I have written a code that uses patternsearch to optimize a complex aircraft geometry for a desired flight condition. The code itself is extremely long, so I’m writing a simpler version below. The function of interest takes multiple geometric variables as inputs, and outputs lift (the objective) and volume (the constraint). I have followed some guides, and have my code setup in a way like this at the moment (this is an extremely simplified version for brevity):
obj = fcn2optimexpr(@lift_fun,x1,x2,x3,x4,x5);
prob = optimproblem("Objective",1/obj);

const1 = fcn2optimexpr(@volume_fun,x1,x2,x3,x4,x5);
prob.Constraints.vol = const1 >= 5;

x0 = [1 2 3 4 5];

[sol] = solve(prob,x0,"Solver","patternsearch","Options",options);

function [lift] = lift_fun(w,n,beta,epsS,p2,p3,Mdesign)
[lift,~] = master_function(x1,x2,x3,x4,x5);
end

function [volume] = volume_fun(w,n,beta,epsS,p2,p3,Mdesign)
[~,volume] = master_function(x1,x2,x3,x4,x5);
end
As I understand it, this code is calling the function ‘master_function’ twice for every optimization loop. This is a problem because this function takes up to a minute to run every time. Because of this I am wondering if it is possible to edit this code so that ‘master_function’ is only called once, providing both the objective and the constraint for the patternsearch optimization? Thank you for your helpI have written a code that uses patternsearch to optimize a complex aircraft geometry for a desired flight condition. The code itself is extremely long, so I’m writing a simpler version below. The function of interest takes multiple geometric variables as inputs, and outputs lift (the objective) and volume (the constraint). I have followed some guides, and have my code setup in a way like this at the moment (this is an extremely simplified version for brevity):
obj = fcn2optimexpr(@lift_fun,x1,x2,x3,x4,x5);
prob = optimproblem("Objective",1/obj);

const1 = fcn2optimexpr(@volume_fun,x1,x2,x3,x4,x5);
prob.Constraints.vol = const1 >= 5;

x0 = [1 2 3 4 5];

[sol] = solve(prob,x0,"Solver","patternsearch","Options",options);

function [lift] = lift_fun(w,n,beta,epsS,p2,p3,Mdesign)
[lift,~] = master_function(x1,x2,x3,x4,x5);
end

function [volume] = volume_fun(w,n,beta,epsS,p2,p3,Mdesign)
[~,volume] = master_function(x1,x2,x3,x4,x5);
end
As I understand it, this code is calling the function ‘master_function’ twice for every optimization loop. This is a problem because this function takes up to a minute to run every time. Because of this I am wondering if it is possible to edit this code so that ‘master_function’ is only called once, providing both the objective and the constraint for the patternsearch optimization? Thank you for your help I have written a code that uses patternsearch to optimize a complex aircraft geometry for a desired flight condition. The code itself is extremely long, so I’m writing a simpler version below. The function of interest takes multiple geometric variables as inputs, and outputs lift (the objective) and volume (the constraint). I have followed some guides, and have my code setup in a way like this at the moment (this is an extremely simplified version for brevity):
obj = fcn2optimexpr(@lift_fun,x1,x2,x3,x4,x5);
prob = optimproblem("Objective",1/obj);

const1 = fcn2optimexpr(@volume_fun,x1,x2,x3,x4,x5);
prob.Constraints.vol = const1 >= 5;

x0 = [1 2 3 4 5];

[sol] = solve(prob,x0,"Solver","patternsearch","Options",options);

function [lift] = lift_fun(w,n,beta,epsS,p2,p3,Mdesign)
[lift,~] = master_function(x1,x2,x3,x4,x5);
end

function [volume] = volume_fun(w,n,beta,epsS,p2,p3,Mdesign)
[~,volume] = master_function(x1,x2,x3,x4,x5);
end
As I understand it, this code is calling the function ‘master_function’ twice for every optimization loop. This is a problem because this function takes up to a minute to run every time. Because of this I am wondering if it is possible to edit this code so that ‘master_function’ is only called once, providing both the objective and the constraint for the patternsearch optimization? Thank you for your help optimization, functions MATLAB Answers — New Questions

​

Hello guys,
Firt timer here asking a question. I have a 12-bit .raw image file save from our experiment – this normall consist of up to 10,000 frames.
I am trying to us matlab to read this .raw file and reconstruct it back into frame images, e.g. Tiff ones. But, as you may expect already, I kept on failing. I have attached a single frame example here.
What I tried is to reconstruct this single frame first.
file=fopen(‘Example.raw’,’r’);
ImgFile=fread(file,’*ubit12′);
Just to read this what I got is a 2621440×1 single array unit 16. My image is 1280×1024, so for some reason I dont know, the element count doubled. I tried some method to pick 1310720 elements out and use reshape to get an image, but never got what I need.
Some assistance here would be greatly appreciated. cheersHello guys,
Firt timer here asking a question. I have a 12-bit .raw image file save from our experiment – this normall consist of up to 10,000 frames.
I am trying to us matlab to read this .raw file and reconstruct it back into frame images, e.g. Tiff ones. But, as you may expect already, I kept on failing. I have attached a single frame example here.
What I tried is to reconstruct this single frame first.
file=fopen(‘Example.raw’,’r’);
ImgFile=fread(file,’*ubit12′);
Just to read this what I got is a 2621440×1 single array unit 16. My image is 1280×1024, so for some reason I dont know, the element count doubled. I tried some method to pick 1310720 elements out and use reshape to get an image, but never got what I need.
Some assistance here would be greatly appreciated. cheers Hello guys,
Firt timer here asking a question. I have a 12-bit .raw image file save from our experiment – this normall consist of up to 10,000 frames.
I am trying to us matlab to read this .raw file and reconstruct it back into frame images, e.g. Tiff ones. But, as you may expect already, I kept on failing. I have attached a single frame example here.
What I tried is to reconstruct this single frame first.
file=fopen(‘Example.raw’,’r’);
ImgFile=fread(file,’*ubit12′);
Just to read this what I got is a 2621440×1 single array unit 16. My image is 1280×1024, so for some reason I dont know, the element count doubled. I tried some method to pick 1310720 elements out and use reshape to get an image, but never got what I need.
Some assistance here would be greatly appreciated. cheers image processing, .raw, image analysis MATLAB Answers — New Questions

​

I have a matrix A with n values, and each value has a corresponding weight in matrix B. How do I create a weighted histogram in this case? I would like to not specify the number of bins, only the binwidth.
Thank you.
Note: There are no repeating values in A, and alos no repeating values in B.
A = randperm(100); % list of values I need a histogram of
B = randperm(100); % weights for every value in A

h = hist(A)
h.BinWidth = 10;I have a matrix A with n values, and each value has a corresponding weight in matrix B. How do I create a weighted histogram in this case? I would like to not specify the number of bins, only the binwidth.
Thank you.
Note: There are no repeating values in A, and alos no repeating values in B.
A = randperm(100); % list of values I need a histogram of
B = randperm(100); % weights for every value in A

h = hist(A)
h.BinWidth = 10; I have a matrix A with n values, and each value has a corresponding weight in matrix B. How do I create a weighted histogram in this case? I would like to not specify the number of bins, only the binwidth.
Thank you.
Note: There are no repeating values in A, and alos no repeating values in B.
A = randperm(100); % list of values I need a histogram of
B = randperm(100); % weights for every value in A

h = hist(A)
h.BinWidth = 10; histogram, weighted histogram MATLAB Answers — New Questions

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Dear all,
I am using the BLOCKPROC command to segment an input image (21828x54644x3) by passing blocks to the SEGMANTICSEG() function. In this case, I found that the smallest block size that executes without error is [2048,2048]. However, when using an even larger input image (22609x24064x3), the command does not execute if I keep the same block size of [2048,2048]. I need to increase the block size slightly, and it worked with a block size of [2100,2100]. I would like to understand the reason for this and how I can determine the smallest block size based on the image size. Could someone help me?
Thank you.
IMG = imread(‘image.tif’);
BlockSize = [2048 2048];

fun_proc_semanticseg = @(bloco)semanticseg(bloco.data, net, ExecutionEnvironment="gpu",
MiniBatchSize=16,OutputType="uint8");

C = blockproc(IMG, BlockSize, fun_proc_semanticseg);Dear all,
I am using the BLOCKPROC command to segment an input image (21828x54644x3) by passing blocks to the SEGMANTICSEG() function. In this case, I found that the smallest block size that executes without error is [2048,2048]. However, when using an even larger input image (22609x24064x3), the command does not execute if I keep the same block size of [2048,2048]. I need to increase the block size slightly, and it worked with a block size of [2100,2100]. I would like to understand the reason for this and how I can determine the smallest block size based on the image size. Could someone help me?
Thank you.
IMG = imread(‘image.tif’);
BlockSize = [2048 2048];

fun_proc_semanticseg = @(bloco)semanticseg(bloco.data, net, ExecutionEnvironment="gpu",
MiniBatchSize=16,OutputType="uint8");

C = blockproc(IMG, BlockSize, fun_proc_semanticseg); Dear all,
I am using the BLOCKPROC command to segment an input image (21828x54644x3) by passing blocks to the SEGMANTICSEG() function. In this case, I found that the smallest block size that executes without error is [2048,2048]. However, when using an even larger input image (22609x24064x3), the command does not execute if I keep the same block size of [2048,2048]. I need to increase the block size slightly, and it worked with a block size of [2100,2100]. I would like to understand the reason for this and how I can determine the smallest block size based on the image size. Could someone help me?
Thank you.
IMG = imread(‘image.tif’);
BlockSize = [2048 2048];

fun_proc_semanticseg = @(bloco)semanticseg(bloco.data, net, ExecutionEnvironment="gpu",
MiniBatchSize=16,OutputType="uint8");

C = blockproc(IMG, BlockSize, fun_proc_semanticseg); blocksize, matlab, blockproc, semanticseg MATLAB Answers — New Questions

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using table2array I get a string array, but I need an array of numbers where the booleans become 0 or 1 respectivly. double(table2array(arr)) results in NaN for every boolean valueusing table2array I get a string array, but I need an array of numbers where the booleans become 0 or 1 respectivly. double(table2array(arr)) results in NaN for every boolean value using table2array I get a string array, but I need an array of numbers where the booleans become 0 or 1 respectivly. double(table2array(arr)) results in NaN for every boolean value table, array MATLAB Answers — New Questions

​

I am relatively new to filter design so patience is appreciated.
I have an extremely long time series (e.g. ~100 million data points) with a sample rate of 0.2 Hz and I want to apply a series of lowpass filters to the signal at query frequencies of e.g. 1/10 Hz, 1/100 Hz, 1/1000 Hz, 1/10000 Hz, and so on. However, the filtered datasets look roughly identical regardless of what lowpass filter I use.

Fig 1: Zoom view of section of time series to show small differences in filtered time series.
The spectrum of the signal suggests that there is plenty of power at the lower frequencies, so I would think that the results should be different depending on which lowpass cutoff I use.

Fig 2: Spectrum of signal

I wonder if it has something to do with trying to filter sub-mHz levels. Most of the filter questions/answers that I have found are dealing with audio sampling and much higher frequencies. Are there some defaults in MATLAB which design filters to be better behaved at higher frequencies and these assumptions break down at lower frequencies?
I have not done filter design before. All my previous filtering experiences has just used plug-and-play MATLAB functions like lowpass, highpass, bandpass. How would I go about designing an adequate filter for these low frequency signals?
Alternatively, is there nothing wrong with the result and my intuition about what the filter results should look like is just wrong?

Any help is appreciated.I am relatively new to filter design so patience is appreciated.
I have an extremely long time series (e.g. ~100 million data points) with a sample rate of 0.2 Hz and I want to apply a series of lowpass filters to the signal at query frequencies of e.g. 1/10 Hz, 1/100 Hz, 1/1000 Hz, 1/10000 Hz, and so on. However, the filtered datasets look roughly identical regardless of what lowpass filter I use.

Fig 1: Zoom view of section of time series to show small differences in filtered time series.
The spectrum of the signal suggests that there is plenty of power at the lower frequencies, so I would think that the results should be different depending on which lowpass cutoff I use.

Fig 2: Spectrum of signal

I wonder if it has something to do with trying to filter sub-mHz levels. Most of the filter questions/answers that I have found are dealing with audio sampling and much higher frequencies. Are there some defaults in MATLAB which design filters to be better behaved at higher frequencies and these assumptions break down at lower frequencies?
I have not done filter design before. All my previous filtering experiences has just used plug-and-play MATLAB functions like lowpass, highpass, bandpass. How would I go about designing an adequate filter for these low frequency signals?
Alternatively, is there nothing wrong with the result and my intuition about what the filter results should look like is just wrong?

Any help is appreciated. I am relatively new to filter design so patience is appreciated.
I have an extremely long time series (e.g. ~100 million data points) with a sample rate of 0.2 Hz and I want to apply a series of lowpass filters to the signal at query frequencies of e.g. 1/10 Hz, 1/100 Hz, 1/1000 Hz, 1/10000 Hz, and so on. However, the filtered datasets look roughly identical regardless of what lowpass filter I use.

Fig 1: Zoom view of section of time series to show small differences in filtered time series.
The spectrum of the signal suggests that there is plenty of power at the lower frequencies, so I would think that the results should be different depending on which lowpass cutoff I use.

Fig 2: Spectrum of signal

I wonder if it has something to do with trying to filter sub-mHz levels. Most of the filter questions/answers that I have found are dealing with audio sampling and much higher frequencies. Are there some defaults in MATLAB which design filters to be better behaved at higher frequencies and these assumptions break down at lower frequencies?
I have not done filter design before. All my previous filtering experiences has just used plug-and-play MATLAB functions like lowpass, highpass, bandpass. How would I go about designing an adequate filter for these low frequency signals?
Alternatively, is there nothing wrong with the result and my intuition about what the filter results should look like is just wrong?

Any help is appreciated. filter, time series, fft, data, signal processing MATLAB Answers — New Questions

​

This is 2D Planar PIV data at a freestream velocity of ~ 13 m/s.
My code currently selects a velocity profile where max(profile_u) = 12.41 m/s. I want to use a velocity profile where max(profile_u) is closer to 13 m/s.
Scatter(u,y) will show all the velocity profiles I am working with.
clc;
close all;
clear all;

%% BASELINE
% Load the data
opts = detectImportOptions(‘FlatPlate_Baseline_avg0001(in).csv’);
opts.VariableNamingRule = ‘preserve’;
data = readtable(‘FlatPlate_Baseline_avg0001(in).csv’, opts);

% Extract variables
x = data.x / 1000; % Convert to meters
y = data.y / 1000; % Convert to meters
u = data.u;
v = data.v;

%%
% Filter data for x values between 0.05m to 0.08m
filter_indices = (x >= 0.05) & (x <= 0.08);
x_filtered = x(filter_indices);
y_filtered = y(filter_indices);
u_filtered = u(filter_indices);
v_filtered = v(filter_indices);

% Find unique x and y values within the filtered range
unique_x = unique(x_filtered);
unique_y = unique(y_filtered);

nu = 1.5e-5;
kappa = 0.41;
B = 5.0;
profile_spacing = 2 / 1000;

profile_x_positions = unique_x(unique_x >= 0.05);

wall_shear_stress = zeros(size(profile_x_positions));
u_tau_values = zeros(size(profile_x_positions));
delta_99_values = zeros(size(profile_x_positions));
delta_star_values = zeros(size(profile_x_positions)); % Displacement thickness
theta_values = zeros(size(profile_x_positions)); % Momentum thickness

for i = 1:length(profile_x_positions)
profile_indices = find(abs(x – profile_x_positions(i)) < 1e-6);
profile_y = y(profile_indices);
profile_u = u(profile_indices);

valid_indices = profile_y > 0 & profile_y * max(profile_u) / nu > 1e-6;
profile_y = profile_y(valid_indices);
profile_u = profile_u(valid_indices);

if isempty(profile_y) || isempty(profile_u)
fprintf(‘No valid data points at x = %f meters. Skipping…n’, profile_x_positions(i));
continue;
end

% Estimate u_inf (freestream velocity) as the max value of the profile
u_inf = max(profile_u);

% f(eta) = u / u_inf
f_eta = profile_u / u_inf;
[f_eta_unique, unique_indices] = unique(f_eta);
profile_y_unique = profile_y(unique_indices);

% Find y_99 where f(eta) = 0.99
y_99 = interp1(f_eta_unique, profile_y_unique, 0.99, ‘linear’, ‘extrap’);
eta_99 = 0.99;

% delta_99
delta_99 = y_99 / eta_99;
delta_99_values(i) = delta_99;

% displacement thickness (delta_star)
delta_star = abs(trapz(profile_y, (1 – f_eta)));
delta_star_values(i) = delta_star;

% momentum thickness (theta)
theta = abs(trapz(profile_y, f_eta .* (1 – f_eta)));
theta_values(i) = theta;
Re_theta = (u_inf * theta)/nu;

% Calculate wall shear stress and friction velocity
fun_u_tau = @(u_tau) mean(profile_u – (u_tau / kappa) * log(profile_y * u_tau / nu + 1) – B * u_tau);
try
options = optimset(‘Display’, ‘off’);
u_tau = fzero(fun_u_tau, [0.01, 10], options);
u_tau_values(i) = u_tau;
catch err
fprintf(‘Cannot converge at x = %f meters: %sn’, profile_x_positions(i), err.message);
continue;
end

rho = 1.225;
tau_wall = rho * u_tau^2;

wall_shear_stress(i) = tau_wall;

% % Plot y/delta vs u/u_inf
% figure;
% y_over_delta = profile_y / delta_99;
% u_over_u_inf = profile_u / u_inf;
% plot(u_over_u_inf, y_over_delta, ‘-o’);
% title(sprintf(‘Velocity Profile at x = %.3f meters’, profile_x_positions(i)));
% xlabel(‘u / u_{infty}’);
% ylabel(‘y / delta’);
% grid on;
end

% Display the results
results_table = table(profile_x_positions, delta_99_values, delta_star_values, theta_values);
disp(results_table);
disp([‘Momentum Thickness Reynolds Number (Re_theta): ‘, num2str(Re_theta)]);This is 2D Planar PIV data at a freestream velocity of ~ 13 m/s.
My code currently selects a velocity profile where max(profile_u) = 12.41 m/s. I want to use a velocity profile where max(profile_u) is closer to 13 m/s.
Scatter(u,y) will show all the velocity profiles I am working with.
clc;
close all;
clear all;

%% BASELINE
% Load the data
opts = detectImportOptions(‘FlatPlate_Baseline_avg0001(in).csv’);
opts.VariableNamingRule = ‘preserve’;
data = readtable(‘FlatPlate_Baseline_avg0001(in).csv’, opts);

% Extract variables
x = data.x / 1000; % Convert to meters
y = data.y / 1000; % Convert to meters
u = data.u;
v = data.v;

%%
% Filter data for x values between 0.05m to 0.08m
filter_indices = (x >= 0.05) & (x <= 0.08);
x_filtered = x(filter_indices);
y_filtered = y(filter_indices);
u_filtered = u(filter_indices);
v_filtered = v(filter_indices);

% Find unique x and y values within the filtered range
unique_x = unique(x_filtered);
unique_y = unique(y_filtered);

nu = 1.5e-5;
kappa = 0.41;
B = 5.0;
profile_spacing = 2 / 1000;

profile_x_positions = unique_x(unique_x >= 0.05);

wall_shear_stress = zeros(size(profile_x_positions));
u_tau_values = zeros(size(profile_x_positions));
delta_99_values = zeros(size(profile_x_positions));
delta_star_values = zeros(size(profile_x_positions)); % Displacement thickness
theta_values = zeros(size(profile_x_positions)); % Momentum thickness

for i = 1:length(profile_x_positions)
profile_indices = find(abs(x – profile_x_positions(i)) < 1e-6);
profile_y = y(profile_indices);
profile_u = u(profile_indices);

valid_indices = profile_y > 0 & profile_y * max(profile_u) / nu > 1e-6;
profile_y = profile_y(valid_indices);
profile_u = profile_u(valid_indices);

if isempty(profile_y) || isempty(profile_u)
fprintf(‘No valid data points at x = %f meters. Skipping…n’, profile_x_positions(i));
continue;
end

% Estimate u_inf (freestream velocity) as the max value of the profile
u_inf = max(profile_u);

% f(eta) = u / u_inf
f_eta = profile_u / u_inf;
[f_eta_unique, unique_indices] = unique(f_eta);
profile_y_unique = profile_y(unique_indices);

% Find y_99 where f(eta) = 0.99
y_99 = interp1(f_eta_unique, profile_y_unique, 0.99, ‘linear’, ‘extrap’);
eta_99 = 0.99;

% delta_99
delta_99 = y_99 / eta_99;
delta_99_values(i) = delta_99;

% displacement thickness (delta_star)
delta_star = abs(trapz(profile_y, (1 – f_eta)));
delta_star_values(i) = delta_star;

% momentum thickness (theta)
theta = abs(trapz(profile_y, f_eta .* (1 – f_eta)));
theta_values(i) = theta;
Re_theta = (u_inf * theta)/nu;

% Calculate wall shear stress and friction velocity
fun_u_tau = @(u_tau) mean(profile_u – (u_tau / kappa) * log(profile_y * u_tau / nu + 1) – B * u_tau);
try
options = optimset(‘Display’, ‘off’);
u_tau = fzero(fun_u_tau, [0.01, 10], options);
u_tau_values(i) = u_tau;
catch err
fprintf(‘Cannot converge at x = %f meters: %sn’, profile_x_positions(i), err.message);
continue;
end

rho = 1.225;
tau_wall = rho * u_tau^2;

wall_shear_stress(i) = tau_wall;

% % Plot y/delta vs u/u_inf
% figure;
% y_over_delta = profile_y / delta_99;
% u_over_u_inf = profile_u / u_inf;
% plot(u_over_u_inf, y_over_delta, ‘-o’);
% title(sprintf(‘Velocity Profile at x = %.3f meters’, profile_x_positions(i)));
% xlabel(‘u / u_{infty}’);
% ylabel(‘y / delta’);
% grid on;
end

% Display the results
results_table = table(profile_x_positions, delta_99_values, delta_star_values, theta_values);
disp(results_table);
disp([‘Momentum Thickness Reynolds Number (Re_theta): ‘, num2str(Re_theta)]); This is 2D Planar PIV data at a freestream velocity of ~ 13 m/s.
My code currently selects a velocity profile where max(profile_u) = 12.41 m/s. I want to use a velocity profile where max(profile_u) is closer to 13 m/s.
Scatter(u,y) will show all the velocity profiles I am working with.
clc;
close all;
clear all;

%% BASELINE
% Load the data
opts = detectImportOptions(‘FlatPlate_Baseline_avg0001(in).csv’);
opts.VariableNamingRule = ‘preserve’;
data = readtable(‘FlatPlate_Baseline_avg0001(in).csv’, opts);

% Extract variables
x = data.x / 1000; % Convert to meters
y = data.y / 1000; % Convert to meters
u = data.u;
v = data.v;

%%
% Filter data for x values between 0.05m to 0.08m
filter_indices = (x >= 0.05) & (x <= 0.08);
x_filtered = x(filter_indices);
y_filtered = y(filter_indices);
u_filtered = u(filter_indices);
v_filtered = v(filter_indices);

% Find unique x and y values within the filtered range
unique_x = unique(x_filtered);
unique_y = unique(y_filtered);

nu = 1.5e-5;
kappa = 0.41;
B = 5.0;
profile_spacing = 2 / 1000;

profile_x_positions = unique_x(unique_x >= 0.05);

wall_shear_stress = zeros(size(profile_x_positions));
u_tau_values = zeros(size(profile_x_positions));
delta_99_values = zeros(size(profile_x_positions));
delta_star_values = zeros(size(profile_x_positions)); % Displacement thickness
theta_values = zeros(size(profile_x_positions)); % Momentum thickness

for i = 1:length(profile_x_positions)
profile_indices = find(abs(x – profile_x_positions(i)) < 1e-6);
profile_y = y(profile_indices);
profile_u = u(profile_indices);

valid_indices = profile_y > 0 & profile_y * max(profile_u) / nu > 1e-6;
profile_y = profile_y(valid_indices);
profile_u = profile_u(valid_indices);

if isempty(profile_y) || isempty(profile_u)
fprintf(‘No valid data points at x = %f meters. Skipping…n’, profile_x_positions(i));
continue;
end

% Estimate u_inf (freestream velocity) as the max value of the profile
u_inf = max(profile_u);

% f(eta) = u / u_inf
f_eta = profile_u / u_inf;
[f_eta_unique, unique_indices] = unique(f_eta);
profile_y_unique = profile_y(unique_indices);

% Find y_99 where f(eta) = 0.99
y_99 = interp1(f_eta_unique, profile_y_unique, 0.99, ‘linear’, ‘extrap’);
eta_99 = 0.99;

% delta_99
delta_99 = y_99 / eta_99;
delta_99_values(i) = delta_99;

% displacement thickness (delta_star)
delta_star = abs(trapz(profile_y, (1 – f_eta)));
delta_star_values(i) = delta_star;

% momentum thickness (theta)
theta = abs(trapz(profile_y, f_eta .* (1 – f_eta)));
theta_values(i) = theta;
Re_theta = (u_inf * theta)/nu;

% Calculate wall shear stress and friction velocity
fun_u_tau = @(u_tau) mean(profile_u – (u_tau / kappa) * log(profile_y * u_tau / nu + 1) – B * u_tau);
try
options = optimset(‘Display’, ‘off’);
u_tau = fzero(fun_u_tau, [0.01, 10], options);
u_tau_values(i) = u_tau;
catch err
fprintf(‘Cannot converge at x = %f meters: %sn’, profile_x_positions(i), err.message);
continue;
end

rho = 1.225;
tau_wall = rho * u_tau^2;

wall_shear_stress(i) = tau_wall;

% % Plot y/delta vs u/u_inf
% figure;
% y_over_delta = profile_y / delta_99;
% u_over_u_inf = profile_u / u_inf;
% plot(u_over_u_inf, y_over_delta, ‘-o’);
% title(sprintf(‘Velocity Profile at x = %.3f meters’, profile_x_positions(i)));
% xlabel(‘u / u_{infty}’);
% ylabel(‘y / delta’);
% grid on;
end

% Display the results
results_table = table(profile_x_positions, delta_99_values, delta_star_values, theta_values);
disp(results_table);
disp([‘Momentum Thickness Reynolds Number (Re_theta): ‘, num2str(Re_theta)]); aerodynamics, indexing, plotting, vectors MATLAB Answers — New Questions

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I’m in a situation that I need to be able to randomly rotate (0-359 angle degrees) different images. I’m currently using _imrotate()_ but considering that the images are small and the quality is important, I noticed that the images lose their qualities, specially in curve lines. I can still rotate my images 0, 90, 180 and 270 degrees without losing any qualities but I wanted to know if there is any way to rotate an image with more degrees without losing the quality.I’m in a situation that I need to be able to randomly rotate (0-359 angle degrees) different images. I’m currently using _imrotate()_ but considering that the images are small and the quality is important, I noticed that the images lose their qualities, specially in curve lines. I can still rotate my images 0, 90, 180 and 270 degrees without losing any qualities but I wanted to know if there is any way to rotate an image with more degrees without losing the quality. I’m in a situation that I need to be able to randomly rotate (0-359 angle degrees) different images. I’m currently using _imrotate()_ but considering that the images are small and the quality is important, I noticed that the images lose their qualities, specially in curve lines. I can still rotate my images 0, 90, 180 and 270 degrees without losing any qualities but I wanted to know if there is any way to rotate an image with more degrees without losing the quality. image processing, rotate, imrotate, quality, imagequality MATLAB Answers — New Questions

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While simulating model in Simulink I got error as "External input specified in the Configuration Parameters dialog box on the Data Import/Export page (or as an option to SIM command) to a model containing no root level input ports"While simulating model in Simulink I got error as "External input specified in the Configuration Parameters dialog box on the Data Import/Export page (or as an option to SIM command) to a model containing no root level input ports" While simulating model in Simulink I got error as "External input specified in the Configuration Parameters dialog box on the Data Import/Export page (or as an option to SIM command) to a model containing no root level input ports" to workspace, from workspace MATLAB Answers — New Questions

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how to plot these signals in matlab
y1(t)=x(t-3)
y2(t)=x(t+5)
y3(t)=x(-t)how to plot these signals in matlab
y1(t)=x(t-3)
y2(t)=x(t+5)
y3(t)=x(-t) how to plot these signals in matlab
y1(t)=x(t-3)
y2(t)=x(t+5)
y3(t)=x(-t) signal MATLAB Answers — New Questions

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