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interpolation of a sinusoidal tunnel data

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ran
ran 2024-6-8
评论： ran 2024-6-13
hallo eveyone, i have fluent data of a flow instive a wavy tunnel as present here. I export the data as ACII file and i want to crate matrixs so i can work on the data preaty easely. the problem is that all the scatterd interpolation mathods do not work smothly on a wavy wall, wich asagnifetly decline. i need bater mathod to work with that interpolat on the wall in between those data points and not in the normal x y deraction? any segestions ??? may data is simpal scatterd data of x,y,and u
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Mathieu NOE
Mathieu NOE 2024-6-10
pls share your data and yur code if you have one
ran
ran 2024-6-10
Sorry, but the main point is that I am attempting to interpolate data from Fluent, which are on a sinusoidal wall (sinusoidal tunnel). The code and data are straightforward and I hope they are helpful. those waves are not origin in the fluent results and artifact from interpolation, you can see some "steps on the wall wich i asumed creat those
% Let the user choose the folder containing the text files
folder_path = uigetdir('Select the folder containing the text files');
% Check if the user canceled folder selection
if folder_path == 0
disp('Folder selection canceled. Exiting...');
return;
end
% Get a list of all the text files in the folder
file_list = dir(fullfile(folder_path, '*.txt'));
% Initialize cell arrays to store data for each variable
x_cell = cell(length(file_list), 1);
y_cell = cell(length(file_list), 1);
u_cell = cell(length(file_list), 1);
v_cell = cell(length(file_list), 1);
T_cell = cell(length(file_list), 1);
P_cell = cell(length(file_list), 1);
Tw_cell = cell(length(file_list), 1);
% rho_cell = cell(length(file_list), 1);
% k_cell = cell(length(file_list), 1);
% dissRate_cell = cell(length(file_list), 1);
% Visxosity_cell = cell(length(file_list), 1);
% Cp_cell = cell(length(file_list), 1);
% thermal_cell = cell(length(file_list), 1);
% Loop through each file and read its contents
for i = 1:length(file_list)
% Get the file name
file_name = fullfile(folder_path, file_list(i).name);
% Read the data from the file
data = readtable(file_name);
% Store the data in the cell arrays with the file name as the cell name
[~, file_name_only, ~] = fileparts(file_name);
x_cell{i} = struct('file_name', file_name_only, 'data', data.x_coordinate);
y_cell{i} = struct('file_name', file_name_only, 'data', data.y_coordinate);
u_cell{i} = struct('file_name', file_name_only, 'data', data.x_velocity);
v_cell{i} = struct('file_name', file_name_only, 'data', data.y_velocity);
T_cell{i} = struct('file_name', file_name_only, 'data', data.temperature);
P_cell{i} = struct('file_name', file_name_only, 'data', data.pressure);
Tw_cell{i} = struct('file_name', file_name_only, 'data', data.wall_temperature);
% rho_cell{i} = struct('file_name', file_name_only, 'data', data.density);
% k_cell{i} = struct('file_name', file_name_only, 'data', data.turb_kinetic_energy);
% dissRate_cell{i} = struct('file_name', file_name_only, 'data', data.turb_diss_rate);
% Visxosity_cell{i} = struct('file_name', file_name_only, 'data', data.viscosity_lam);
% Cp_cell{i} = struct('file_name', file_name_only, 'data', data.specific_heat_cp);
% thermal_cell{i} = struct('file_name', file_name_only, 'data', data.thermal_conductivity_lam);
end
%%
% Initialize structures to store wall coordinates and number of y coordinates
wall_coords = struct('x', {}, 'y', {});
num_y_coordinates = struct('file_name', {}, 'num_y', {});
y_coordinates_end_tunnel = struct('file_name', {}, 'num_y', {});
for i = 1:length(file_list)
% Find wall coordinates
xwall = x_cell{i}.data(Tw_cell{i}.data > 0);
ywall = y_cell{i}.data(Tw_cell{i}.data > 0);
% Find the largest wall X-coordinate (end of the tunnel)
largest_wall_X = max(xwall);
% Find the index of the largest wall X-coordinate
largest_wall_X_idx = find(xwall,1, 'last');
% Determine the distance between the largest wall X-coordinate and the preceding one
distance_between = max(gradient(sort(xwall)));
% Define the search range for X-coordinates
EndOfTunnel = find(largest_wall_X - 0.7 * distance_between < x_cell{i}.data & x_cell{i}.data <= largest_wall_X);
% Extract the corresponding y-coordinates
y_coordinates_end_tunnel(i).file_name = x_cell{i}.file_name;
y_coordinates_end_tunnel(i).num_y = y_cell{i}.data(EndOfTunnel);
% Store wall coordinates
wall_coords(i).x = xwall;
wall_coords(i).y = ywall;
% Count the number of y-coordinates
num_y_coordinates(i).file_name = x_cell{i}.file_name;
num_y_coordinates(i).num_y = length(y_coordinates_end_tunnel(i).num_y);
end
%%
% Initialize structures to store interpolated data
interp_u_cell = cell(length(file_list), 1);
interp_v_cell = cell(length(file_list), 1);
interp_T_cell = cell(length(file_list), 1);
interp_P_cell = cell(length(file_list), 1);
interp_Tw_cell = cell(length(file_list), 1);
interp_x_cell = cell(length(file_list), 1);
interp_y_cell = cell(length(file_list), 1);
for i = 1:length(file_list)
% Find wall coordinates
xwall = x_cell{i}.data(Tw_cell{i}.data > 0);
ywall = y_cell{i}.data(Tw_cell{i}.data > 0);
% Find the largest wall X-coordinate (end of the tunnel)
largest_wall_X = max(xwall);
% Calculate the distance between the maximum and minimum wall X-coordinates
distance_total = max(xwall) - min(xwall);
% Define the wavelength
wavelength = 28.8/1000; % m
% Calculate the number of sections based on the wavelength
num_sections = floor(distance_total / wavelength);
% Initialize structures to store interpolated data for each section
interp_u = cell(num_sections, 1);
interp_v = cell(num_sections, 1);
interp_T = cell(num_sections, 1);
interp_P = cell(num_sections, 1);
interp_Tw = cell(num_sections, 1);
interp_x= cell(num_sections, 1);
interp_y = cell(num_sections, 1);
% Initialize starting point for creating sections
current_x = largest_wall_X;
% Loop to create sections and interpolate data within each section
for j = 1:num_sections
% Find the end point of the current section
end_x = current_x - wavelength;
% Find the indices of x coordinates within the current section
section_indices = find(x_cell{i}.data >= end_x & x_cell{i}.data < current_x);
wall_length = length(xwall(xwall >= end_x & xwall < current_x));
% Extract the x and y coordinates within the current section
section_x = x_cell{i}.data(section_indices);
section_y = y_cell{i}.data(section_indices);
section_u = u_cell{i}.data(section_indices);
section_v = v_cell{i}.data(section_indices);
section_T = T_cell{i}.data(section_indices);
section_P = P_cell{i}.data(section_indices);
section_Tw = Tw_cell{i}.data(section_indices);
% Create linearly spaced vectors for interpolation
% interp_X = linspace(min(section_x), max(section_x), wall_length * 5);
% interp_Y = linspace(min(section_y), max(section_y), num_y_coordinates(i).num_y * 3);
% [interp_X_grid, interp_Y_grid] = meshgrid(interp_X, interp_Y);
interp_X = linspace(min(section_x), max(section_x), 2500);
interp_Y = linspace(min(section_y), max(section_y),2500*0.694444444);
% Construct meshgrid
[interp_x{j}, interp_y{j}] = meshgrid(interp_X, interp_Y);
% Initialize matrix to hold combined upper and lower wall boundaries
% Interpolation using scatteredInterpolant on normalized coordinates
F_u = scatteredInterpolant(section_x, section_y, section_u, 'natural', 'none');
F_v = scatteredInterpolant(section_x, section_y, section_v, 'natural', 'none');
F_T = scatteredInterpolant(section_x, section_y, section_T, 'natural', 'none');
F_P = scatteredInterpolant(section_x, section_y, section_P, 'natural', 'none');
% Interpolate and re-normalize the data
interp_u{j} = F_u(interp_x{i}, interp_y{i});
interp_v{j} = F_v(interp_x{i}, interp_y{i});
interp_T{j} = F_T(interp_x{i}, interp_y{i});
interp_P{j} = F_P(interp_x{i}, interp_y{i});
% Update current_x for the next section
current_x = end_x;
end
% Update cell arrays to contain the combined matrices
interp_u_cell{i} = struct('file_name', x_cell{i}.file_name, 'data', interp_u);
interp_v_cell{i} = struct('file_name', x_cell{i}.file_name, 'data', interp_v);
interp_T_cell{i} = struct('file_name', x_cell{i}.file_name, 'data', interp_T);
interp_P_cell{i} = struct('file_name', x_cell{i}.file_name, 'data', interp_P);
interp_x_cell{i} = struct('file_name', x_cell{i}.file_name, 'data', interp_x);
interp_y_cell{i} = struct('file_name', x_cell{i}.file_name, 'data', interp_y);
end
folder_path = uigetdir('Select the folder containing the text files');
% Define the file name without leading space characters
% Define the file name without leading space characters
filename = 'StaedyFlowM028Re8000.mat';
% Concatenate the folder path and file name to create the full path
full_path = fullfile(folder_path, filename);
% Save the structures to the file using MAT-file version 7.3
save(full_path, 'interp_u_cell', 'interp_v_cell', 'interp_T_cell', 'interp_P_cell', 'interp_Tw_cell', 'interp_x_cell', 'interp_y_cell', '-v7.3');

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回答（2 个）

Mathieu NOE
Mathieu NOE 2024-6-11
hello again
so I looked a bit some of your data , and my first question would be , why not first plot your (scattered) data with quiver or a colored quiver function, before doing some meshgrid & interpolation
here I used this Fex submission : Quiver - magnitude-dependent color in 2D and 3D - File Exchange - MATLAB Central (mathworks.com)
and you can see that there are no artifacts in the plot rendering.
then , if you really want to do meshgrid & interpolation, I noticed than 500 or 2500 points of interpolation, nor the method used had a great effect on reducing (to zero) the little "steps" close to the boundary; the only fix I could suggest here is to add a bit of 2D smoothing
here I used this Fex submission, but there are other alternatives if you have your own prefered function for this task : smooth2a - File Exchange - MATLAB Central (mathworks.com)
Quiver plot :
Filled contour plot - before smoothing :
Filled contour plot - after smoothing :
load('matlab.mat')
% Name Size Bytes Class Attributes
%
% section_P 37502x1 300016 double
% section_T 37502x1 300016 double
% section_Tw 37502x1 300016 double
% section_u 37502x1 300016 double
% section_v 37502x1 300016 double
% section_x 37502x1 300016 double
% section_y 37502x1 300016 double
figure(1) % quiver plot (with colors)
quiverC2D(section_x,section_y,section_u,section_v,1);
% Fex : https://fr.mathworks.com/matlabcentral/fileexchange/58527-quiver-magnitude-dependent-color-in-2d-and-3d?s_tid=ta_fx_results
colorbar('vert');
N = 1000; % 2500
interp_X = linspace(min(section_x), max(section_x), N);
interp_Y = linspace(min(section_y), max(section_y),round(N*0.694444444)); % "round" added
% Construct meshgrid
[interp_x, interp_y] = meshgrid(interp_X, interp_Y);
% velocity field
velo = sqrt(section_u.^2+section_v.^2).*sign(section_u);
% NB sign of section_u is used to show forward or backward oriented flow
% Interpolation using scatteredInterpolant on normalized coordinates
method = 'natural';
F_velo = scatteredInterpolant(section_x, section_y, velo, method, 'none');
Z = F_velo(interp_x, interp_y);
figure(2) % contourf plot
grid on
[hq,hqlbl] = contourf(interp_X,interp_Y,Z);
clabel(hq,hqlbl)
colorbar('vert');
% contourf plot with smoothing
ZS = smooth2a(Z,8,8);
figure(3) % contourf plot
grid on
[hq,hqlbl] = contourf(interp_X,interp_Y,ZS);
clabel(hq,hqlbl)
colorbar('vert');
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ran
ran 2024-6-13
i thx u allot my frined but i found a new problem, as u see in the pic the intarpulation is problematic even more beacuse it intarpulat from the inlet outlet to the wall. need diper sulution

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ran
ran 2024-6-13
using delaunay

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