1 Statement of Stokes' Theorem 2 Examples
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Lecture 11 Line Integrals of Vector-Valued Functions (Cont'd)
Lecture 11 Line integrals of vector-valued functions (cont’d) In the previous lecture, we considered the following physical situation: A force, F(x), which is not necessarily constant in space, is acting on a mass m, as the mass moves along a curve C from point P to point Q as shown in the diagram below. z Q P m F(x(t)) x(t) C y x The goal is to compute the total amount of work W done by the force. Clearly the constant force/straight line displacement formula, W = F · d , (1) where F is force and d is the displacement vector, does not apply here. But the fundamental idea, in the “Spirit of Calculus,” is to break up the motion into tiny pieces over which we can use Eq. (1 as an approximation over small pieces of the curve. We then “sum up,” i.e., integrate, over all contributions to obtain W . The total work is the line integral of the vector field F over the curve C: W = F · dx . (2) ZC Here, we summarize the main steps involved in the computation of this line integral: 1. Step 1: Parametrize the curve C We assume that the curve C can be parametrized, i.e., x(t)= g(t) = (x(t),y(t), z(t)), t ∈ [a, b], (3) so that g(a) is point P and g(b) is point Q. From this parametrization we can compute the velocity vector, ′ ′ ′ ′ v(t)= g (t) = (x (t),y (t), z (t)) . (4) 1 2. Step 2: Compute field vector F(g(t)) over curve C F(g(t)) = F(x(t),y(t), z(t)) (5) = (F1(x(t),y(t), z(t), F2(x(t),y(t), z(t), F3(x(t),y(t), z(t))) , t ∈ [a, b] . -
1 Curl and Divergence
Sections 15.5-15.8: Divergence, Curl, Surface Integrals, Stokes' and Divergence Theorems Reeve Garrett 1 Curl and Divergence Definition 1.1 Let F = hf; g; hi be a differentiable vector field defined on a region D of R3. Then, the divergence of F on D is @ @ @ div F := r · F = ; ; · hf; g; hi = f + g + h ; @x @y @z x y z @ @ @ where r = h @x ; @y ; @z i is the del operator. If div F = 0, we say that F is source free. Note that these definitions (divergence and source free) completely agrees with their 2D analogues in 15.4. Theorem 1.2 Suppose that F is a radial vector field, i.e. if r = hx; y; zi, then for some real number p, r hx;y;zi 3−p F = jrjp = (x2+y2+z2)p=2 , then div F = jrjp . Theorem 1.3 Let F = hf; g; hi be a differentiable vector field defined on a region D of R3. Then, the curl of F on D is curl F := r × F = hhy − gz; fz − hx; gx − fyi: If curl F = 0, then we say F is irrotational. Note that gx − fy is the 2D curl as defined in section 15.4. Therefore, if we fix a point (a; b; c), [gx − fy](a; b; c), measures the rotation of F at the point (a; b; c) in the plane z = c. Similarly, [hy − gz](a; b; c) measures the rotation of F in the plane x = a at (a; b; c), and [fz − hx](a; b; c) measures the rotation of F in the plane y = b at the point (a; b; c). -
A Brief Tour of Vector Calculus
A BRIEF TOUR OF VECTOR CALCULUS A. HAVENS Contents 0 Prelude ii 1 Directional Derivatives, the Gradient and the Del Operator 1 1.1 Conceptual Review: Directional Derivatives and the Gradient........... 1 1.2 The Gradient as a Vector Field............................ 5 1.3 The Gradient Flow and Critical Points ....................... 10 1.4 The Del Operator and the Gradient in Other Coordinates*............ 17 1.5 Problems........................................ 21 2 Vector Fields in Low Dimensions 26 2 3 2.1 General Vector Fields in Domains of R and R . 26 2.2 Flows and Integral Curves .............................. 31 2.3 Conservative Vector Fields and Potentials...................... 32 2.4 Vector Fields from Frames*.............................. 37 2.5 Divergence, Curl, Jacobians, and the Laplacian................... 41 2.6 Parametrized Surfaces and Coordinate Vector Fields*............... 48 2.7 Tangent Vectors, Normal Vectors, and Orientations*................ 52 2.8 Problems........................................ 58 3 Line Integrals 66 3.1 Defining Scalar Line Integrals............................. 66 3.2 Line Integrals in Vector Fields ............................ 75 3.3 Work in a Force Field................................. 78 3.4 The Fundamental Theorem of Line Integrals .................... 79 3.5 Motion in Conservative Force Fields Conserves Energy .............. 81 3.6 Path Independence and Corollaries of the Fundamental Theorem......... 82 3.7 Green's Theorem.................................... 84 3.8 Problems........................................ 89 4 Surface Integrals, Flux, and Fundamental Theorems 93 4.1 Surface Integrals of Scalar Fields........................... 93 4.2 Flux........................................... 96 4.3 The Gradient, Divergence, and Curl Operators Via Limits* . 103 4.4 The Stokes-Kelvin Theorem..............................108 4.5 The Divergence Theorem ...............................112 4.6 Problems........................................114 List of Figures 117 i 11/14/19 Multivariate Calculus: Vector Calculus Havens 0. -
Appendix a Short Course in Taylor Series
Appendix A Short Course in Taylor Series The Taylor series is mainly used for approximating functions when one can identify a small parameter. Expansion techniques are useful for many applications in physics, sometimes in unexpected ways. A.1 Taylor Series Expansions and Approximations In mathematics, the Taylor series is a representation of a function as an infinite sum of terms calculated from the values of its derivatives at a single point. It is named after the English mathematician Brook Taylor. If the series is centered at zero, the series is also called a Maclaurin series, named after the Scottish mathematician Colin Maclaurin. It is common practice to use a finite number of terms of the series to approximate a function. The Taylor series may be regarded as the limit of the Taylor polynomials. A.2 Definition A Taylor series is a series expansion of a function about a point. A one-dimensional Taylor series is an expansion of a real function f(x) about a point x ¼ a is given by; f 00ðÞa f 3ðÞa fxðÞ¼faðÞþf 0ðÞa ðÞþx À a ðÞx À a 2 þ ðÞx À a 3 þÁÁÁ 2! 3! f ðÞn ðÞa þ ðÞx À a n þÁÁÁ ðA:1Þ n! © Springer International Publishing Switzerland 2016 415 B. Zohuri, Directed Energy Weapons, DOI 10.1007/978-3-319-31289-7 416 Appendix A: Short Course in Taylor Series If a ¼ 0, the expansion is known as a Maclaurin Series. Equation A.1 can be written in the more compact sigma notation as follows: X1 f ðÞn ðÞa ðÞx À a n ðA:2Þ n! n¼0 where n ! is mathematical notation for factorial n and f(n)(a) denotes the n th derivation of function f evaluated at the point a. -
Curl, Divergence and Laplacian
Curl, Divergence and Laplacian What to know: 1. The definition of curl and it two properties, that is, theorem 1, and be able to predict qualitatively how the curl of a vector field behaves from a picture. 2. The definition of divergence and it two properties, that is, if div F~ 6= 0 then F~ can't be written as the curl of another field, and be able to tell a vector field of clearly nonzero,positive or negative divergence from the picture. 3. Know the definition of the Laplace operator 4. Know what kind of objects those operator take as input and what they give as output. The curl operator Let's look at two plots of vector fields: Figure 1: The vector field Figure 2: The vector field h−y; x; 0i: h1; 1; 0i We can observe that the second one looks like it is rotating around the z axis. We'd like to be able to predict this kind of behavior without having to look at a picture. We also promised to find a criterion that checks whether a vector field is conservative in R3. Both of those goals are accomplished using a tool called the curl operator, even though neither of those two properties is exactly obvious from the definition we'll give. Definition 1. Let F~ = hP; Q; Ri be a vector field in R3, where P , Q and R are continuously differentiable. We define the curl operator: @R @Q @P @R @Q @P curl F~ = − ~i + − ~j + − ~k: (1) @y @z @z @x @x @y Remarks: 1. -
How to Compute Line Integrals Scalar Line Integral Vector Line Integral
How to Compute Line Integrals Northwestern, Spring 2013 Computing line integrals can be a tricky business. First, there's two types of line integrals: scalar line integrals and vector line integrals. Although these are related, we compute them in different ways. Also, there's two theorems flying around, Green's Theorem and the Fundamental Theorem of Line Integrals (as I've called it), which give various other ways of computing line integrals. Knowing what to use when and where and why can be confusing, so hopefully this will help to keep things straight. I won't say much about what line integrals mean geometrically or otherwise. Hopefully this is something you have some idea about from class or the book, but I'm always happy to talk about this more in office hours. Here the focus is on computation. Key Point: When you are trying to apply one of these techniques, make sure the technique you are using is actually applicable! (This was a major source of confusion on Quiz 5.) Scalar Line Integral • Arises when integrating a function f over a curve C. • Notationally, is always denoted by something like Z f ds; C where the s in ds is just a normal s, as opposed to ds or d~s. • To compute, find parametric equations x(t); a ≤ t ≤ b for C and use Z Z b f ds = f(x(t)) kx0(t)k dt: C a • In the special case where f is the constant function 1, Z ds = arclength of C: C • For scalar line integrals, the orientation (i.e. -
Line Integrals
Line Integrals Kenneth H. Carpenter Department of Electrical and Computer Engineering Kansas State University September 12, 2008 Line integrals arise often in calculations in electromagnetics. The evaluation of such integrals can be done without error if certain simple rules are followed. This rules will be illustrated in the following. 1 Definitions 1.1 Definite integrals In calculus, for functions of one dimension, the definite integral is defined as the limit of a sum (the “Riemann” integral – other kinds are possible – see an advanced calculus text if you are curious about this :). Z b X f(x)dx = lim f(ξi)∆xi (1) ∆xi!0 a i where the ξi is a value of x within the incremental ∆xi, and all the incremental ∆xi add up to the length along the x axis from a to b. This is often pictured graphically as the area between the curve drawn as f(x) and the x axis. 1 EECE557 Line integrals – supplement to text - Fall 2008 2 1.2 Evaluation of definite integrals in terms of indefinite inte- grals The fundamental theorem of integral calculus relates the indefinite integral to the definite integral (with certain restrictions as to when these exist) as Z b dF f(x)dx = F (b) − F (a); when = f(x): (2) a dx The proof of eq.(2) does not depend on having a < b. In fact, R b f(x)dx = R a a − b f(x)dx follows from the values of ∆xi being signed so that ∆xi equals the value of x on the side of the increment next to b minus the value of x on the side of the increment next to a. -
10A– Three Generalizations of the Fundamental Theorem of Calculus MATH 22C
10A– Three Generalizations of the Fundamental Theorem of Calculus MATH 22C 1. Introduction In the next four sections we present applications of the three generalizations of the Fundamental Theorem of Cal- culus (FTC) to three space dimensions (x, y, z) 3,a version associated with each of the three linear operators,2R the Gradient, the Curl and the Divergence. Since much of classical physics is framed in terms of these three gener- alizations of FTC, these operators are often referred to as the three linear first order operators of classical physics. The FTC in one dimension states that the integral of a function over a closed interval [a, b] is equal to its anti- derivative evaluated between the endpoints of the interval: b f 0(x)dx = f(b) f(a). − Za This generalizes to the following three versions of the FTC in two and three dimensions. The first states that the line integral of a gradient vector field F = f along a curve , (in physics the work done by F) is exactlyr equal to the changeC in its potential potential f across the endpoints A, B of : C F Tds= f(B) f(A). (1) · − ZC The second, called Stokes Theorem, says that the flux of the Curl of a vector field F through a two dimensional surface in 3 is the line integral of F around the curve that S R 1 C 2 bounds : S CurlF n dσ = F Tds (2) · · ZZS ZC And the third, called the Divergence Theorem, states that the integral of the Divergence of F over an enclosed vol- ume is equal to the flux of F outward through the two dimensionalV closed surface that bounds : S V DivF dV = F n dσ. -
Approaching Green's Theorem Via Riemann Sums
APPROACHING GREEN’S THEOREM VIA RIEMANN SUMS JENNIE BUSKIN, PHILIP PROSAPIO, AND SCOTT A. TAYLOR ABSTRACT. We give a proof of Green’s theorem which captures the underlying intuition and which relies only on the mean value theorems for derivatives and integrals and on the change of variables theorem for double integrals. 1. INTRODUCTION The counterpoint of the discrete and continuous has been, perhaps even since Eu- clid, the essence of many mathematical fugues. Despite this, there are fundamental mathematical subjects where their voices are difficult to distinguish. For example, although early Calculus courses make much of the passage from the discrete world of average rate of change and Riemann sums to the continuous (or, more accurately, smooth) world of derivatives and integrals, by the time the student reaches the cen- tral material of vector calculus: scalar fields, vector fields, and their integrals over curves and surfaces, the voice of discrete mathematics has been obscured by the coloratura of continuous mathematics. Our aim in this article is to restore the bal- ance of the voices by showing how Green’s Theorem can be understood from the discrete point of view. Although Green’s Theorem admits many generalizations (the most important un- doubtedly being the Generalized Stokes’ Theorem from the theory of differentiable manifolds), we restrict ourselves to one of its simplest forms: Green’s Theorem. Let S ⊂ R2 be a compact surface bounded by (finitely many) simple closed piecewise C1 curves oriented so that S is on their left. Suppose that M F = is a C1 vector field defined on an open set U containing S. -
External Aerodynamics of the Magnetosphere
’ .., NASA TECHNICAL NOTE NASA TN- I- &: I -I LOAN COPY: R AFWL (W KIRTLAND AFI EXTERNAL AERODYNAMICS OF THE MAGNETOSPHERE by John R. Spreiter, A Zbertu Y. A Zksne, and Audrey L. Summers Awes Research Center Moffett Fie@ CuZ$ NATIONAL AERONAUTICS AND SPACE ADMINISTRATION WASHINGTON, D. C. JUNE 1968 i TECH LIBRARY KAFB, NM Illll1lllll111lll lllll lilll llll lllll 1111 Ill 0133359 EXTERNAL AERODYNAMICS OF THE MAGNETOSPHERE By John R. Spreiter, Alberta Y. Alksne, and Audrey L. Summers Ames Research Center Moffett Field, Calif. NATIONAL AERONAUTICS AND SPACE ADMINISTRATION For sale by the Clearinghouse for Federal Scientific and Technical Information Springfield, Virginia 22151 - CFSTl price $3.00 EXTERNAL AERODYNAMICS OF THE MAGNETOSPHERE* By John R. Spreiter, Alberta Y. Alksne, and Audrey L. Summers Ames Research Center SUMMARY A comprehensive survey is given of the continuum fluid theory of the solar wind and its interaction with the Earth's magnetic field, and the rela- tion between the calculated results and those actually measured in space. A unified basis for the entire discussion is provided by the equations of mag- netohydrodynamics, augmented by relations from kinetic theory for certain small-scale details of the flow. While the full complexity of magnetohydrodynamics is required for the formulation of the model and the establishment of the proper conditions to apply at the magnetosphere boundary, it is shown that the magnetic field actually experienced in space is usually sufficiently small that an adequate approximation to the solution can be obtained by first solving the simpler equations of gasdynamics for the flow and then using the results to calculate the deformation of the magnetic field. -
Stokes' Theorem
V13.3 Stokes’ Theorem 3. Proof of Stokes’ Theorem. We will prove Stokes’ theorem for a vector field of the form P (x, y, z) k . That is, we will show, with the usual notations, (3) P (x, y, z) dz = curl (P k ) · n dS . � C � �S We assume S is given as the graph of z = f(x, y) over a region R of the xy-plane; we let C be the boundary of S, and C ′ the boundary of R. We take n on S to be pointing generally upwards, so that |n · k | = n · k . To prove (3), we turn the left side into a line integral around C ′, and the right side into a double integral over R, both in the xy-plane. Then we show that these two integrals are equal by Green’s theorem. To calculate the line integrals around C and C ′, we parametrize these curves. Let ′ C : x = x(t), y = y(t), t0 ≤ t ≤ t1 be a parametrization of the curve C ′ in the xy-plane; then C : x = x(t), y = y(t), z = f(x(t), y(t)), t0 ≤ t ≤ t1 gives a corresponding parametrization of the space curve C lying over it, since C lies on the surface z = f(x, y). Attacking the line integral first, we claim that (4) P (x, y, z) dz = P (x, y, f(x, y))(fxdx + fydy) . � C � C′ This looks reasonable purely formally, since we get the right side by substituting into the left side the expressions for z and dz in terms of x and y: z = f(x, y), dz = fxdx + fydy. -
Generalized Stokes' Theorem
Chapter 4 Generalized Stokes’ Theorem “It is very difficult for us, placed as we have been from earliest childhood in a condition of training, to say what would have been our feelings had such training never taken place.” Sir George Stokes, 1st Baronet 4.1. Manifolds with Boundary We have seen in the Chapter 3 that Green’s, Stokes’ and Divergence Theorem in Multivariable Calculus can be unified together using the language of differential forms. In this chapter, we will generalize Stokes’ Theorem to higher dimensional and abstract manifolds. These classic theorems and their generalizations concern about an integral over a manifold with an integral over its boundary. In this section, we will first rigorously define the notion of a boundary for abstract manifolds. Heuristically, an interior point of a manifold locally looks like a ball in Euclidean space, whereas a boundary point locally looks like an upper-half space. n 4.1.1. Smooth Functions on Upper-Half Spaces. From now on, we denote R+ := n n f(u1, ... , un) 2 R : un ≥ 0g which is the upper-half space of R . Under the subspace n n n topology, we say a subset V ⊂ R+ is open in R+ if there exists a set Ve ⊂ R open in n n n R such that V = Ve \ R+. It is intuitively clear that if V ⊂ R+ is disjoint from the n n n subspace fun = 0g of R , then V is open in R+ if and only if V is open in R . n n Now consider a set V ⊂ R+ which is open in R+ and that V \ fun = 0g 6= Æ.