Predicting the future with Newton s Second Law

Similar documents
Differential Equations

4.9 Anti-derivatives. Definition. An anti-derivative of a function f is a function F such that F (x) = f (x) for all x.

Change & Rates of Change

Physics 121 for Majors

2.2 Average vs. Instantaneous Description

Coordinate systems and vectors in three spatial dimensions

Lecture 6.1 Work and Energy During previous lectures we have considered many examples, which can be solved using Newtonian approach, in particular,

The Basics of Physics with Calculus Part II. AP Physics C

Motion in One Dimension

Exam Question 10: Differential Equations. June 19, Applied Mathematics: Lecture 6. Brendan Williamson. Introduction.

The acceleration of gravity is constant (near the surface of the earth). So, for falling objects:

Parametric Functions and Vector Functions (BC Only)

Example (#1) Example (#1) Example (#2) Example (#2) dv dt

Introduction to Differential Equations

CHAPTER 6 VECTOR CALCULUS. We ve spent a lot of time so far just looking at all the different ways you can graph

Introduction to First Order Equations Sections

Applications of the definite integral to rates, velocities and densities

PHYS 211 Lecture 9 - Examples of 3D motion 9-1

Resonance and response

Sierzega: Kinematics 10 Page 1 of 14

Topic 5 Notes Jeremy Orloff. 5 Homogeneous, linear, constant coefficient differential equations

Springs: Part I Modeling the Action The Mass/Spring System

Derivation of Kinematic Equations. View this after Motion on an Incline Lab

Systems of Differential Equations: General Introduction and Basics

Sample Questions, Exam 1 Math 244 Spring 2007

Math 142 (Summer 2018) Business Calculus 6.1 Notes

Solving Differential Equations: First Steps

Figure 1: Doing work on a block by pushing it across the floor.

y 2y = 4 x, Name Form Solution method

What will you learn?

Chapter 3 Acceleration

Math Shifting theorems

KINEMATICS IN ONE DIMENSION p. 1

F = ma, F R + F S = mx.

2t t dt.. So the distance is (t2 +6) 3/2

APPLICATIONS OF INTEGRATION

Chapter 13 - Inverse Functions

Linear algebra and differential equations (Math 54): Lecture 20

Exercise 4) Newton's law of cooling is a model for how objects are heated or cooled by the temperature of an ambient medium surrounding them.

MATH 100 Introduction to the Profession

The Starting Point: Basic Concepts and Terminology

Chapter 0 of Calculus ++, Differential calculus with several variables

Area. A(2) = sin(0) π 2 + sin(π/2)π 2 = π For 3 subintervals we will find

Math Lab 10: Differential Equations and Direction Fields Complete before class Wed. Feb. 28; Due noon Thu. Mar. 1 in class

Chapter 13 Lecture. Essential University Physics Richard Wolfson 2 nd Edition. Oscillatory Motion Pearson Education, Inc.

Particle Systems. University of Texas at Austin CS384G - Computer Graphics

APPLICATIONS OF DIFFERENTIATION

PHYSICS 107. Lecture 5 Newton s Laws of Motion

n=0 ( 1)n /(n + 1) converges, but not

PUM Physics II - Kinematics Lesson 12 Solutions Page 1 of 16

Revision History Date Version Description Author 03/06/ Initial Version Sergio Miguel Martin. Table of Contents

DIFFERENTIAL EQUATIONS

Calculus of Variations Summer Term 2016

Calculus with business applications, Lehigh U, Lecture 01 notes Summer

Math 5a Reading Assignments for Sections

How to Use Calculus Like a Physicist

Computational Neuroscience. Session 1-2

MATH CALCULUS I 4.1: Area and Distance

Math Week 1 notes

Daily Lessons and Assessments for AP* Calculus AB, A Complete Course Page 584 Mark Sparks 2012

Differential Equations

Systems of Linear ODEs

Contents. Contents. Contents

Section 11.1 What is a Differential Equation?

Definition of Tangent Line with Slope m: If f is defined on an open interval containing x, and if the limit y f( c x) f( c) f( c x) f( c) lim lim lim

Math 106 Answers to Exam 3a Fall 2015

2nd-Order Linear Equations

MITOCW MITRES18_005S10_DiffEqnsMotion_300k_512kb-mp4

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

Calculus and Parametric Equations

Chapter 7: Trigonometric Equations and Identities

CHAPTER 1. First-Order Differential Equations and Their Applications. 1.1 Introduction to Ordinary Differential Equations

Chapter 7. Homogeneous equations with constant coefficients

Magnetic Flux. Conference 8. Physics 102 General Physics II

Solutions to the Review Questions

Chapter 7: Trigonometric Equations and Identities

MATH 18.01, FALL PROBLEM SET #5 SOLUTIONS (PART II)

3.9 Derivatives of Exponential and Logarithmic Functions

Dimensions. 1 gt. y(t) = 2

Section 3.1 Second Order Linear Homogeneous DEs with Constant Coefficients

Physics 101 Discussion Week 3 Explanation (2011)

MTH4100 Calculus I. Week 6 (Thomas Calculus Sections 3.5 to 4.2) Rainer Klages. School of Mathematical Sciences Queen Mary, University of London

Work sheet / Things to know. Chapter 3

Calculus Review. v = x t

Module 4: One-Dimensional Kinematics

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

Differential Forms. Introduction

Unforced Mechanical Vibrations

Lecture 9: Eigenvalues and Eigenvectors in Classical Mechanics (See Section 3.12 in Boas)

Chapter 3 Acceleration

ENGI 3424 First Order ODEs Page 1-01

Kinematics Introduction

Separable First-Order Equations

Old Math 330 Exams. David M. McClendon. Department of Mathematics Ferris State University

Physics 299: Computational Physics II Project II

Chapter 3 Acceleration

Vectors and Coordinate Systems

Please read for extra test points: Thanks for reviewing the notes you are indeed a true scholar!

Even-Numbered Homework Solutions

Integration of Differential Equations

Transcription:

Predicting the future with Newton s Second Law To represent the motion of an object (ignoring rotations for now), we need three functions x(t), y(t), and z(t), which describe the spatial coordinates of the object for each possible time. Since there are an infinite number of possible times (between any starting and ending time), giving the coordinates at all possible times is actually an infinite amount of information! Part of the magic of physics is that we can predict all of this information knowing only the initial position r(t = 0) = (x(t = 0), y(t = 0), z(t = 0)) and velocity v(t = 0) = (v x (t = 0), v y (t = 0), v z (t = 0)) of an object, plus the information about the object s environment (specifically, what forces are acting on it). The key tool is Newton s Second Law, which we write as: 1 a = 1 m F NET. (1) This says that we can predict the acceleration of an object by knowing the forces on the object. We are assuming that the object s environment is understood well enough that we can predict the forces on the object from the object s position and velocity. Why we can predict the future To see why Newton s Second Law allows us to predict the future, we need to remember that acceleration is defined to be the rate of change of velocity a = d v/. So given the acceleration a at some time t, we can say that in a time δt, the velocity will change by aδt. If the velocity at time t is v(t), the velocity at the later time (t + δt) will then be v(t + δ) v(t) + δt a. (2) In exactly the same way, using the definition of velocity as the rate of change of position, we get r(t + δ) r(t) + δt v. (3) We put approximately equals to ( ) here because the acceleration and/or velocity might be changing with time. In this case, the equations become exact only in the limit where δt 0, but in practice, we just need to take δt small enough to get whatever accuracy we desire. Let s understand why (2) and (3) together with Newton s Law (1) are so powerful. Remembering that Newton s law allows us to predict acceleration from the force on the object, and that the force at some time is determined by the position and velocity of the object at that time (we ll write the force as F ( r(t), v(t)) to remind ourselves of this), we can summarize (2) and (3) as v(t + δ) v(t) + δ m F ( r(t), v(t)) 1 Here, we are assuming that speed is small compared with the speed of light, so that we can use p = m v to rewrite Newton s Law from its original form d p/ = F to the familiar F = m a. 1

r(t + δ) r(t) + δt v. Here, the right hand sides are all things that we can calculate given the position and velocity of the object at time t. The left hand sides are the position at velocity at a slightly later time. So if we know position and velocity now, we can predict what they will be slightly later! We can do this as many times as we want to predict what the object will do in the future. To summarize, we get the following recipe for predicting the motion. Start with the position r(t) and the velocity v(t) at some time. Given these, determine the net force on the object. Using (4), calculate the position and velocity r(t + δt) and the velocity v(t + δt) at some slightly later time. Repeat. By taking the time step δt to be sufficiently small, we can predict the position and velocity of an object at some specified later time with arbitrary precision. 2 In many cases, there are simpler ways to actually predict the motion than using this repetitive procedure. But our discussion here shows that we can always predict the motion in principle no matter how complicated the forces are. Differential equations of motion The equations (4) are equivalent to the following exact equations 3 d v d r = 1 m F ( r(t), v(t)) = v. Since these equations contain derivatives, they are known as DIFFERENTIAL EQUA- TIONS. The solutions to equations like this are functions (in this case r(t) and v(t)). What we have seen is that given some INITIAL CONDITIONS that is, the position and velocity r( ) and v( ) at some initial time there will be a unique solution (that we can find in principle using the repetitive method) to these equations. This solution describes the motion of the object at all later times. For this reason, the equations (4) are known as the EQUATIONS OF MOTION for the object. 4 2 Note that in some systems, for large enough times into the future, achieving such precision would require taking a δt that is impractically small. 3 Here the second one is just the definition of velocity and the first combines the definition of acceleration with Newton s first Law. These were exactly what we used to derive (4) 4 Sometimes, the two equations (4) are combined into one by using the second to eliminate v from the first. This gives d 2 r 2 = 1 F m ( r(t), v(t)), but the equations of motion are a little less clear to interpret in this form. 2

Methods for solving the equations of motion Let s now discuss various procedures for actually solving the equations of motion. 1) The Euler method In the most difficult cases (actually, in most realistic cases), the repetitive procedure we have described in the previous section (or some closely related procedure) may be the only way of predicting the motion. While this procedure is tedious to do by hand, it is very easy to implement on a computer. The specific procedure described is known as the Euler method for solving the differential equations. If we predict the position and/or velocity of an object at some later time using this method, the accuracy of our result (difference from the exact result) is generally proportional to the size of our time step δt. For some other numerical methods, the error decreases more quickly as we take δt to zero. 2) Solving the differential equations directly In some cases, the differential equations are simple enough that we can solve them directly by finding a family of functions that satisfies the equations and then choosing the ones that have the right initial conditions. Various higher-level math courses (or books on differential equations) teach you techniques for doing this. In the simplest cases, it s possible just to guess a solution and check that it works. As an example, if we had dv/ = Bv (e.g. for a resistive force proportional to the speed of the object), we could recall that exponential functions have a derivative proportional to the function itself and then guess the family of solutions v(t) = Ae Bt, using the initial velocity to determine the constant A. 3) Cases where the force is some known function of time - antidifferentiation method A special case where we can directly find a solution is when the force is some known function of time, which does not explicitly refer to the position or the velocity of the object. 5 This includes cases where the forces are constant, but also cases where they can change with time. In these cases, the equations of motion simplify to dv x dx = A x (t) = v x. where A x (t) is known, and we may have similar equations for y and z. Note that A x (t) is not allowed to depend on v or r. In this case we can use the following method: 5 Examples of forces that do depend explicitly on position or velocity are the drag force (with magnitude proportional to v 2 ), or the force from a spring, which changes as the object moves and the spring stretches. 3

Find a function g(t) whose derivative is A x (t). Since the derivative of v x (t) is also A x (t), the graphs of g(t) and v x (t) have the same slope everywhere. This means that v x (t) = g(t) + C for some constant C. To find C, we use the information about what v x is at the initial time. For example, if we know v x is v 1 at time, then we need to choose C = v( ) g( ), so that v x (t) = g(t) g( ) + v( ). (4) Once we know v x (t) we repeat the same procedure to find x(t). Use the same approach independently for y and z if necessary. 4) Cases where the force is some known function of time - area method Remarkably, there is another equivalent method for determining the velocity at later times if we are given the acceleration as a function of time. This may be summarized as t2 v x (t 2 ) = v x ( ) + A x (t), (5) where the expression t 2 A x (t) means the area under the the graph of A x (t) between and t 2. Similarly, once we know v x (t), we can write x(t 2 ) = x( ) + t2 v x (t). In practice, we might use this method if the acceleration is given to us in the form of a graph of a very simple function for which we can immediately read off the area. Origin of the area method Where does this come from? It s actually just the Euler method in disguise! Let s say we have dv = A(t), (6) and the velocity at time is equal to v 1. Then we can use (2) to write the velocity at time v( + δt) as v( + δt) = v( ) + δt A( ). Repeating for the later time + 2δt, we get v( + 2δt) = v( + δt) + δt A( + δt) = v( ) + δt A( ) + δt A( + δt) 4

Figure 1: Rectangles of thickness δt under graph of A(t) from to t 2. Area of the first rectangle is δt A( ), area of the second rectangle is δt A( + δt), and so forth. where we have used our previous result in the last line. If we keep repeating this all the way to some later time t 2, we ll get v(t 2 ) = v( ) + δta( ) + δt A( + δt) + δt A( + 2δt) +... + δt A(t 2 δt). Now, looking at figure 1, we notice that each term here with a δt is exactly the area of one of the rectangles under the graph. In the limit where δt goes to zero (where the Euler method becomes exact), the rectangles become infinitely thin and completely fill in the region under the curve between and t 2 (i.e. there are no gaps). Thus, the sum of the areas is the area under the curve, and we have derived the formula (5). The fundamental theorem of calculus We ve actually just figured out a completely AMAZING MATHEMATICAL FACT. Combining what we ve learned so far, we ve actually come up with a method for how to calculate the area under the curve of a function! Let s review: we saw in the previous section that if v satisfies the equation (6), then the change in v from time to time t 2 is equal to the area under the graph of A(t) from to t 2. But we d already given a completely different method for finding this change in v. According to the other method, if we find a function g(t) whose derivative is A(t), then the change in velocity from to t 2 is g(t 2 ) g( ) (using equation (4). Since both methods are correct, it must be that t2 A(t) = g(t 2 ) g( ) where g is any function whose derivative is A(t). This amazing result connects differentiation with integration (finding areas) and is part of THE FUNDAMENTAL THEOREM OF CALCULUS. 5

Example: constant acceleration The methods above all work when the acceleration is constant, and reproduce all the standard kinematics formulae for constant acceleration. To see this, suppose we have an object with initial position x 0 and velocity v 0 at time t = 0, and suppose that the acceleration is a constant value A. Then we have dv = A so using the antidifferentiation method, we have v(t) = At + C. where C is a constant. To get the correct value v = v 0 at t = 0, we must have C = v 0, so we get v(t) = v 0 + At. From this, we have dx = v 0 + At, so using antidifferentiation again, we find x(t) = C + v 0 t + 1 2 At2. Since x(0) = x 0, we must have C = x 0, so we finally obtain the familiar formula x(t) = x 0 + v 0 t + 1 2 At2. It is important to note that this (and other formulae like v1 2 correct if acceleration is constant! = v 2 0 + 2Ad) are only 6