This example shows the finite element mesh for a NASA airfoil, including two trailing flaps. More information about the history of airfoils is available at NACA Airfoils (nasa.gov).
The data is stored in the file
airfoil.mat. The data consists of 4253 pairs of (x,y) coordinates of the mesh points. It also contains an array of 12,289 pairs of indices, (i,j), specifying connections between the mesh points.
Load the data file into the workspace.
y by to bring them into the range . Then form a sparse adjacency matrix from the (i,j) connections and make it positive definite. Finally, plot the adjacency matrix using (x,y) as the coordinates for the vertices (mesh points).
% Scaling x and y x = pow2(x,-32); y = pow2(y,-32); % Forming the sparse adjacency matrix and making it positive definite n = max(max(i),max(j)); A = sparse(i,j,-1,n,n); A = A + A'; d = abs(sum(A)) + 1; A = A + diag(sparse(d)); % Plotting the finite element mesh gplot(A,[x y]) title('Airfoil Cross-Section')
You can use
spy to visualize the nonzero elements in a matrix, so it is a particularly useful function to see the sparsity pattern in sparse matrices.
spy(A) plots the sparsity pattern of the matrix
spy(A) title('Airfoil Adjacency Matrix')
symrcm uses the Reverse Cuthill-McKee technique for reordering the adjacency matrix.
r = symrcm(A) returns a permutation vector
r such that
A(r,r) tends to have its diagonal elements closer to the diagonal than
A. This form is a good preordering for LU or Cholesky factorization of matrices that come from "long, skinny" problems. It works for both symmetric and nonsymmetric matrices.
r = symrcm(A); spy(A(r,r)) title('Reverse Cuthill-McKee')
j = COLPERM(A) to return a permutation vector that reorders the columns of the sparse matrix
A in nondecreasing order of nonzero count. This form is sometimes useful as a preordering for LU factorization, as in
j = colperm(A); spy(A(j,j)) title('Column Count Reordering')
symamd gives a symmetric approximate minimum degree permutation. For a symmetric positive definite matrix
A, the command
p = symamd(S) returns the permutation vector
p such that
S(p,p) tends to have a sparser Cholesky factor than
symamd works well for symmetric indefinite matrices too.
m = symamd(A); spy(A(m,m)) title('Approximate Minimum Degree')