GG303 Lecture 6 8/27/09 1 SCALARS, VECTORS, AND TENSORS

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1 GG303 Lecture 6 8/27/09 1 SCALARS, VECTORS, AND TENSORS I Main Topics A Why deal with tensors? B Order of scalars, vectors, and tensors C Linear transformation of scalars and vectors (and tensors) II Why deal with tensors? A They broaden how we can look at the world; geologists who dont become acquainted with them are handi-capped. B They can be extremely useful III Order of scalars, vectors, and tensors A Scalars (magnitudes) 1 Numbers with no associated direction (zero-order tensors) 2 No subscripts in notation 3 Examples: Time, mass, length volume 4 Matrix representation: 1x1 matrix [ x] B Vectors (magnitude and a direction) 1 Quantities with one associated direction (first-order tensors) 2 One subscript in notation (e.g., ux) 3 Examples: Displacement, velocity, acceleration 4 Matrix representation: 1xn row matrix, or nx1 column matrix, with n components a Two-dimensional vector (2 components): [ x y] or x1 x2 [ ] 1 row, 2 columns x = component in x-direction, y = component in y-direction x1 = component in x-direction, x2 = component in y-direction Stephen Martel (Fall 2009: Conrad) 6-1 University of Hawaii

2 GG303 Lecture 6 8/27/09 2 b Three-dimensional vector (3 components): [ x y z] or [ x 2 x 3 ] 1 row, 3 columns 5 Dont confuse the dimensionality of a tensor with its order C Tensors (magnitude and two directions) 1 Quantities with two associated directions (second-order tensors) 2 Two subscripts in notation (e.g.,! ij ) 3 Examples: Stress, strain, permeability 4 Matrix representation: nxn matrix, with n 2 components a Two-dimensional tensor (4 components):! xx! xy! 11! 12 or \$! yx! yy \$! 21! 22 2 rows, 2 columns b Three-dimensional vector (9 components):! xx! xy! xz! 11! 12! 13 \$! yx! yy! yz or \$! 21! 22! 23 \$! zx! zy! zz \$! 31! 32! 33 3 rows, 3 columns 5 An n-dimension tensor consists of n rows of n-dimension vectors IV Linear transformation of scalars, vectors, and tensors A Transformations refers to how components change when the coordinate system changes in which the quantities are measured. Linear means the transformation depends on the length of the components, not, for example, on the square of the component lengths. Transformations are used to when we change reference frames in order to present physical quantities from a different (clearer) perspective. Stephen Martel (Fall 2009: Conrad) 6-2 University of Hawaii

3 GG303 Lecture 6 8/27/09 3 B Scalars 1 Scalar quantities dont change in response to a transformation of coordinates; they are invariant 2 Examples: The mass, volume, and density of a body are independent of the reference frame orientation. C Vectors 1 Vector components change with a transformation of coordinates (see Figures for examples of a rotation) 2 Example: the components of a position vector change from a coordinate system where x = east, y = north, z = up to a new system where x = north, y =west, z = down (see Fig. 2.1) 3 Transformation rule for vectors (See Figs ) a Expanded form (explicit but can be tedious to write) x = a x + a x i x 2 = a 21 + a 22 x 2 (2-D) = a 11 + a 12 x 2 + a 13 x 3 x 2 = a 21 + a 22 x 2 + a 2 3 x 3 (3-D) x 3 = a 31 + a 32 x 2 + a 3 3 x 3 a ji is the component of a unit vector in the i-direction that points in the j -direction (i.e., a direction cosine). ii Every component in the unprimed reference frame can contribute to each component in the primed reference frame. The direction cosines are weighting factors that specify how much each unprimed component contributes to a primed component. Stephen Martel (Fall 2009: Conrad) 6-3 University of Hawaii

4 GG303 Lecture 6 8/27/09 4 n b Summation notation x j = a ji x i i =1 The resultant vector in the x j direction is the sum of the components that act in that direction c Matrix form [ x ] = [ a] [ x] (or in Matlab X=a*x) where [a] is a rotation matrix containing the direction cosines of the angles between (unit vectors along) the x,y,z axes and the x,y,z axes. In expanded matrix form \$ x 2 = a 11 a 12 \$ a 21 a 22 \$ x 2 (2-D) In expanded matrix form x 1 a 11 a 12 a 1 3 x 1 \$ x \$ 2 = \$ a 21 a 22 a \$ 2 3 x \$ \$ 2 x 3 a 31 a 32 a 3 3 x 3 (3-D) To multiply two matrices [A] and [B], the row elements in [A] multiply the column elements in B. So if [A] is an nxm matrix (n = of rows, m = of columns), [B] must be an mxp matrix (m = of rows, p = of columns). The number of columns in the first matrix must match the number of rows in the second matrix. The solution [C] will be an nxp matrix, (n = of rows, p = of columns). The elements of [C] are given by: k =m Cij =! Aik Bkj k =1 Stephen Martel (Fall 2009: Conrad) 6-4 University of Hawaii

5 GG303 Lecture 6 8/27/09 5 Examples A 1x2 matrix times a 2x1 matrix gives a 1x1 matrix! [ 1 2] 3 \$ 4 = [ (1)(3) + (2)(4) ] = [ 11] A 2x1 matrix times a 1x2 matrix gives a 2x2 matrix! 3\$ [ ] =!(3)(1) (3)(2) \$ (4)(1) (4)(2) =! 3 6 \$ 4 8 **A 2x2 matrix times a 2x1 matrix gives a 2x1 matrix** 1 2 \$ \$ 5 (1)(5) +(2)(6) = \$ = 17 \$ (3)(5) +(4)(6) 39 **A 2x2 matrix times a 2x2 matrix gives a 2x2 matrix** 1 2 \$ 1 0 (1)(1) +(2)(0) (1)(0) +(2)(1) \$ = \$ = 1 2 \$ (3)(1) +(4)(0) (3)(0)+ (4)(1) 3 4 A 3x2 matrix times a 2x2 matrix gives a 3x2 matrix! 1 2\$!(1)(7) +(2)(9) (1)(8) + (2)(10) \$! \$! 7 8 \$ = (3)(7) + (4)(9) (3)(8) + (4)(10) = (5)(7) + (6)(9) (5)(8) + (6)(10) A 3x2 matrix times a 2x3 matrix gives a 3x3 matrix! 1 2\$!(1)(7) + (2)(10) (1)(8) + (2)(11) (1)(9) + (2)(12) \$! \$! \$ = (3)(7) + (4)(10) (3)(8) + (4)(11) (3)(9) + (4)(12) = (5)(7) + (6)(10) (5)(8) + (6)(11) (5)(9) + (6)(12) d Tensor notation x j = a ji x i ; double subscripts imply summation Stephen Martel (Fall 2009: Conrad) 6-5 University of Hawaii

6 GG303 Lecture 6 8/27/09 6 D Second-order tensors (to be covered later) 1 Components change with a transformation of coordinates 2 Transformation rule for tensors is somewhat analogous to that for vectors except there are two rotations involved. 3 In tensor notation: i j = a ik a jl kl = a ik kl a jl ; i,j,k,l = (1,2,3) Consider any two vectors u and v that we wish to transform into a new coordinate system u i = a i k u k 3x1 matrix = (3x3 matrix)(3x1 matrix) column matrix v j = a jl v l 3x1 matrix = (3x3 matrix)(3x1 matrix) column matrix v j = v l a j l 1x3 matrix = (1x3 matrix)(3x3 matrix) row matrix Now suppose each component of u i is to be multiplied by each component of v j to yield a total of 9 components. To do this, we need to multiply a 3x1 (column) matrix by a 1x3 (row) matrix. u i v j = a i k u k ( ) ( ) v l a jl In this tensor notation we can rearrange the order of the terms u i v j = a ik a j l u k v l Now we let the products u i v j be called A i j, and u k u l be called A kl A i j = a i k a j l A kl Quantities that transform like A i j, and A kl are known as second-order tensors. 4 Transformation by matrix multiplication (watch the order of the matrices) [ A i j ] = [ a i k] [ A kl ][ a ik ] T a 11 a 12 a a 11 a 21 a ( 2 3 ( = a 21 a 22 a ( 23 ( ( 23 a ( 12 a 22 a ( 32 ( \$ \$ a 31 a 32 a 33 \$ \$ a 13 a 23 a 3 3 Stephen Martel (Fall 2009: Conrad) 6-6 University of Hawaii

7 GG303 Lecture 6 8/27/09 7 Stephen Martel (Fall 2009: Conrad) 6-7 University of Hawaii

8 GG303 Lecture 6 8/27/09 8 Stephen Martel (Fall 2009: Conrad) 6-8 University of Hawaii

9 GG303 Lecture 6 8/27/09 9 Stephen Martel (Fall 2009: Conrad) 6-9 University of Hawaii

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