Mercurial > repos > public > sbplib
view +sbp/D2VariablePeriodic.m @ 1253:89dad61cad22 feature/poroelastic
Make Elastic2dVariable faster and more memory efficient
author | Martin Almquist <malmquist@stanford.edu> |
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date | Tue, 04 Feb 2020 10:15:42 -0800 |
parents | 5ccf6aaf6d6b |
children | bf2554f1825d |
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classdef D2VariablePeriodic < sbp.OpSet properties D1 % SBP operator approximating first derivative H % Norm matrix HI % H^-1 Q % Skew-symmetric matrix e_l % Left boundary operator e_r % Right boundary operator D2 % SBP operator for second derivative M % Norm matrix, second derivative d1_l % Left boundary first derivative d1_r % Right boundary first derivative m % Number of grid points. h % Step size x % grid borrowing % Struct with borrowing limits for different norm matrices end methods function obj = D2VariablePeriodic(m,lim,order) x_l = lim{1}; x_r = lim{2}; L = x_r-x_l; obj.h = L/m; x = linspace(x_l,x_r,m+1)'; obj.x = x(1:end-1); switch order case 6 [obj.H, obj.HI, obj.D1, obj.D2, obj.e_l,... obj.e_r, obj.d1_l, obj.d1_r] = ... sbp.implementations.d2_variable_periodic_6(m,obj.h); obj.borrowing.M.d1 = 0.1878; obj.borrowing.R.delta_D = 0.3696; % Borrowing e^T*D1 - d1 from R case 4 [obj.H, obj.HI, obj.D1, obj.D2, obj.e_l,... obj.e_r, obj.d1_l, obj.d1_r] = ... sbp.implementations.d2_variable_periodic_4(m,obj.h); obj.borrowing.M.d1 = 0.2505765857; obj.borrowing.R.delta_D = 0.577587500088313; % Borrowing e^T*D1 - d1 from R case 2 [obj.H, obj.HI, obj.D1, obj.D2, obj.e_l,... obj.e_r, obj.d1_l, obj.d1_r] = ... sbp.implementations.d2_variable_periodic_2(m,obj.h); obj.borrowing.M.d1 = 0.3636363636; % Borrowing const taken from Virta 2014 obj.borrowing.R.delta_D = 1.000000538455350; % Borrowing e^T*D1 - d1 from R otherwise error('Invalid operator order %d.',order); end obj.borrowing.H11 = obj.H(1,1)/obj.h; % First element in H/h, obj.m = m; obj.M = []; end function str = string(obj) str = [class(obj) '_' num2str(obj.order)]; end end end