Exam in TMA4195 Mathematical Modeling Solutions

Similar documents
Examination paper for TMA4195 Mathematical Modeling

TMA4195 Mathematical modelling Autumn 2012

Poincaré Map, Floquet Theory, and Stability of Periodic Orbits

MATH 425, FINAL EXAM SOLUTIONS

STABILITY. Phase portraits and local stability

Dimensional Analysis - Concepts

EG4321/EG7040. Nonlinear Control. Dr. Matt Turner

EECS 700: Exam # 1 Tuesday, October 21, 2014

Coupled differential equations

Final Exam. Monday March 19, 3:30-5:30pm MAT 21D, Temple, Winter 2018

Interactions. Yuan Gao. Spring Applied Mathematics University of Washington

UNIVERSITY OF MANITOBA

MATH 220: MIDTERM OCTOBER 29, 2015

BIFURCATION PHENOMENA Lecture 1: Qualitative theory of planar ODEs

154 Chapter 9 Hints, Answers, and Solutions The particular trajectories are highlighted in the phase portraits below.

Examination paper for TMA4195 Mathematical Modeling

Dynamical Systems. August 13, 2013

Math 126 Final Exam Solutions

SOLUTIONS TO THE FINAL EXAM. December 14, 2010, 9:00am-12:00 (3 hours)

Math 46, Applied Math (Spring 2008): Final

MATH 173: PRACTICE MIDTERM SOLUTIONS

Entrance Exam, Differential Equations April, (Solve exactly 6 out of the 8 problems) y + 2y + y cos(x 2 y) = 0, y(0) = 2, y (0) = 4.

Math Exam III - Spring

Preliminary Exam 2018 Solutions to Morning Exam

x + ye z2 + ze y2, y + xe z2 + ze x2, z and where T is the

Practice Problems for Final Exam

Applied Math Qualifying Exam 11 October Instructions: Work 2 out of 3 problems in each of the 3 parts for a total of 6 problems.

Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations

M340 HW 2 SOLUTIONS. 1. For the equation y = f(y), where f(y) is given in the following plot:

2.20 Fall 2018 Math Review

Math 46, Applied Math (Spring 2009): Final

Solutions to Dynamical Systems 2010 exam. Each question is worth 25 marks.

Exam 4 SCORE. MA 114 Exam 4 Spring Section and/or TA:

MATH H53 : Final exam

Math 342 Partial Differential Equations «Viktor Grigoryan

Geometry and Motion Selected answers to Sections A and C Dwight Barkley 2016

Chapter #4 EEE8086-EEE8115. Robust and Adaptive Control Systems

Stability of Feedback Solutions for Infinite Horizon Noncooperative Differential Games

Differential equations

Math Partial Differential Equations 1

Problem set 7 Math 207A, Fall 2011 Solutions

ENGI Partial Differentiation Page y f x

Direction of maximum decrease = P

Green s Functions and Distributions

Modeling with differential equations

Integration - Past Edexcel Exam Questions

MATH 32A: MIDTERM 2 REVIEW. sin 2 u du z(t) = sin 2 t + cos 2 2

16.2 Line Integrals. Lukas Geyer. M273, Fall Montana State University. Lukas Geyer (MSU) 16.2 Line Integrals M273, Fall / 21

In this chapter we study elliptical PDEs. That is, PDEs of the form. 2 u = lots,

Salmon: Lectures on partial differential equations

PH.D. PRELIMINARY EXAMINATION MATHEMATICS

Multivariable Calculus Notes. Faraad Armwood. Fall: Chapter 1: Vectors, Dot Product, Cross Product, Planes, Cylindrical & Spherical Coordinates

MS 2001: Test 1 B Solutions

Ordinary Differential Equations

Homework 2. Due Friday, July We studied the logistic equation in class as a model of population growth. It is given by dn dt = rn 1 N

Workshop on Theoretical Ecology and Global Change March 2009

Practice Problems for Exam 3 (Solutions) 1. Let F(x, y) = xyi+(y 3x)j, and let C be the curve r(t) = ti+(3t t 2 )j for 0 t 2. Compute F dr.

REVIEW OF DIFFERENTIAL CALCULUS

Variational Methods & Optimal Control

FIRST-ORDER SYSTEMS OF ORDINARY DIFFERENTIAL EQUATIONS III: Autonomous Planar Systems David Levermore Department of Mathematics University of Maryland

Homework #3 Solutions

MTH4101 CALCULUS II REVISION NOTES. 1. COMPLEX NUMBERS (Thomas Appendix 7 + lecture notes) ax 2 + bx + c = 0. x = b ± b 2 4ac 2a. i = 1.

The functions in all models depend on two variables: time t and spatial variable x, (x, y) or (x, y, z).

TMA 4195 Mathematical Modeling, 30 November 2006 Solution with additional comments

Solution of Additional Exercises for Chapter 4

1 (t + 4)(t 1) dt. Solution: The denominator of the integrand is already factored with the factors being distinct, so 1 (t + 4)(t 1) = A

Math 142, Final Exam. 12/7/10.

1.2. Direction Fields: Graphical Representation of the ODE and its Solution Let us consider a first order differential equation of the form dy

Half of Final Exam Name: Practice Problems October 28, 2014

Streamline calculations. Lecture note 2

1. (i) Determine how many periodic orbits and equilibria can be born at the bifurcations of the zero equilibrium of the following system:

2D-Volterra-Lotka Modeling For 2 Species

THE WAVE EQUATION. d = 1: D Alembert s formula We begin with the initial value problem in 1 space dimension { u = utt u xx = 0, in (0, ) R, (2)

APPM 2350 FINAL EXAM FALL 2017

Final exam (practice) UCLA: Math 31B, Spring 2017

DO NOT BEGIN THIS TEST UNTIL INSTRUCTED TO START

Final Exam Due on Sunday 05/06

11 Chaos in Continuous Dynamical Systems.

Dynamical Systems and Chaos Part II: Biology Applications. Lecture 6: Population dynamics. Ilya Potapov Mathematics Department, TUT Room TD325

Exercises for Multivariable Differential Calculus XM521

MATH COURSE NOTES - CLASS MEETING # Introduction to PDEs, Spring 2018 Professor: Jared Speck

Exercise Set 4. D s n ds + + V. s dv = V. After using Stokes theorem, the surface integral becomes

DRAFT - Math 101 Lecture Note - Dr. Said Algarni

An Application of Perturbation Methods in Evolutionary Ecology

Solutions to Section 1.1

Systems of Ordinary Differential Equations

Differential equations, comprehensive exam topics and sample questions

Math Ordinary Differential Equations

1. (30 points) In the x-y plane, find and classify all local maxima, local minima, and saddle points of the function. f(x, y) = 3y 2 2y 3 3x 2 + 6xy.

PH.D. PRELIMINARY EXAMINATION MATHEMATICS

A Very Brief Introduction to Conservation Laws

2.152 Course Notes Contraction Analysis MIT, 2005

Global Stability Analysis on a Predator-Prey Model with Omnivores

Nonlinear Control. Nonlinear Control Lecture # 2 Stability of Equilibrium Points

Final Exam May 4, 2016

LAURENTIAN UNIVERSITY UNIVERSITÉ LAURENTIENNE

Math 5588 Final Exam Solutions

SOLUTIONS, PROBLEM SET 11

The death of an epidemic

Math 220A - Fall 2002 Homework 5 Solutions

Transcription:

Norwegian University of Science and Technology Department of Mathematical Sciences Page of 9 Exam in TMA495 Mathematical Modeling 6..07 Solutions Problem a Here x, y are two populations varying with time t. The equation for ẋ consists of: rx( x K logistic growth term of rate r and capacity K, axy c + x death term of rate a and dependent on both x and y, while the equation for ẏ consists of: my death term, of rate a, + bxy c + x growth term of rate b and dependent on both x and y. Possible models include: x prey, y predators, x humans, y bacteria, x lemmings, y foxes, x fish, y fishermen. b We have [ẋ ] ẏ [ x( x y = F (x, y := ] +x y( + x +x ( Equilibrium points (x e, y e are constant solutions of ( and therefore the solutions of ( x x y = 0, + x ( y + x = 0. + x

TMA495 Mathematical Modeling, 6..07, solutions Page of 9 The second equation is satisfied exactly when y = 0 or x =. For y = 0, the first equation is satisfied exactly when x = 0 or x =, while if x = the first equation is satisfied only for y =. Thus the equilibrium points are given by: (0, 0, (, 0, (,. OBS: By carelessly multiplying the equations with ( + x, one might conclude that (, 0 is also an equilibrium point, which is not the case (check!. To calculate the stability of the points we look at the Jacobian of F : DF (x, y := [ ( x y y (+x (+x x +x + x +x We calculate the eigenvalues λ, λ of DF in the different equilibrium points: [ ] 0 DF (0, 0 = where λ 0 =, λ =, [ ] /3 DF (, 0 = where λ 0 /3 =, λ = /3, [ ] / / DF (, = where λ 0 = 7 4 + i 4, λ = 7 4 i 4. The eigenvalues of the last matrix can be find by solving det(df (, λi = 0 ( / λ( λ ( / = 0 λ + λ + = 0. An equilibrium point is stable if max{reλ,reλ } < 0 and unstable if max{reλ,reλ } > 0. Consequently we see that (0, 0 and (, 0 are unstable while (, is stable. ] Problem Performing regular perturbation we write y as y(x = y 0 (x + εy (x + O(ε, where y 0, y are not dependent on ε. As f is smooth we can Taylor expand f about 0 to obtain f(εy = f(0 + f (0εy + O(ε, which in turn can be written as f(εy = f(0 + f (0ε ( y 0 (t + εy (t + O(ε + O(ε = f(0 + f (0εy 0 (t + O(ε.

TMA495 Mathematical Modeling, 6..07, solutions Page 3 of 9 Inserting these two representations in the differential equation we obtain while the initial conditions become y 0 + εy + f(0 + f (0εy 0 + O(ε = 0, y 0 (0 + εy (0 + O(ε = 0, y 0 ( + εy ( + O(ε = ε. Equating terms of different order in ε, we obtain the system of equations: O( : y 0 + f(0 = 0, y 0 (0 = 0, y 0 ( = 0, O(ε : y + f (0y 0 = 0, y 0 (0 = 0, y ( =. Solving first for y 0 we obtain y 0 (x = f(0x + Ax + B, where we must take A = f(0 and B = 0 to satisfy the boundary conditions for y 0. We can now insert for y 0 in the expression for y to obtain y + f (0f(0(x x = 0, = y (x = f (0f(0 ( x 4 x3 6 + Cx + D, and the boundary conditions gives C = + 4 f (0f(0 and D = 0. We conclude that y(x = y 0 (x + εy (x + O(ε = f(0(x x + ε ( x + 4 f (0f(0(x 4 x 3 + x + O(ε. Problem 3 We set z(t = ρ(x(t, t and find that the characteristic equations in this case are given by ẋ = j (z = e z, x(0 = x 0 and ż = 0, z(0 = ρ 0 (x 0. Solving we get z(t = ρ 0 (x 0 and x(t = e ρ 0(x 0 t + x 0. a In this first case, we obtain the two family of (x-characteristics x(t = x 0 + et, x 0 < 0, x(t = x 0 + t, x 0 > 0.

TMA495 Mathematical Modeling, 6..07, solutions Page 4 of 9 The left characteristics overtake the right characteristics (collisions in the region t < x < et, so the physical solution ρ is a shock solution, x < s(t, ρ(x, t = 0, x > s(t, where the shock curve s(t starts at s = 0 and satisfies the Rankine-Hugoniot condition: ṡ = j( j(0 0 = e, s(0 = 0 = s(t = (e t. b In the second case, the two families of characteristics are given by x(t = x 0 + t, x 0 < 0, x(t = x 0 + et, x 0 > 0. The left characteristics are slower than the right characteristics so there is a region t < x < et not reached by any characteristic (a dead sector. Hence the physical solution is given by a rarefaction wave ρ(x, t = ϕ(x/t. From the PDE for ρ we find that x t ϕ + e ϕ t ϕ = 0 = φ 0 We then get the solution: e ϕ = x t 0, x < t, ρ(x, t = ln(x/t, t < x < et,, et < x. = ϕ(x/t = ln(x/t. Problem 4 a For simplicity we drop the -notation in this problem and let x, y denote points in R 3. We fix a point x in the rock formation and consider the ball B r := B(x, r, centered at x with radius r > 0, whose volume will be denoted B r. By conservation of mass we have d φρ dy = (j ˆndσ. ( dt B r B r Since we have a bounded smooth domain and smooth integrands, we may change the order of differentiation and integration and use the divergence theorem to get d φρ dy = φρ t dy, dt B r B r (j ˆndσ = ( j dy. B r B r

TMA495 Mathematical Modeling, 6..07, solutions Page 5 of 9 Consequently, ( can be rewritten as B r (φρ t + j dy = 0. (3 Let f := φρ t + j, and divide by B r, and add and subtract f(x in (3 to get f(x + (f(y f(x dy = 0. (4 B r B r Since f is continuous f(y f(x for y B r and r, and hence for r, B (f(y f(x dy 0 r dy B r B r B r = φρ t (x, t + j(x, t = f(x 0. Sending r 0 we get equality and the first equation for ρ in the problem. [A rigorous argument which is not required for this exam, it given below: f(y f(x dy B r = ( f(x + s(y x (y x ds dy, B r B r B r 0 max f r ds dy = B r 0 R 3 B r B r max f r 0 as r 0. ] R 3 B r Combining Darcy s law and ideal gas law, we get j = ρ k µ p = ρk RT ρ, µ where we used that R, T are constants. Inserting this into the equation for ρ, we obtain ρ t = krt µφ b Let x = Xx, y = Y y, z = Zz, t = tt, ρ = ρρ, and (ρ ρ =: κ (ρ ρ. (5 x, y, z, t, ρ, ρ x, ρ xx, ρ y, ρ yy, ρ z, ρ zz (scaling assumption. By ideal gas law and max p = p, max ρ = max p RT = p RT. Thus ρ = p RT is a natural scale for ρ. Good scales for x, y, z are X = L, Y = Z = 500 m = εl.

TMA495 Mathematical Modeling, 6..07, solutions Page 6 of 9 The time scale is determined introducing the scaled quatities into (5 and balancing, ( ρ ρ t ρ t = κ = ρ t = κρt L L (ρρ x x + ρ ε L (ρρ y y + ρ ε L (ρρ z z ( (ρρ x x + ε [(ρρ y y + (ρρ z z ]. To arrive at the desired equation, we set t = L /κρ. c When x > s(t, ρ F = 0, and we immediately get (ρ F t = 0 = (ρ F (ρ F x x. When x < s(t, ρ F = ct 3 6 x t, and we calculate to conclude that (ρ F t = 3 ct 4 3 + 6 x t, (ρ F x = 3 xt, (ρ F xx = 3 t, (ρ F (ρ F x x = ((ρ F x + ρ F (ρ F xx = 9 x t 3 ct 4 3 + 8 x t = 3 ct 4 3 + 6 x t = (ρ F t. Now we will show that ρ F solves the conservation law in integral form on any 0 < a < s(t < b: The time change of mass inside [a, b] is equal the influx at x = a (i.e. +j(a, t plus the influx at x = b (i.e. j(b, t, and the (scaled flux here is j = ρρ x (see also the hint. Hence we have d b ρ dx = j(b, t + j(a, t = (ρρ x (b, t (ρρ x (a, t. (6 dt a Since ρ F is a (classical solution for x (a, s(t and ρ = 0 for x [s(t, b, we get d ρ F dx = d ( s(t b ρ F dx + ρ F dx dt [a,b] dt a s(t }{{} = ρ F (s(t, ts (t + }{{} = =0 s(t a =0 s(t a (ρ F (ρ F x x dx = ρρ x s(t a = ρρ x b a, (ρ F t dx (Leibniz rule

TMA495 Mathematical Modeling, 6..07, solutions Page 7 of 9 and ρ F satisfies (6. Since ρ F = 0 for x > s(t, we first note that s(t s(t ρ F dx = ρ F dx =. Then since also ρ F 0 and lim t 0 s(t = 0, for any continuous function f, s(t ρ F (x, tf(x dx f(0 = ρ F (x, t ( f(x f(0 dx s(t s(t = max f(x f(0 ρ F (x, t dx x s(t s(t = max f(x f(0 0 as t 0. x s(t The function ρ F is a fundamental solution of equation (5 on the exam, if it is a solution with initial condition equal to the delta function. The meaning of the initial condition is that lim t 0 ρ F (x, tf(x = f(0 for all continuous functions f. Both requirements are satisfied by the previous parts of problem c, and hence ρ F is a fundamental solution. d The idea of the method of intermediate asymptotics is to rescale the problem for large (but finite! times in such a way that the rescaled intitial data approximates the δ- function. Then the solution of the rescaled problem will be close to the fundamental solution of this problem. Going back to original variables, we get an approximation valid for large times. Let t = 3 months and t = 5 days. Here we want to rescale the initial value problem such that ρ t + κ(ρ ρ x x = 0, ρ (x, t = ρ 0(x, (i the time scale is t = 3 months (large compared to t, (ii the scaled equation becomes equation (5 on the exam (to be able to use part c, (iii the scaled initial data ρ(x, 0 δ(x, the δ-function. That is, we seek a scaled problem of the form ρ t + (ρρ x x = 0, ρ(x, 0 δ(x, (7 If we can do that, we can conclude (by stability of the PDE that the scaled solution ρ ρ F, where ρ F is the fundamental solution given in part c. Going back to original variables, we find an approximation of the solution ρ at t = t = 3 months.

TMA495 Mathematical Modeling, 6..07, solutions Page 8 of 9 Let us now show that (7 holds after scaling. Take ρ = ρρ, t = T t, and x = Xx. The time scale T = t, and we choose X and ρ such that (7 holds. From the equation, we find that ρ t + κ(ρ ρ x x = 0 = ρ t ρ t + κρ X (ρρ x x = 0 = ρ t + κρ t X (ρρ x x = 0, and then we get (7 if κρ t X =. Next we look at the initial condition. Recall that ρ 0 = 0 for x > l := 5 m and of total mass M := ρ dx. We want to choose a scaling such that ρ dx = and ρ(x, 0 = 0 for x > ε for some 0 < ε, or in other words, ρ(x, 0 δ(x. The integral conditions is satisfied if M = ρ dx = ρx ρ dx = ρx. We now have two equations for two unknowns X and ρ, and the solution is ( M 3 ρ =, X = ( κm t 3. κ t Note that ρ(x, t t = ρ ρ (Xx, t = 0 for Xx l or x l X. Since t = 3months 8 0 6 s, a quick calculation shows that X = ( 3 κmt (0 5 0 8 8 0 6 3 m = 000 m 5 m = l. Hence l X, and since also t t, it follows that ρ(x, 0 ρ(x, t t δ(x. We conclude that an approximation of ρ at t = t = 3 months, is given by ( x ρ (x, t ρρ F X, t t ( M ( 3 x = ρ F κ t (κm t 3,. As the rock formation is surrounded by impermeable rock, there can be no flux at the boundaries, x = ± L. Thus the boundary conditions are 0 = j (± L, T = ρ (± L, T ρ x (± L, T.

TMA495 Mathematical Modeling, 6..07, solutions Page 9 of 9 This is indeed satisfied by our approximation since L X = L (κm t 3 =.5 >.65 (6c = s(, and then since ρ F (x, t = 0 for x > s(t, ρ (± L, t = ρρ F ( L X, = 0.