Divergence and Curl "Del", ∇ - a Defined Operator ∂ ∂ ∂ ∇ = , , ∂X ∂ Y ∂ Z

Total Page:16

File Type:pdf, Size:1020Kb

Divergence and Curl Divergence and Curl "Del", ∇ - A defined operator ∂ ∂ ∂ ∇ = , , ∂x ∂ y ∂ z The gradient of a function (at a point) is a vec tor that points in the direction in which the function increases most rapidly. A vector field is a vector function that can be thou ght of as a velocity field of a fluid. At each point it assigns a vector that represents the velocity of a particle at that point. The flux of a vector field is the volume of fluid flowing through an element of surface area per unit time. The divergence of a vector field is the flux per u nit volume. The divergence of a vector field is a number that can be thought of as a measure of the rate of change of the density of the flu id at a point. The curl of a vector field measures the tendency of the vector field to rotate about a point. The curl of a vector field at a point is a vector that points in the direction of the axis of rotation and has magnitude represents the speed of the rotation. Vector Field Scalar Funct ion F = Pxyz()()(),,, Qxyz ,,, Rxyz ,, f( x, yz, ) Gra dient grad( f ) ∇f = fffx, y , z Div ergence div (F ) ∂∂∂ ∂∂∂P Q R ∇⋅=F , , ⋅PQR, , =++=++ PQR ∂∂∂xyz ∂∂∂ xyz x y z Curl curl (F ) i j k ∂ ∂ ∂ ∇×=F =−−−+−()RQyz ijk() RP xz() QP xy ∂x ∂ y ∂ z P Q R ∇×=F RQy − z, −() RPQP xzxy −, − F( x,, y z) = xe−z ,4 yz2 ,3 ye − z ∂ ∂ ∂ div()F=∇⋅= F ,, ⋅ xe−z ,4,3 yz2 ye − z = e−z+4 z2 − 3 ye − z ∂x ∂ y ∂ z i j k ∂ ∂ ∂ curl ()F=∇×= F = 3e− z − 8 yz 2 , − 0 − ( − xe − z ) , 0 ∂x ∂ y ∂ z ( ) xe−z4 yz2 3 ye − z 3e−z− 8, yz2 − xe − z ,0 grad( scalar function ) = Vector Field div(Vector Field ) = scalarfunction curl (Vector Field) = Vector Field Which of the 9 ways to combine grad, div and curl by taking one of each. Which of these combinations make sense? grad( grad( f )) div( grad( f )) curl( grad( f )) 0 Vector Field Vector Field Vector Field vector grad( div (F )) div( div (F )) curl( div (F )) scalar function scalar function scalar function grad( curl (F )) div( curl (F )) 0 curl( curl (F )) Vector Field Vector Field scalar Vector Field 2 of the above are always zero. Verify the given identity. Assume conti nuity of all partial derivatives. curl grad f = 0 ()() . i j k grad( f) = f, f , f ∂ ∂ ∂ x y z curl() grad() f=∇×∇ f = ∂x ∂ y ∂ z fx f y f z ∇×∇=fffzy − yz, −( ffff zx − xz) , yx − xy = 0 since mixed partial derivatives are equal. Verify the given identity. Assume conti nuity of all partial derivatives. div() curl ()F = 0. Let F = Pxyz( ,,,) Qxyz( ,,,) Rxyz( ,, ) curl(F) =− Ry Q zz, P − R xx , Q − P y i j k ∂ ∂ ∂ curl ()F=∇× F = divcurl( (F)) =−( Ryz Q) +−( PR zx) +−( Q xy P ) ∂x ∂ y ∂ z x y z P Q R div( curl(F)) = Ryx − Q zx +− P zy R xy + Q xz − P yz div( curl(F)) = R yx − Qzx + Pzy − Rxy + Qxz − Pyz = 0.
Recommended publications
  • 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).
    [Show full text]
  • The Matrix Calculus You Need for Deep Learning
    The Matrix Calculus You Need For Deep Learning Terence Parr and Jeremy Howard July 3, 2018 (We teach in University of San Francisco's MS in Data Science program and have other nefarious projects underway. You might know Terence as the creator of the ANTLR parser generator. For more material, see Jeremy's fast.ai courses and University of San Francisco's Data Institute in- person version of the deep learning course.) HTML version (The PDF and HTML were generated from markup using bookish) Abstract This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math. Don't worry if you get stuck at some point along the way|just go back and reread the previous section, and try writing down and working through some examples. And if you're still stuck, we're happy to answer your questions in the Theory category at forums.fast.ai. Note: There is a reference section at the end of the paper summarizing all the key matrix calculus rules and terminology discussed here. arXiv:1802.01528v3 [cs.LG] 2 Jul 2018 1 Contents 1 Introduction 3 2 Review: Scalar derivative rules4 3 Introduction to vector calculus and partial derivatives5 4 Matrix calculus 6 4.1 Generalization of the Jacobian .
    [Show full text]
  • Symmetry and Tensors Rotations and Tensors
    Symmetry and tensors Rotations and tensors A rotation of a 3-vector is accomplished by an orthogonal transformation. Represented as a matrix, A, we replace each vector, v, by a rotated vector, v0, given by multiplying by A, 0 v = Av In index notation, 0 X vm = Amnvn n Since a rotation must preserve lengths of vectors, we require 02 X 0 0 X 2 v = vmvm = vmvm = v m m Therefore, X X 0 0 vmvm = vmvm m m ! ! X X X = Amnvn Amkvk m n k ! X X = AmnAmk vnvk k;n m Since xn is arbitrary, this is true if and only if X AmnAmk = δnk m t which we can rewrite using the transpose, Amn = Anm, as X t AnmAmk = δnk m In matrix notation, this is t A A = I where I is the identity matrix. This is equivalent to At = A−1. Multi-index objects such as matrices, Mmn, or the Levi-Civita tensor, "ijk, have definite transformation properties under rotations. We call an object a (rotational) tensor if each index transforms in the same way as a vector. An object with no indices, that is, a function, does not transform at all and is called a scalar. 0 A matrix Mmn is a (second rank) tensor if and only if, when we rotate vectors v to v , its new components are given by 0 X Mmn = AmjAnkMjk jk This is what we expect if we imagine Mmn to be built out of vectors as Mmn = umvn, for example. In the same way, we see that the Levi-Civita tensor transforms as 0 X "ijk = AilAjmAkn"lmn lmn 1 Recall that "ijk, because it is totally antisymmetric, is completely determined by only one of its components, say, "123.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Vector Fields
    Vector Calculus Independent Study Unit 5: Vector Fields A vector field is a function which associates a vector to every point in space. Vector fields are everywhere in nature, from the wind (which has a velocity vector at every point) to gravity (which, in the simplest interpretation, would exert a vector force at on a mass at every point) to the gradient of any scalar field (for example, the gradient of the temperature field assigns to each point a vector which says which direction to travel if you want to get hotter fastest). In this section, you will learn the following techniques and topics: • How to graph a vector field by picking lots of points, evaluating the field at those points, and then drawing the resulting vector with its tail at the point. • A flow line for a velocity vector field is a path ~σ(t) that satisfies ~σ0(t)=F~(~σ(t)) For example, a tiny speck of dust in the wind follows a flow line. If you have an acceleration vector field, a flow line path satisfies ~σ00(t)=F~(~σ(t)) [For example, a tiny comet being acted on by gravity.] • Any vector field F~ which is equal to ∇f for some f is called a con- servative vector field, and f its potential. The terminology comes from physics; by the fundamental theorem of calculus for work in- tegrals, the work done by moving from one point to another in a conservative vector field doesn’t depend on the path and is simply the difference in potential at the two points.
    [Show full text]
  • Policy Gradient
    Lecture 7: Policy Gradient Lecture 7: Policy Gradient David Silver Lecture 7: Policy Gradient Outline 1 Introduction 2 Finite Difference Policy Gradient 3 Monte-Carlo Policy Gradient 4 Actor-Critic Policy Gradient Lecture 7: Policy Gradient Introduction Policy-Based Reinforcement Learning In the last lecture we approximated the value or action-value function using parameters θ, V (s) V π(s) θ ≈ Q (s; a) Qπ(s; a) θ ≈ A policy was generated directly from the value function e.g. using -greedy In this lecture we will directly parametrise the policy πθ(s; a) = P [a s; θ] j We will focus again on model-free reinforcement learning Lecture 7: Policy Gradient Introduction Value-Based and Policy-Based RL Value Based Learnt Value Function Implicit policy Value Function Policy (e.g. -greedy) Policy Based Value-Based Actor Policy-Based No Value Function Critic Learnt Policy Actor-Critic Learnt Value Function Learnt Policy Lecture 7: Policy Gradient Introduction Advantages of Policy-Based RL Advantages: Better convergence properties Effective in high-dimensional or continuous action spaces Can learn stochastic policies Disadvantages: Typically converge to a local rather than global optimum Evaluating a policy is typically inefficient and high variance Lecture 7: Policy Gradient Introduction Rock-Paper-Scissors Example Example: Rock-Paper-Scissors Two-player game of rock-paper-scissors Scissors beats paper Rock beats scissors Paper beats rock Consider policies for iterated rock-paper-scissors A deterministic policy is easily exploited A uniform random policy
    [Show full text]
  • Introduction to a Line Integral of a Vector Field Math Insight
    Introduction to a Line Integral of a Vector Field Math Insight A line integral (sometimes called a path integral) is the integral of some function along a curve. One can integrate a scalar-valued function1 along a curve, obtaining for example, the mass of a wire from its density. One can also integrate a certain type of vector-valued functions along a curve. These vector-valued functions are the ones where the input and output dimensions are the same, and we usually represent them as vector fields2. One interpretation of the line integral of a vector field is the amount of work that a force field does on a particle as it moves along a curve. To illustrate this concept, we return to the slinky example3 we used to introduce arc length. Here, our slinky will be the helix parameterized4 by the function c(t)=(cost,sint,t/(3π)), for 0≤t≤6π, Imagine that you put a small-magnetized bead on your slinky (the bead has a small hole in it, so it can slide along the slinky). Next, imagine that you put a large magnet to the left of the slink, as shown by the large green square in the below applet. The magnet will induce a magnetic field F(x,y,z), shown by the green arrows. Particle on helix with magnet. The red helix is parametrized by c(t)=(cost,sint,t/(3π)), for 0≤t≤6π. For a given value of t (changed by the blue point on the slider), the magenta point represents a magnetic bead at point c(t).
    [Show full text]
  • Calculus Terminology
    AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential
    [Show full text]
  • High Order Gradient, Curl and Divergence Conforming Spaces, with an Application to NURBS-Based Isogeometric Analysis
    High order gradient, curl and divergence conforming spaces, with an application to compatible NURBS-based IsoGeometric Analysis R.R. Hiemstraa, R.H.M. Huijsmansa, M.I.Gerritsmab aDepartment of Marine Technology, Mekelweg 2, 2628CD Delft bDepartment of Aerospace Technology, Kluyverweg 2, 2629HT Delft Abstract Conservation laws, in for example, electromagnetism, solid and fluid mechanics, allow an exact discrete representation in terms of line, surface and volume integrals. We develop high order interpolants, from any basis that is a partition of unity, that satisfy these integral relations exactly, at cell level. The resulting gradient, curl and divergence conforming spaces have the propertythat the conservationlaws become completely independent of the basis functions. This means that the conservation laws are exactly satisfied even on curved meshes. As an example, we develop high ordergradient, curl and divergence conforming spaces from NURBS - non uniform rational B-splines - and thereby generalize the compatible spaces of B-splines developed in [1]. We give several examples of 2D Stokes flow calculations which result, amongst others, in a point wise divergence free velocity field. Keywords: Compatible numerical methods, Mixed methods, NURBS, IsoGeometric Analyis Be careful of the naive view that a physical law is a mathematical relation between previously defined quantities. The situation is, rather, that a certain mathematical structure represents a given physical structure. Burke [2] 1. Introduction In deriving mathematical models for physical theories, we frequently start with analysis on finite dimensional geometric objects, like a control volume and its bounding surfaces. We assign global, ’measurable’, quantities to these different geometric objects and set up balance statements.
    [Show full text]
  • 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.
    [Show full text]
  • The Divergence of a Vector Field.Doc 1/8
    9/16/2005 The Divergence of a Vector Field.doc 1/8 The Divergence of a Vector Field The mathematical definition of divergence is: w∫∫ A(r )⋅ds ∇⋅A()rlim = S ∆→v 0 ∆v where the surface S is a closed surface that completely surrounds a very small volume ∆v at point r , and where ds points outward from the closed surface. From the definition of surface integral, we see that divergence basically indicates the amount of vector field A ()r that is converging to, or diverging from, a given point. For example, consider these vector fields in the region of a specific point: ∆ v ∆v ∇⋅A ()r0 < ∇ ⋅>A (r0) Jim Stiles The Univ. of Kansas Dept. of EECS 9/16/2005 The Divergence of a Vector Field.doc 2/8 The field on the left is converging to a point, and therefore the divergence of the vector field at that point is negative. Conversely, the vector field on the right is diverging from a point. As a result, the divergence of the vector field at that point is greater than zero. Consider some other vector fields in the region of a specific point: ∇⋅A ()r0 = ∇ ⋅=A (r0) For each of these vector fields, the surface integral is zero. Over some portions of the surface, the normal component is positive, whereas on other portions, the normal component is negative. However, integration over the entire surface is equal to zero—the divergence of the vector field at this point is zero. * Generally, the divergence of a vector field results in a scalar field (divergence) that is positive in some regions in space, negative other regions, and zero elsewhere.
    [Show full text]