 # 1 Solutions to sample quiz problems and assigned problems Sample Quiz Problems

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Solutions to sample quiz problems and assigned problems
Sample Quiz Problems
Quiz Problem 1. Prove the expression for the Carnot efficiency for a perfectly reversible Carnot cycle using an
ideal gas.
Solution: The ideal Carnot cycle consists of four segments as follows (1) An isothermal expansion during which
heat QH is added to the system at temperature TH ; (2) an adiabatic expansion during which the gas cools from
temperature TH to TC ; (3) An isothermal compression during which heat QC is extracted from the system to a cold
reservoir at temperature TC ; (4) Adiabatic compression during which the temperature of the gas rises from TC to TH .
The efficiency of the engine is given by η = W/QH , where W is the useful work done by the system and QH is the
heat added to the system. This is a thermodynamic cycle provided it is carried out reversibly (i.e. kept very close
to equilibrium), and then the internal energy is the same at the beginning and the end of the cycle. In that case,
dU = 0, and Wtotal = QH − QL , so we can also write the efficiency as
η =1−
QL
TL ∆S
TL
=1−
=1−
QH
TH ∆S
TH
(1)
The introduction of Entropy by Clausius greatly simplified understanding of engines.
—————–
Quiz Problem 2. Show
ρln(ˆ
ρ)) reduces to
P that in the eigenfunction basis, the von Neuman entropy S = −kB tr(ˆ
the Gibbs form S = −kB i pi ln(pi ) where the sum is over all eigenstates.
Solution. See Eq. (12) of Lecture notes.
—————–
Quiz Problem 3. Use Stirling’s approximation to show that,
N
ln(
) ≈ −N [pln(p) + (1 − p)ln(1 − p)],
n
where
p = n/N
Solution. Using Stirling’s approximation we have,
N
ln(
) = ln(N !) − ln(n!) − ln(N − n)! = N ln(N ) − N − nln(n) + n − (N − n)ln(N − n) + (N − n)
n
(2)
(3)
which reduces to
N
ln(
) = N ln(N ) − nln(n) − (N − n)ln(N − n).
n
Defining p = n/N gives,
N
ln(
) = N ln(N ) − pN ln(pN ) − N (1 − p)ln(N (1 − p)) = −N [pln(p) + (1 − p)ln(1 − p)]
n
(4)
(5)
—————–
Quiz Problem 4. If S = −kB N [pln(p) + (1 − p)ln(1 − p)], by doing a variation with respect to p find the value
of p that gives the maximum entropy. Demonstrate that it is a maximum by showing that the second derivative with
respect to p is negative.
Solution. Taking a derivative with respect to p gives,
∂S
= −kB N [ln(p) − ln(1 − p)]
∂p
(6)
2
The extrema occur at values of p = p∗ where this expression is zero, which yields p∗ = 1/2. To determine whether
this is a minimum of maximum, we take the second derivative to find that,
1
∂2S
1
= −kB N [ +
]p=p∗ = −kB N
2
∂p
p 1−p
(7)
Since the second derivative is negative the extremum at p∗ = 1/2 is a maximum. We have thus found the value of
the probability at which the entropy is a maximum.
—————–
Quiz Problem 5. Use the Master equation to prove the second law of thermodynamics, i.e. in a closed system
dS/dt ≥ 0.
Solution. see lecture notes page 8.
—————–
Quiz Problem 6. Give three examples of systems where the ergodic hypothesis fails. Explain why it fails in these
cases.
Solution. Non-interacting classical gas, as in the absence of interactions the systems is non-chaotic. Coupled
harmonic oscillators with no other interactions, as the normal modes do not share energy. Any system with spontaneous symmetry breaking at low temperature, e.g. magnetic systems. Once in the up magnetized state at low
enough temperature the system stays in that state. Similarly once a crystal has formed from the gas phase at low
temperature it almost never transforms to another crystal phase.
—————–
Quiz Problem 7. Why is the ideal gas law still a good starting point for high temperature gases, even though a
non-interacting gas is non-ergodic?
Solution. Even very small interactions can make the system chaotic. These interactions have little effect on
the equilibrium thermodynamic behavior though they are critical to the transport properties.
—————–
Quiz Problem 8. Using pi = e−βEi /Z in the Gibbs
P form for the entropy, show that F = −kB T ln(Z), where
F = U − T S is the Helmholtz free energy. Here Z = i e−βEi is the canonical partition function.
Solution. The Gibbs formula for the entropy is
S = −kB
X
pi ln(pi ).
(8)
i
Using the Boltzmann probability in the canonical ensemble pi = exp(−βEi )/Z, we have,
S = −kB
X
pi [
i
where we used
P
i
pi = 1, U =
P
i
U
−Ei
− ln(Z)] =
+ kB ln(Z)
kB T
T
so
U − T S = −kB T ln(Z),
(9)
pi Ei
—————–
Quiz Problem 9. If x = e−β(Ei −Ej ) , so that wji → f (x). Show that the Metropolis algorithm is given by
f (x) = min(x, 1).
Solution With this notation, the Metropolis algorithm is: If x > 1, wji = 1, and if x < 1, wji = x. This can be
put in one equation as wji = f (x) = min(1, x).
3
—————–
Quiz Problem 10. If x = e−β(Ei −Ej ) , so that wji → f (x). Show that detailed balance is satisfied for any f (x)
satisfying f (x)/f (1/x) = x. Moreover, show that f (x) = x/(1 + x), the heat bath algorithm, is one such solution.
Solution Since wij = 1/wji , if f (x) = wji , and if f (1/x) = wij , then detailed balance is satisfied provided
f (x)/f (1/x) = x. For the heat bath case,
f (x)/f (1/x) = (x/1 + x)/(1/x/(1 + 1/x)) = x
(10)
—————–
Quiz Problem 11. For a monatomic interacting classical gas, with interactions that only depend on the particle
co-ordinates, derive the Maxwell Boltzmann distribution of velocities and show that the average kinetic energy is
given by < KE >= 3N kB T /2.
Solution. See Eqs. (94,95) of the notes.
—————–
Quiz Problem 12. Using the fact that δE 2 =< E 2 > − < E >2 = kB T 2 CV show that δE/E is proportional 1/N 1/2 .
Solution. See Eqs. (134) of the notes.
—————–
Quiz Problem 13. Write down the central difference form of the second derivative. Using this expression for the
acceleration, write down the basic form of the Verlet algorithm for Molecular dynamics.
Solution. See. Eq. (86) of the notes.
—————–
Quiz Problem 14. Given that the virial is defined to be G =
derivative is expected to be zero at long times.
P
k
p~k · ~rk . Explain why the average of its time
Solution. The average of the time derivative of the virial gives,
Z
dG
1 τ dG
<
>=
dt = (G(τ ) − G(0))/τ
dt
τ 0 dt
where the average is taken over the time interval τ . Since G is bounded, as τ goes to infinity, the average of the time
derivative of G goes to zero.
—————–
Quiz Problem 15. The ground state structure of Argon is a fcc crystal. If a MD simulation is carried out where
a high temperature structure is quenched to a low temperature where the fcc structure is the equilibrium state. If
the starting high temperature state in your MD simulation is a liquid phase, do you expect the simulation to find the
correct fcc state? Explain.
Solution. For most particle systems it is difficult to find the low temperature ground state, so the fcc single crystal
state is usually not found. In experiments a polycrystal is quit often found but it is harder to make single crystals.
In MD simulations, the timescale is very short so unless special conditions are applied, the low temperature state is
a frozen glass state.
4
—————–
Quiz Problem 16. In a MC calculation of the ferromagnetic Ising model on a square lattice, you calculate the
magnetization as a function of temperature as accurately as you can. However your simulations remain rounded at
the critical point, instead of showing the behavior m ∼ |Tc − T |β expected in the thermodynamic limit. Explain the
physical origins of the rounding that you observe. Your experimental colleague does a measurement of a model Ising
magnet that consists of quantum dots with around 1000 spins in each quantum dot. She also observes a rounding
of the behavior of the magnetization. Provide some reasons why her magnetization experiment also gives a rounded
behavior near the critical point.
Solution. Three reasons for rounding of the transition are finites size effects, sample inhomogeneity and nonequilibrium effects. Non-equilibrium effects occur when the system is not fully equilibrated. In experiments all three
effects occur, while in simulations the sample inhomogeneity effect is avoided.
—————–
Problem 17. Derive the relation,
P V = N kB T +
1
3
internal
X
~rk · F~k
(11)
k
Explain the physical origin of each of the three terms. This relation is not always true for periodic boundary conditions, though rather fortuitiously it is true for periodic boundaries provided the interaction potential is a pair potential.
Solution. See Eqs. (98-101) of the notes.
—————–
Problem 18. Show that
(δE)2 = kB T 2 CV
(12)
Solution. See Eq. (129-132) of the notes.
—————–
Solutions to assigned problems
Problem 1. Find the relation between pressure and volume for an ideal gas under going an adiabatic process.
Solution:. In an adiabatic process no heat is added to the system, so dU +dW = 0, where dW = P dV . Combining
the ideal gas law and the equipartion theorem, we have
dU = αN kB dT = αd(P V ) = α(V dP + P dV )
(13)
where α = DOF/2. Using the first law, we then have,
−P dV = α(V dP + P dV ) or
α
dV
dP
= −(α + 1)
P
V
(14)
Integrating gives,
V0
P
= ( )1+1/α
P0
V
(15)
which may be written in its most common form,
α+1
(16)
α
For a mono-atomic ideal gas in three dimensions there are three translational degrees of freedom, so DOF = 3,
α = 3/2, and γ = 5/3.
P V γ = constant where γ =
5
—————–
Problem 2: Derive the ideal monatomic gas law using kinetic theory. You may use the equipartition result
U = 23 N kB T .
Solution: The internal energy of an ideal gas is purely kinetic energy, so that,
U=
1X
1
3
N kB T =
m < [(vxi )2 + (vyi )2 + (vzi )2 ] >= N m < ~v 2 >
2
2 i
2
(17)
The pressure is calculated by considering a particle incident normally on a perfectly reflecting wall,
Fx = max = m
∆px
2mvx
=
δt
δt
(18)
The time taken for the particle to strike the wall again is δt = 2L/vx , so that
P i
P
m i (vxi 2 )
2mvx2
i Fx
Fwall =
so that P =
=
2L
A
AL
P i 2
Noting that V = AL, with i (vx ) = 13 < ~v 2 > and combining (12) and (14) yields,
PV = m
(19)
X
(vxi )2 = N kB T
(20)
i
—————–
Problem 3. Find the degeneracy Ω(E, N ) for N a spin 1/2 Ising system with Hamiltonian H = −h
P
i
Si .
Solution: The energy of a spin state depends only on the number of up spins Nu , and the number of down spins
Nd , but not their arrangement. We then have,
h
E = − (Nu − Nd ),
2
with degeneracy
Ω(E) =
N!
Nu !Nd !
(21)
We also have N = Nu + Nd , so that
h
E = − (2Nu − N )
2
or Nu =
N
E
E
N
− , Nd =
−
2
h
h
2
(22)
Since entropy is the kB ln(Ω(E)), we use Stirling’s approximation to write,
ln(Ω(E)) = N lnN − N − Nu lnNu + Nu − Nd lnNd + Nd = N lnN − Nu lnNu − Nd lnNd
(23)
It is useful to introduce the fraction f = Nu /N , so that Nd /N = 1 − f and
ln(Ω) = N lnN − f N ln(f N ) − (1 − f )N ln[(1 − f )N ] = N [f ln(f ) + (1 − f )ln(1 − f )]
(24)
which shows that the entropy in this model is extensive. In terms of f , E = (N h/2)(2f − 1). We define = E/N , so
that f = 1/2 − /h. Finally the entropy is,
S = −kB ln(Ω(E)) = −kB N [(1/2 − /h)ln(1/2 − /h) + (/h − 1/2)ln(/h − 1/2)]
—————–
(25)
6
Problem 4. Consider a random walk on a one dimensional lattice where the probability of stepping to the right
or to the left is 1/2. Find the probability that after N steps the walker is at position x lattice units from the starting
position. Use Stirling’s approximation to show that for large N the probability distribution is a Gaussian in x. Find
the average mean square distance the walker is from the origin as a function of N .
Solution: Let us define the number of steps that the student takes to be N = t/δt. The number of steps taken in
the positive x-direction, n, and the number taken in the negative x-direction, m, must add up to N , so that,
N =n+m
(26)
The x position of the student at time t = N δt is given by x = dδx, where
d = (n − m).
(27)
But what is the probability that the student reaches this position at time t?
P (d, N ) =
N!
1 N!
1
= N
N
2 m!n!
2 ((N + d)/2)!((N − d)/2)!
(28)
Stirling’s approximation:
Log(n!) ≈ nln(n) − n
(29)
We then have,
Ln(P (d, N )) ≈ −N Ln(2) + N Ln(N ) − N
−
−(
N +d
Ln((N + d)/2) + (N + d)/2
2
N −d
)Log((N − d)/2) + (N − d)/2
2
(30)
This reduces to,
Ln(P (d, N )) ≈ −N Ln(2) + N Ln(N )
−
N +d
[Ln(N ) + Ln(1 + d/N ) − Ln(2)]
2
−(
N −d
)[Ln(N ) + Ln(1 − d/N ) − Ln(2)]
2
(31)
Defining y = d/N and expanding for small y yields,
ln(P (d, N )) = −
N
N
1
1
[(1 + y)ln(1 + y) + (1 − y)ln(1 − y)] ≈ − [(1 + y)(y − y 2 ) + ...) + (1 − y)(−y − y 2 ) + ...)] (32)
2
2
2
2
ln(P (d, N )) ≈ −N y 2 = −
d2
N
(33)
or,
2
P (d, N ) ≈ e−d
/N
→ e−x
2
δt/(δx)2 t
.
(34)
Comparing to the solutions to the continuum diffusion equation in one dimension, this shows that the diffusion
constant is related to the stochastic parameters δx and δt through,
D ≈ (δx)2 /δt.
(35)
From the probability distribution (28), it is easy to see that the average displacement of an ink particle is < x >= 0,
but the typical distance from the origin is (< x2 > − < x >2 )1/2 ≈ N 1/2 δx.
7
—————–
Problem 5. By taking a time P
derivative of the definition of the density operator in terms of a set of wavefunctions
drawn from an ensemble, ρˆ(t) = j |ψj (t) >< ψj (t)|, derive the von Neumann time evolution equation (12).
Solution. Take a time derivative of the definition to find,
∂ < ψj (t)|
∂ ρˆ(t) X ∂|ψj (t) >
=
< ψj (t)| + |ψj (t) >
∂t
∂t
∂t
j
Using the Schr¨
odinger equation i¯
h∂|ψ(t) > /∂t = H|ψ(t) > and its adjoint, yields,
∂ ρˆ(t)
1 X ˆ
ˆ or i¯h ∂ ρˆ(t) = [H,
ˆ ρˆ]
= [
H|ψj (t) >< ψj (t)| − |ψj (t) >< ψj (t)|H]
∂t
i¯
h j
∂t
(36)
(37)
—————–
Problem 6. Prove the central limit theorem for a set of N independent subsystems that each have an energy Ei
drawn from a Gaussian distribution with standard deviation σ. You will need to use the integral,
Z ∞
2
b2
π
e−ax +bx dx = ( )1/2 e 4a
(38)
a
−∞
Solution: Consider any intensive thermodynamic variable Xi (e.g. E/N ) that has a different value in each of N
subsystems. Consider each subsystem to have the same volume and that each subsystem is statistically equivalent.
Each subsystem is assumed to have a statistical behavior that is Gaussian distributed, so that,
P1 (Xi ) = 2πσ 2
−1/2
X2
i
e− 2σ2
(39)
where we assumed that each subsystem has the same statistical variations, quantified by σ. We would like to calculate
the statistical behavior of the quantity
N
1 X
Xi
N i=1
XN =
(40)
The statistical behavior of X N is given by,
PN (X N ) = 2πσ 2
−N/2
∞
Z
X2
X
i
Xi − N X N )
dXi e− 2σ2 δ(
Y
inf ty
(41)
i
i
Using the delta function representation,
δ(α) =
1
2π
Z
∞
eiyα dy
(42)
−∞
we find,
−N/2
PN (X N ) = 2πσ 2
Z
∞
inf ty
1
2π
Z
∞
dy
−∞
Y
P
X2
i
dXi e− 2σ2 eiy( i Xi −N X N )
(43)
i
which is equivalent to
PN (X N ) = 2πσ
2 −N/2
Z
∞
−∞
dy −iyN X N N
e
[I]
2π
(44)
where
Z
∞
I=
X2
dXe− 2σ2 eiyX = 2πσ 2
1/2
e−
σ2 y2
2
(45)
−∞
Finally,
Z
∞
PN (X N ) =
−∞
where σN = σ/N 1/2
2
2
dy −iyN X N − N σ2 y2
1
2
e
e
=
e−X N /2σN
1/2
2π
(2πN ) σ
(46)
8
—————–
Problem 7. Find the entropy of mixing
Pm for a system consisting of m different atom types, with ni of each atom
type and with the total of atoms, N = i ni .
Solution: The number of ways of arranging the system is given by the multinomial,
Ω({ni }) =
N!
n1 !n2 !...nm !
(47)
Using Stirling’s approximation we find,
ln(Ω) = N lnN − N − [
X
X
(ni ln(ni ) − ni )] = N lnN − N − [ (N pi ln(N pi ) − N pi )]
i
where pi = ni /N . Using
P
i
(48)
i
pi = 1 this simplifies to,
S = kB ln(Ω) = −kB
X
pi ln(pi )
(49)
i
which is the same as the Gibbs entropy.
—————–
Problem 8. Derive Stirling’s approximation ln(N !) = N ln(N ) − N .
Solution. Expanding the logarithm of N ! as a sum we write,
ln(N !) =
N
X
N
Z
ln(x)dx = [xln(x) − x]|N
1 = N ln(N ) − N + 1
ln(i) ≈
i=1
(50)
1
In the large N limit the constant “one” is negligible.
—————–
Problem 9. Which of the following is an exact differential? a) dU (x, y) = 2xdx + 3xydy; b) dU (x, y) =
(−1/y)dx + (x/y 2 )dy; c) dU (x, y) = −ydx + xdy ? For these three examples, set dU = 0 and solve the differential
equations to find y(x). In cases where the differential is exact, find U (x, y).
Solution:
a) We have
∂U
= 2x;
∂x
and
∂U
= 3xy
∂y
(51)
so that
∂2U
∂2U
= 1/y 2 6=
= 3y
∂x∂y
∂y∂x
(52)
Therefore dU IS NOT an exact differential.
b) We have
∂U
−1
=
;
∂x
y
and
∂U
x
= 2
∂y
y
(53)
so that
∂2U
1
∂2U
1
= 2 =
= 2
∂x∂y
y
∂y∂x
y
(54)
9
Therefore dU IS an exact differential.
c) We have
∂U
= −y;
∂x
and
∂U
=x
∂y
(55)
so that
∂2U
∂2U
= −1 6=
=1
∂x∂y
∂y∂x
(56)
Therefore dU IS NOT an exact differential.
Now we solve the three cases and if possible find U .
a) We find,
dy
−2
=
,
dx
3y
with solution
y=
4
4
(c − x)1/2 ; or y = − (c + x)1/2
3
3
(57)
b) In this case,
y
dy
= ,
dx
x
with solution
y = cx
(58)
This is an exact differential, where U (x, y) = −x/y.
c) The solution for dU = 0 is the same as b), but for finite U , it is not an exact differential so we cannot find
U (x, y) to satisfy the equation.
—————–
Problem 10. By using the fact that S is extensive show that
∂S
∂S
∂S
N
+V
+U
=S
∂N V,U
∂V N,U
∂U N,V
(59)
and hence that N µ = U + P V − T S.
Solution. Taking a derivative with respect to lambda of the homogeneous equation,
λS(U, V, N ) = S(λU, λV, λN )
and then setting λ = 1 yields Eq. (56). From the fundamental thermodynamic relation we have,
1
P
µ
∂S
∂S
∂S
dS = dU + dV − dN =
dU +
dV +
dN
T
T
T
∂U N,V
∂V N,U
∂N V,U
(60)
(61)
Using the relations inferred from this equation in Eq. (56) yields N µ = U + P V − T S.
—————–
Problem 11. Using the fundamental thermodynamic relation and N µ = U + P V − T S prove the Gibbs-Duhem
relation.
Solution. From the fundamental thermodynamic relation we have,
dU = T dS − P dV + µdN = d(T S − P V + µN )
(62)
From the equality of the last two expressions we deduce
SdT − V dP + N dµ = 0
—————–
(63)
10
Problem 12. From the fundamental thermodynamic relation show that,
∂V
∂µ
=
.
∂P S,N
∂N S,P
Solution: Since the independent variables are S, P, N , we choose the enthalpy and write,
∂H
∂H
∂H
dH = T dS + V dP + µdN =
dS +
dP +
dN
∂S P,N
∂P S,N
∂N S,P
(64)
(65)
hence,
∂H
∂P
=V;
S,N
∂H
∂N
= µ.
(66)
S,P
By doing a derivative with respect to N of the first equation in (65) we find,
!
∂V
∂
∂H
=
.
∂N ∂P S,N
∂N S,P
(67)
S,P
while a derivative with respect to P of the second equation in (65) yields
!
∂µ
∂
∂H
=
.
∂P ∂N S,P
∂P S,N
(68)
S,N
Because dH is an exact differential, the order of the derivatives of H in Eq. (66) and (67) does not matter so these
expressions are equivalent hence proving the Maxwell relation (65).
—————–
Problem 13. From the fundamental thermodynamic relation show that,
2 ∂CP
∂ V
= −T
.
∂P T,N
∂T 2 P,N
(69)
Solution: Since the independent variables in the expression are T, P , we consider the Gibb’s free energy G(T, P, N ),
so that,
∂G
∂G
∂G
dT +
dP +
dN
(70)
dG = −SdT + V dP + µdN =
∂T P,N
∂P T,N
∂N T,P
hence,
∂G
∂T
= −S;
P,N
∂G
∂P
∂V
∂T
= V.
(71)
T,N
This leads to the Maxwell relation,
−
∂S
∂P
=
T,N
(72)
P,N
Now take a temperature derivative at constant pressure of this equation, to find,
!
2 ∂
∂S
∂ V
−
=
∂T ∂P T,N
∂T 2 P,N
(73)
P,N
We can change the order of the derivatives of the expression on the left hand side, and use the fact that CP =
T (∂S/∂T )P to write,
2 2 ∂(CP /T )
∂ V
∂CP
∂ V
−
=
; or
= −T
(74)
2
∂P
∂T
∂P
∂T 2 P,N
T,N
P,N
T,N
which solves the problem.
11
—————–
Problem 14. From the fundamental thermodynamic relation show that (here we use β = 1/T )
∂E
∂N
=
T
∂T V,βµ
∂µ T,V
(75)
Solution: Since the independent variables are β, V, βµ, we start with the entropy form of the fundamental relation,
dS = βdU + βP dV − βµdN
(76)
where β = 1/T . Since the independent variables in the desired expression are β, V, βµ, we define, Y = S −βU +(βµ)N ,
so that,
∂Y
∂Y
∂Y
dβ +
dV +
d(βµ)
(77)
dY (β, V, βµ) = −U dβ + βP dV + N d(βµ) =
∂β V,βµ
∂V β,βµ
∂βµ β,V
hence,
∂Y
∂β
= −U ;
V,βµ
∂Y
∂(βµ)
= N,
(78)
β,V
that leads to the Maxwell relation,
−
∂U
∂(βµ)
=
β,V
∂N
∂β
(79)
V,βµ
Since β is constant for the left hand side derivative, we can take it outside. For the right hand derivative, we use the
chain rule to find
−1 ∂U
∂N
∂T
=
(80)
β
∂µ β,V
∂T βµ,V ∂β βµ,V
which reduces to the equation posed in the problem.
—————–
Problem 15. From the fundmental thermodynamic relation show that
∂N
∂(S/N )
∂P
1
∂T
2
=−
and −ρ
=
T 2 ∂βµ E,V
∂E βµ,V
∂ρ
∂T ρ,N
T,N
(81)
where ρ = N/V is the number density.
Solution: For the first equation, the variables held constant are βµ and E, so we use the entropy form of the
fundamental relation, dS = βdU + βP dV − βµdN . We also define Y = S + (βµ)N to find a relation that has βµ as
an independent variable. We then have,
∂Y
∂Y
∂Y
dY (U, V, βµ) = βdU + βP dV + N d(βµ) =
dV +
dV +
d(βµ)
(82)
∂U V,βµ
∂V U,βµ
∂βµ U,V
which leads to the Maxwell relation,
∂β
∂(βµ)
=
U,V
∂N
∂U
(83)
V,βµ
Using the chain rule on the left hand side yields,
∂β
∂β
∂T
1
∂T
=
=− 2
∂(βµ) U,V
∂T U,V ∂(βµ) U,V
T
∂(βµ) U,V
(84)
12
which is the first relation of Eq. (81). The second relation of equation (81) has T, V, N as independent variables, so
we use the Helmholtz free energy,
∂F
∂F
∂F
dF = −SdT − P dV + µdN =
dT +
dV +
dN
(85)
∂T V,N
∂V T,N
∂N T,V
∂S
∂V
=
T,N
∂P
∂T
(86)
V,N
The expression on the left hand side may be rewritten using the chain rule,
∂S
∂S
∂V
V 2 ∂S
=
=−
∂ρ T,N
∂V T,N ∂ρ T,N
N ∂V T,N
(87)
Equation (86) is then,
N2
− 2
V
∂(S/N )
∂V
= −ρ
2
T,N
∂(S/N )
∂V
=
T,N
∂P
∂T
=
V,N
∂P
∂T
(88)
ρ,N
which is the second relation in Eq. (81)
—————–
Problem 16. From the second result of problem 15 show that
∂P
∂P
NT
∂P
=
+[
]2 2
∂ρ S/N,N
∂ρ T,N
∂T ρ,N
ρ CV
(89)
Solution: We use the relation for a non-natural derivative,
∂P
∂P
∂P
∂T
=
+
∂ρ S/N,N
∂ρ T,N
∂T ρ,N ∂ρ S/N,N
(90)
Using the triple product rule, we have,
∂ρ
∂(S/N )
∂ρ
= −1
∂T S/N,N
∂T
∂(S/N ) T,N
ρ,N
(91)
This yields,
∂ρ
∂T
=−
S/N,N
∂T
∂(S/N )
ρ,N
∂(S/N )
∂ρ
=
T,N
NT 1
CV ρ2
∂P
∂T
(92)
ρ,N
Using the second expression in (81) and (92) in (90) yields the desired result.
—————–
Problem 17. Consider a system with an order parameter, x. If the volume(V ), energy(E) and particle number(N )
are fixed, then the system will choose the value of x with the maximum entropy. Now consider that the same system
is connected to a reservoir that has temperature T and chemical potential µ, so it can exchange energy and particles
with the reservoir. Show that the total entropy, that is the sum of the entropy of the reservoir and the system, is
maximized when
∂P
=0
(93)
∂x T,µ,V
Hint: You can assume the system and reservoir are at the same temperature and chemical potential as required by
equilibrium. Moreover you may use P V = T S − E + µN .
13
Solution: Since the variables that are kept fixed are T, µ, V , we use the grand potential ΦG = U − T S − µN , so
that,
dΦG (T, V, µ) = −SdT + P dV − N dµ = −d(P V )
(94)
Now we take a derivative of this expression with respect to x at fixed T, V, µ. Since the entropy is maximized, the
Grand potential is minimized, so that
∂P
∂ΦG
= 0 = −V
(95)
∂x T,V,µ
∂x T,V,µ
—————–
Problem 18. Consider a system where the thermodynamics are a function of temperature and another variable x
which can be varied by an experimentalist. Assuming that
∂E
= T ; and using F = E − T S
(96)
∂S x
show that,
∂E
∂x
=
S
∂F
∂x
(97)
T
Solution: We take a derivative of the equation F = E − T S with respect to x at constant T , to find,
∂E
∂S
∂F
=
−T
∂x T
∂x T
∂x T
Using the formula for a non-natural derivative, we have,
∂E
∂E
∂S
∂E
=
+
∂x T
∂S x ∂x T
∂x S
(98)
(99)
Using this result, along with the first of Eq. (96), in Eq. (98) proves Eq. (97).
—————–
Problem 19. Show that
CV = −T
∂2F
∂T 2
(100)
N,V
Solution: The specific heat at constant volume is given by,
∂S
CV = T
∂T V,N
(101)
The Helmholtz free energy is given by,
F = U − T S;
so
dF = −SdT − P dV + µdN =
∂F
∂T
dT +
V,N
∂F
∂V
dV +
T,N
∂F
∂N
dN
(102)
T,V
so that,
S=−
∂F
∂T
;
V,N
and
∂S
∂T
Multiplying by T and using Eq. (101) proves the relation (100).
—————–
=−
V,N
∂2F
∂T 2
(103)
V,N
14
Problem 20. Show that
U=
∂(F/T )
∂(1/T )
(104)
N,V
Solution: Defining β = 1/T , we have,
∂(βF )
∂F
∂F
=F +β
=F −T
∂β
∂β N,V
∂T N,V
N,V
where the last expression on the right hand side is found using the chain rule. We also have,
∂F
∂F
∂F
dF = −SdT − P dV + µdN =
dT +
dV +
dN
∂T V,N
∂V T,N
∂N T,V
(105)
(106)
so that
∂F
∂T
= −S.
(107)
V,N
Therefore,
∂(βF )
∂β
= F + TS = U
(108)
N,V
as posed in Eq. (104).
—————–
Problem 21. Show that
V
∂P
∂T
= S;
and V
µ,N
∂P
∂µ
=N
(109)
T,N
Now express the pressure of the ideal classical gas in terms of the variables µ and T and using this expression verify
the equations above.
Solution: We use the fact that the Gibbs free energy is µN to write
∂G
∂µ
=N
∂P T,N
∂P T,N
(110)
We also have,
dG =
∂G
∂T
dT +
P,N
∂G
∂P
dP +
T,N
∂G
∂N
dN = −SdT + V dP + µdN
(111)
T,P
so that
∂G
∂P
= V.
(112)
T,N
Using this relation in Eq. (110) proves the second identity in (109). To prove the first relation in (109), we use the
triple product identity,
∂P
∂µ
∂T
= −1.
(113)
∂T µ,N ∂P T,N ∂µ P,N
From Eq. (114) and G = µN , we have,
−S =
∂G
∂T
=N
P,N
∂µ
∂T
(114)
P,N
Using the second relation in Eq. (109) for the second term in the product (113) and using (114) leads to the first
relation in (109).
15
—————–
Problem 22. From the fundmental thermodynamic relation show that
∂S
∂V
=−
∂P T,µ
∂T P,µ
(115)
Solution: The fundamental thermodynamic relation is dU = T dS − P dV + µdN , so the independent variables are
S, V, N . To find the desired expression using a Maxwell relation approach, we need independent variables T, P, µ, so
we define Y = U − T S + P V − µN , so that,
∂Y
∂Y
∂Y
dT +
dP +
dµ
(116)
dY = −SdT + V dP − N dµ =
∂T P,µ
∂P T,µ
∂µ T,P
so that,
∂Y
∂T
= −S;
P,µ
∂Y
∂P
=V;
T,µ
∂Y
∂µ
= −N
(117)
T,P
and hence,
∂
∂P
∂Y
∂T
!
=−
P,µ
T,µ
∂S
∂P
T,µ
∂
∂T
∂Y
∂P
!
=
T,µ
P,µ
∂V
∂T
(118)
P,µ
Since the order of differentiation does not matter, as dY is an exact derivative, the relation (115) is proven.
—————–
``` # 1 Solutions to sample quiz problems and assigned problems Sample Quiz Problems # the landauer limit and thermodynamics of biological systems # Why the Normal Distribution? Raul Rojas Freie Universit¨ at Berlin 