2.12 HW 12

  2.12.1 Problems listing
  2.12.2 Problem 4, section 1.4
  2.12.3 Problem 17, section 1.4
  2.12.4 Problem 19, section 1.4
  2.12.5 Problem 33, section 1.4
  2.12.6 Problem 43, section 1.4
  2.12.7 Problem 3, section 1.5
  2.12.8 Problem 17, section 1.5
  2.12.9 Problem 37, section 1.5
  2.12.10 Problem 15, section 2.1
  2.12.11 Problem 16, section 2.1
  2.12.12 Problem 17, section 2.1
  2.12.13 Additional problem 1
  2.12.14 Additional problem 2
  2.12.15 key solution for HW12

2.12.1 Problems listing

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2.12.2 Problem 4, section 1.4

Find general solutions (implicit if necessary, explicit if convenient) of the differential equations in Problems 1 through 18. Primes denote derivatives with respect to x.(1+x)dydx=4y Solution

This is separable as it can be written asdydx=F(x)G(y) Where in this case G(y)=y and F(x)=41+x. Assuming x1. Therefore we can now separate and writedydx1G(y)=F(x)dyG(y)=F(x)dx

Integrating both sides givesdyG(y)=F(x)dx Replacing G(y)=y and F(x)=41+x, the above becomesdyy=41+xdxln|y|=ln|(1+x)4|+c

Taking the exponential of both sides|y|=eln|(1+x)4|+c=eceln|(1+x)4|

Let ec=c1 and since (1+x)4 can not be negative, therefore the above simplifies to|y|=c1eln(1+x)4=c1(1+x)4

Let the sign ± be absorbed into the constant of integration. The above simplifies toy(x)=c1(1+x)4x1

2.12.3 Problem 17, section 1.4

Find general solutions (implicit if necessary, explicit if convenient) of the differential equations in Problems 1 through 18. Primes denote derivatives with respect to x.dydx=1+x+y+xy Solution

Writing the above asdydx=(1+x)(1+y) This is separable. It can be written asdydx=F(x)G(y) Where in this case G(y)=(1+y) and F(x)=(1+x). Therefore we can now separate and writedydx1G(y)=F(x)dyG(y)=F(x)dx

Integrating both sides givesdyG(y)=F(x)dx Replacing G(y)=(1+y) and F(x)=(1+x), the above becomesdy(1+y)=(1+x)dxln|1+y|=x+x22+c

Taking the exponential of both sides|1+y|=ex+x22+c=ecex+x22

Let ec=c1 the above becomes|1+y|=c1ex+x22 Let the sign ± be absorbed into the constant of integration. The above simplifies to1+y=c1ex+x22y=c1ex+x221

2.12.4 Problem 19, section 1.4

Find explicit particular solutions of the initial value problems in Problems 19 through 28.dydx=yexy(0)=2e

Solution

This is separable because it can be written asdydx=F(x)G(y) Where in this case G(y)=y and F(x)=ex. Therefore we can now separate and writedydx1G(y)=F(x)dyG(y)=F(x)dx

Integrating both sides givesdyG(y)=F(x)dx Replacing G(y)=y and F(x)=ex, the above becomesdyy=exdxln|y|=ex+c

Taking the exponential of both sides|y|=eex+c=eceex

Let ec=c1, therefore the above simplifies to|y|=c1eex Let the sign ± be absorbed into the constant of integration. The above simplifies to(1)y(x)=c1eex Now we apply initial conditions to find c1. Since y(0)=2e then the above solution becomes2e=c1ec1=2

Hence the general solution (1) now becomesy(x)=2eex

2.12.5 Problem 33, section 1.4

A certain city had a population of 25,000 in 1960 and a population of 30,000 in 1970. Assume that its population will continue to grow exponentially at a constant rate. What population can its city planners expect in the year 2000?

Solution

The differential equation model is  dPdt=kP Where P(t) is the population at time t. The initial conditions are P(0)=25000 where t=0 is taken as the year 1960. We are also given that P(10)=30000. We are asked to determine P(40) which is the year 2000. First we solve the ode. This is both linear and separable. Using the separable method, it can be written asdPdt=F(t)G(P) Where in this case G(P)=P and F(t)=k. Therefore we can now separate and writedPdt1G(P)=F(t)dPG(P)=F(t)dt

Integrating both sides givesdPG(P)=F(t)dt Replacing G(P)=P and F(t)=k, the above becomesdPP=kdtlnP=kt+c

No need for absolute sign here, since P can not be negative. Taking exponential of both sides gives(1)P(t)=cekt Applying initial conditions P(0)=25000 the above gives25000=c Hence (1) now becomes(2)P(t)=25000ekt Applying second condition P(10)=30000 to the above gives30000=25000e10k3000025000=e10k65=e10k

Taking natural log of both sidesln(65)=10kk=110ln(65)

Hence (2) becomesP(t)=25000e(110ln(65))t At t=40P(40)=25000e(110ln(65))40 Using calculator it givesP(40)=51840 Hence the population in year 2000 is 51840.

2.12.6 Problem 43, section 1.4

Cooling. A pitcher of buttermilk initially at 25 C is to be cooled by setting it on the front porch, where the temperature is 0 C. Suppose that the temperature of the buttermilk has dropped to 15 C after 20 min. When will it be at 5 C?

Solution

Cooling of object is governed by the Newton’s law cooling dTdt=k(ToutT) Where Tout is the ambient temperature, which is 0 C in this problem and k is positive constant. Hence the above becomesdTdt=kT This is separable (and also linear in T). Solving it as separable, it can be written asdTdt=F(t)G(T) Where in this case G(T)=T and F(t)=k. Therefore we can now separate and writedTdt1G(T)=F(t)dTG(T)=F(t)dt

Integrating both sides givesdTG(T)=F(t)dt Replacing G(T)=T and F(t)=k, the above becomesdyT=kdtln|T|=kt+c

Taking the exponential of both sides|T|=ekt+c=ecekt

Let ec=c1, therefore the above simplifies to|T|=c1ekt Let the sign ± be absorbed into the constant of integration. The above simplifies to(1)T(t)=c1ekt Now initial conditions are used to determine c1. At t=0, we are given T(0)=25. The above becomes25=c1 Therefore (1) becomes(2)T(t)=25ekt Now the second condition  T(20)=15 is used to determine k. The above becomes15=25e20k1525=e20k35=e20k

Taking natural log of both sides gives (using property lnef(x)=f(x))ln(35)=20kk=120ln(35)=120ln53

Substituting the above value of k back into (2) givesT(t)=25e(120ln53)t=25e(120ln35)t

To answer the final part, let T(t)=5 and we need to solve for t from the above.5=25e(120ln35)t15=e(120ln35)t

Taking natural log of both sides givesln(15)=(120ln35)tt=ln(15)ln(35)120

Using the calculator givest=63.013 min

2.12.7 Problem 3, section 1.5

Find general solutions of the differential equations in Problems 1 through 25. If an initial condition is given, find the corresponding particular solution. Throughout, primes denote derivatives with respect to x.

(1)y+3y=2xe3x Solution

This is of the form y+p(x)y=q(x). Hence it is linear in y. Where p(x)=3q(x)=2xe3x

The integrating factor is ρ=epdx=e3dx=e3x

Multiplying both sides of (1) by the integration factor givesddx(yρ)=ρ(2xe3x)ddx(e3xy)=e3x(2xe3x)ddx(e3xy)=2x

Integrating givesd(e3xy)=2xdxe3xy=x2+cy(x)=e3x(x2+c)

The above is the general solution.

2.12.8 Problem 17, section 1.5

Find general solutions of the differential equations in Problems 1 through 25. If an initial condition is given, find the corresponding particular solution. Throughout, primes denote derivatives with respect to x.(1)(1+x)y+y=cosxy(0)=1

Solution

Dividing both sides of (1) by (1+x) where x1 gives(2)y+11+xy=cosx1+x This is now in the form y+p(x)y=q(x). Hence it is linear in y. Where p(x)=11+xq(x)=cosx1+x

The integrating factor is ρ=epdx=e11+xdx=eln(1+x)=1+x

Multiplying both sides of (2) by the above integration factor givesddx(yρ)=ρ(cosx1+x)ddx((1+x)y)=(1+x)(cosx1+x)ddx((1+x)y)=cosx

Integrating givesd((1+x)y)=cosxdx(1+x)y=sinx+c(3)y(x)=11+x(sinx+c)x1

The above is the general solution. Now we use initial conditions to determine c. Since we are given that y(0)=1 then (3) becomes1=(sin0+c)c=1

Therefore (3) becomesy(x)=11+x(1+sinx)x1

2.12.9 Problem 37, section 1.5

A 400-gal tank initially contains 100 gal of brine containing 50 lb of salt. Brine containing 1 lb of salt per gallon enters the tank at the rate of 5 gal/s, and the well-mixed brine in the tank flows out at the rate of 3 gal/s. How much salt will the tank contain when it is full of brine?

Solution

Let x(t) be mass of salt in lb at time t in the tank. The differential equation that describes how the mass of salt changes in time is therefore(1)dxdt=(5)(1)(3)xV(t) But V(t)=100+(5t3t)=100+2t

Therefore (1) becomesdxdt=53x100+2t(2)dxdt+3100+2tx=5

This is now in the form x+p(t)x=q(t). Hence it is linear in x. Where p(t)=3100+2tq(t)=5

The integrating factor is ρ=epdt=e31100+2tdt

Let 100+2t=u. Hence dudt=2. The integral becomes 1100+2tdt becomes 1udu2=12ln(u)=12ln(100+2t). The above becomesρ=e32ln(100+2t)=(100+2t)32

Multiplying both sides of (2) by the above integration factor givesddt(xρ)=5ρddt((100+2t)32x)=5(100+2t)32

Integrating gives(100+2t)32x=5(100+2t)32dt Let 100+2t=u hence dudt=2 and the integral on the right becomes u322du=12u5252=15u52. Hence the above now becomes(100+2t)32x=5(15u52)+c=u52+c=(100+2t)52+c

Solving for x(t) givesx=(100+2t)5232+c(100+2t)32(3)=(100+2t)+c(100+2t)32

Now we find c from initial conditions. At t=0 we are told that x=50. Hence50=(100)+c(100)3250=c10032c=(50)(10032)=50000

Therefore (3) becomes(4)x(t)=(100+2t)50000(100+2t)32 The above gives the mass of salt as function of time. We now to find the time when the tank is full. From the volume function we know that V(t)=100+2t. Since the tank size is 400 gal, then we solve for t from400=100+2tt=3002=150 sec

So the tank fills up after 150 seconds. Substituting this value of time in (4) givesx(t)=(100+2(150))50000(100+2(150))32=(100+300)50000(100+300)32=4005000040032=15754=393.75 lb

2.12.10 Problem 15, section 2.1

Consider a population P(t) satisfying the logistic equation dPdt=aPbP2, where B=aP is the time rate at which births occur and D=bP2 is the rate at which deaths occur. If the initial population P(0)=P0, and B0 births per month and D0 deaths per month are occurring at time t=0, show that the limiting population is M=B0P0D0

Solution

We are given the logistic equation in he form dPdt=aPbP2=a(PbaP2)(1)=aP(1baP)

Comparing (1) to the other standard form given in textbook which is(2)dPdt=kP(MP) Where in this form M is the limiting population. Factoring M out from (2) gives(3)dPdt=(kM)P(1PM) Comparing (1) and (3) shows that, by inspection that a=kM(4)M=ab

But we are told that a=BP. At time t=0 this gives (5)a=B0P0 And we are told that b=DP2 which at t=0 gives (6)b=D0P02 Substituting (5,6) back in (4) gives M=B0P0D0P02=B0P02P0D0

OrM=B0P0D0 Which is what we are asked to show.

2.12.11 Problem 16, section 2.1

Consider a rabbit population P(t) satisfying the logistic equation as in Problem 15. If the initial population is 120 rabbits and there are 8 births per month and 6 deaths per month occurring at time t=0, how many months does it take for P(t) to reach 95% of the limiting population M ?

Solution

We have P(0)=120 and B=aP=8 per month and D=bP2=6 per month. Hence a=BP=8P(0)=8120=115 The limiting population is M=B0P0D0=(8)(120)6=160

Therefore, we need to find the time the population reaches 95% of the above value, or 95100(160)=152 rabbits. The solution to the logistic equation is given in equation (7) page 77 asP(t)=MP0P0+(MP0)ekMt This was derived from the form  dPdt=kp(MP). But as we found in the last problem, k=aM and a=115 in this problem. Hence k=115M. The above solution now becomesP(t)=MP0P0+(MP0)e115t But M=160 and P0=120. The above becomesP(t)=(160)(120)120+(160120)e115t=19200120+40e115t

We want to find t when P(t)=152. Hence152=19200120+40e115t We need to solve the above for t. 152(120+40e115t)=192006080e115t+18240=19200e115t=19200182406080=319

Taking natural log gives115t=ln(319)t=15ln(319)

Using the calculator the above givest=27.687 months

2.12.12 Problem 17, section 2.1

Consider a rabbit population P(t)satisfying the logistic equation as in Problem 15. If the initial population is 240 rabbits and there are 9 births per month and 12 deaths per month occurring at time t=0. How many months does it take for P(t) to reach 105% of the limiting population M ?

Solution

This is similar to the above problem. We have P(0)=240 and B=aP=9 per month and D=bP2=12 per month. Hence a=BP=9P(0)=9240=380 The limiting population is M=B0P0D0=(9)(240)12=180

Therefore, we need to find the time the population reaches 105% of the above value, or 105100(180)=189 rabbits. The solution to the logistic equation is given in equation (7) page 77 asP(t)=MP0P0+(MP0)ekMt This was derived from the form  dPdt=kp(MP). But as we found in the last problem, k=aM and a=380 in this problem. Hence k=380M. The above solution now becomesP(t)=MP0P0+(MP0)e380t But M=180 and P0=240. The above becomesP(t)=(180)(240)240+(180240)e380t=4320024060e380t

We want to find t when P(t)=189. Hence189=4320024060e9140t We need to solve the above for t. 189(24060e380t)=432004536011340e380t=43200e380t=432004536011340=421

Taking natural log gives380t=ln(421)t=803ln(421)

Using the calculator the above givest=44.219 months

2.12.13 Additional problem 1

   2.12.13.1 Part a
   2.12.13.2 Part b
   2.12.13.3 Part c
   2.12.13.4 Part d
   2.12.13.5 Part e
   2.12.13.6 Part f

Solution

2.12.13.1 Part a

y+y=0 The characteristic equation is r+1=0 The root is r=1. Therefore the general solution is given byyh(x)=Cerx=Cex

Where C is arbitrary constant.

2.12.13.2 Part b

(1)y+y=ex From part (a) we found that ex is basis solution for the homogeneous ODE. The RHS in this ode is ex. No duplication. Therefore we let yp=Aex Substituting this in (1) givesAex+Aex=ex2A=1A=12

Therefore yp=12ex

2.12.13.3 Part c

The general solution is the sum of the homogeneous solution (part a) and the particular solution (part b). Thereforey=yh+yp=Cex+12ex

2.12.13.4 Part d

The ODE(1)y+y=ex Has the form y+P(x)y=Q(x) Which implies that P(x)=1Q(x)=ex

2.12.13.5 Part e

The integrating factor is therefore ρ=eP(x)dx=edx=ex. Multiplying both sides of (1) by ρ results inddx(ρy)=ρexd(ρy)=(ρex)dxd(exy)=e2xdx

Integrating givesexy=e2xdxexy=12e2x+C

Thereforey=12ex+Cex

2.12.13.6 Part f

Comparing the solution obtained in part (c) and (e) shows they are the same solution.

2.12.14 Additional problem 2

   2.12.14.1 Part (a)
   2.12.14.2 Part (b)
   2.12.14.3 Part (c)
   2.12.14.4 Part (d)
   2.12.14.5 Part (e)

Solution

2.12.14.1 Part (a)

(1)dPdt=kP(MP) The solution P(t), where P(t) is number of positive cases at time t should satisfy the above ODE, with P(0)=5000.

2.12.14.2 Part (b)

The ODE in part(a) is separable. It has the formdPdt=F(t)G(P) Where F(t)=1G(P)=kP(MP)

Therefore the ODE (1) can be written asdPG(P)=F(t)dtdPkP(MP)=dt(2)dPkP(MP)=dt

To integrate the left side will use partial fractions. Let 1kP(MP)=AkP+BMP ThereforeA=1MP|P=0=1M

AndB=1kP|P=M=1kM

Hence (2) becomes1M1kP+1kM1MPdP=t+C1kMln|P|1kMln|MP|=t+C1kMln|PMP|=t+Cln|PMP|=kMt+C2

Where C2=CkM a new constant. The above now can be written asPMP=C3ekMt Where the ± sign it taken care of by the constant C3. HenceP=C3ekMt(MP)P=C3MekMtC3PekMtP+C3PekMt=C3MekMtP(1+C3ekMt)=C3MekMtP(t)=C3MekMt1+C3ekMt(3)=C3MekMt+C3

When t=0,P=P0. Hence the above becomesP0=C3M1+C3P0+P0C3=C3MC3(P0M)=P0C3=P0MP0

Substituting this back in (3) givesP(t)=P0MP0MekMt+P0MP0=P0MekMt(MP0)+P0

OrP(t)=MP0P0+(MP0)ekMt Which is the solution given in the textbook. Now, using P0=5000 given in this problem givesP(t)=5000M5000+(M5000)ekMt But M=100000 which is the limiting capacity (total population). The above simplifies toP(t)=(5000)(100000)5000+(1000005000)e100000kt=(5000)(100000)5000+(95000)e100000kt=1000001+(950005000)e100000kt(4)=1000001+19e100000kt

The above is the solution we will use for the rest of the problem.

2.12.14.3 Part (c)

We are told there are 500 new cases on first day. This means P(1)=5000+500=5500. Using the solution found above we now solve for k. Let t=1, we obtain5500=1000001+19e100000ke100000k=100000=1000005500(19)(5500)=189209

Hencek100000=ln(189209)k=1100000ln(189209)=1×106

2.12.14.4 Part (d)

We now need to find the time t where P(t)=50000. Therefore, using (4)P(t)=1000001+19e100000kt And replacing k by value found in part(c) and P(t) by 50000 gives50000=1000001+19e100000(1×106)t50000=1000001+19e110t50000(1+19e110t)=1000001+19e110t=100000500001+19e110t=2e110t=119

Therefore110t=ln(119)t=10ln(119)=29.444

Therefore it will take about 29 days for the half the population to be infected.

2.12.14.5 Part (e)

The model

dPdt=kP(MP)

Says that the rate of infection depends on MP where P is current size of infected population and M is limiting size of the population that could become infected, which is assumed to be the total population, and this is assumed to remain constant all the time. Hence as more population is infected, the value MP becomes smaller and smaller, since P(t) is increasing, but M is fixed. This means the rate at which people get infected becomes smaller as more people are infected. This is a good model, assuming people who get infected remain infected all the time, which is the case here, and assuming M remain constant. This model does not account for death or birth of the overall population and any migration from outside. A more accurate model would account for this.

This model gives useful information for predicting how many of the population will become infected in the future given initial conditions. 

2.12.15 key solution for HW12

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