ENGG2013 Unit 1 Overview Jan, 2011. Course info • Textbook: “Advanced Engineering Mathematics” 9th edition, by Erwin Kreyszig. • Lecturer: Kenneth Shum – Office: SHB 736 – Ext: 8478 – Office hour: Mon, Tue 2:00~3:00 • Tutor: Li Huadong, Lou Wei • Grading: – Bi-Weekly homework (12%) – Midterm (38%) – Final Exam (50%) Erwin O. Kreyszig (6/1/1922~12/12/2008) • Before midterm: Linear algebra • After midterm: Differential equations kshum ENGG2013 2 Academic Honesty • Attention is drawn to University policy and regulations on honesty in academic work, and to the disciplinary guidelines and procedures applicable to breaches of such policy and regulations. Details may be found at http://www.cuhk.edu.hk/policy/academichon esty/ kshum ENGG2013 3 System of Linear Equations Two variables, two equations 7 6 5 4 y 3 2 1 0 -1 -2 -3 kshum 0 0.2 0.4 ENGG2013 0.6 0.8 1 x 1.2 1.4 1.6 1.8 2 4 System of Linear Equations Three variables, three equations 6 4 2 z 0 -2 -4 -6 -8 1 0 -1 -2 y kshum ENGG2013 -2 -1.5 -1 -0.5 0 0.5 x 5 System of Linear Equations Multiple variables, multiple equations How to solve? kshum ENGG2013 6 Determinant • Area of parallelogram (c,d) (a,b) kshum ENGG2013 7 3x3 Determinant • Volume of parallelepiped (g,h,i) (d,e,f) (a,b,c) kshum ENGG2013 8 Nutrition problem • Find a combination of food A, B, C and D in order to satisfy the nutrition requirement exactly. Food A Food B Food C Food D Requirement Protein 9 8 3 3 5 Carbohydrate 15 11 1 4 5 Vitamin A 0.02 0.003 0.01 0.006 0.01 Vitamin C 0.01 0.01 0.005 0.05 0.01 How to solve it using linear algebra? kshum ENGG2013 9 Electronic Circuit (Static) • Find the current through each resistor System of linear equations kshum ENGG2013 10 Electronic Circuit (dynamic) • Find the current through each resistor inductor alternating current System of differential equations kshum ENGG2013 11 Spring-mass system • Before t=0, the two springs and three masses are at rest on a frictionless surface. • A horizontal force cos(wt) is applied to A for t>0. • What is the motion of C? A C B Second-order differential equation kshum ENGG2013 12 System Modeling Physical System Reality Physical Laws + Simplifying assumptions Mathematical description kshum ENGG2013 Theory 13 How to model a typhoon? Lots of partial differential equations are required. kshum ENGG2013 14 Example: Simple Pendulum • • • • L = length of rod m = mass of the bob = angle g = gravitational constant L m mg sin mg kshum ENGG2013 15 Example: Simple Pendulum • arc length = s = L • velocity = v = L d/dt • acceleration = a = L d2/dt2 • Apply Newton’s law F=ma to the tangential axis: L m mg sin mg kshum ENGG2013 16 What are the assumptions? • • • • • • The bob is a point mass Mass of the rod is zero The rod does not stretch No air friction The motion occurs in a 2-D plane* Atmosphere pressure is neglected * Foucault pendulum @ wiki kshum ENGG2013 17 Further simplification • Small-angle assumption – When is small, (in radian) is very close to sin . Solutions are elliptic functions. simplifies to Solutions are sinusoidal functions. kshum ENGG2013 18 Modeling the pendulum modeling or Continuous-time dynamical system for small angle kshum ENGG2013 19 Discrete-time dynamical system • Compound interest – r = interest rate per month – p(t) = money in your account – t = 0,1,2,3,4 Time is discrete kshum ENGG2013 20 Discrete-time dynamical system • Logistic population growth – n(t) = population in the t-th year – t = 0,1,2,3,4 0.25 An example for K=1 Graph of n(1-n) 0.2 -0.05 -0.1 -0.15 negative growth 0 Slow growth 0.05 Slow growth Proportionality constant 0.1 n*(1-n/K) Increase in population fast growth 0.15 -0.2 -0.25 0 kshum ENGG2013 0.2 0.4 0.6 0.8 n 1 1.2 1.4 21 Sample population growth Initialized at n(1) = 0.01 Monotonically increasing a=0.8, K=1 Oscillating a=2, K=1 1 1.4 1.2 0.8 1 0.6 n(t) n(t) 0.8 0.6 0.4 0.4 0.2 0.2 0 0 kshum 5 10 t 15 20 ENGG2013 0 0 5 10 15 20 25 t 30 35 40 45 50 22 Sample population growth Initialized at n(1) = 0.01 a=2.8, K=1 1.4 1.2 1 Chaotic n(t) 0.8 0.6 0.4 0.2 0 0 10 20 30 40 50 t kshum ENGG2013 23 Rough classification System Static Probabilistic systems are treated in ENGG2040 kshum Dynamic Continuoustime ENGG2013 Discrete-time 24 Determinism • From wikipedia: “…if you knew all of the variables and rules you could work out what will happen in the future.” • There is nothing called randomness. • Even flipping a coin is deterministic. – We cannot predict the result of coin flipping because we do not know the initial condition precisely. kshum ENGG2013 25