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The mathematics of the previous problem can also be applied to a one-dimensional random walk: a journey consisting of Nsteps, all the same sic, cache chosen randomly to be cither forward or backward. (The usual mental image is that of a drunk stumbling along an alley.)

(a) Where are you most likely to find yourself, after the end of a long random walk?

(b) Suppose you take a random walk of 10,000steps (say each a yard long). About how far from your starting point would you expect to be at the end?

(c) A good example of a random walk in nature is the diffusion of a molecule through a gas; the average step length is then the mean free path, as computed in Section 1.7.Using this model, and neglecting any small numerical factors that might arise from the varying step size and the multidimensional nature of the path, estimate the expected net displacement of an air molecule (or perhaps a carbon monoxide molecule traveling through air) in one second, at room temperature and atmospheric pressure. Discuss how your estimate would differ if the clasped time or the temperature were different. Check that your estimate is consistent with the treatment of diffusion in Section1.7.

Short Answer

Expert verified

(a) Its most probable scenario is, as is normal, an equally lot of actions to the left and right, signifying that its trip will most definitely conclude at its initial state (assuming a fair number of steps). Because the possibility line can even be modelled by a Gaussian it around apex, it is also realistic to expect that you will likely land up just few steps from either end of a origin.

(b) The place to begin would you expect to be at the top is 70x+70

(c) Estimate is according to the treatment of diffusiond=N2=6.1mm

Step by step solution

01

Flipping (a)

(a) This method is technically analogous to the coin turning operation in difficulty 2.24, with a step here to right yielding in a very head or a move to the left yielding in an exceedingly tail. As a corollary, the range of possible step patterns in an exceedingly chaotic system of Nsteps is 2N, and also the risk of coming away n steps first from start is

P=Ω(N,n)Ωmax

Ω(N,n)=n+N1nΩmax=2N

Ω(N,n)n+Nn=N!n!(Nn)!

P=12NN!n!(Nn)!

Ω2Ne2x2N

xwidth=N2

Ω(N,n)=n+N1nΩmax=2N

02

stochastic process   (b)

(b) for less than a variate of N=10000steps, we are able to find yourself later territory:

N2x+N2

100002x+100002

70x+70

03

Diffusion (c)

(c) The diffusion rate in a perfect gas is one instance of a stochastic process . We calculate the mean free path of a gas increases of radius r and used the identical model as in section 1.7:

14πr2VVmoγ

where could we be? The frequency of air molecules during a volume Vis denoted by Nmol. The underlying speed determined through kinetic law is employed to live the common movement of either a particle, which is:

v¯=3KTm

=1.5×107m

v¯=500m81

We must always count the number of steps at a time tbecause the number of steps in in an exceedingly chaotic system to depict diffusion as a random process.

N=v¯t

dxwidth=N2

dv¯t2=12v¯t

d12×500×1.5×107=0.0061m

d6.1mm

D12v¯

(Δx)2DΔtΔx12v¯Δt

04

The amount of moves

it is also merit note that when a gas molecule diffuses 6mmin 1second doesn't indicate it's doing so at a continuing speed of 6mm per second. Since this distance diffused is adequate the inverse of your time, the diffusion speed slows over time. A substance takes roughly 4months to permeate the length of a 10mmroom, which is noticeably slower than 6mmper second, as seen in problem 1.68. If we warm the earth while keeping everything else constant, the speed rises from v¯=3KTm, but still the phase velocity remains constant, therefore the the amount of moves during a given moment rises, implying that the molecules moves.

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Most popular questions from this chapter

Fun with logarithms.
a Simplify the expressionealnb. (That is, write it in a way that doesn't involve logarithms.)
b Assuming that b<<a, prove that ln(a+b)(lna)+(b/a). (Hint: Factor out the afrom the argument of the logarithm, so that you can apply the approximation of part d of the previous problem.)

A black hole is a region of space where gravity is so strong that nothing, not even light, can escape. Throwing something into a black hole is therefore an irreversible process, at least in the everyday sense of the word. In fact, it is irreversible in the thermodynamic sense as well: Adding mass to a black hole increases the black hole's entropy. It turns out that there's no way to tell (at least from outside) what kind of matter has gone into making a black hole. Therefore, the entropy of a black hole must be greater than the entropy of any conceivable type of matter that could have been used to create it. Knowing this, it's not hard to estimate the entropy of a black hole.
aUse dimensional analysis to show that a black hole of mass Mshould have a radius of order GM/c2, where Gis Newton's gravitational constant and cis the speed of light. Calculate the approximate radius of a one-solar-mass black holeM=2×1030kg .
bIn the spirit of Problem 2.36, explain why the entropy of a black hole, in fundamental units, should be of the order of the maximum number of particles that could have been used to make it.

cTo make a black hole out of the maximum possible number of particles, you should use particles with the lowest possible energy: long-wavelength photons (or other massless particles). But the wavelength can't be any longer than the size of the black hole. By setting the total energy of the photons equal toMc2 , estimate the maximum number of photons that could be used to make a black hole of mass M. Aside from a factor of 8π2, your result should agree with the exact formula for the entropy of a black hole, obtained* through a much more difficult calculation:

Sb.h.=8π2GM2hck

d Calculate the entropy of a one-solar-mass black hole, and comment on the result.

For a single large two-state paramagnet, the multiplicity function is very sharply peaked about N=N/2.

(a) Use Stirling's approximation to estimate the height of the peak in the multiplicity function.

(b) Use the methods of this section to derive a formula for the multiplicity function in the vicinity of the peak, in terms of xN(N/2). Check that your formula agrees with your answer to part (a) when x=0.

(c) How wide is the peak in the multiplicity function?

(d) Suppose you flip 1,000,000coins. Would you be surprised to obtain heads and 499,000 tails? Would you be surprised to obtain 510,000 heads and 490,000 tails? Explain.

Calculate the number of possible five-card poker hands, dealt from a deck of 52 cards. (The order of cards in a hand does not matter.) A royal flush consists of the five highest-ranking cards (ace, king, queen, jack, 10) of any one of the four suits. What is the probability of being dealt a royal flush (on the first deal)?

For either a monatomic ideal gas or a high-temperature Einstein solid, the entropy is given by times some logarithm. The logarithm is never large, so if all you want is an order-of-magnitude estimate, you can neglect it and just say . That is, the entropy in fundamental units is of the order of the number of particles in the system. This conclusion turns out to be true for most systems (with some important exceptions at low temperatures where the particles are behaving in an orderly way). So just for fun, make a very rough estimate of the entropy of each of the following: this book (a kilogram of carbon compounds); a moose of water ; the sun of ionized hydrogen .

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