Ordinary differentiall equations

Size: px
Start display at page:

Download "Ordinary differentiall equations"

Transcription

1 Ordinary differentiall equations

2

3 Finite element method Divide macroscopic object into small pieces Can investigate real-size objects in real world time regimes Disregarding of atomic nature of matter and long-range interactions

4 Case study Reactor with reaction

5 More complicated problem Accumulation = inputs - outputs

6 inputs outputs outputs More complicated problem Accumulation = inputs - outputs Q 55 = 2 m3 min Q 15 = 3 m3 min outputs outputs inputs c 5 Q 54 = 2 m3 min Q 25 = 1 m3 min outputs Q 44 = 11 m3 min Q 01 = 5 m3 min Q 12 = 3 c 01 = 20 mg/m 3 min m 3 c 1 c 2 c 4 inputs outputs inputs outputs inputs outputs Q 31 = 1 m3 min outputs Q 23 = 1 m3 min Q 34 = 8 m3 min Q 03 = 8 m3 min c 03 = 20 mg/m 3 inputs c 3 outputs

7 outputs How to solve that problem? Accumulation = inputs - outputs Q 15 = 3 m3 min So? outputs Q 01 = 5 m3 min Q 12 = 3 c 01 = 20 mg/m 3 min inputs c 1 outputs m 3 V 1 dc 1 dt = c 01Q 01 + c 3 Q 31 c 1 Q 12 c 1 Q 15 Q01c01 = 50 mg/min, Q03c03 = 160 mg/min, V1 =50 m3, V2 = 20 m3, V3 = 40 m3, V4 = 80 m3, and V5 = 100 m3 Q 31 = 1 m3 min

8 outputs How to solve that problem? Accumulation = inputs - outputs Q 15 = 3 m3 min So? outputs Q 01 = 5 m3 min Q 12 = 3 c 01 = 20 mg/m 3 min inputs c 1 outputs m 3 V 1 dc 1 dt = c 01Q 01 + c 3 Q 31 c 1 Q 12 c 1 Q 15 Q01c01 = 50 mg/min, Q03c03 = 160 mg/min, V1 =50 m3, V2 = 20 m3, V3 = 40 m3, V4 = 80 m3, and V5 = 100 m3 Q 31 = 1 m3 min V 1 dc 1 dt = 0.12c c 3 + 1

9 outputs How to solve that problem? Accumulation = inputs - outputs Q 15 = 3 m3 min So? outputs Q 01 = 5 m3 min Q 12 = 3 c 01 = 20 mg/m 3 min inputs c 1 outputs m 3 V 1 dc 1 dt = c 01Q 01 + c 3 Q 31 c 1 Q 12 c 1 Q 15 Q 01 c 01 = 50 mg min, Q 03c 03 = 160 mg min, V 1 = 50 m 3, V 2 = 20 m 3, V 3 = 40 m 3, V 4 = 80 m 3, V 5 = 100 m 3 Q 31 = 1 m3 min dc 1 dt = 0.12c c 3 + 1

10 How to solve that problem? Accumulation = inputs - outputs For other reactors dc 1 dt = 0.12c c dc 2 dt = 0.15c c 2 dc 3 dt = 0.025c c dc 4 dt = 0.1c c c 5 dc 5 dt = 0.03c c c 5 Initially the concentration in all reactors will be zero. What will be the evolution of concentrations in time? How to solve this problem?

11 A really simple example Converstion of cyclopropane to propane cyclopropane propane k = s, T = 500o C 1st order dc dt = c t If the initial concentration c(0) of cyclopropane is 0.05 M, what is the concentration after 30 min?

12 How to calculate c(t)? Differential equation dc dt = c t

13 How to calculate c(t)? Differential equation dc dt = c t t 1 න 0 c dc dt dt = න 0 t dt c t dc න c(0) c = න 0 t dt

14 How to calculate c(t)? Differential equation dc dt c t dc න c(0) c = c t = න 0 t dt ln c t ln c 0 = t

15 How to calculate c(t)? Differential equation dc dt c t dc න c(0) c = c t = න 0 t dt ln c t ln c 0 c t c 0 = e t = t

16 How to calculate c(t)? Differential equation dc dt c t dc න c(0) c = c t = න 0 ln c t ln c 0 c t c 0 = e t c t = c 0 e t t dt = t

17 Different approach Euler s scheme df f (t + Dt) - f (t) f (t + Dt) - f (t) dt = lim Dt 0 Dt» Dt df dt = f i+1 - f i Dt

18 Instead of solving function analytically step by step procedure f t + Δt = f t + f t Δt f Analytical solution t

19 Instead of solving function analytically step by step procedure f t + Δt = f t + f t Δt f Analytical solution f Point-by-point solution t t

20 How does it works we start from f(0) 1. Choose step Δt = 1 2. Compute next point using chain rule f t + Δt = f t + f t Δt Example f t = 2 t f t = 2

21 How does it works we start from f(0) f 0 + Δt = 1 = = 2 f f(1) 1 t

22 How does it works we start from f(0) f = = 4 f f(2) f(1) t 2 2

23 And so on point by point we reproduce solution f f(t) = 2t f(2) f(1) t 2 2

24 Problems? - Accuracy Example derivative varies (change of the function s shape) f Wrong value! t

25 Solution? More points, shorter interval Example derivative varies (change of the function s shape) f Problem? Computing efficiency! t

26 How to calculate c(t)? Different approach finite difference method Δc Δt = c t

27 How to calculate c(t)? Different approach finite difference method Δc Δt = c t c t + Δt c t Δt = c t

28 How to calculate c(t)? Different approach finite difference method Δc Δt = c t c t + Δt c t Δt = c t c t + Δt c t = Δtc t

29 How to calculate c(t)? Different approach finite difference method, explicit scheme Δc Δt = c t c t + Δt c t Δt = c t c t + Δt c t = Δtc t c t + Δt = c t Δtc t = c t 1 Δt

30 c t + Δt = c t Δtc t = c t 1 Δt c n = c n 1 1 Δt c t

31 c t + Δt = c t Δtc t = c t 1 Δt Δt = 0. 1 c 0 = c 0 = 1 c n = c n 1 1 Δt c 1 t

32 c t + Δt = c t Δtc t = c t 1 Δt Δt = 0. 1 c 0 = c 0 = 1 c n = c n 1 1 Δt c 1 = c 0 1 Δt = = 0. 9 c t

33 c t + Δt = c t Δtc t = c t 1 Δt Δt = 0. 1 c 0 = c 0 = 1 c n = c n 1 1 Δt c 1 = c 0 1 Δt = = 0. 9 c c 2 = c 1 1 Δt = = t

34 c t + Δt = c t Δtc t = c t 1 Δt Δt = 0. 1 c 0 = c 0 = 1 c n = c n 1 1 Δt c 1 = c 0 1 Δt = = 0. 9 c 1 c 2 = c 1 1 Δt = = c 3 = c 2 1 Δt = = t

35 c t + Δt = c t Δtc t = c t 1 Δt Δt = 0. 1 c 0 = c 0 = c n = c n 1 1 Δt c 1 = c 0 1 Δt = = 0. 9 c 2 = c 1 1 Δt = = c 3 = c 2 1 Δt = = c explicit exp(-kt)

36 Problems? c t + Δt = c t Δtc t = c t 1 Δt Say Δt = 1 then c 1 = c = 0 c 2 = c = 0 Say Δt > 1 Δt = 2 then c 1 = c = c 0 < 0 negative concentration. Unphysical!!! c t + Δt = c t 1 kδt 1 kδt > 0 Δt > 1/k c exp(-kt)

37 Solution? Implicit scheme f f t Δt 0 df dt t Δf Δt t or Δf Δt t + Δt or anywhere inbetween Δf f t + Δt Δt Slope in point t Δf tgβ = lim Δt 0 Δt = df dt t α t + Δt Average slope on Δt interval β t tgα = Δf Δt

38 Solution? Implicit scheme Before Now Δc Δc = c t = c t + Δt Δt Δt c t + Δt c t c t + Δt c t = c t = c t + Δt Δt Δt c t + Δt = c t Δtc t + Δt c t + Δt = c t Δtc t

39 Solution? Implicit scheme c t + Δt = c t Δtc t + Δt c t + Δt + Δtc t + Δt = c t c t + Δt 1 + Δt = c t c t + Δt = c t 1 + Δt Always positive

40 Solutions, comparison dc = c t dt c t c t + Δt = c t Δtc t c t + Δt = 1 + Δt Δt = 0. 1 c t = c 0 e t Δt = c explicit c implicit exp(-kt)

41 Problems? - Accuracy Example derivative varies (change of the function s shape) f Wrong value! t

42 But what about set of ODE? Same approach combined with linear equations dc 1 dt = 0.12c c dc 2 dt = 0.15c c 2 dc 3 dt = 0.025c c dc 4 dt = 0.1c c c 5 dc 5 dt = 0.03c c c 5 dc 1 dt dc 2 dt dc 3 dt dc 4 dt dc 5 dt = c 1 c 2 c 3 c 4 c

43 dc 1 dt dc 2 dt dc 3 dt dc 4 dt dc 5 dt = But what about set of ODE? Same approach combined with linear equations c 1 c 2 c 3 c 4 c d dt c 1 c 2 c 3 c 4 c 5 = c 1 c 2 c 3 c 4 c

44 But what about set of ODE? Same approach combined with linear equations d dt c 1 c 2 c 3 c 4 c 5 = c 1 c 2 c 3 c 4 c തc A തc ഥb dതc dt = Aതc + ഥb

45 What next? Finite difference approach but on vectors! dതc dt = Aതc Δതc Δt = Aതc

46 What next? Finite difference approach but with vectors! Δതc Δt = Aതc തc t + Δt ҧ c(t) Δt = Aതc തc t + Δt = തc t + ΔtAതc

47 What next? Finite difference approach but with vectors! Explicit scheme തc = തc(t) തc t + Δt = തc t + ΔtAതc(t) തc t + Δt = I + ΔtA cҧ t തc t + Δt = B ҧ c t B = I + ΔtA

48 What next? Finite difference approach but with vectors! Explicit scheme തc = തc(t) തc t + Δt = B ҧ c t B = I + ΔtA Define c(0) Compute B = I + ΔtA Problem? Unstable! Compute c in the next time frame c nδt = Bc( n 1 Δt)) n + 1

49 What next? Finite difference approach but on vectors! Implicit scheme തc = തc(t + Δt) തc t + Δt = തc t + ΔtAതc(t + Δt) തc t + Δt ΔtAതc(t + Δt) = തc t I ΔtA തc t + Δt = തc t

50 What next? Finite difference approach but on vectors! Implicit scheme തc = തc(t + Δt) I ΔtA തc t + Δt = തc t Bതc t + Δt = തc t തc t + Δt = B 1 തc t B = I ΔtA

51 Implicit scheme തc = തc(t) തc t + Δt = B 1 ҧ c t Define c(0) B = I ΔtA Problem? Computing B 1 Compute B = I + ΔtA Compute B 1 Compute c in the next time frame c nδt = B 1 c( n 1 Δt)) n + 1

52 Working example implicite scheme A k 1=1 B k 2 =1 C da dt = A db = A B dt dc dt = B d dt Initially A = 1, B = 0, C = 0 A B C cҧ 0 = = A B C

53 Working example 1. Initiate, calculate B d dt A B C = A k 1=1 B k 2 =1 C A B C cҧ 0 = B = I ΔtA Δt=1 cҧ 0 = B = =

54 Working example 2. Calculate B 1 B = B 1 =

55 Working example 3. Iterate n=1 cҧ nδt = B 1 cҧ n 1 Δt cҧ 1 = B 1 cҧ 0 = = A B C

56 Working example 3. Iterate n=2 cҧ nδt = B 1 cҧ n 1 Δt cҧ 2 = B 1 cҧ 1 = =

57 Working example 3. Iterate n=3 cҧ nδt = B 1 cҧ n 1 Δt cҧ 3 = B 1 cҧ 2 = =

58 Working example 3. Iterate n=4 cҧ nδt = B 1 cҧ n 1 Δt cҧ 4 = B 1 cҧ 3 = =

Math 225 Differential Equations Notes Chapter 1

Math 225 Differential Equations Notes Chapter 1 Math 225 Differential Equations Notes Chapter 1 Michael Muscedere September 9, 2004 1 Introduction 1.1 Background In science and engineering models are used to describe physical phenomena. Often these

More information

1.1. Bacteria Reproduce like Rabbits. (a) A differential equation is an equation. a function, and both the function and its

1.1. Bacteria Reproduce like Rabbits. (a) A differential equation is an equation. a function, and both the function and its G NAGY ODE January 7, 2018 1 11 Bacteria Reproduce like Rabbits Section Objective(s): Overview of Differential Equations The Discrete Equation The Continuum Equation Summary and Consistency 111 Overview

More information

BAE 820 Physical Principles of Environmental Systems

BAE 820 Physical Principles of Environmental Systems BAE 820 Physical Principles of Environmental Systems Acquisition of reaction rate data Dr. Zifei Liu Uncertainties in real world reaction rate data Most interesting reaction systems involves multiple reactions,

More information

Ordinary Differential Equations I

Ordinary Differential Equations I Ordinary Differential Equations I CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Ordinary Differential Equations I 1 / 27 Theme of Last Three

More information

Fundamentals Physics

Fundamentals Physics Fundamentals Physics And Differential Equations 1 Dynamics Dynamics of a material point Ideal case, but often sufficient Dynamics of a solid Including rotation, torques 2 Position, Velocity, Acceleration

More information

1.1. Bacteria Reproduce like Rabbits. (a) A differential equation is an equation. a function, and both the function and its

1.1. Bacteria Reproduce like Rabbits. (a) A differential equation is an equation. a function, and both the function and its G. NAGY ODE August 28, 2018 1 1.1. Bacteria Reproduce like Rabbits Section Objective(s): Overview of Differential Equations. The Discrete Equation. The Continuum Equation. Summary and Consistency. 1.1.1.

More information

ESS Dirac Comb and Flavors of Fourier Transforms

ESS Dirac Comb and Flavors of Fourier Transforms 6. Dirac Comb and Flavors of Fourier ransforms Consider a periodic function that comprises pulses of amplitude A and duration τ spaced a time apart. We can define it over one period as y(t) = A, τ / 2

More information

Solutions to Math 53 First Exam April 20, 2010

Solutions to Math 53 First Exam April 20, 2010 Solutions to Math 53 First Exam April 0, 00. (5 points) Match the direction fields below with their differential equations. Also indicate which two equations do not have matches. No justification is necessary.

More information

Review for the Final Exam

Review for the Final Exam Math 171 Review for the Final Exam 1 Find the limits (4 points each) (a) lim 4x 2 3; x x (b) lim ( x 2 x x 1 )x ; (c) lim( 1 1 ); x 1 ln x x 1 sin (x 2) (d) lim x 2 x 2 4 Solutions (a) The limit lim 4x

More information

115.3 Assignment #9 Solutions

115.3 Assignment #9 Solutions 115. Assignment #9 Solutions-1 115. Assignment #9 Solutions 8.1-12 Solve the differential equation d dx = 2(1 ), where 0 = 2 for x 0 = 0. d 1 = 2dx d 1 = 2dx ln 1 =2x + C Find C b inserting the Initial

More information

Ordinary Differential Equation Theory

Ordinary Differential Equation Theory Part I Ordinary Differential Equation Theory 1 Introductory Theory An n th order ODE for y = y(t) has the form Usually it can be written F (t, y, y,.., y (n) ) = y (n) = f(t, y, y,.., y (n 1) ) (Implicit

More information

Ordinary Differential Equations I

Ordinary Differential Equations I Ordinary Differential Equations I CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Ordinary Differential Equations I 1 / 32 Theme of Last Few

More information

ENGI Partial Differentiation Page y f x

ENGI Partial Differentiation Page y f x ENGI 344 4 Partial Differentiation Page 4-0 4. Partial Differentiation For functions of one variable, be found unambiguously by differentiation: y f x, the rate of change of the dependent variable can

More information

10.34 Numerical Methods Applied to Chemical Engineering. Quiz 2

10.34 Numerical Methods Applied to Chemical Engineering. Quiz 2 10.34 Numerical Methods Applied to Chemical Engineering Quiz 2 This quiz consists of three problems worth 35, 35, and 30 points respectively. There are 4 pages in this quiz (including this cover page).

More information

First Order Linear Ordinary Differential Equations

First Order Linear Ordinary Differential Equations First Order Linear Ordinary Differential Equations The most general first order linear ODE is an equation of the form p t dy dt q t y t f t. 1 Herepqarecalledcoefficients f is referred to as the forcing

More information

(1) Rate of change: A swimming pool is emptying at a constant rate of 90 gal/min.

(1) Rate of change: A swimming pool is emptying at a constant rate of 90 gal/min. CHAPTER 1 Introduction 1. Bacground Models of physical situations from Calculus (1) Rate of change: A swimming pool is emptying at a constant rate of 90 gal/min. With V = volume in gallons and t = time

More information

Solutions to Homework 5

Solutions to Homework 5 Solutions to Homework 5 1. Let z = f(x, y) be a twice continuously differentiable function of x and y. Let x = r cos θ and y = r sin θ be the equations which transform polar coordinates into rectangular

More information

Implicit Differentiation

Implicit Differentiation Implicit Differentiation Sometimes we have a curve in the plane defined by a function Fxy= (, ) 0 (1) that involves both x and y, possibly in complicated ways. Examples. We show below the graphs of the

More information

ECE257 Numerical Methods and Scientific Computing. Ordinary Differential Equations

ECE257 Numerical Methods and Scientific Computing. Ordinary Differential Equations ECE257 Numerical Methods and Scientific Computing Ordinary Differential Equations Today s s class: Stiffness Multistep Methods Stiff Equations Stiffness occurs in a problem where two or more independent

More information

Math Spring 2014 Homework 2 solution

Math Spring 2014 Homework 2 solution Math 3-00 Spring 04 Homework solution.3/5 A tank initially contains 0 lb of salt in gal of weater. A salt solution flows into the tank at 3 gal/min and well-stirred out at the same rate. Inflow salt concentration

More information

Systems of Linear ODEs

Systems of Linear ODEs P a g e 1 Systems of Linear ODEs Systems of ordinary differential equations can be solved in much the same way as discrete dynamical systems if the differential equations are linear. We will focus here

More information

Chapter 9b: Numerical Methods for Calculus and Differential Equations. Initial-Value Problems Euler Method Time-Step Independence MATLAB ODE Solvers

Chapter 9b: Numerical Methods for Calculus and Differential Equations. Initial-Value Problems Euler Method Time-Step Independence MATLAB ODE Solvers Chapter 9b: Numerical Methods for Calculus and Differential Equations Initial-Value Problems Euler Method Time-Step Independence MATLAB ODE Solvers Acceleration Initial-Value Problems Consider a skydiver

More information

Calculus II Practice Test Questions for Chapter , 9.6, Page 1 of 9

Calculus II Practice Test Questions for Chapter , 9.6, Page 1 of 9 Calculus II Practice Test Questions for Chapter 9.1 9.4, 9.6, 10.1 10.4 Page 1 of 9 This is in no way an inclusive set of problems there can be other types of problems on the actual test. To prepare for

More information

How to Use Calculus Like a Physicist

How to Use Calculus Like a Physicist How to Use Calculus Like a Physicist Physics A300 Fall 2004 The purpose of these notes is to make contact between the abstract descriptions you may have seen in your calculus classes and the applications

More information

Euler s Method (BC Only)

Euler s Method (BC Only) Euler s Method (BC Only) Euler s Method is used to generate numerical approximations for solutions to differential equations that are not separable by methods tested on the AP Exam. It is necessary to

More information

1 Implicit Differentiation

1 Implicit Differentiation 1 Implicit Differentiation In logarithmic differentiation, we begin with an equation y = f(x) and then take the logarithm of both sides to get ln y = ln f(x). In this equation, y is not explicitly expressed

More information

Modeling with First-Order Equations

Modeling with First-Order Equations Modeling with First-Order Equations MATH 365 Ordinary Differential Equations J. Robert Buchanan Department of Mathematics Spring 2018 Radioactive Decay Radioactive decay takes place continuously. The number

More information

. For each initial condition y(0) = y 0, there exists a. unique solution. In fact, given any point (x, y), there is a unique curve through this point,

. For each initial condition y(0) = y 0, there exists a. unique solution. In fact, given any point (x, y), there is a unique curve through this point, 1.2. Direction Fields: Graphical Representation of the ODE and its Solution Section Objective(s): Constructing Direction Fields. Interpreting Direction Fields. Definition 1.2.1. A first order ODE of the

More information

EAD 115. Numerical Solution of Engineering and Scientific Problems. David M. Rocke Department of Applied Science

EAD 115. Numerical Solution of Engineering and Scientific Problems. David M. Rocke Department of Applied Science EAD 115 Numerical Solution of Engineering and Scientific Problems David M. Rocke Department of Applied Science Transient Response of a Chemical Reactor Concentration of a substance in a chemical reactor

More information

On linear and non-linear equations. (Sect. 1.6).

On linear and non-linear equations. (Sect. 1.6). On linear and non-linear equations. (Sect. 1.6). Review: Linear differential equations. Non-linear differential equations. The Picard-Lindelöf Theorem. Properties of solutions to non-linear ODE. The Proof

More information

Partitioned Methods for Multifield Problems

Partitioned Methods for Multifield Problems C Partitioned Methods for Multifield Problems Joachim Rang, 6.7.2016 6.7.2016 Joachim Rang Partitioned Methods for Multifield Problems Seite 1 C One-dimensional piston problem fixed wall Fluid flexible

More information

APPM 2360: Midterm exam 1 February 15, 2017

APPM 2360: Midterm exam 1 February 15, 2017 APPM 36: Midterm exam 1 February 15, 17 On the front of your Bluebook write: (1) your name, () your instructor s name, (3) your recitation section number and () a grading table. Text books, class notes,

More information

APPLICATIONS OF FD APPROXIMATIONS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS

APPLICATIONS OF FD APPROXIMATIONS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS LECTURE 10 APPLICATIONS OF FD APPROXIMATIONS FOR SOLVING ORDINARY DIFFERENTIAL EQUATIONS Ordinary Differential Equations Initial Value Problems For Initial Value problems (IVP s), conditions are specified

More information

Modeling and Solving Constraints. Erin Catto Blizzard Entertainment

Modeling and Solving Constraints. Erin Catto Blizzard Entertainment Modeling and Solving Constraints Erin Catto Blizzard Entertainment Basic Idea Constraints are used to simulate joints, contact, and collision. We need to solve the constraints to stack boxes and to keep

More information

Math 122 Fall Handout 11: Summary of Euler s Method, Slope Fields and Symbolic Solutions of Differential Equations

Math 122 Fall Handout 11: Summary of Euler s Method, Slope Fields and Symbolic Solutions of Differential Equations 1 Math 122 Fall 2008 Handout 11: Summary of Euler s Method, Slope Fields and Symbolic Solutions of Differential Equations The purpose of this handout is to review the techniques that you will learn for

More information

Numerical Methods - Initial Value Problems for ODEs

Numerical Methods - Initial Value Problems for ODEs Numerical Methods - Initial Value Problems for ODEs Y. K. Goh Universiti Tunku Abdul Rahman 2013 Y. K. Goh (UTAR) Numerical Methods - Initial Value Problems for ODEs 2013 1 / 43 Outline 1 Initial Value

More information

Modeling radiocarbon in the Earth System. Radiocarbon summer school 2012

Modeling radiocarbon in the Earth System. Radiocarbon summer school 2012 Modeling radiocarbon in the Earth System Radiocarbon summer school 2012 Outline Brief introduc9on to mathema9cal modeling Single pool models Mul9ple pool models Model implementa9on Parameter es9ma9on What

More information

Initial value problems for ordinary differential equations

Initial value problems for ordinary differential equations Initial value problems for ordinary differential equations Xiaojing Ye, Math & Stat, Georgia State University Spring 2019 Numerical Analysis II Xiaojing Ye, Math & Stat, Georgia State University 1 IVP

More information

LECTURE 4-1 INTRODUCTION TO ORDINARY DIFFERENTIAL EQUATIONS

LECTURE 4-1 INTRODUCTION TO ORDINARY DIFFERENTIAL EQUATIONS 130 LECTURE 4-1 INTRODUCTION TO ORDINARY DIFFERENTIAL EQUATIONS DIFFERENTIAL EQUATIONS: A differential equation (DE) is an equation involving an unknown function and one or more of its derivatives. A differential

More information

Calculus for the Life Sciences

Calculus for the Life Sciences Improved Calculus for the Life Sciences ntial Equations Joseph M. Mahaffy, jmahaffy@mail.sdsu.edu Department of Mathematics and Statistics Dynamical Systems Group Computational Sciences Research Center

More information

Transistor Noise Lecture 10 High Speed Devices

Transistor Noise Lecture 10 High Speed Devices Transistor Noise 1 Transistor Noise A very brief introduction to circuit and transistor noise. I an not an expert regarding noise Maas: Noise in Linear and Nonlinear Circuits Lee: The Design of CMOS RFIC

More information

Introduction to multiscale modeling and simulation. Explicit methods for ODEs : forward Euler. y n+1 = y n + tf(y n ) dy dt = f(y), y(0) = y 0

Introduction to multiscale modeling and simulation. Explicit methods for ODEs : forward Euler. y n+1 = y n + tf(y n ) dy dt = f(y), y(0) = y 0 Introduction to multiscale modeling and simulation Lecture 5 Numerical methods for ODEs, SDEs and PDEs The need for multiscale methods Two generic frameworks for multiscale computation Explicit methods

More information

Functional differentiation

Functional differentiation Functional differentiation March 22, 2016 1 Functions vs. functionals What distinguishes a functional such as the action S [x (t] from a function f (x (t, is that f (x (t is a number for each value of

More information

Workbook for Calculus I

Workbook for Calculus I Workbook for Calculus I By Hüseyin Yüce New York 2007 1 Functions 1.1 Four Ways to Represent a Function 1. Find the domain and range of the function f(x) = 1 + x + 1 and sketch its graph. y 3 2 1-3 -2-1

More information

AP Calculus BC Chapter 4 AP Exam Problems. Answers

AP Calculus BC Chapter 4 AP Exam Problems. Answers AP Calculus BC Chapter 4 AP Exam Problems Answers. A 988 AB # 48%. D 998 AB #4 5%. E 998 BC # % 5. C 99 AB # % 6. B 998 AB #80 48% 7. C 99 AB #7 65% 8. C 998 AB # 69% 9. B 99 BC # 75% 0. C 998 BC # 80%.

More information

MIDTERM 1 PRACTICE PROBLEM SOLUTIONS

MIDTERM 1 PRACTICE PROBLEM SOLUTIONS MIDTERM 1 PRACTICE PROBLEM SOLUTIONS Problem 1. Give an example of: (a) an ODE of the form y (t) = f(y) such that all solutions with y(0) > 0 satisfy y(t) = +. lim t + (b) an ODE of the form y (t) = f(y)

More information

8.a: Integrating Factors in Differential Equations. y = 5y + t (2)

8.a: Integrating Factors in Differential Equations. y = 5y + t (2) 8.a: Integrating Factors in Differential Equations 0.0.1 Basics of Integrating Factors Until now we have dealt with separable differential equations. Net we will focus on a more specific type of differential

More information

B8.3 Mathematical Models for Financial Derivatives. Hilary Term Solution Sheet 2

B8.3 Mathematical Models for Financial Derivatives. Hilary Term Solution Sheet 2 B8.3 Mathematical Models for Financial Derivatives Hilary Term 18 Solution Sheet In the following W t ) t denotes a standard Brownian motion and t > denotes time. A partition π of the interval, t is a

More information

A population is modeled by the differential equation

A population is modeled by the differential equation Math 2, Winter 2016 Weekly Homework #8 Solutions 9.1.9. A population is modeled by the differential equation dt = 1.2 P 1 P ). 4200 a) For what values of P is the population increasing? P is increasing

More information

1. By the Product Rule, in conjunction with the Chain Rule, we compute the derivative as follows: and. So the slopes of the tangent lines to the curve

1. By the Product Rule, in conjunction with the Chain Rule, we compute the derivative as follows: and. So the slopes of the tangent lines to the curve MAT 11 Solutions TH Eam 3 1. By the Product Rule, in conjunction with the Chain Rule, we compute the derivative as follows: Therefore, d 5 5 d d 5 5 d 1 5 1 3 51 5 5 and 5 5 5 ( ) 3 d 1 3 5 ( ) So the

More information

Fourth Order RK-Method

Fourth Order RK-Method Fourth Order RK-Method The most commonly used method is Runge-Kutta fourth order method. The fourth order RK-method is y i+1 = y i + 1 6 (k 1 + 2k 2 + 2k 3 + k 4 ), Ordinary Differential Equations (ODE)

More information

P1 Calculus II. Partial Differentiation & Multiple Integration. Prof David Murray. dwm/courses/1pd

P1 Calculus II. Partial Differentiation & Multiple Integration. Prof David Murray.   dwm/courses/1pd P1 2017 1 / 39 P1 Calculus II Partial Differentiation & Multiple Integration Prof David Murray david.murray@eng.ox.ac.uk www.robots.ox.ac.uk/ dwm/courses/1pd 4 lectures, MT 2017 P1 2017 2 / 39 Motivation

More information

On linear and non-linear equations.(sect. 2.4).

On linear and non-linear equations.(sect. 2.4). On linear and non-linear equations.sect. 2.4). Review: Linear differential equations. Non-linear differential equations. Properties of solutions to non-linear ODE. The Bernoulli equation. Review: Linear

More information

Inputs (F) State variable (Q) Outputs (F)

Inputs (F) State variable (Q) Outputs (F) Inputs (F) State variable (Q) Outputs (F) Leaning objectives Define what are dynamic deterministic models and explain why they are popular Write a model using compartmental model diagram Translate a model

More information

PowerPoints organized by Dr. Michael R. Gustafson II, Duke University

PowerPoints organized by Dr. Michael R. Gustafson II, Duke University Part 6 Chapter 20 Initial-Value Problems PowerPoints organized by Dr. Michael R. Gustafson II, Duke University All images copyright The McGraw-Hill Companies, Inc. Permission required for reproduction

More information

MATH 353 LECTURE NOTES: WEEK 1 FIRST ORDER ODES

MATH 353 LECTURE NOTES: WEEK 1 FIRST ORDER ODES MATH 353 LECTURE NOTES: WEEK 1 FIRST ORDER ODES J. WONG (FALL 2017) What did we cover this week? Basic definitions: DEs, linear operators, homogeneous (linear) ODEs. Solution techniques for some classes

More information

Ordinary Differential Equations

Ordinary Differential Equations Chapter 13 Ordinary Differential Equations We motivated the problem of interpolation in Chapter 11 by transitioning from analzying to finding functions. That is, in problems like interpolation and regression,

More information

Lecture 1. Scott Pauls 1 3/28/07. Dartmouth College. Math 23, Spring Scott Pauls. Administrivia. Today s material.

Lecture 1. Scott Pauls 1 3/28/07. Dartmouth College. Math 23, Spring Scott Pauls. Administrivia. Today s material. Lecture 1 1 1 Department of Mathematics Dartmouth College 3/28/07 Outline Course Overview http://www.math.dartmouth.edu/~m23s07 Matlab Ordinary differential equations Definition An ordinary differential

More information

PowerPoints organized by Dr. Michael R. Gustafson II, Duke University

PowerPoints organized by Dr. Michael R. Gustafson II, Duke University Part 6 Chapter 20 Initial-Value Problems PowerPoints organized by Dr. Michael R. Gustafson II, Duke University All images copyright The McGraw-Hill Companies, Inc. Permission required for reproduction

More information

AP Calculus BC Spring Final Part IA. Calculator NOT Allowed. Name:

AP Calculus BC Spring Final Part IA. Calculator NOT Allowed. Name: AP Calculus BC 6-7 Spring Final Part IA Calculator NOT Allowed Name: . Find the derivative if the function if f ( x) = x 5 8 2x a) f b) f c) f d) f ( ) ( x) = x4 40 x 8 2x ( ) ( x) = x4 40 +x 8 2x ( )

More information

Lecture 2 The Centralized Economy

Lecture 2 The Centralized Economy Lecture 2 The Centralized Economy Economics 5118 Macroeconomic Theory Kam Yu Winter 2013 Outline 1 Introduction 2 The Basic DGE Closed Economy 3 Golden Rule Solution 4 Optimal Solution The Euler Equation

More information

CHEMICAL ENGINEERING KINETICS/REACTOR DESIGN. Tony Feric, Kathir Nalluswami, Manesha Ramanathan, Sejal Vispute, Varun Wadhwa

CHEMICAL ENGINEERING KINETICS/REACTOR DESIGN. Tony Feric, Kathir Nalluswami, Manesha Ramanathan, Sejal Vispute, Varun Wadhwa CHEMICAL ENGINEERING KINETICS/REACTOR DESIGN Tony Feric, Kathir Nalluswami, Manesha Ramanathan, Sejal Vispute, Varun Wadhwa Presentation Overview Kinetics Reactor Design Non- Isothermal Design BASICS OF

More information

MA 214 Calculus IV (Spring 2016) Section 2. Homework Assignment 2 Solutions

MA 214 Calculus IV (Spring 2016) Section 2. Homework Assignment 2 Solutions MA 214 Calculus IV (Spring 2016) Section 2 Homework Assignment 2 Solutions 1 Boyce and DiPrima, p 60, Problem 2 Solution: Let M(t) be the mass (in grams) of salt in the tank after t minutes The initial-value

More information

Solutions to Homework 1, Introduction to Differential Equations, 3450: , Dr. Montero, Spring y(x) = ce 2x + e x

Solutions to Homework 1, Introduction to Differential Equations, 3450: , Dr. Montero, Spring y(x) = ce 2x + e x Solutions to Homewor 1, Introduction to Differential Equations, 3450:335-003, Dr. Montero, Spring 2009 problem 2. The problem says that the function yx = ce 2x + e x solves the ODE y + 2y = e x, and ass

More information

Assignment 1: Optimization and mathematical modeling

Assignment 1: Optimization and mathematical modeling Math 360 Winter 2017 Section 101 Assignment 1: Optimization and mathematical modeling 0.1 (Due Thursday Sept 28, 2017) Let P = (, y) be any point on the straight line y = 4 3. (a) Show that the distance

More information

AMATH 351 Mar 15, 2013 FINAL REVIEW. Instructor: Jiri Najemnik

AMATH 351 Mar 15, 2013 FINAL REVIEW. Instructor: Jiri Najemnik AMATH 351 Mar 15, 013 FINAL REVIEW Instructor: Jiri Najemni ABOUT GRADES Scores I have so far will be posted on the website today sorted by the student number HW4 & Exam will be added early next wee Let

More information

AP Calculus Chapter 3 Testbank (Mr. Surowski)

AP Calculus Chapter 3 Testbank (Mr. Surowski) AP Calculus Chapter 3 Testbank (Mr. Surowski) Part I. Multiple-Choice Questions (5 points each; please circle the correct answer.). If f(x) = 0x 4 3 + x, then f (8) = (A) (B) 4 3 (C) 83 3 (D) 2 3 (E) 2

More information

Name Class. 5. Find the particular solution to given the general solution y C cos x and the. x 2 y

Name Class. 5. Find the particular solution to given the general solution y C cos x and the. x 2 y 10 Differential Equations Test Form A 1. Find the general solution to the first order differential equation: y 1 yy 0. 1 (a) (b) ln y 1 y ln y 1 C y y C y 1 C y 1 y C. Find the general solution to the

More information

20.3. Further Laplace Transforms. Introduction. Prerequisites. Learning Outcomes

20.3. Further Laplace Transforms. Introduction. Prerequisites. Learning Outcomes Further Laplace Transforms 2.3 Introduction In this Section we introduce the second shift theorem which simplifies the determination of Laplace and inverse Laplace transforms in some complicated cases.

More information

Vibration Testing. Typically either instrumented hammers or shakers are used.

Vibration Testing. Typically either instrumented hammers or shakers are used. Vibration Testing Vibration Testing Equipment For vibration testing, you need an excitation source a device to measure the response a digital signal processor to analyze the system response Excitation

More information

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

Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations S. Y. Ha and J. Park Department of Mathematical Sciences Seoul National University Sep 23, 2013 Contents 1 Logistic Map 2 Euler and

More information

1.1 Motivation: Why study differential equations?

1.1 Motivation: Why study differential equations? Chapter 1 Introduction Contents 1.1 Motivation: Why stu differential equations?....... 1 1.2 Basics............................... 2 1.3 Growth and decay........................ 3 1.4 Introduction to Ordinary

More information

Ordinary Differential Equations II

Ordinary Differential Equations II Ordinary Differential Equations II CS 205A: Mathematical Methods for Robotics, Vision, and Graphics Justin Solomon CS 205A: Mathematical Methods Ordinary Differential Equations II 1 / 33 Almost Done! Last

More information

AP Exam Practice Questions for Chapter 6

AP Exam Practice Questions for Chapter 6 AP Eam Practice Questions for Chapter 6 AP Eam Practice Questions for Chapter 6. To find which graph is a slope field for, 5 evaluate the derivative at selected points. At ( 0, ),.. 3., 0,. 5 At ( ) At

More information

Lecture 10. (2) Functions of two variables. Partial derivatives. Dan Nichols February 27, 2018

Lecture 10. (2) Functions of two variables. Partial derivatives. Dan Nichols February 27, 2018 Lecture 10 Partial derivatives Dan Nichols nichols@math.umass.edu MATH 233, Spring 2018 University of Massachusetts February 27, 2018 Last time: functions of two variables f(x, y) x and y are the independent

More information

Modeling with First-Order Equations

Modeling with First-Order Equations Modeling with First-Order Equations MATH 365 Ordinary Differential Equations J. Robert Buchanan Department of Mathematics Fall 2018 Radioactive Decay Radioactive decay takes place continuously. The number

More information

First Order ODEs, Part II

First Order ODEs, Part II Craig J. Sutton craig.j.sutton@dartmouth.edu Department of Mathematics Dartmouth College Math 23 Differential Equations Winter 2013 Outline Existence & Uniqueness Theorems 1 Existence & Uniqueness Theorems

More information

Ordinary Differential Equations

Ordinary Differential Equations Chapter 7 Ordinary Differential Equations Differential equations are an extremely important tool in various science and engineering disciplines. Laws of nature are most often expressed as different equations.

More information

Chapter 8. Numerical Solution of Ordinary Differential Equations. Module No. 1. Runge-Kutta Methods

Chapter 8. Numerical Solution of Ordinary Differential Equations. Module No. 1. Runge-Kutta Methods Numerical Analysis by Dr. Anita Pal Assistant Professor Department of Mathematics National Institute of Technology Durgapur Durgapur-71309 email: anita.buie@gmail.com 1 . Chapter 8 Numerical Solution of

More information

Math 31S. Rumbos Fall Solutions to Exam 1

Math 31S. Rumbos Fall Solutions to Exam 1 Math 31S. Rumbos Fall 2011 1 Solutions to Exam 1 1. When people smoke, carbon monoxide is released into the air. Suppose that in a room of volume 60 m 3, air containing 5% carbon monoxide is introduced

More information

Technical Calculus I Homework. Instructions

Technical Calculus I Homework. Instructions Technical Calculus I Homework Instructions 1. Each assignment is to be done on one or more pieces of regular-sized notebook paper. 2. Your name and the assignment number should appear at the top of the

More information

5. Ordinary Differential Equations. Indispensable for many technical applications!

5. Ordinary Differential Equations. Indispensable for many technical applications! Indispensable for many technical applications! Numerisches Programmieren, Hans-Joachim Bungartz page 1 of 30 5.1. Introduction Differential Equations One of the most important fields of application of

More information

Global Optimization of Ordinary Differential Equations Models

Global Optimization of Ordinary Differential Equations Models Global Optimization of Ordinary Differential Equations Models Angelo Lucia*, Meghan L. Bellows and Leah M. Octavio Department of Chemical Engineering, University of Rhode Island, Kingston, RI 02881 Abstract

More information

Time: 1 hour 30 minutes

Time: 1 hour 30 minutes Paper Reference(s) 6666/0 Edexcel GCE Core Mathematics C4 Silver Level S Time: hour 30 minutes Materials required for examination papers Mathematical Formulae (Green) Items included with question Nil Candidates

More information

Review for Exam 2 Ben Wang and Mark Styczynski

Review for Exam 2 Ben Wang and Mark Styczynski Review for Exam Ben Wang and Mark Styczynski This is a rough approximation of what we went over in the review session. This is actually more detailed in portions than what we went over. Also, please note

More information

Finite Difference Methods for

Finite Difference Methods for CE 601: Numerical Methods Lecture 33 Finite Difference Methods for PDEs Course Coordinator: Course Coordinator: Dr. Suresh A. Kartha, Associate Professor, Department of Civil Engineering, IIT Guwahati.

More information

1 The Derivative and Differrentiability

1 The Derivative and Differrentiability 1 The Derivative and Differrentiability 1.1 Derivatives and rate of change Exercise 1 Find the equation of the tangent line to f (x) = x 2 at the point (1, 1). Exercise 2 Suppose that a ball is dropped

More information

Math 308 Exam I Practice Problems

Math 308 Exam I Practice Problems Math 308 Exam I Practice Problems This review should not be used as your sole source of preparation for the exam. You should also re-work all examples given in lecture and all suggested homework problems..

More information

Coordinate systems and vectors in three spatial dimensions

Coordinate systems and vectors in three spatial dimensions PHYS2796 Introduction to Modern Physics (Spring 2015) Notes on Mathematics Prerequisites Jim Napolitano, Department of Physics, Temple University January 7, 2015 This is a brief summary of material on

More information

Approximation of the Continuous Time Fourier Transform. Signals & Systems Lab 8

Approximation of the Continuous Time Fourier Transform. Signals & Systems Lab 8 Approximation of the Continuous Time Fourier Transform Signals & Systems Lab 8 Continuous Time Fourier Transform (CTFT) X f = x t e j2πft dt x t = X f e j2πft df Uncle Fourier Riemann Definite Integral

More information

Lecture 4. Mole balance: calculation of membrane reactors and unsteady state in tank reactors. Analysis of rate data

Lecture 4. Mole balance: calculation of membrane reactors and unsteady state in tank reactors. Analysis of rate data Lecture 4 Mole balance: calculation of membrane reactors and unsteady state in tank reactors. nalysis of rate data Mole alance in terms of Concentration and Molar Flow Rates Working in terms of number

More information

Linear Second Order ODEs

Linear Second Order ODEs Chapter 3 Linear Second Order ODEs In this chapter we study ODEs of the form (3.1) y + p(t)y + q(t)y = f(t), where p, q, and f are given functions. Since there are two derivatives, we might expect that

More information

Chap. 20: Initial-Value Problems

Chap. 20: Initial-Value Problems Chap. 20: Initial-Value Problems Ordinary Differential Equations Goal: to solve differential equations of the form: dy dt f t, y The methods in this chapter are all one-step methods and have the general

More information

A Brief Introduction to Numerical Methods for Differential Equations

A Brief Introduction to Numerical Methods for Differential Equations A Brief Introduction to Numerical Methods for Differential Equations January 10, 2011 This tutorial introduces some basic numerical computation techniques that are useful for the simulation and analysis

More information

Introduction to PDEs and Numerical Methods Tutorial 5. Finite difference methods equilibrium equation and iterative solvers

Introduction to PDEs and Numerical Methods Tutorial 5. Finite difference methods equilibrium equation and iterative solvers Platzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen Introduction to PDEs and Numerical Methods Tutorial 5. Finite difference methods equilibrium equation and iterative solvers Dr. Noemi

More information

The University of Sydney Math1003 Integral Calculus and Modelling. Semester 2 Exercises and Solutions for Week

The University of Sydney Math1003 Integral Calculus and Modelling. Semester 2 Exercises and Solutions for Week The University of Sydney Math1003 Integral Calculus and Modelling Semester Exercises and s for Week 11 011 Assumed Knowledge Integration techniques. Objectives (10a) To be able to solve differential equations

More information

Outline Calculus for the Life Sciences II. Pollution in a Lake 1. Introduction. Lecture Notes Numerical Methods for Differential Equations

Outline Calculus for the Life Sciences II. Pollution in a Lake 1. Introduction. Lecture Notes Numerical Methods for Differential Equations Improved Improved Outline Calculus for the Life Sciences II tial Equations Joseph M. Mahaffy, mahaffy@math.sdsu.edu Department of Mathematics and Statistics Dynamical Systems Group Computational Sciences

More information

Chemical Reaction Engineering. Lecture 2

Chemical Reaction Engineering. Lecture 2 hemical Reaction Engineering Lecture 2 General algorithm of hemical Reaction Engineering Mole balance Rate laws Stoichiometry Energy balance ombine and Solve lassification of reactions Phases involved:

More information

Dynamical Systems. August 13, 2013

Dynamical Systems. August 13, 2013 Dynamical Systems Joshua Wilde, revised by Isabel Tecu, Takeshi Suzuki and María José Boccardi August 13, 2013 Dynamical Systems are systems, described by one or more equations, that evolve over time.

More information

Sample Solutions of Assignment 3 for MAT3270B: 2.8,2.3,2.5,2.7

Sample Solutions of Assignment 3 for MAT3270B: 2.8,2.3,2.5,2.7 Sample Solutions of Assignment 3 for MAT327B: 2.8,2.3,2.5,2.7 1. Transform the given initial problem into an equivalent problem with the initial point at the origin (a). dt = t2 + y 2, y(1) = 2, (b). dt

More information