Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Vector Calculus.

34,253 views

Published on

We discussed most of what one wishes to learn in vector calculus at the undergraduate engineering level. Its also useful for the Physics ‘honors’ and ‘pass’ students.

This was a course I delivered to engineering first years, around 9th November 2009. But I have added contents to make it more understandable, eg I added all the diagrams and many explanations only now; 14-18th Aug 2015.

More such lectures will follow soon. Eg electromagnetism and electromagnetic waves !

Published in: Education
  • Do This Simple 2-Minute Ritual To Loss 1 Pound Of Belly Fat Every 72 Hours ■■■ https://tinyurl.com/bkfitness4u
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Vector Calculus.

  1. 1. MANMOHAN DASH, PHYSICIST, TEACHER ! Physics for ‘Engineers and Physicists’ “A concise course of important results” Lecture - 1 Vector Calculus and Operations Lectures around 9.Nov.2009 + further content developments this week; 14-18 Aug 2015 !
  2. 2. Fields and Functions What are Fields: A field is a ‘field’ of values of a variable called a ‘function’, across parameters; such as space, time, location, distance, speed and momentum or spin, just like a paddy field is a field of paddy across a region. What are Functions; A function is an infinite set of values, for each specification of another value. Eg y is a variable which is a function of values of x. We write y = f(x) and say ‘y is a function of x’. So a field is a collection of values of a function of values of another variable spread across a chosen parameter. A field is a function. y = f(x) is a field.
  3. 3. Fields and Functions
  4. 4. Vector and Scalar Fields There are 2 kinds of fields because there are 2 kinds of functions; SCALAR and VECTOR. Scalar as the name suggests “scales”. It scales up, scales down. It’s a magnitude of anything. Just a magnitude of any quantity. It can only increase or decrease (or stay constant). But there are no other properties associated with it. Eg; time takes to complete a task is a scalar. Its just a number with no other special attributes.
  5. 5. Vector and Scalar Fields A vector on the other hand is a carrier of a quantity. Hence a vector carries magnitudes, in as many degrees of freedom, as possible. A vector is a quantity defined by a specifed number of scalars, in different independent directions. These are called as Degrees of Freedom (DOF) or dimension (Dim). An usual vector in space and time is 1, 2 or 3-dimensional. But in general we define n-dimensional vectors where n is any number from [1 – infinity].
  6. 6. Vector and Scalar Fields A vector field is a vector function and a scalar field is a scalar function. Scalar Field; T = f(h), temperature as a function of altitude. Vector Field; v = f(t), velocity as a function of instant of time. A scalar function is one where the function is defined by only one value at each parameter-specification. Eg y = f(x). Physical variables that are scalar are ‘energy’, ‘temperature’ and ‘magnitude of any Physical variable’ A vector function is one where the function is defined by a vector, that is, n independent scalar values, along n independent directions, which also satisfy additional rules.
  7. 7. Scalar Functions
  8. 8. Vector Functions
  9. 9. Time derivative of fields Now that we discussed scalar and vector fields, we can define their time derivatives. A vector field has 3 components as stated above and its time-derivative is given as; A scalar field looks like this; A vector field looks like this; ),( trff   ),( trAA   So a vector field can be written as; )(ˆ)(ˆ)(ˆ)( tAktAjtAitAA zyx   Derivative of a vector; t A k t A j t A i t tAttA dt Ad zyx t              ˆˆˆ)()( lim 0  t A k t A j t A i dt Ad zyx          ˆˆˆ 
  10. 10. Direction of time derivative of fields Time derivative of a vector field is thus a vector; what angle it makes with the original vector field? 3 situations arise to answer this question >> 1. The vector A changes magnitude but not direction; then resultant time derivative dA/dt is along the original vector A. 2. The vector A changes direction, but not magnitude; then resultant time derivative dA/dt is perpendicular to the original vector A. 3. Both the direction and magnitude of A changes, dA/dt makes arbitrary angles with A.
  11. 11. When A does not change direction
  12. 12. When A does not change magnitude
  13. 13. When A changes in a generic fashion
  14. 14. Time derivative of ‘operations on vectors’ We will discuss 4 results of taking time derivatives of different operations of vectors or vector fields A, B. 1. 2. 3. 4. dt Ad c dt Acd   )( A dt df dt Ad f dt Afd    )( dt Bd AB dt Ad dt BAd      )( dt Bd AB dt Ad dt BAd      )( c = constant of time f = function of time ‘•’ = dot product ‘x’ = cross product
  15. 15. Differential operations on fields We will first discuss results of differential operations on scalar fields. We will discuss the operator grad, nabla or del. 1. 2. 3. nabladelgrad   z ˆ y ˆ x ˆ          kji  z ˆ y ˆ x ˆ          V k V j V iV  The gradient, grad, del or nabla operator. Here grad is acting on a scalar function or field V and grad is defined from (x, y, z) = r. In next slide we will show an important result on gradient of scalar function >> rdVdV  
  16. 16. Differential of a scalar field Lets see how the results we stated in last slide is valid. The differential of V is dV. Since and So we have dzkdyjdird ˆˆxˆ   z ˆ y ˆ x ˆ          V k V j V iV  It’s a well known mathematical result that differentials are given in terms of partials. if V = V(x, y, z); dz V dy VV dV zy dx x          rdVdV  
  17. 17. Direction of ‘gradient of scalar field’ The direction of gradient is always along the maximal change of the scalar field. Since The direction of gradient or above vector E, is normal to surface of constant V. E is in the direction along which most rapid change of V occurs. VE   Or in the given definition, in direction opposite to the most rapid change. These surfaces are called as equipotential surface. The directional derivative of V is its rate of change along n given by; Vn dn dV   ˆ
  18. 18. Uniform and non-uniform fields
  19. 19. Relation between V and E is comprehensive V = scalar field <<-->> E = vector field For every V field there is an E field and vice-a-versa ---------O --------- V = Potential Energy; E = Corresponding Force V = Electric Potential; E = Electric Field V = Gravitational Potential; E = Gravitational Field V = Simple Harmonic Pot; E = Simple Harmonic Force V = Molecular Pot. Energy; E = Molecular Force ---------O --------- etc … …
  20. 20. Gradient of ‘operations on scalar field’ The gradient can easily be calculated for addition, product and ratio of scalar fields in the following way. i. ii. iii. WVWV   )( The ‘grad’ is a 3-Dimensional differentiation. The usual rules of differentiation for products and ratio etc are valid. In the next slide lets prove the following important identity for gradient of a scalar; )()()( WVWVWV   2 )( W WVVW W V     r V rrV    ˆ)( 
  21. 21. Proof of the following identity Lets prove the identity; ) z ˆ y ˆ x ˆ( rzr ˆ yr ˆ xr ˆ z ˆ y ˆ x ˆ                                    r k r j r i VrV k rV j rV i V k V j V iV  r ˆ)(:QED r ˆ r ) zˆyˆxˆ( r )( z z , y y , x x )x(;ˆˆxˆ 2/1222                       V rrV V r r rV r k r j r i V rV r r r r r r zyrzkyjir    r V rrV    ˆ)( 
  22. 22. Lets discuss operations on vector field For vectors basically two operations are possible. One is called divergence and one is called curl. Lets discuss divergence 1st. Definitions; Relations for divergence; Solenoidal Field; divergence of a vector vanishes everywhere. zyx )ˆˆˆ() z ˆ y ˆ x ˆ( x x                    zy zy AAA AAdiv AkAjAikjiAAdiv   )()()(. )(. AVAVAVii BABAi    
  23. 23. Divergence on vector field Divergence of a vector field such as that of an electric dipole offers +ve, –ve and zero divergences at different flux points.
  24. 24. Curl of a vector field Here is how curl of a vector, vector function or vector field is defined. Definitions; Relations for curl; Irrotational Field; curl of a vector vanishes everywhere. )-(kˆ)-(jˆ)-(iˆ; z , y , x where ˆˆˆ xyxxzxzy zyx x zyx AAAAAAASo AAA kji AAcurl yzyz zy                        )()()(. )(. AVAVAVii BABAi    
  25. 25. Homework Two short home work questions, if you have carried yourself this far ! Prove the following; 3r 0r    
  26. 26. Curl Me ! If vector A is on x–y plane in the given fashion, its curl will span along z axis. Curl always follows right-hand-rule. Palm curls from one vector to another then right hand thumb gives resultaing curl vector.
  27. 27. All that we got! Now that we learned curl, divergence, grad as the differential operations possible on vector and scalar fields we have following combinations that are possible, that we will discuss one by one. 1. Laplacian [On scalar and vectors fields] 2. Curl of grad [of scalar field] 3. Divergence of curl [of vector field] 4. Curl of curl [of vector field] 5. Divergence [of cross product of two vector fields]
  28. 28. 1. Laplacian The divergence of the del operator can act on both scalars and vectors. Its simply a double differentiation wrt given parameters, for our discussion space parameters; (x, y, z)=r. Called as Laplacian it looks like this; So we have two types of quantities; A V 2 2   2 2 2 2 2 2 22 zyx          
  29. 29. 2. Curl of grad of scalar field is zero Curl of grad of a scalar field is zero. This is because order of partial differentiation does not matter. i.e. It’s a very important result; if curl of a vector is zero (such vector is called irrotational) then this vector A can always be represented as grad of any scalar field. i.e. Conversely; If a vector field is a grad of a scalar field, its curl vanishes (its irrotational) Example; 0 V  yzz VV      2 y 2 VA   0 EVE 
  30. 30. 3. Div of Curl of a vector field is zero Divergence of curl of a vector field is zero. Here is another important result; if a vector field is a curl of another vector its divergence is zero (its a solenoid field). Conversely; If the divergence of a vector field is zero (its a solenoid field) then it can be expressed as curl of a vector. 0 A 
  31. 31. Curl of Curl and divergence of cross prod. 4. Curl of curl of a vector field; 5. Divergence of cross product of two vector fields; * If for a scalar field f, its Laplacian is zero, then grad-f is both solenoidal (divergence = 0) and irrotational (curl = 0) ! AAA  2 )(  )()()( BAABBA   0)(,0)( and is 02    ff alirrotation solenoidalf f  
  32. 32. Integrations on vector fields We can define 3 kinds of integral operation on a vector field; 1. Line or path integral 2. Surface integral 3. Volume integral ! These definitions come from the kind of differential element we use for these integrations, a length, an area or a volume. Lets discuss the line/path integral of a vector field first !
  33. 33. Line/Path Integral of vector fields Line integral of a vector field A between a, b along a given path (L); 1. I depends on points a, b and the path between a, b: in general. 2. If I is independent of the path of integration the vector field A is called a ‘conservative field’. 3. Line integral of a conservative vector field vanishes along a closed contour. 4. Example of line integral in physics;    b a b a zyL dzAdyAdAldAI )x( x    0ldA    QP ldFWork 
  34. 34. Line/Path Integral of vector fields
  35. 35. Surface Integral of vector fields Differential surface elements dS are vectors with directions along the outward normals, n to the surface. An is the projection of vector A along n and thus the normal component of vector A. Only the normal component of vector A contributes to the surface integral. nAAdSAdSnAI dSnSdSdAI n S n S S S S ˆ,ˆ ˆ,       Flux of the electric field and work done on a surface are the example of surface integrals.    SQP SdESdFW  E,
  36. 36. Integral form of curl, div, grad First lets state the volume integral; dV is the differential element of Volume. We have seen the curl, grad, div etc defined as differential operators. We can also cast them into line, surface and volume integrals. We define areas that enclose relevant volumes for surface integrals and surfaces that are bounded by closed paths for line intergrals. i. Gradient ii. Divergence iii. Curl  V V dVAI  lim 0 V dS S V        lim 0 V SdA A S V        n S ldA A C S ˆlim 0       
  37. 37. Integral and differential theorems 1. Gauss Divergence Theorem; The ‘volume integral’ of the ‘divergence of vector A’ over a given volume is equal to the surface integral of the vector over a closed area enclosing the volume.   SV SdAdVA  1. Stoke’s Theorem;   CS ldASdA  )( The ‘surface integral’ of the ‘curl of vector A’ over a given surface is equal to the line integral of the vector over a closed contour/line enclosing the surface.
  38. 38. Green’s Theorem for 2 scalar fields We saw this for divergence; Lets write A as a gradient of f; Then we will get; Subtract -2 from -1 and take volume integral on both sides; Apply Gauss Div theorem on LHS; Now we have Green’s Theorem; )()()( AfAfAf   gA   2-)( 1-)( 2 2 fgfgfg gfgfgf     )]()([)( V 22   dVfggfdVfggf V    SV SdfggfdVfggf  )()( )]()([)( V 22   dVfggfSdfggf S 
  39. 39. Thank you We discussed most of what one wishes to learn in vector calculus at the undergraduate engineering level. Its also useful for the Physics ‘honors’ and ‘pass’ students. This was a course I delivered to engineering first years, around 9th November 2009. But I have added contents to make it more understandable, eg I added all the diagrams and many explanations only now; 14-18th Aug 2015. More such lectures will follow soon. Eg electromagnetism and electromagnetic waves !

×