# Numerical methods challenge: Day 26

During October (2017) I will write a program per day for some well-known numerical methods in both Python and Julia. It is intended to be an exercise then don't expect the code to be good enough for real use. Also, I should mention that I have almost no experience with Julia, so it probably won't be idiomatic Julia but more Python-like Julia.

## Boundary element method

Today we have the Boundary element method , or, an attempt. We want so solve the equation

with

For this method, we need to use an integral representation of the equation, that in this case is

with

and

Then, we can form a system of equations

that we obtain by discretization of the boundary. If we take constant variables over the discretization, the integral can be computed analytically by

and

for points \(n\) and $m$ in different elements, and the subindices \(A,B\) refer to the endpoints of the evaluation element. For diagonal terms the integrals evaluate to

and

with $L$ the size of the element.

Below is the Python code. In this case, I did not succeed and the code is not working right now.

from __future__ import division, print_function import numpy as np from numpy import log, sin, cos, arctan2, pi, mean, arctan from numpy.linalg import norm, solve import matplotlib.pyplot as plt def assem(coords, elems): nelems = elems.shape[0] Gmat = np.zeros((nelems, nelems)) Fmat = np.zeros((nelems, nelems)) for ev_cont, elem1 in enumerate(elems): print(coords[elem1[0]], coords[elem1[1]]) for col_cont, elem2 in enumerate(elems): pt_col = mean(coords[elem2], axis=0) if ev_cont == col_cont: L = norm(coords[elem1[1]] - coords[elem1[0]]) Gmat[ev_cont, ev_cont] = - L/(2*pi)*(log(L/2) - 1) Fmat[ev_cont, ev_cont] = - 0.5 else: Gij, Fij = influence_coeff(elem1, coords, pt_col) Gmat[ev_cont, col_cont] = Gij Fmat[ev_cont, col_cont] = Fij return Gmat, Fmat def influence_coeff(elem, coords, pt_col): r_A = coords[elem[0]] - pt_col r_B = coords[elem[1]] - pt_col theta_A = arctan2(r_A[1], r_A[0]) theta_B = arctan2(r_B[1], r_B[0]) G_coeff = r_B[1]*(log(norm(r_B)) - 1) + theta_B*r_B[0] -\ (r_A[1]*(log(norm(r_A)) - 1) + theta_A*r_A[0]) F_coeff = theta_B - theta_A return -G_coeff/(2*pi), F_coeff/(2*pi) def eval_sol(ev_coords, coords): nelems = elems.shape[0] Gmat = np.zeros((nelems, nelems)) Fmat = np.zeros((nelems, nelems)) for ev_cont, elem1 in enumerate(elems): L = norm(coords[elem1[1]] - coords[elem1[0]]) for col_cont, elem2 in enumerate(elems): pt_col = mean(coords[elem2], axis=0) if ev_cont == col_cont: Gmat[ev_cont, ev_cont] = - L/(2*pi)*(log(L/2) - 1) Fmat[ev_cont, ev_cont] = - 0.5 else: Gmat[ev_cont, col_cont], Fmat[ev_cont, col_cont] = \ influence_coeff(elem1, coords, pt_col) nelems = 3 rad = 1.0 theta = np.linspace(0, 2*pi, nelems, endpoint=False) coords = rad * np.vstack((cos(theta), sin(theta))).T elems = np.array([[cont, (cont + 1)%nelems] for cont in range(nelems)]) Gmat, Fmat = assem(coords, elems) u_boundary = np.ones_like(theta) q_boundary = solve(Gmat, Fmat.dot(u_boundary))

## Comments

Comments powered by Disqus