Editor at large – Make magazine. Creative director – Adafruit Industries, contributing editor – Popular Science. Previously: Founded – Hack-a-Day, how-to editor – Engadget, Director of product development – Fallon Worldwide, Technology Director – Braincraft.

Hello, I just watched this video and it was pretty interesting. I would like to add some questions to this. Does this take into account random close pack? If so how would you know if this was not randomly close packed? What if while this jar was being filled and shaken while filling? That would produce some different results.

I believe that’s discussed in the paper (but I don’t have access as I’m home for the summer). Basically you just end up with a different packing fractions (that can all be measured experimentally).

This is actually brought up in the article mentioned in Phillip Torrone’s video above, “Improving the Density of Jammed Disordered Packings Using Ellipsoids” (see reference 1)

Many experimental and computational algorithms
produce a relatively robust packing fraction
(relative density) = 0.64 for randomly
packed monodisperse spheres as they proceed to
their limiting density (8). This number, widely
designated as the random close packing (RCP)
density, is not universal but generally depends
on the packing protocol (9). RCP is an ill-defined
concept because higher packing fractions
are obtained as the system becomes ordered, and
a definition for randomness has been lacking. A
more recent concept is that of the maximally
random jammed (MRJ) state, corresponding to
the least ordered among all jammed packings
(9). For a variety of order metrics, it appears that
the MRJ state has a density of 0.637 and is
consistent with what has traditionally been
thought of as RCP (10). Henceforth, we refer to
this random form of packing as the MRJ state.

We report on the density of the MRJ state
of ellipsoid packings as asphericity is introduced.
For both oblate and prolate spheroids,
and Z (the average number of touching
neighbors per particle) increase rapidly, in a
cusp-like manner, as the particles deviate
from perfect spheres. Both reach high densities
such as 0.71, and general ellipsoids
pack randomly to a remarkable 0.735,
approaching the density of the crystal with
the highest possible density for spheres
(11) = pi/rad.18 = 0.7405. The rapid increases
are unrelated to any observable increase
in order in these systems that develop
neither crystalline (periodic) nor liquid crystalline
(nematic or orientational) order.

——–
Ref. 1
Donev, A., Cisse, I., Sachs, D., Variano, E.A., Stillinger, F.H., Connelly, R., Torquato, S., Chaikin. P.M. (2004). Improving the Density of Jammed Disordered Packings Using Ellipsoids. Science, Vol. 303. no. 5660, pp. 990 – 993 http://www.sciencemag.org/cgi/content/full/303/5660/990 http://onesci.com

I think you can apply the law of large number in this case and take the midpoint of the packing fraction range and use that as the value to calculate the number of m&m’s in the jar. After all it’s an estimate.

I watched this video and understand what he is saying, but hoped to end up with an equation that would help me win the M&Ms. Can someone put together that equation for me?

In order to calculate how much M&M’s would be needed to fill up the entire jar, you would take the “volume of the jar”/”volume of one m&m”

This value would give you how many m&m’s it would take to fill the jar completely, that’s excluding the spaces of air in between the m&m’s. This is where the packing fraction comes in. It pretty much says how much of the total volume of the jar is taken up my only the m&m’s. In this case, from the paper, it was 66.5%. This means that 33.5% is made up of air in between the m&m’s.

So your final equation to figure out the number of m&m’s is:

(VOLjar/VOLmm)*PackingFraction

Remember that the packing fraction is different types of candies. IE, peanut mm’s will have a different packing fraction from regular ones.

Hello, I just watched this video and it was pretty interesting. I would like to add some questions to this. Does this take into account random close pack? If so how would you know if this was not randomly close packed? What if while this jar was being filled and shaken while filling? That would produce some different results.

http://en.wikipedia.org/wiki/Random_close_pack

I believe that’s discussed in the paper (but I don’t have access as I’m home for the summer). Basically you just end up with a different packing fractions (that can all be measured experimentally).

http://www.sciencemag.org/cgi/content/abstract/303/5660/990

@Simon Kokosza

This is actually brought up in the article mentioned in Phillip Torrone’s video above, “Improving the Density of Jammed Disordered Packings Using Ellipsoids” (see reference 1)

Many experimental and computational algorithms

produce a relatively robust packing fraction

(relative density) = 0.64 for randomly

packed monodisperse spheres as they proceed to

their limiting density (8). This number, widely

designated as the random close packing (RCP)

density, is not universal but generally depends

on the packing protocol (9). RCP is an ill-defined

concept because higher packing fractions

are obtained as the system becomes ordered, and

a definition for randomness has been lacking. A

more recent concept is that of the maximally

random jammed (MRJ) state, corresponding to

the least ordered among all jammed packings

(9). For a variety of order metrics, it appears that

the MRJ state has a density of 0.637 and is

consistent with what has traditionally been

thought of as RCP (10). Henceforth, we refer to

this random form of packing as the MRJ state.

We report on the density of the MRJ state

of ellipsoid packings as asphericity is introduced.

For both oblate and prolate spheroids,

and Z (the average number of touching

neighbors per particle) increase rapidly, in a

cusp-like manner, as the particles deviate

from perfect spheres. Both reach high densities

such as 0.71, and general ellipsoids

pack randomly to a remarkable 0.735,

approaching the density of the crystal with

the highest possible density for spheres

(11) = pi/rad.18 = 0.7405. The rapid increases

are unrelated to any observable increase

in order in these systems that develop

neither crystalline (periodic) nor liquid crystalline

(nematic or orientational) order.

——–

Ref. 1

Donev, A., Cisse, I., Sachs, D., Variano, E.A., Stillinger, F.H., Connelly, R., Torquato, S., Chaikin. P.M. (2004). Improving the Density of Jammed Disordered Packings Using Ellipsoids. Science, Vol. 303. no. 5660, pp. 990 – 993

http://www.sciencemag.org/cgi/content/full/303/5660/990

http://onesci.com

I think you can apply the law of large number in this case and take the midpoint of the packing fraction range and use that as the value to calculate the number of m&m’s in the jar. After all it’s an estimate.

I watched this video and understand what he is saying, but hoped to end up with an equation that would help me win the M&Ms. Can someone put together that equation for me?

In order to calculate how much M&M’s would be needed to fill up the entire jar, you would take the “volume of the jar”/”volume of one m&m”

This value would give you how many m&m’s it would take to fill the jar completely, that’s excluding the spaces of air in between the m&m’s. This is where the packing fraction comes in. It pretty much says how much of the total volume of the jar is taken up my only the m&m’s. In this case, from the paper, it was 66.5%. This means that 33.5% is made up of air in between the m&m’s.

So your final equation to figure out the number of m&m’s is:

(VOLjar/VOLmm)*PackingFraction

Remember that the packing fraction is different types of candies. IE, peanut mm’s will have a different packing fraction from regular ones.

Yeah, that works perfectly… Until somebody opens the thing up and finds a partially inflated balloon in the middle of the M&Ms.