I am trying to solve for heat transfer coefficients so that I can use them to calculate gas and tank wall temperatures. Currently, I am using constant heat transfer coefficients to enable the calculation of the desired temperatures with ode45.
% Convective heat transfer coefficients
h_in = 25; % Convective heat transfer coefficient inside the tank (W/(m^2·K))
h_out = 6; % Convective heat transfer coefficient outside the tank (W/(m^2·K))

% Solve the coupled ODEs and PDE
[t, y] = ode45(@(t, y) odes(t, y, R, C_p_gas, k_liner, k_CFRP, rho_liner, rho_CFRP, C_p_liner, C_p_CFRP, h_in, h_out, T_ambient, A_in, A_out, dx_liner, dx_CFRP, N_liner, N_CFRP, V, mdot_out), tspan, initial_conditions);

% Extract time histories for inside and outside wall temperatures
T_wall_liner_interface = y(:,3); % Inside wall temperature (first point of the liner)
T_wall_CFRP_interface = y(:,end); % Outside wall temperature (last point of the CFRP layer)
What I would like to do is to use the output temperatures from the ode45 to solve for h_in and h_out, which will in turn be used by ode45 to solve for new temperatures. The h_in and h_out calculations look like this:
function [h_in, h_out]=heat_transfer_coefficients
Di = 0.230; % Internal diameter in meters
Do = 0.279; % External diameter in meters

T_gf = 266.17; %(K) – Type III hydrogen gas T at film temp
rho_gf = 39.16; %(kg/m^3) – Type III hydrogen gas density @ T_gf

Cp_gf = 0.01514*((T_gf/100)^4.789)+1002;
mu_gf = (-4.127*10^-7)*((T_gf/100)^2)+((7.258*10^-6)*(T_gf/100))+(4.094*10^-7);
lambda_gf = ((-3.77*10^-4)*(T_gf/100)^2)+((1.004*10^-2)*(T_gf/100))-(4.776*10^-4);
beta_gf = 1/T_gf; %volumetric thermal expansion coefficient
g = 9.81; %gravitational acceleration (m/s^2)

Rai = (rho_gf^2*(Di^3)*beta_gf*g*(T_wall_liner_interface-T_gas)*Cp_gf)/(mu_gf*lambda_gf); %Rayleigh number at inner wall

if Rai < 10^8
Nu_i = 1.15*Rai.^0.22;
else
Nu_i = 0.14*Rai.^0.333;
end

ki = 168.9e3; %thermal conductivity inside tank at T_g, Type III (W/m*K)
h_in = (Nu_i*ki)/Di;

T_ambf = 279.43; %(K) – Type III
rho_ambf = 1.263; %(kg/m^3) – density @ T_ambf
c_wind = 0; %(m/s) – cross wind velocity

Cp_ambf = 0.01514*((T_ambf/100)^4.789)+1002;
mu_ambf = (-4.127*10^-7)*((T_ambf/100)^2)+((7.258*10^-6)*(T_ambf/100))+(4.094*10^-7);
lambda_ambf = ((-3.77*10^-4)*(T_ambf/100)^2)+((1.004*10^-2)*(T_ambf/100))-(4.776*10^-4);
beta_ambf = 1/T_ambf; %volumetric thermal expansion coefficient

Re_Do = (rho_ambf*c_wind*Do)/(mu_ambf);
Pr_ambf = (mu_ambf*Cp_ambf)/lambda_ambf;

if Re_Do == 0
Nu_cylforced = 0;
Nu_sphforced = 0;
else
Nu_cylforced = 0.3+((0.62*(Re_Do^0.5)*(Pr_ambf^(1/3)))/(1+(0.4/((Pr_ambf))^(2/3)))^(1/4))…
*(1+(Re_Do/282000)^5/8)^4/5;
Nu_sphforced = 2 + ((Re_Do/4)+((3*10^-4)*Re_Do^1.6))^0.5;
end

Rao = (rho_ambf^2*(Do^3)*beta_ambf*g*(T_amb-T_wall_CFRP_interface)*Cp_ambf)/(mu_ambf*lambda_ambf); %Rayleigh number at inner wall

Nu_oforced = ((Nu_cylforced*0.72)+(Nu_sphforced*0.24))/(0.72+0.24);
Nu_ofree = (0.6+((0.387*(Rao.^(1/6)))/(1+(0.559/Pr_ambf)^(9/16)).^(8/27))).^2;
Nu_o = ((Nu_ofree.^4)+(Nu_oforced^4)).^0.25;

ko = 24.84e3; %thermal conductivity outside tank at T_wo, Type III (W/m*K)
h_out = (Nu_o*ko)/Do;
end
As can be seen in the above code, the h_in and h_out calculations require T_gas, T_wall_liner interface and T_wall_CFRP_interface, which currently are being calculated after h_in and h_out. Can somebody please show me a way I can incorporate the h_in and h_out calculations into the first section of code? For ease of explanation and solving, please feel free to use your own numbers for the constants in the ode45 solver line.I am trying to solve for heat transfer coefficients so that I can use them to calculate gas and tank wall temperatures. Currently, I am using constant heat transfer coefficients to enable the calculation of the desired temperatures with ode45.
% Convective heat transfer coefficients
h_in = 25; % Convective heat transfer coefficient inside the tank (W/(m^2·K))
h_out = 6; % Convective heat transfer coefficient outside the tank (W/(m^2·K))

% Solve the coupled ODEs and PDE
[t, y] = ode45(@(t, y) odes(t, y, R, C_p_gas, k_liner, k_CFRP, rho_liner, rho_CFRP, C_p_liner, C_p_CFRP, h_in, h_out, T_ambient, A_in, A_out, dx_liner, dx_CFRP, N_liner, N_CFRP, V, mdot_out), tspan, initial_conditions);

% Extract time histories for inside and outside wall temperatures
T_wall_liner_interface = y(:,3); % Inside wall temperature (first point of the liner)
T_wall_CFRP_interface = y(:,end); % Outside wall temperature (last point of the CFRP layer)
What I would like to do is to use the output temperatures from the ode45 to solve for h_in and h_out, which will in turn be used by ode45 to solve for new temperatures. The h_in and h_out calculations look like this:
function [h_in, h_out]=heat_transfer_coefficients
Di = 0.230; % Internal diameter in meters
Do = 0.279; % External diameter in meters

T_gf = 266.17; %(K) – Type III hydrogen gas T at film temp
rho_gf = 39.16; %(kg/m^3) – Type III hydrogen gas density @ T_gf

Cp_gf = 0.01514*((T_gf/100)^4.789)+1002;
mu_gf = (-4.127*10^-7)*((T_gf/100)^2)+((7.258*10^-6)*(T_gf/100))+(4.094*10^-7);
lambda_gf = ((-3.77*10^-4)*(T_gf/100)^2)+((1.004*10^-2)*(T_gf/100))-(4.776*10^-4);
beta_gf = 1/T_gf; %volumetric thermal expansion coefficient
g = 9.81; %gravitational acceleration (m/s^2)

Rai = (rho_gf^2*(Di^3)*beta_gf*g*(T_wall_liner_interface-T_gas)*Cp_gf)/(mu_gf*lambda_gf); %Rayleigh number at inner wall

if Rai < 10^8
Nu_i = 1.15*Rai.^0.22;
else
Nu_i = 0.14*Rai.^0.333;
end

ki = 168.9e3; %thermal conductivity inside tank at T_g, Type III (W/m*K)
h_in = (Nu_i*ki)/Di;

T_ambf = 279.43; %(K) – Type III
rho_ambf = 1.263; %(kg/m^3) – density @ T_ambf
c_wind = 0; %(m/s) – cross wind velocity

Cp_ambf = 0.01514*((T_ambf/100)^4.789)+1002;
mu_ambf = (-4.127*10^-7)*((T_ambf/100)^2)+((7.258*10^-6)*(T_ambf/100))+(4.094*10^-7);
lambda_ambf = ((-3.77*10^-4)*(T_ambf/100)^2)+((1.004*10^-2)*(T_ambf/100))-(4.776*10^-4);
beta_ambf = 1/T_ambf; %volumetric thermal expansion coefficient

Re_Do = (rho_ambf*c_wind*Do)/(mu_ambf);
Pr_ambf = (mu_ambf*Cp_ambf)/lambda_ambf;

if Re_Do == 0
Nu_cylforced = 0;
Nu_sphforced = 0;
else
Nu_cylforced = 0.3+((0.62*(Re_Do^0.5)*(Pr_ambf^(1/3)))/(1+(0.4/((Pr_ambf))^(2/3)))^(1/4))…
*(1+(Re_Do/282000)^5/8)^4/5;
Nu_sphforced = 2 + ((Re_Do/4)+((3*10^-4)*Re_Do^1.6))^0.5;
end

Rao = (rho_ambf^2*(Do^3)*beta_ambf*g*(T_amb-T_wall_CFRP_interface)*Cp_ambf)/(mu_ambf*lambda_ambf); %Rayleigh number at inner wall

Nu_oforced = ((Nu_cylforced*0.72)+(Nu_sphforced*0.24))/(0.72+0.24);
Nu_ofree = (0.6+((0.387*(Rao.^(1/6)))/(1+(0.559/Pr_ambf)^(9/16)).^(8/27))).^2;
Nu_o = ((Nu_ofree.^4)+(Nu_oforced^4)).^0.25;

ko = 24.84e3; %thermal conductivity outside tank at T_wo, Type III (W/m*K)
h_out = (Nu_o*ko)/Do;
end
As can be seen in the above code, the h_in and h_out calculations require T_gas, T_wall_liner interface and T_wall_CFRP_interface, which currently are being calculated after h_in and h_out. Can somebody please show me a way I can incorporate the h_in and h_out calculations into the first section of code? For ease of explanation and solving, please feel free to use your own numbers for the constants in the ode45 solver line. I am trying to solve for heat transfer coefficients so that I can use them to calculate gas and tank wall temperatures. Currently, I am using constant heat transfer coefficients to enable the calculation of the desired temperatures with ode45.
% Convective heat transfer coefficients
h_in = 25; % Convective heat transfer coefficient inside the tank (W/(m^2·K))
h_out = 6; % Convective heat transfer coefficient outside the tank (W/(m^2·K))

% Solve the coupled ODEs and PDE
[t, y] = ode45(@(t, y) odes(t, y, R, C_p_gas, k_liner, k_CFRP, rho_liner, rho_CFRP, C_p_liner, C_p_CFRP, h_in, h_out, T_ambient, A_in, A_out, dx_liner, dx_CFRP, N_liner, N_CFRP, V, mdot_out), tspan, initial_conditions);

% Extract time histories for inside and outside wall temperatures
T_wall_liner_interface = y(:,3); % Inside wall temperature (first point of the liner)
T_wall_CFRP_interface = y(:,end); % Outside wall temperature (last point of the CFRP layer)
What I would like to do is to use the output temperatures from the ode45 to solve for h_in and h_out, which will in turn be used by ode45 to solve for new temperatures. The h_in and h_out calculations look like this:
function [h_in, h_out]=heat_transfer_coefficients
Di = 0.230; % Internal diameter in meters
Do = 0.279; % External diameter in meters

T_gf = 266.17; %(K) – Type III hydrogen gas T at film temp
rho_gf = 39.16; %(kg/m^3) – Type III hydrogen gas density @ T_gf

Cp_gf = 0.01514*((T_gf/100)^4.789)+1002;
mu_gf = (-4.127*10^-7)*((T_gf/100)^2)+((7.258*10^-6)*(T_gf/100))+(4.094*10^-7);
lambda_gf = ((-3.77*10^-4)*(T_gf/100)^2)+((1.004*10^-2)*(T_gf/100))-(4.776*10^-4);
beta_gf = 1/T_gf; %volumetric thermal expansion coefficient
g = 9.81; %gravitational acceleration (m/s^2)

Rai = (rho_gf^2*(Di^3)*beta_gf*g*(T_wall_liner_interface-T_gas)*Cp_gf)/(mu_gf*lambda_gf); %Rayleigh number at inner wall

if Rai < 10^8
Nu_i = 1.15*Rai.^0.22;
else
Nu_i = 0.14*Rai.^0.333;
end

ki = 168.9e3; %thermal conductivity inside tank at T_g, Type III (W/m*K)
h_in = (Nu_i*ki)/Di;

T_ambf = 279.43; %(K) – Type III
rho_ambf = 1.263; %(kg/m^3) – density @ T_ambf
c_wind = 0; %(m/s) – cross wind velocity

Cp_ambf = 0.01514*((T_ambf/100)^4.789)+1002;
mu_ambf = (-4.127*10^-7)*((T_ambf/100)^2)+((7.258*10^-6)*(T_ambf/100))+(4.094*10^-7);
lambda_ambf = ((-3.77*10^-4)*(T_ambf/100)^2)+((1.004*10^-2)*(T_ambf/100))-(4.776*10^-4);
beta_ambf = 1/T_ambf; %volumetric thermal expansion coefficient

Re_Do = (rho_ambf*c_wind*Do)/(mu_ambf);
Pr_ambf = (mu_ambf*Cp_ambf)/lambda_ambf;

if Re_Do == 0
Nu_cylforced = 0;
Nu_sphforced = 0;
else
Nu_cylforced = 0.3+((0.62*(Re_Do^0.5)*(Pr_ambf^(1/3)))/(1+(0.4/((Pr_ambf))^(2/3)))^(1/4))…
*(1+(Re_Do/282000)^5/8)^4/5;
Nu_sphforced = 2 + ((Re_Do/4)+((3*10^-4)*Re_Do^1.6))^0.5;
end

Rao = (rho_ambf^2*(Do^3)*beta_ambf*g*(T_amb-T_wall_CFRP_interface)*Cp_ambf)/(mu_ambf*lambda_ambf); %Rayleigh number at inner wall

Nu_oforced = ((Nu_cylforced*0.72)+(Nu_sphforced*0.24))/(0.72+0.24);
Nu_ofree = (0.6+((0.387*(Rao.^(1/6)))/(1+(0.559/Pr_ambf)^(9/16)).^(8/27))).^2;
Nu_o = ((Nu_ofree.^4)+(Nu_oforced^4)).^0.25;

ko = 24.84e3; %thermal conductivity outside tank at T_wo, Type III (W/m*K)
h_out = (Nu_o*ko)/Do;
end
As can be seen in the above code, the h_in and h_out calculations require T_gas, T_wall_liner interface and T_wall_CFRP_interface, which currently are being calculated after h_in and h_out. Can somebody please show me a way I can incorporate the h_in and h_out calculations into the first section of code? For ease of explanation and solving, please feel free to use your own numbers for the constants in the ode45 solver line. function MATLAB Answers — New Questions

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Hello, trying to understand which strategy works best here.
I am using the mapping toolbox for a research project, which includes the calculation of the average distance/time for traveling from a number of polygonal ROIs (e.g. area of cities) to a given destination. This will in turn provide a measurement for the average cost that residents in the ROI have to endure in order to travel to the destination.
My script goes:
Consider one boundary polygon and geolocate it.
Build a grid of equally spaced geopoints within the polygon.
For each point in step 2, query the OSRM routing engine to the destination and store the response.
Average the distances/times.
I don’t like the current version of step 4, as the algorithm is applying the same weight to points in downtown and in the deep countryside. I would like to achieve a weighted average, where the weights represent a measure of the probability that someone lives nearby a given query point. This basically means detecting residential areas.
I’m not sure how to tackle this, as I’ve never worked with image processing (I’m assuming that’s the way, but I could be wrong). I thought about using geobasemap in ‘streets-light’ mode, and process the amount of pure white color in the picture, which would represent a measure of how much public road exists in a given portion of my ROI. The more roads are present, the less likely it is that we’re in the deep countryside.
I’m pretty sure there are more appropriate workarounds. Has anyone encountered a similar problem? Thank you very much.Hello, trying to understand which strategy works best here.
I am using the mapping toolbox for a research project, which includes the calculation of the average distance/time for traveling from a number of polygonal ROIs (e.g. area of cities) to a given destination. This will in turn provide a measurement for the average cost that residents in the ROI have to endure in order to travel to the destination.
My script goes:
Consider one boundary polygon and geolocate it.
Build a grid of equally spaced geopoints within the polygon.
For each point in step 2, query the OSRM routing engine to the destination and store the response.
Average the distances/times.
I don’t like the current version of step 4, as the algorithm is applying the same weight to points in downtown and in the deep countryside. I would like to achieve a weighted average, where the weights represent a measure of the probability that someone lives nearby a given query point. This basically means detecting residential areas.
I’m not sure how to tackle this, as I’ve never worked with image processing (I’m assuming that’s the way, but I could be wrong). I thought about using geobasemap in ‘streets-light’ mode, and process the amount of pure white color in the picture, which would represent a measure of how much public road exists in a given portion of my ROI. The more roads are present, the less likely it is that we’re in the deep countryside.
I’m pretty sure there are more appropriate workarounds. Has anyone encountered a similar problem? Thank you very much. Hello, trying to understand which strategy works best here.
I am using the mapping toolbox for a research project, which includes the calculation of the average distance/time for traveling from a number of polygonal ROIs (e.g. area of cities) to a given destination. This will in turn provide a measurement for the average cost that residents in the ROI have to endure in order to travel to the destination.
My script goes:
Consider one boundary polygon and geolocate it.
Build a grid of equally spaced geopoints within the polygon.
For each point in step 2, query the OSRM routing engine to the destination and store the response.
Average the distances/times.
I don’t like the current version of step 4, as the algorithm is applying the same weight to points in downtown and in the deep countryside. I would like to achieve a weighted average, where the weights represent a measure of the probability that someone lives nearby a given query point. This basically means detecting residential areas.
I’m not sure how to tackle this, as I’ve never worked with image processing (I’m assuming that’s the way, but I could be wrong). I thought about using geobasemap in ‘streets-light’ mode, and process the amount of pure white color in the picture, which would represent a measure of how much public road exists in a given portion of my ROI. The more roads are present, the less likely it is that we’re in the deep countryside.
I’m pretty sure there are more appropriate workarounds. Has anyone encountered a similar problem? Thank you very much. image processing, matlab, figure, image analysis, geobasemap MATLAB Answers — New Questions

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