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

Find an expression for the entropy of the two-dimensional ideal gas considered in Problem 2.26. Express your result in terms of U,AandN.

Suppose you flip 20 fair coins.

(a) How many possible outcomes (microstates) are there?

(b) What is the probability of getting the sequence HTHHTTTHTHHHTHHHHTHT (in exactly that order)?

(c) What is the probability of getting 12 heads and 8 tails (in any order)?

This problem gives an alternative approach to estimating the width of the peak of the multiplicity function for a system of two large Einstein solids.

(a) Consider two identical Einstein solids, each with Noscillators, in thermal contact with each other. Suppose that the total number of energy units in the combined system is exactly 2N. How many different macrostates (that is, possible values for the total energy in the first solid) are there for this combined system?

(b) Use the result of Problem2.18to find an approximate expression for the total number of microstates for the combined system. (Hint: Treat the combined system as a single Einstein solid. Do not throw away factors of "large" numbers, since you will eventually be dividing two "very large" numbers that are nearly equal. Answer: 24N/8πN.)

(c) The most likely macrostate for this system is (of course) the one in which the energy is shared equally between the two solids. Use the result of Problem 2.18to find an approximate expression for the multiplicity of this macrostate. (Answer:24N/(4πN) .)

(d) You can get a rough idea of the "sharpness" of the multiplicity function by comparing your answers to parts (b) and (c). Part (c) tells you the height of the peak, while part (b) tells you the total area under the entire graph. As a very crude approximation, pretend that the peak's shape is rectangular. In this case, how wide would it be? Out of all the macrostates, what fraction have reasonably large probabilities? Evaluate this fraction numerically for the case N=1023.

Problem 2.20. Suppose you were to shrink Figure2.7until the entire horizontal scale fits on the page. How wide would the peak be?

Use Stirling's approximation to show that the multiplicity of an Einstein solid, for any large values ofNandlocalid="1650383388983" q,is approximately

Omega(N,q)q+Nqqq+NNN2πq(q+N)/N

The square root in the denominator is merely large, and can often be neglected. However, it is needed in Problem2.22. (Hint: First show thatΩ=Nq+N(q+N)!q!N!. Do not neglect the2πNin Stirling's approximation.)

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