of the radius in order to keep the speed constant during the second experiment. I like this a lot as well.
  • Students still have trouble reconciling their two models ( and ) to determine the complete function of v and r. Even when they have figured out the units of the two constants, the connection is hard for them to make. I'm very open to suggestions of ways to make this go more easily - I don't have a great handle on what the conceptual difficulty is for them here. In the second section, I framed those two models as "OK, so a is proportional to v-squared, and a is proportional to 1/r," and that may have helped.
  • Overall, the quantitative modeling went much more quickly, had some good conceptual things to think about, and was good practice with function modeling, so I'm pretty happy about it, at this point. We'll see how things go over the next couple of weeks; did this begin to build lasting understanding?

Saturday, November 29, 2014

Circular Motion Simulation

I've been through several variations of circular motion paradigm labs over the years. Lots of approaches, lots of pros and cons.

Here's where I'm landing this year:

  • Preliminary investigation: a qualitative exploration, using basketballs and "science hammers" (lab rods with clamps on the ends) - differentiating between the effects of forces parallel to and perpendicular to the velocity. This establishes the conceptual foundation of uniform circular motion, and comes back later in the year during the energy transfer model (work)
  • Quick conceptual investigation: using either a visual accelerometer or a wireless dynamics probe/LabQuest, determine the qualitative effects of various variables on the acceleration of an object in UCM. Narrow it down to radius and (linear) speed. Angular speed can be a more natural variable for this (and easier to design an experiment to control for), but I've found that students have a lot of difficulty differentiating between angular and linear speeds at this point, and that they later confuse an angle in a banked turn with the angle "around the circle." 
  • While designing a real-world experiment with constant angular velocity is easier, using an applet can make experimental design with linear velocity as a variable just as easy. It's also quicker and more reliable (whirligig experiments can be a little dicey with data quality), putting the emphasis on the data analysis. (Save the whirligig for a practicum later!)
The various circular motion applets that I've used before are pretty much inaccessible now, because of Java's waning usability. So, I wrote one using Glowscript

Here's a screenshot - click through to use the applet.

Saturday, September 20, 2014

Graphical Solutions _and_ Symbolic Algebra in Physics

Graphical solutions are terrific tools to build understanding in motion, momentum, energy, and nearly every area of physics. Even better than that, they allow a large number of students to successfully solve problems that really struggle with a strictly algebraic approach. Kelly O'shea and others have some great resources for you to peruse; the approach can be eye-opening for those of us brought up in the traditional system.

One thing that I struggled with a few years ago when first exposed to graphical problem-solving was all of the numbers. With my honors physics classes, I put a lot of emphasis on building their skills in exclusively symbolic solutions to problems - students are expected to do every solution with "no numbers until the end." That is, they have to first derive an expression giving the desired quantity in terms of nothing but given quantities; only then can they use any numbers. This approach has several big advantages:

  • It's a pain to carry around units in the algebra (and necessary, because it's unthinkable to have "naked numbers"); symbolic algebra means that we don't have to
  • We can re-evaluate for different values of the parameters easily
  • We can check how our solution depends on the variables - should the Atwood's acceleration increase or decrease when this mass increases?
  • We can learn how our solution depends on the variables - maybe we didn't know that the mass would cancel out of the expression for the minimum speed in the Gravitron!
  • We can check how our solution behaves in special cases - should the acceleration go to g if that mass goes to zero?
  • We can learn how the solution behaves in special cases - hey, the position function of a falling ball with drag becomes linear in the large-t limit!
  • We can still check the units of the answer, without having to carry them through the algebra - if the units don't work, then it's not even worth plugging in the numbers
  • This is a skill that ultimately is a part of how "big kids" do science - it's an great skill to have going into college science and math courses (the course that I took this summer had, out of about sixty HW and exam exercises, exactly three problems that involved numbers!)
  • It helps to refine and develop student algebra skills (the more abstract end of the concrete-to-abstract progression of their skill development)
These advantages don't have to be lost in graphical problem-solving - students can still label the graph (naming every labeled quantity, whether it's known or not) and can still build slope and area relationships from the graph (involving those variables that they labeled). Solving these symbolically gets them the best of both worlds: graphical analysis and its lower barrier to entry and improved understanding, and symbolic algebra, with the skills listed above.

What does it look like? Here's a bit of student work, from a recent assessment on CVPM (constant velocity particle model) and the a whiteboard with the first (!) CAPM (constant acceleration particle model) problem that three students did this year.





Tuesday, September 16, 2014

VPython, Energy, and Stability

The content in AP Physics C about stability and its relationship to energy is a pretty thin introduction to a fairly deep idea. Classifying equilibria by looking at the derivative of potential energy can be a quick add-on, or we can make it a little deeper with the help of VPython.

When it first comes up, we go through the stable/unstable/neutral equilibrium discussion and the corresponding shapes of potential energy graphs, but I really wanted a way for students to apply that. I've thought of a few, only three of which I have tried out so far:

  • Have VPython plot a potential energy vs. tip angle curve for a box that's standing on its end - compare for different heights/widths of box
  • Have VPython graph total potential energy vs. stretch amount for a mass on a vertical spring
  • Have VPython graph potential energy for a mass of a spring that doesn't necessarily stay vertical (on y vs. x axes, with U value signified by color)
  • Investigate the Lenard-Jones (interatomic) potential, both for meaning and location of equilibrium points and to fit an approximate quadratic to it near the equilibrium point, justifying the use of the ball-and-spring model of matter.
  • Later in the year, during rotation, have them create a physical pendulum program with a box on a pivot not through its center - modify this to show the graph of potential energy as a function of angle
For the first and fifth, I have some results using VPython to show. (The fourth is great, too, especially if you're using Matter and Interactions!) For the box tipping, the graph is neat, not least of which because of the discontinuity in the derivative at 0. Calculus wouldn't find this minimum on without intervention from the student, and it's easy to forget to check those endpoints! This gives a nice graphical reminder. My one concern is that the geometry is fairly harrowing - determining the height of the CM as a function of angle isn't super-easy, and requires a high-quality diagram. 

Here's a screen shot:


When you vary the parameters, that central well can become deeper (more stable) or shallower (less stable), and vanishes as the box gets narrower (and becomes a pencil).

For the final one, I think that the program, being a modification of a previous one, isn't too difficult to do, though it does bring in the complication of having a "meta loop," where the whole simulation runs several times, with different placements of the axis. 

Here's a screen shot that links to a video of the program running:

The graph here is incredibly rich. Not only do we see two different kinds of equilibrium (three, if you let the pivot be at the middle for the first run through), but we see how the system is forced away from or towards those equilibrium more or less violently as the pivot moves. This helps to bring together their common-sense understandings of stability and the physical principle that we're trying to investigate.

Saturday, August 30, 2014

Drag Graph Checking

I previously posted about a class exercise where my AP C students pair up, pick two random objects, and try to draw qualitatively correct position, velocity, and acceleration graphs for them falling through the air. The idea is to get a qualitative feel for drag graphs and to check qualitative results for terminal velocities.

I had written a VPython script to do this, but it required my intervention to change the values each time, and everyone had to watch all of the graphs. I don't have enough students for that to be super-terrible, but I wanted a way for them to do it themselves.

Enter Glowscript; I've ported the VPython script to there (and made some improvements), and students can now check these graphs for themselves!

There's a screenshot below which links to the simulation - feel free to use it and drop me a line if you do! Definitely let me know if you find any bugs!