Category: News
Outlook Folder Names in Foreign Language
I recently switched to the new version of Outlook. I run two accounts there – my business account and my personal Gmail account. I notice under the Gmail account, the folder names are in Korean, for some weird reason. I’m an English-speaking Australian. This didn’t happen under the old version of Outlook. The content of the emails is all in English. Just the folder names are in Korea. I’ve checked language in the settings. And it says English. Anyone have any ideas about what is causing this?
I recently switched to the new version of Outlook. I run two accounts there – my business account and my personal Gmail account. I notice under the Gmail account, the folder names are in Korean, for some weird reason. I’m an English-speaking Australian. This didn’t happen under the old version of Outlook. The content of the emails is all in English. Just the folder names are in Korea. I’ve checked language in the settings. And it says English. Anyone have any ideas about what is causing this? Read More
رقم شيخ روحاني مخفي / 5770 0096657838 / سحر الغضب الشديد
رقم شيخ روحاني مخفي / 5770 0096657838 / سحر الغضب الشديد
رقم شيخ روحاني مخفي / 5770 0096657838 / سحر الغضب الشديد Read More
Add SINGLE element to array or vector
I have a vector of the format:
x = [xval(1) xval(2) … xval(n)]
, and I want to add an element to the end, xval(n+1). How do I do that?I have a vector of the format:
x = [xval(1) xval(2) … xval(n)]
, and I want to add an element to the end, xval(n+1). How do I do that? I have a vector of the format:
x = [xval(1) xval(2) … xval(n)]
, and I want to add an element to the end, xval(n+1). How do I do that? append valur to vector, deep learning MATLAB Answers — New Questions
how to remove noise from audio using fourier transform and filter and to obtain back the original audio signal
[y,Fs]=audioread(‘audio.wav’);
%Normal sound
sound(y,Fs);
subplot(3,1,1);
plot(y);
x=y(1:2:length(y));%x=y(1:2:end)
%Decimated sound
sound(x,Fs);
subplot(3,1,2);
plot(x);
xn=randn(384362,1);
yn=y+xn;
sound(yn,Fs);
x1(1)=y(1);
g=length(y)-1;
j=2;
i=2;
while g~=0
m=rem(i,2);
if m==0
x1(i)=0;
else
x1(i)=y(j);
j=j+1;
g=g-1;
end
i=i+1;
end
%Interpolated sound
sound(x1,Fs);
subplot(3,1,3);
plot(x1);
srate = 10000;
time = 0:1/srate:2;
npnts = length(time);
% signal
signal = y;
% Fourier spectrum
signalX = fft(signal);
hz = linspace(0,srate/2,npnts);
% amplitude
ampl = abs(signalX(1:length(hz)));
figure(1);
stem(signal);
figure(2), clf
stem(hz,ampl,’ks-‘,’linew’,3,’markersize’,10,’markerfacecolor’,’w’)
% make plot look a bit nicer
set(gca,’xlim’,[0 10])
xlabel(‘Frequency (Hz)’), ylabel(‘Amplitude (a.u.)’)[y,Fs]=audioread(‘audio.wav’);
%Normal sound
sound(y,Fs);
subplot(3,1,1);
plot(y);
x=y(1:2:length(y));%x=y(1:2:end)
%Decimated sound
sound(x,Fs);
subplot(3,1,2);
plot(x);
xn=randn(384362,1);
yn=y+xn;
sound(yn,Fs);
x1(1)=y(1);
g=length(y)-1;
j=2;
i=2;
while g~=0
m=rem(i,2);
if m==0
x1(i)=0;
else
x1(i)=y(j);
j=j+1;
g=g-1;
end
i=i+1;
end
%Interpolated sound
sound(x1,Fs);
subplot(3,1,3);
plot(x1);
srate = 10000;
time = 0:1/srate:2;
npnts = length(time);
% signal
signal = y;
% Fourier spectrum
signalX = fft(signal);
hz = linspace(0,srate/2,npnts);
% amplitude
ampl = abs(signalX(1:length(hz)));
figure(1);
stem(signal);
figure(2), clf
stem(hz,ampl,’ks-‘,’linew’,3,’markersize’,10,’markerfacecolor’,’w’)
% make plot look a bit nicer
set(gca,’xlim’,[0 10])
xlabel(‘Frequency (Hz)’), ylabel(‘Amplitude (a.u.)’) [y,Fs]=audioread(‘audio.wav’);
%Normal sound
sound(y,Fs);
subplot(3,1,1);
plot(y);
x=y(1:2:length(y));%x=y(1:2:end)
%Decimated sound
sound(x,Fs);
subplot(3,1,2);
plot(x);
xn=randn(384362,1);
yn=y+xn;
sound(yn,Fs);
x1(1)=y(1);
g=length(y)-1;
j=2;
i=2;
while g~=0
m=rem(i,2);
if m==0
x1(i)=0;
else
x1(i)=y(j);
j=j+1;
g=g-1;
end
i=i+1;
end
%Interpolated sound
sound(x1,Fs);
subplot(3,1,3);
plot(x1);
srate = 10000;
time = 0:1/srate:2;
npnts = length(time);
% signal
signal = y;
% Fourier spectrum
signalX = fft(signal);
hz = linspace(0,srate/2,npnts);
% amplitude
ampl = abs(signalX(1:length(hz)));
figure(1);
stem(signal);
figure(2), clf
stem(hz,ampl,’ks-‘,’linew’,3,’markersize’,10,’markerfacecolor’,’w’)
% make plot look a bit nicer
set(gca,’xlim’,[0 10])
xlabel(‘Frequency (Hz)’), ylabel(‘Amplitude (a.u.)’) audio MATLAB Answers — New Questions
جلب الحبيب بسرعة خارقة – 578385770 : 966 + – شيخ روحاني معروف
جلب الحبيب بسرعة خارقة – 578385770 : 966 + – شيخ روحاني معروف
جلب الحبيب بسرعة خارقة – 578385770 : 966 + – شيخ روحاني معروف Read More
جـلب الـحبيب مضمون * 34028443 : 973 +* شيخ روحاني مضمون
جـلب الـحبيب مضمون * 34028443 : 973 +* شيخ روحاني مضمون
جـلب الـحبيب مضمون * 34028443 : 973 +* شيخ روحاني مضمون Read More
جلب الحبيب بالمسك والعنبر – 578385770 : 966 +💞 شيخ روحاني مضمون للزواج
جلب الحبيب بالمسك والعنبر – 578385770 : 966 + شيخ روحاني مضمون للزواج
جلب الحبيب بالمسك والعنبر – 578385770 : 966 + شيخ روحاني مضمون للزواج Read More
رقم هاتف شيخ روحاني – 578385770 : 966 + رقم هاتف معالج روحاني
رقم هاتف شيخ روحاني – 578385770 : 966 + رقم هاتف معالج روحاني
رقم هاتف شيخ روحاني – 578385770 : 966 + رقم هاتف معالج روحاني Read More
جلب الحبيب بالتوكيدات – 578385770 : 966 + رقم هاتف شيخ روحاني
جلب الحبيب بالتوكيدات – 578385770 : 966 + رقم هاتف شيخ روحاني
جلب الحبيب بالتوكيدات – 578385770 : 966 + رقم هاتف شيخ روحاني Read More
جلب الحبيب بعلم الأرقام 34028443 : 973 +❣️ رقم هاتف معالج روحاني
جلب الحبيب بعلم الأرقام 34028443 : 973 +:heavy_heart_exclamation: رقم هاتف معالج روحاني
جلب الحبيب بعلم الأرقام 34028443 : 973 +:heavy_heart_exclamation: رقم هاتف معالج روحاني Read More
شيخ روحاني محترف / 5770 0096657838 / وصفات المحبة المغربية
شيخ روحاني محترف / 5770 0096657838 / وصفات المحبة المغربية
شيخ روحاني محترف / 5770 0096657838 / وصفات المحبة المغربية Read More
Is it possible to implement FOC plant model on Arduino?
I’m trying implement a FOC plant model on Arduino using S32K144 as input pulse generator. I tried looking into some examples but I’m not finding clarity on what blocksets to use.I’m trying implement a FOC plant model on Arduino using S32K144 as input pulse generator. I tried looking into some examples but I’m not finding clarity on what blocksets to use. I’m trying implement a FOC plant model on Arduino using S32K144 as input pulse generator. I tried looking into some examples but I’m not finding clarity on what blocksets to use. power_electronics_control MATLAB Answers — New Questions
Hypervolume computation with PlatEMO
Does anybody know how can i apply this set of code (HV.m) from PlatEMO (https://github.com/BIMK/PlatEMO/blob/1274e2530e1c5afa928f3691c65af7d2f7efe099/PlatEMO/Metrics/HV.m)
I’ve tried to execute it with the population solutions obtained from my algorithm and the optimum values obtained across several benchmark dataset as follows, however the output for hypervolume value that i’ve got is extremely low, what could be the problem?
inputs:
solutions = [43,176,38;43,177,37;42,188,38;46,168,38;43,184,36;42,178,48;42,169,49;42,179,42];
Population.best = struct(‘objs’, []);
% Fill the structure array with the solutions
for i = 1:size(solutions, 1)
Population.best(i).objs = solutions(i, :);
end
optimum = [40, 162, 38 ; 40, 164, 37 ;40, 167, 36; 40,171,36; 40,165,37; 40,169,36 ; 41, 160, 38 ;41, 163, 37 ;42, 157, 40 ;42, 158, 39 ;42, 165, 36 ; 42,162,42 ; 43, 155, 40 ;44, 154, 40 ;45, 153, 42];
hv = HV(Population, optimum);
disp([‘Hypervolume: ‘, num2str(hv)]);
in HV.m:
function score = HV(Population,optimum)
PopObj = vertcat(Population.best.objs);
… and the rest of the code from (https://github.com/BIMK/PlatEMO/blob/1274e2530e1c5afa928f3691c65af7d2f7efe099/PlatEMO/Metrics/HV.m)Does anybody know how can i apply this set of code (HV.m) from PlatEMO (https://github.com/BIMK/PlatEMO/blob/1274e2530e1c5afa928f3691c65af7d2f7efe099/PlatEMO/Metrics/HV.m)
I’ve tried to execute it with the population solutions obtained from my algorithm and the optimum values obtained across several benchmark dataset as follows, however the output for hypervolume value that i’ve got is extremely low, what could be the problem?
inputs:
solutions = [43,176,38;43,177,37;42,188,38;46,168,38;43,184,36;42,178,48;42,169,49;42,179,42];
Population.best = struct(‘objs’, []);
% Fill the structure array with the solutions
for i = 1:size(solutions, 1)
Population.best(i).objs = solutions(i, :);
end
optimum = [40, 162, 38 ; 40, 164, 37 ;40, 167, 36; 40,171,36; 40,165,37; 40,169,36 ; 41, 160, 38 ;41, 163, 37 ;42, 157, 40 ;42, 158, 39 ;42, 165, 36 ; 42,162,42 ; 43, 155, 40 ;44, 154, 40 ;45, 153, 42];
hv = HV(Population, optimum);
disp([‘Hypervolume: ‘, num2str(hv)]);
in HV.m:
function score = HV(Population,optimum)
PopObj = vertcat(Population.best.objs);
… and the rest of the code from (https://github.com/BIMK/PlatEMO/blob/1274e2530e1c5afa928f3691c65af7d2f7efe099/PlatEMO/Metrics/HV.m) Does anybody know how can i apply this set of code (HV.m) from PlatEMO (https://github.com/BIMK/PlatEMO/blob/1274e2530e1c5afa928f3691c65af7d2f7efe099/PlatEMO/Metrics/HV.m)
I’ve tried to execute it with the population solutions obtained from my algorithm and the optimum values obtained across several benchmark dataset as follows, however the output for hypervolume value that i’ve got is extremely low, what could be the problem?
inputs:
solutions = [43,176,38;43,177,37;42,188,38;46,168,38;43,184,36;42,178,48;42,169,49;42,179,42];
Population.best = struct(‘objs’, []);
% Fill the structure array with the solutions
for i = 1:size(solutions, 1)
Population.best(i).objs = solutions(i, :);
end
optimum = [40, 162, 38 ; 40, 164, 37 ;40, 167, 36; 40,171,36; 40,165,37; 40,169,36 ; 41, 160, 38 ;41, 163, 37 ;42, 157, 40 ;42, 158, 39 ;42, 165, 36 ; 42,162,42 ; 43, 155, 40 ;44, 154, 40 ;45, 153, 42];
hv = HV(Population, optimum);
disp([‘Hypervolume: ‘, num2str(hv)]);
in HV.m:
function score = HV(Population,optimum)
PopObj = vertcat(Population.best.objs);
… and the rest of the code from (https://github.com/BIMK/PlatEMO/blob/1274e2530e1c5afa928f3691c65af7d2f7efe099/PlatEMO/Metrics/HV.m) transferred MATLAB Answers — New Questions
Hypervolume indicator with Yi Cao
I am working with Multi-objective optimization problem and i come across with this code (https://www.mathworks.com/matlabcentral/fileexchange/19651-hypervolume-indicator), however, does anyone know how to get the function ‘paretofront’ from the exisiting code to obtain the hypervolume value
thanksI am working with Multi-objective optimization problem and i come across with this code (https://www.mathworks.com/matlabcentral/fileexchange/19651-hypervolume-indicator), however, does anyone know how to get the function ‘paretofront’ from the exisiting code to obtain the hypervolume value
thanks I am working with Multi-objective optimization problem and i come across with this code (https://www.mathworks.com/matlabcentral/fileexchange/19651-hypervolume-indicator), however, does anyone know how to get the function ‘paretofront’ from the exisiting code to obtain the hypervolume value
thanks transferred MATLAB Answers — New Questions
how to display the calling relation ship between simulink function and function caller
hello, as follwoing system shows, how to configure to display the relation ship between function caller an simulink function in the system diagram.hello, as follwoing system shows, how to configure to display the relation ship between function caller an simulink function in the system diagram. hello, as follwoing system shows, how to configure to display the relation ship between function caller an simulink function in the system diagram. simulink function, function caller MATLAB Answers — New Questions
Antlion optimization algorithm for fuel cost optimization
How Can i use the antlion optimizer tool for fuel cost optimization for a thermal power plant If not possible, please i need a matlab code to do thatHow Can i use the antlion optimizer tool for fuel cost optimization for a thermal power plant If not possible, please i need a matlab code to do that How Can i use the antlion optimizer tool for fuel cost optimization for a thermal power plant If not possible, please i need a matlab code to do that antlion optimization MATLAB Answers — New Questions
Migration of system mailboxes from 2013 to 2019?
It is a hybrid environment with exchange 2013. User mailboxes have been migrated to exchange online.
We want to migrate to exchange 2019. do we need to migrate the system mailboxes from exchange 2013 to 2019? Or do I just need to recreate the system mailboxes in exchange 2019?
It is a hybrid environment with exchange 2013. User mailboxes have been migrated to exchange online.We want to migrate to exchange 2019. do we need to migrate the system mailboxes from exchange 2013 to 2019? Or do I just need to recreate the system mailboxes in exchange 2019? Read More
maximum Lyapunov exponent diagram
hello, I’m working on discrete dynamical system in 2 dimesntion
t’m trying to plot a digram a paramete versus the maximum lyapunov exponent, i searched more about it but didn’t reach to anything.
any one have idea or could help me and thank you.hello, I’m working on discrete dynamical system in 2 dimesntion
t’m trying to plot a digram a paramete versus the maximum lyapunov exponent, i searched more about it but didn’t reach to anything.
any one have idea or could help me and thank you. hello, I’m working on discrete dynamical system in 2 dimesntion
t’m trying to plot a digram a paramete versus the maximum lyapunov exponent, i searched more about it but didn’t reach to anything.
any one have idea or could help me and thank you. dynamical system MATLAB Answers — New Questions
Timestep stability in a 1D heat diffusion model
Hi,
I have a 1D heat diffusion code which I was using on a timescale of 10s of years and I am now trying to use the same code to work on a scale of millions of years. Obviously if I keep my timestep the same this will take ages to calculate but if I increase my timestep I encounter numerical stability issues.
My questions are:
How should I approach this problem?
What affects the maximum stable timestep? And how do I calculate this?
Many thanks,
Alex
close all
clear all
dx = 4; % discretization step in m
dt = 0.0000001; % timestep in Myrs
h=1000; % height of box in m
nx=h/dx+1;
model_lenth=1; %length of model in Myrs
nt=ceil(model_lenth/dt)+1; % number of tsteps to reach end of model
kappa = 1e-6; % thermal diffusivity
x=0:dx:0+h; % finite difference mesh
T=38+0.05.*x; % initial T=Tm everywhere …
time=zeros(1,nt);
t=0;
Tnew = zeros(1,nx);
%Lower sill
sill_1_thickness=18;
Sill_1_top_position=590;
Sill_1_top=ceil(Sill_1_top_position/dx);
Sill_1_bottom=ceil((Sill_1_top_position+sill_1_thickness)/dx);
%Upper sill
sill_2_thickness=10;
Sill_2_top_position=260;
Sill_2_top=ceil(Sill_2_top_position/dx);
Sill_2_bottom=ceil((Sill_2_top_position+sill_2_thickness)/dx);
%Temperature of dolerite intrusions
Tm=1300;
T(Sill_1_top:Sill_1_bottom)=Tm; %Apply temperature to intrusion 1
% unit conversion to SI:
secinmyr=24*3600*365*1000000; % dt in sec
dt=dt*secinmyr;
%Plot initial conditions
figure(1), clf
f1 = figure(1); %Make full screen
set(f1,’Units’, ‘Normalized’, ‘OuterPosition’, [0 0 1 1]);
plot (T,x,’LineWidth’,2)
xlabel(‘T [^oC]’)
ylabel(‘x[m]’)
axis([0 1310 0 1000])
title(‘ Initial Conditions’)
set(gca,’YDir’,’reverse’);
%Main calculation
for it=1:nt
%Apply temperature to upper intrusion
if it==10;
T(Sill_2_top:Sill_2_bottom)=Tm;
end
for i = 2:nx-1
Tnew(i) = T(i) + kappa*dt*(T(i+1) – 2*T(i) + T(i-1))/dx/dx;
end
Tnew(1) = T(1);
Tnew(nx) = T(nx);
time(it) = t;
T = Tnew; %Set old Temp to = new temp for next loop
tmyears=(t/secinmyr);
%Plot a figure which updates in the loop of temperature against depth
figure(2), clf
plot (T,x,’LineWidth’,2)
xlabel(‘T [^oC]’)
ylabel(‘x[m]’)
title([‘ Temperature against Depth after ‘,num2str(tmyears),’ Myrs’])
axis([0 1300 0 1000])
set(gca,’YDir’,’reverse’);%Reverse y axis
%Make full screen
f2 = figure(2);
set(f2,’Units’, ‘Normalized’, ‘OuterPosition’, [0 0 1 1]);
drawnow
t=t+dt;
endHi,
I have a 1D heat diffusion code which I was using on a timescale of 10s of years and I am now trying to use the same code to work on a scale of millions of years. Obviously if I keep my timestep the same this will take ages to calculate but if I increase my timestep I encounter numerical stability issues.
My questions are:
How should I approach this problem?
What affects the maximum stable timestep? And how do I calculate this?
Many thanks,
Alex
close all
clear all
dx = 4; % discretization step in m
dt = 0.0000001; % timestep in Myrs
h=1000; % height of box in m
nx=h/dx+1;
model_lenth=1; %length of model in Myrs
nt=ceil(model_lenth/dt)+1; % number of tsteps to reach end of model
kappa = 1e-6; % thermal diffusivity
x=0:dx:0+h; % finite difference mesh
T=38+0.05.*x; % initial T=Tm everywhere …
time=zeros(1,nt);
t=0;
Tnew = zeros(1,nx);
%Lower sill
sill_1_thickness=18;
Sill_1_top_position=590;
Sill_1_top=ceil(Sill_1_top_position/dx);
Sill_1_bottom=ceil((Sill_1_top_position+sill_1_thickness)/dx);
%Upper sill
sill_2_thickness=10;
Sill_2_top_position=260;
Sill_2_top=ceil(Sill_2_top_position/dx);
Sill_2_bottom=ceil((Sill_2_top_position+sill_2_thickness)/dx);
%Temperature of dolerite intrusions
Tm=1300;
T(Sill_1_top:Sill_1_bottom)=Tm; %Apply temperature to intrusion 1
% unit conversion to SI:
secinmyr=24*3600*365*1000000; % dt in sec
dt=dt*secinmyr;
%Plot initial conditions
figure(1), clf
f1 = figure(1); %Make full screen
set(f1,’Units’, ‘Normalized’, ‘OuterPosition’, [0 0 1 1]);
plot (T,x,’LineWidth’,2)
xlabel(‘T [^oC]’)
ylabel(‘x[m]’)
axis([0 1310 0 1000])
title(‘ Initial Conditions’)
set(gca,’YDir’,’reverse’);
%Main calculation
for it=1:nt
%Apply temperature to upper intrusion
if it==10;
T(Sill_2_top:Sill_2_bottom)=Tm;
end
for i = 2:nx-1
Tnew(i) = T(i) + kappa*dt*(T(i+1) – 2*T(i) + T(i-1))/dx/dx;
end
Tnew(1) = T(1);
Tnew(nx) = T(nx);
time(it) = t;
T = Tnew; %Set old Temp to = new temp for next loop
tmyears=(t/secinmyr);
%Plot a figure which updates in the loop of temperature against depth
figure(2), clf
plot (T,x,’LineWidth’,2)
xlabel(‘T [^oC]’)
ylabel(‘x[m]’)
title([‘ Temperature against Depth after ‘,num2str(tmyears),’ Myrs’])
axis([0 1300 0 1000])
set(gca,’YDir’,’reverse’);%Reverse y axis
%Make full screen
f2 = figure(2);
set(f2,’Units’, ‘Normalized’, ‘OuterPosition’, [0 0 1 1]);
drawnow
t=t+dt;
end Hi,
I have a 1D heat diffusion code which I was using on a timescale of 10s of years and I am now trying to use the same code to work on a scale of millions of years. Obviously if I keep my timestep the same this will take ages to calculate but if I increase my timestep I encounter numerical stability issues.
My questions are:
How should I approach this problem?
What affects the maximum stable timestep? And how do I calculate this?
Many thanks,
Alex
close all
clear all
dx = 4; % discretization step in m
dt = 0.0000001; % timestep in Myrs
h=1000; % height of box in m
nx=h/dx+1;
model_lenth=1; %length of model in Myrs
nt=ceil(model_lenth/dt)+1; % number of tsteps to reach end of model
kappa = 1e-6; % thermal diffusivity
x=0:dx:0+h; % finite difference mesh
T=38+0.05.*x; % initial T=Tm everywhere …
time=zeros(1,nt);
t=0;
Tnew = zeros(1,nx);
%Lower sill
sill_1_thickness=18;
Sill_1_top_position=590;
Sill_1_top=ceil(Sill_1_top_position/dx);
Sill_1_bottom=ceil((Sill_1_top_position+sill_1_thickness)/dx);
%Upper sill
sill_2_thickness=10;
Sill_2_top_position=260;
Sill_2_top=ceil(Sill_2_top_position/dx);
Sill_2_bottom=ceil((Sill_2_top_position+sill_2_thickness)/dx);
%Temperature of dolerite intrusions
Tm=1300;
T(Sill_1_top:Sill_1_bottom)=Tm; %Apply temperature to intrusion 1
% unit conversion to SI:
secinmyr=24*3600*365*1000000; % dt in sec
dt=dt*secinmyr;
%Plot initial conditions
figure(1), clf
f1 = figure(1); %Make full screen
set(f1,’Units’, ‘Normalized’, ‘OuterPosition’, [0 0 1 1]);
plot (T,x,’LineWidth’,2)
xlabel(‘T [^oC]’)
ylabel(‘x[m]’)
axis([0 1310 0 1000])
title(‘ Initial Conditions’)
set(gca,’YDir’,’reverse’);
%Main calculation
for it=1:nt
%Apply temperature to upper intrusion
if it==10;
T(Sill_2_top:Sill_2_bottom)=Tm;
end
for i = 2:nx-1
Tnew(i) = T(i) + kappa*dt*(T(i+1) – 2*T(i) + T(i-1))/dx/dx;
end
Tnew(1) = T(1);
Tnew(nx) = T(nx);
time(it) = t;
T = Tnew; %Set old Temp to = new temp for next loop
tmyears=(t/secinmyr);
%Plot a figure which updates in the loop of temperature against depth
figure(2), clf
plot (T,x,’LineWidth’,2)
xlabel(‘T [^oC]’)
ylabel(‘x[m]’)
title([‘ Temperature against Depth after ‘,num2str(tmyears),’ Myrs’])
axis([0 1300 0 1000])
set(gca,’YDir’,’reverse’);%Reverse y axis
%Make full screen
f2 = figure(2);
set(f2,’Units’, ‘Normalized’, ‘OuterPosition’, [0 0 1 1]);
drawnow
t=t+dt;
end time step; heat diffusion MATLAB Answers — New Questions
How to set the theorem of floquet for Linear variational equation with periodic equation?
I’m trying to analyze the stability regions of the equation of motion of a system with periodic coefficients (whirlflutter). I have only written its polynomial equation and examined its roots, but my plot did not match the reference implementation correctly.
If I wanna write the Hill function and use the ode45 code, is it possible for someone to correct my code and my approach to finding the monodromy matrix and calculating the Floquet multipliers?
clc;
clear all;
% Define parameters
N = 2; % Number of blades
I_thetaoverI_b = 2; % Moment of inertia pitch axis over I_b
I_psioverI_b = 2; % Moment of inertia yaw axis over I_b
C_thetaoverI_b = 0.00; % Damping coefficient over I_b
C_psioverI_b = 0.00; % Damping coefficient over I_b
h = 0.3; % rotor mast height, wing tip spar to rotor hub [m]
hoverR = 0.34;
R = h / hoverR; % radius [m]
gamma = 4; % lock number
V_knots = 325; % the rotor forward velocity [knots]
% Convert velocity from [knots] to [meters per second]
% 1 knot = 0.51444 meters per second
V = V_knots * 0.51444;
% Calculate angular velocity in radians per second
omega_rad_s = V / R;
% Convert angular velocity from radians per second to RPM
% 1 radian per second = (60 / (2 * pi)) RPM
Omega = omega_rad_s * (60 / (2 * pi));
freq_1_over_Omega = 1 / Omega;
%the flap moment aerodynamic coefficients for large V
M_b = -(1/10)*V;
M_u = 1/6;
%the propeller aerodynamic coefficients
H_u = V/2;
% Frequency ranges
f_pitch= 5:3:140;
f_yaw= 5:3:140;
divergence_map = [];
Rdivergence_map = [];
unstable = [];
% Modify the loop to iterate over time points
for i = 1:length(f_pitch)
for j = 1:length(f_yaw)
phi_steps = linspace(0, pi, 100); % Time steps within one period
for phi = phi_steps
% Angular frequencies for the current time point
w_omega_pitch = 2 * pi* f_pitch(i);
w_omega_yaw = 2 * pi * f_yaw(j);
K_psi = (w_omega_pitch^2) * I_psioverI_b;
K_theta = (w_omega_yaw^2) * I_thetaoverI_b;
% Define inertia matrix [M]
M_matrix = [I_thetaoverI_b + 1 + cos(2*phi), -sin(2*phi);
-sin(2*phi), I_psioverI_b + 1 – cos(2*phi)];
% Define damping matrix [D]
D11 = C_thetaoverI_b + h^2*gamma*H_u*(1 – cos(2*phi)) – gamma*M_b*(1 + cos(2*phi)) – (2 + 2*h*gamma*M_u)*sin(2*phi);
D12 = h^2*gamma*H_u*sin(2*phi) + gamma*M_b*sin(2*phi) – 2*(1 + cos(2*phi)) – 2*h*gamma*M_u*cos(2*phi);
D21 = h^2*gamma*H_u*sin(2*phi) + gamma*M_b*sin(2*phi) + 2*(1 – cos(2*phi)) – 2*h*gamma*M_u*cos(2*phi);
D22 = C_psioverI_b + h^2*gamma*H_u*(1 + cos(2*phi)) – gamma*M_b*(1 – cos(2*phi)) + (2 + 2*h*gamma*M_u)*sin(2*phi);
D_matrix = [D11, D12;
D21, D22];
% Define stiffness matrix [K]
K11 = K_theta – h*gamma*V*H_u*(1 – cos(2*phi)) + gamma*V*M_u*sin(2*phi);
K12 = -h*V*gamma*H_u*sin(2*phi) + gamma*V*M_u*(1 + cos(2*phi));
K21 = -h*gamma*V*H_u*sin(2*phi) – gamma*V*M_u*(1 – cos(2*phi));
K22 = K_psi – h*gamma*V*H_u*(1 + cos(2*phi)) – gamma*V*M_u*sin(2*phi);
K_matrix = [K11, K12;
K21, K22];
% Compute the system matrices
M11 = M_matrix(1, 1); M12 = M_matrix(1, 2); M21 = M_matrix(2, 1); M22 = M_matrix(2, 2);
D11 = D_matrix(1, 1); D12 = D_matrix(1, 2); D21 = D_matrix(2, 1); D22 = D_matrix(2, 2);
K11 = K_matrix(1, 1); K12 = K_matrix(1, 2); K21 = K_matrix(2, 1); K22 = K_matrix(2, 2);
% Find the roots of the polynomial equation
P0 = M11*M22-M12*M21;
P1 = (- D11*M22*1j – D22*M11*1j + M12*D21*j + D12*M21*j);
P2 = (D11*D22*(1j)^2 – K22*M11 – K11*M22 – D12*D21*(1j)^2 + M12*K21 + M21*K12);
P3 = (D11*K22*1j – D12*K21*1j – D21*K12*1j + D22*K11*1j);
P4 = K11*K22 – K12*K21;
P = roots([P0, P1, P2, P3, P4]);
r = 1 * P;
%Flutter
for k = 1:length(r)
if (real(r(k)) > 0)
if (imag(r(k)) <= 0)
unstable = [unstable; phi, K_psi, K_theta];
% Proximity check for 1/Ω divergence
if abs(real(r(k)) – freq_1_over_Omega) < 0.1
Rdivergence_map = [Rdivergence_map; phi, K_psi, K_theta];
end
end
end
end
%Divergence
if (real(det(K_matrix)) < 0)
divergence_map = [divergence_map; phi, K_psi, K_theta];
end
end
end
end
% Plotting section
figure;
hold on;
scatter(unstable(:,2), unstable(:,3), ‘filled’);
scatter(divergence_map(:,2), divergence_map(:,3), ‘filled’, ‘r’);
scatter(Rdivergence_map(:,2), Rdivergence_map(:,3), ‘filled’, ‘g’);
xlabel(‘K_psi’);
ylabel(‘K_theta’);
title(‘Whirl Flutter Diagram’);
legend(‘Flutter area’, ‘Divergence area’, ‘1/Ω Divergence area’);
hold off;I’m trying to analyze the stability regions of the equation of motion of a system with periodic coefficients (whirlflutter). I have only written its polynomial equation and examined its roots, but my plot did not match the reference implementation correctly.
If I wanna write the Hill function and use the ode45 code, is it possible for someone to correct my code and my approach to finding the monodromy matrix and calculating the Floquet multipliers?
clc;
clear all;
% Define parameters
N = 2; % Number of blades
I_thetaoverI_b = 2; % Moment of inertia pitch axis over I_b
I_psioverI_b = 2; % Moment of inertia yaw axis over I_b
C_thetaoverI_b = 0.00; % Damping coefficient over I_b
C_psioverI_b = 0.00; % Damping coefficient over I_b
h = 0.3; % rotor mast height, wing tip spar to rotor hub [m]
hoverR = 0.34;
R = h / hoverR; % radius [m]
gamma = 4; % lock number
V_knots = 325; % the rotor forward velocity [knots]
% Convert velocity from [knots] to [meters per second]
% 1 knot = 0.51444 meters per second
V = V_knots * 0.51444;
% Calculate angular velocity in radians per second
omega_rad_s = V / R;
% Convert angular velocity from radians per second to RPM
% 1 radian per second = (60 / (2 * pi)) RPM
Omega = omega_rad_s * (60 / (2 * pi));
freq_1_over_Omega = 1 / Omega;
%the flap moment aerodynamic coefficients for large V
M_b = -(1/10)*V;
M_u = 1/6;
%the propeller aerodynamic coefficients
H_u = V/2;
% Frequency ranges
f_pitch= 5:3:140;
f_yaw= 5:3:140;
divergence_map = [];
Rdivergence_map = [];
unstable = [];
% Modify the loop to iterate over time points
for i = 1:length(f_pitch)
for j = 1:length(f_yaw)
phi_steps = linspace(0, pi, 100); % Time steps within one period
for phi = phi_steps
% Angular frequencies for the current time point
w_omega_pitch = 2 * pi* f_pitch(i);
w_omega_yaw = 2 * pi * f_yaw(j);
K_psi = (w_omega_pitch^2) * I_psioverI_b;
K_theta = (w_omega_yaw^2) * I_thetaoverI_b;
% Define inertia matrix [M]
M_matrix = [I_thetaoverI_b + 1 + cos(2*phi), -sin(2*phi);
-sin(2*phi), I_psioverI_b + 1 – cos(2*phi)];
% Define damping matrix [D]
D11 = C_thetaoverI_b + h^2*gamma*H_u*(1 – cos(2*phi)) – gamma*M_b*(1 + cos(2*phi)) – (2 + 2*h*gamma*M_u)*sin(2*phi);
D12 = h^2*gamma*H_u*sin(2*phi) + gamma*M_b*sin(2*phi) – 2*(1 + cos(2*phi)) – 2*h*gamma*M_u*cos(2*phi);
D21 = h^2*gamma*H_u*sin(2*phi) + gamma*M_b*sin(2*phi) + 2*(1 – cos(2*phi)) – 2*h*gamma*M_u*cos(2*phi);
D22 = C_psioverI_b + h^2*gamma*H_u*(1 + cos(2*phi)) – gamma*M_b*(1 – cos(2*phi)) + (2 + 2*h*gamma*M_u)*sin(2*phi);
D_matrix = [D11, D12;
D21, D22];
% Define stiffness matrix [K]
K11 = K_theta – h*gamma*V*H_u*(1 – cos(2*phi)) + gamma*V*M_u*sin(2*phi);
K12 = -h*V*gamma*H_u*sin(2*phi) + gamma*V*M_u*(1 + cos(2*phi));
K21 = -h*gamma*V*H_u*sin(2*phi) – gamma*V*M_u*(1 – cos(2*phi));
K22 = K_psi – h*gamma*V*H_u*(1 + cos(2*phi)) – gamma*V*M_u*sin(2*phi);
K_matrix = [K11, K12;
K21, K22];
% Compute the system matrices
M11 = M_matrix(1, 1); M12 = M_matrix(1, 2); M21 = M_matrix(2, 1); M22 = M_matrix(2, 2);
D11 = D_matrix(1, 1); D12 = D_matrix(1, 2); D21 = D_matrix(2, 1); D22 = D_matrix(2, 2);
K11 = K_matrix(1, 1); K12 = K_matrix(1, 2); K21 = K_matrix(2, 1); K22 = K_matrix(2, 2);
% Find the roots of the polynomial equation
P0 = M11*M22-M12*M21;
P1 = (- D11*M22*1j – D22*M11*1j + M12*D21*j + D12*M21*j);
P2 = (D11*D22*(1j)^2 – K22*M11 – K11*M22 – D12*D21*(1j)^2 + M12*K21 + M21*K12);
P3 = (D11*K22*1j – D12*K21*1j – D21*K12*1j + D22*K11*1j);
P4 = K11*K22 – K12*K21;
P = roots([P0, P1, P2, P3, P4]);
r = 1 * P;
%Flutter
for k = 1:length(r)
if (real(r(k)) > 0)
if (imag(r(k)) <= 0)
unstable = [unstable; phi, K_psi, K_theta];
% Proximity check for 1/Ω divergence
if abs(real(r(k)) – freq_1_over_Omega) < 0.1
Rdivergence_map = [Rdivergence_map; phi, K_psi, K_theta];
end
end
end
end
%Divergence
if (real(det(K_matrix)) < 0)
divergence_map = [divergence_map; phi, K_psi, K_theta];
end
end
end
end
% Plotting section
figure;
hold on;
scatter(unstable(:,2), unstable(:,3), ‘filled’);
scatter(divergence_map(:,2), divergence_map(:,3), ‘filled’, ‘r’);
scatter(Rdivergence_map(:,2), Rdivergence_map(:,3), ‘filled’, ‘g’);
xlabel(‘K_psi’);
ylabel(‘K_theta’);
title(‘Whirl Flutter Diagram’);
legend(‘Flutter area’, ‘Divergence area’, ‘1/Ω Divergence area’);
hold off; I’m trying to analyze the stability regions of the equation of motion of a system with periodic coefficients (whirlflutter). I have only written its polynomial equation and examined its roots, but my plot did not match the reference implementation correctly.
If I wanna write the Hill function and use the ode45 code, is it possible for someone to correct my code and my approach to finding the monodromy matrix and calculating the Floquet multipliers?
clc;
clear all;
% Define parameters
N = 2; % Number of blades
I_thetaoverI_b = 2; % Moment of inertia pitch axis over I_b
I_psioverI_b = 2; % Moment of inertia yaw axis over I_b
C_thetaoverI_b = 0.00; % Damping coefficient over I_b
C_psioverI_b = 0.00; % Damping coefficient over I_b
h = 0.3; % rotor mast height, wing tip spar to rotor hub [m]
hoverR = 0.34;
R = h / hoverR; % radius [m]
gamma = 4; % lock number
V_knots = 325; % the rotor forward velocity [knots]
% Convert velocity from [knots] to [meters per second]
% 1 knot = 0.51444 meters per second
V = V_knots * 0.51444;
% Calculate angular velocity in radians per second
omega_rad_s = V / R;
% Convert angular velocity from radians per second to RPM
% 1 radian per second = (60 / (2 * pi)) RPM
Omega = omega_rad_s * (60 / (2 * pi));
freq_1_over_Omega = 1 / Omega;
%the flap moment aerodynamic coefficients for large V
M_b = -(1/10)*V;
M_u = 1/6;
%the propeller aerodynamic coefficients
H_u = V/2;
% Frequency ranges
f_pitch= 5:3:140;
f_yaw= 5:3:140;
divergence_map = [];
Rdivergence_map = [];
unstable = [];
% Modify the loop to iterate over time points
for i = 1:length(f_pitch)
for j = 1:length(f_yaw)
phi_steps = linspace(0, pi, 100); % Time steps within one period
for phi = phi_steps
% Angular frequencies for the current time point
w_omega_pitch = 2 * pi* f_pitch(i);
w_omega_yaw = 2 * pi * f_yaw(j);
K_psi = (w_omega_pitch^2) * I_psioverI_b;
K_theta = (w_omega_yaw^2) * I_thetaoverI_b;
% Define inertia matrix [M]
M_matrix = [I_thetaoverI_b + 1 + cos(2*phi), -sin(2*phi);
-sin(2*phi), I_psioverI_b + 1 – cos(2*phi)];
% Define damping matrix [D]
D11 = C_thetaoverI_b + h^2*gamma*H_u*(1 – cos(2*phi)) – gamma*M_b*(1 + cos(2*phi)) – (2 + 2*h*gamma*M_u)*sin(2*phi);
D12 = h^2*gamma*H_u*sin(2*phi) + gamma*M_b*sin(2*phi) – 2*(1 + cos(2*phi)) – 2*h*gamma*M_u*cos(2*phi);
D21 = h^2*gamma*H_u*sin(2*phi) + gamma*M_b*sin(2*phi) + 2*(1 – cos(2*phi)) – 2*h*gamma*M_u*cos(2*phi);
D22 = C_psioverI_b + h^2*gamma*H_u*(1 + cos(2*phi)) – gamma*M_b*(1 – cos(2*phi)) + (2 + 2*h*gamma*M_u)*sin(2*phi);
D_matrix = [D11, D12;
D21, D22];
% Define stiffness matrix [K]
K11 = K_theta – h*gamma*V*H_u*(1 – cos(2*phi)) + gamma*V*M_u*sin(2*phi);
K12 = -h*V*gamma*H_u*sin(2*phi) + gamma*V*M_u*(1 + cos(2*phi));
K21 = -h*gamma*V*H_u*sin(2*phi) – gamma*V*M_u*(1 – cos(2*phi));
K22 = K_psi – h*gamma*V*H_u*(1 + cos(2*phi)) – gamma*V*M_u*sin(2*phi);
K_matrix = [K11, K12;
K21, K22];
% Compute the system matrices
M11 = M_matrix(1, 1); M12 = M_matrix(1, 2); M21 = M_matrix(2, 1); M22 = M_matrix(2, 2);
D11 = D_matrix(1, 1); D12 = D_matrix(1, 2); D21 = D_matrix(2, 1); D22 = D_matrix(2, 2);
K11 = K_matrix(1, 1); K12 = K_matrix(1, 2); K21 = K_matrix(2, 1); K22 = K_matrix(2, 2);
% Find the roots of the polynomial equation
P0 = M11*M22-M12*M21;
P1 = (- D11*M22*1j – D22*M11*1j + M12*D21*j + D12*M21*j);
P2 = (D11*D22*(1j)^2 – K22*M11 – K11*M22 – D12*D21*(1j)^2 + M12*K21 + M21*K12);
P3 = (D11*K22*1j – D12*K21*1j – D21*K12*1j + D22*K11*1j);
P4 = K11*K22 – K12*K21;
P = roots([P0, P1, P2, P3, P4]);
r = 1 * P;
%Flutter
for k = 1:length(r)
if (real(r(k)) > 0)
if (imag(r(k)) <= 0)
unstable = [unstable; phi, K_psi, K_theta];
% Proximity check for 1/Ω divergence
if abs(real(r(k)) – freq_1_over_Omega) < 0.1
Rdivergence_map = [Rdivergence_map; phi, K_psi, K_theta];
end
end
end
end
%Divergence
if (real(det(K_matrix)) < 0)
divergence_map = [divergence_map; phi, K_psi, K_theta];
end
end
end
end
% Plotting section
figure;
hold on;
scatter(unstable(:,2), unstable(:,3), ‘filled’);
scatter(divergence_map(:,2), divergence_map(:,3), ‘filled’, ‘r’);
scatter(Rdivergence_map(:,2), Rdivergence_map(:,3), ‘filled’, ‘g’);
xlabel(‘K_psi’);
ylabel(‘K_theta’);
title(‘Whirl Flutter Diagram’);
legend(‘Flutter area’, ‘Divergence area’, ‘1/Ω Divergence area’);
hold off; stability theory, floquet theorem, characteristic multiplier, periodic coeffcients MATLAB Answers — New Questions
