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# 05_curve_fitting | ||
# 05_Curve_Fitting | ||
ME 3255 Homework 5 | ||
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# Problem 2 | ||
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##Least Squares Function | ||
`` | ||
`setdefaults | ||
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% Part A | ||
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xa=[1 2 3 4 5]'; | ||
ya=[2.2 2.8 3.6 4.5 5.5]'; | ||
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Z=[ones(size(xa)) xa xa.^-1]; | ||
[a_a,fx_a,r2_a] = least_squares_2 (Z,ya) | ||
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xa_fcn = linspace(min(xa),max(xa)); | ||
h = figure(1); | ||
plot(xa,ya,'o',xa_fcn,a_a(1)+a_a(2)*xa_fcn+a_a(3)*xa_fcn.^-1) | ||
saveas(h, 'figure_10.png') | ||
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% Part B | ||
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xb=[0 2 4 6 8 10 12 14 16 18]'; | ||
yb=[21.5 20.84 23.19 22.69 30.27 40.11 43.31 54.79 70.88 89.48]'; | ||
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Z=[ones(size(xb)) xb xb.^2 xb.^3]; | ||
[a_b,fx_b,r2_b] = least_squares_2 (Z,yb) | ||
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xb_fcn = linspace(min(xb),max(xb)); | ||
b = figure(2); | ||
plot(xb,yb,'o',xb_fcn,a_b(1)+a_b(2)*xb_fcn.^1+a_b(3)*xb_fcn.^2+a_b(4)*xb_fcn.^3) | ||
saveas(b, 'figure_11.png') | ||
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% Part C | ||
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xc=[0.5 1 2 3 4 5 6 7 9]'; | ||
yc=[6 4.4 3.2 2.7 2.2 1.9 1.7 1.4 1.1]'; | ||
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Z=[exp(-1.5*xc), exp(-0.3*xc), exp(-0.05*xc)]; | ||
[a_c,fx_c,r2_c] = least_squares_2 (Z,yc) | ||
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xc_function = linspace(min(xc),max(xc)); | ||
c = figure(3); | ||
plot(xc,yc,'o',xc_function,a_c(1)*exp(-1.5.*xc_function) + a_c(2)*exp(-0.3.*xc_function) + a_c(3)*exp(-0.05.*xc_function)); | ||
saveas(c, 'figure_12.png') | ||
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% Part D | ||
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xd=[0.00000000e+00 1.26933037e-01 2.53866073e-01 3.80799110e-01 5.07732146e-01 6.34665183e-01 7.61598219e-01 8.88531256e-01 1.01546429e+00 1.14239733e+00 1.26933037e+00 1.39626340e+00 1.52319644e+00 1.65012947e+00 1.77706251e+00 1.90399555e+00 2.03092858e+00 2.15786162e+00 2.28479466e+00 2.41172769e+00 2.53866073e+00 2.66559377e+00 2.79252680e+00 2.91945984e+00 3.04639288e+00 3.17332591e+00 3.30025895e+00 3.42719199e+00 3.55412502e+00 3.68105806e+00 3.80799110e+00 3.93492413e+00 4.06185717e+00 4.18879020e+00 4.31572324e+00 4.44265628e+00 4.56958931e+00 4.69652235e+00 4.82345539e+00 4.95038842e+00 5.07732146e+00 5.20425450e+00 5.33118753e+00 5.45812057e+00 5.58505361e+00 5.71198664e+00 5.83891968e+00 5.96585272e+00 6.09278575e+00 6.21971879e+00 6.34665183e+00 6.47358486e+00 6.60051790e+00 6.72745093e+00 6.85438397e+00 6.98131701e+00 7.10825004e+00 7.23518308e+00 7.36211612e+00 7.48904915e+00 7.61598219e+00 7.74291523e+00 7.86984826e+00 7.99678130e+00 8.12371434e+00 8.25064737e+00 8.37758041e+00 8.50451345e+00 8.63144648e+00 8.75837952e+00 8.88531256e+00 9.01224559e+00 9.13917863e+00 9.26611167e+00 9.39304470e+00 9.51997774e+00 9.64691077e+00 9.77384381e+00 9.90077685e+00 1.00277099e+01 1.01546429e+01 1.02815760e+01 1.04085090e+01 1.05354420e+01 1.06623751e+01 1.07893081e+01 1.09162411e+01 1.10431742e+01 1.11701072e+01 1.12970402e+01 1.14239733e+01 1.15509063e+01 1.16778394e+01 1.18047724e+01 1.19317054e+01 1.20586385e+01 1.21855715e+01 1.23125045e+01 1.24394376e+01 1.25663706e+01]'; | ||
yd=[9.15756288e-02 3.39393873e-01 6.28875306e-01 7.67713096e-01 1.05094584e+00 9.70887288e-01 9.84265740e-01 1.02589034e+00 8.53218113e-01 6.90197665e-01 5.51277193e-01 5.01564914e-01 5.25455797e-01 5.87052838e-01 5.41394658e-01 7.12365594e-01 8.14839678e-01 9.80181855e-01 9.44430709e-01 1.06728057e+00 1.15166322e+00 8.99464065e-01 7.77225453e-01 5.92618124e-01 3.08822183e-01 -1.07884730e-03 -3.46563271e-01 -5.64836023e-01 -8.11931510e-01 -1.05925186e+00 -1.13323611e+00 -1.11986890e+00 -8.88336727e-01 -9.54113139e-01 -6.81378679e-01 -6.02369117e-01 -4.78684439e-01 -5.88160325e-01 -4.93580777e-01 -5.68747320e-01 -7.51641934e-01 -8.14672884e-01 -9.53191554e-01 -9.55337518e-01 -9.85995556e-01 -9.63373597e-01 -1.01511061e+00 -7.56467517e-01 -4.17379564e-01 -1.22340361e-01 2.16273929e-01 5.16909714e-01 7.77031694e-01 1.00653798e+00 9.35718089e-01 1.00660116e+00 1.11177057e+00 9.85485116e-01 8.54344900e-01 6.26444042e-01 6.28124048e-01 4.27764254e-01 5.93991751e-01 4.79248018e-01 7.17522492e-01 7.35927848e-01 9.08802925e-01 9.38646871e-01 1.13125860e+00 1.07247935e+00 1.05198782e+00 9.41647332e-01 6.98801244e-01 4.03193543e-01 1.37009682e-01 -1.43203880e-01 -4.64369445e-01 -6.94978252e-01 -1.03483196e+00 -1.10261288e+00 -1.12892727e+00 -1.03902484e+00 -8.53573083e-01 -7.01815315e-01 -6.84745997e-01 -6.14189417e-01 -4.70090797e-01 -5.95052432e-01 -5.96497000e-01 -5.66861911e-01 -7.18239679e-01 -9.52873043e-01 -9.37512847e-01 -1.15782985e+00 -1.03858206e+00 -1.03182712e+00 -8.45121554e-01 -5.61821980e-01 -2.83427014e-01 -8.27056140e-02]'; | ||
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Z = [sin(xd), sin(3*xd)]; | ||
[a_d,fx_d,r2_d] = least_squares_2 (Z,yd) | ||
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xd_function = linspace(min(xd),max(xd)); | ||
d = figure(4); | ||
plot(xd,yd,'o',xd_function,a_d(1)*sin(xd_function)+a_d(2)*sin(3*xd_function)); | ||
saveas(d, 'figure_13.png') | ||
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``` | ||
##Output: | ||
```matlab | ||
Part A: r2 = 0.9801 | ||
Part B: r2 = 0.9232 | ||
Part C: r2 = 0.9462 | ||
Part D: r2 = 0.9219 | ||
Part A | ||
![Part A](./Figures/figure_10.png) | ||
Part B | ||
![Part B](./Figures/figure_11.png) | ||
Part C | ||
![Part C](./Figures/figure_12.png) | ||
Part D | ||
![Part D](./Figures/figure_13.png) | ||
``` | ||
# Problem 3 | ||
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```matlab | ||
dart = dlmread('compiled_data.csv',',',1,0); | ||
x_d =dart(:,2).*cosd(dart(:,3)); | ||
y_d =dart(:,2).*sind(dart(:,3)); | ||
ur = dart(:,1); | ||
i = 1; | ||
for r = 0:32 | ||
i_interest = find(ur==r); | ||
mx_ur(i)= mean(x_d(i_interest)); | ||
my_ur(i)= mean(y_d(i_interest)); | ||
i = i +1; | ||
end | ||
acc = mx_ur + my_ur; %accuracy | ||
val = 0; | ||
abs_acc = abs(acc-val); | ||
[~, index] = min(abs_acc) | ||
closest_value = accuracy(index) | ||
dart = dlmread('compiled_data.csv',',',1,0); | ||
x_d =dart(:,2).*cosd(dart(:,3)); | ||
y_d =dart(:,2).*sind(dart(:,3)); | ||
ur = dart(:,1); | ||
i = 1; | ||
for r = 0:32 | ||
i_interest = find(ur==r); | ||
stdx_ur(i)= std(x_d(i_interest)); | ||
stdy_ur(i)= std(y_d(i_interest)); | ||
i = i +1; | ||
end | ||
prec = stdx_ur + stdy_ur; %precision | ||
val = 0; | ||
abs_acc = abs(prec-val); | ||
[~, index] = min(abs_acc) | ||
closest_value = prec(index) | ||
``` | ||
Part A | ||
```matlab | ||
User number 32 was closest with a value of 0.0044. | ||
``` | ||
Part B | ||
```matlab | ||
User number 33 was the most precise with a standard deviation of 3.4073 | ||
``` | ||
# Problem 4 | ||
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||
Part A | ||
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```matlab | ||
function [mean_buckle_load,std_buckle_load]=buckle_monte_carlo(E,r_mean,r_std,L_mean,L_std); | ||
N=100; | ||
r_mean=0.01; | ||
r_std=.001; | ||
L_mean=5; | ||
L_std=L_mean*0.01; | ||
rrand=normrnd(r_mean,r_std,[N,1]); | ||
Lrand=normrnd(L_mean,L_std,[N,1]); | ||
E = 200*10^9; | ||
P_cr=(pi^3*E.*rrand.^4)./(16.*Lrand.^2); | ||
x1 = mean(P_cr) | ||
x2 = std(P_cr) | ||
P_test=1000*9.81/100; | ||
sum(P_cr<P_test) | ||
end | ||
``` | ||
```matlab | ||
Mean Buckle Load = 164.4154 N | ||
Standard Deviation = 63.6238 N | ||
``` | ||
Part B | ||
```matlab | ||
function [mean_buckle_load,std_buckle_load]=buckle_monte_carlo_2(E,r_mean,r_std,P_cr); | ||
N=100; | ||
r_mean=0.01; | ||
r_std=.001; | ||
P_test=1000*9.81/100; | ||
sum(2.5<P_test) | ||
P_cr = 164.4154; | ||
rrand=normrnd(r_mean,r_std,[N,1]); | ||
E = 200*10^9; | ||
L = ((pi^3*E.*rrand.^4)./(16*P_cr)).^0.5; | ||
mean(L) | ||
end | ||
``` | ||
Output | ||
```matlab | ||
L = 5.0108 m | ||
``` | ||
#Problem 5 | ||
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Part A | ||
```matlab | ||
function [Cd_out] = sphere_drag(Re_in,spline_type) | ||
data = [2 0.52 | ||
5.8 0.52 | ||
16.8 0.52 | ||
27.2 0.5 | ||
29.9 0.49 | ||
33.9 0.44 | ||
36.3 0.18 | ||
40 0.074 | ||
46 0.067 | ||
60 0.08 | ||
100 0.12 | ||
200 0.16 | ||
400 0.19]; | ||
Cd_out=interp1(data(:,1),data(:,2),Re_in,spline_type); | ||
rho=1.3;V=linspace(4,40);D=23.5e-2;mu=1.78e-5; | ||
Re=rho*V*D/mu*1e-4; | ||
end | ||
``` | ||
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```matlab | ||
Cd_out1 = sphere_drag(150,'linear') | ||
Cd_out2 = sphere_drag(150,'spline') | ||
Cd_out3 = sphere_drag(150,'pchip') | ||
``` | ||
Output | ||
```matlab | ||
Linear = 0.1400 | ||
Spline = 0.1428 | ||
Pchip = 0.1447 | ||
``` | ||
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Part B Code | ||
```matlab | ||
function [Cd_out] = sphere_drag(Re_in,spline_type) | ||
data = [2 0.52 | ||
5.8 0.52 | ||
16.8 0.52 | ||
27.2 0.5 | ||
29.9 0.49 | ||
33.9 0.44 | ||
36.3 0.18 | ||
40 0.074 | ||
46 0.067 | ||
60 0.08 | ||
100 0.12 | ||
200 0.16 | ||
400 0.19]; | ||
Cd_out=interp1(data(:,1),data(:,2),Re_in,spline_type) | ||
Re=linspace(data(1,1),data(end,1),1000); | ||
rho=1.3;V=linspace(4,40);D=23.5e-2;mu=1.78e-5; | ||
Re=rho*V*D/mu*1e-4; | ||
end | ||
``` | ||
Part B Script | ||
```matlab | ||
Re=linspace(data(1,1),data(end,1),1000); | ||
rho=1.3;V=linspace(4,40);D=23.5e-2;mu=1.78e-5; | ||
Re=rho*V*D/mu*1e-4 | ||
a=figure(1); | ||
cd_interp=sphere_drag(linspace(data(1,1),data(end,1),100),'linear'); | ||
plot(data(:,1),data(:,2),'o',Re,cd_interp,'b-','Linewidth',4,'Markersize',10) | ||
hold on; | ||
cd_interp=sphere_drag(linspace(data(1,1),data(end,1),100),'spline'); | ||
plot(Re,cd_interp,'r-', 'Linewidth',4,'Markersize',10) | ||
cd_interp=sphere_drag(linspace(data(1,1),data(end,1),100),'pchip'); | ||
plot(Re,cd_interp,'g-', 'Linewidth',4,'Markersize',10) | ||
legend('data','linear','spline','pchip') | ||
saveas(a,'figure_14.png') | ||
``` | ||
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Script | ||
```matlab | ||
Cd_out1 = sphere_drag(10,'linear') | ||
Cd_out2 = sphere_drag(10,'pchip') | ||
Cd_out3 = sphere_drag(10,'spline') | ||
plot(data(:,1),data(:,2),'o',Re,Cd_out,'b-','Linewidth',4,'Markersize',10) | ||
plot(data(:,1),data(:,2),Re,Cd_out2,'r-', 'Linewidth',4,'Markersize',10) | ||
plot(data(:,1),data(:,2),Re,Cd_out3,'g-', 'Linewidth',4,'Markersize',10) | ||
legend('data','linear','pchip','spline') | ||
``` | ||
![Part B](./Figures/figure_14.png) | ||
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# Problem 6 | ||
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##Analytical Solution | ||
```matlab | ||
fun = @(x) (1/6).*(x).^3+(1/2).*(x).^2+(x); | ||
q = integral(fun,2,3) | ||
``` | ||
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##Gauss Quadtrature | ||
```matlab | ||
%One Point | ||
x = [0]; | ||
c = [2]; | ||
a = 2; | ||
b = 3; | ||
tildec = (b-a)/2*c; | ||
tildex = (b-a)/2*x + (b+a)/2; | ||
f = (1/6).*(tildex).^3+(1/2).*(tildex).^2+(tildex); | ||
value1 = sum(tildec.*f) | ||
``` | ||
##Two Point | ||
``` | ||
%Two Point | ||
x = [-0.57735,0.57735]; | ||
c = [1,1]; | ||
a = 2; | ||
b = 3; | ||
tildec = (b-a)/2*c; | ||
tildex = (b-a)/2*x + (b+a)/2; | ||
f = (1/6).*(tildex).^3+(1/2).*(tildex).^2+(tildex); | ||
value2 = sum(tildec.*f) | ||
``` | ||
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##Three Point | ||
``` | ||
x = [-0.77459666,0,0.77459666]; | ||
c = [0.5555555,0.88888888,0.55555555]; | ||
a = 2; | ||
b = 3; | ||
tildec = (b-a)/2*c; | ||
tildex = (b-a)/2*x + (b+a)/2; | ||
f = (1/6).*(tildex).^3+(1/2).*(tildex).^2+(tildex); | ||
value3 = sum(tildec.*f) | ||
``` | ||
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``` | ||
matlab | ||
| Method | Value | Error | | ||
| Analytical | 8.3750 | 0% | | ||
| 1 Gauss Point | 8.2292 | 1.74% | | ||
| 2 Gauss Point | 8.3750 | 0% | | ||
| 3 Gauss Point | 8.3750 | 0% | | ||
``` |
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function [mean_buckle_load,std_buckle_load]=buckle_first_pass(E,r_mean,r_std,L_mean,L_std); | ||
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N = 100; | ||
E = 200*10^9; %Pa | ||
r = 0.01; | ||
L = 5;r_mean = 0.01; %meters | ||
r_std = r_mean*0.001; | ||
rmin = r_mean - r_mean*.001; | ||
rmax = r_mean + r_mean*.001; | ||
L_mean = 5; | ||
L_std = L_mean*0.01; | ||
Lmin = L_mean - L_mean*0.01; | ||
Lmax = L_mean + L_mean*0.01; | ||
rrand = normrnd(r_mean, r_std, [N,1]); | ||
Lrand = normrnd(L_mean, L_std, [N,1]); | ||
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P_cr=(pi^3*E.*rrand.^4)/(16.*Lrand.^2); | ||
mean(P_cr) | ||
std(P_cr) | ||
end |
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function [mean_buckle_load,std_buckle_load]=buckle_monte_carlo(E,r_mean,r_std,L_mean,L_std); | ||
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N=100; | ||
r_mean=0.01; | ||
r_std=.001; | ||
L_mean=5; | ||
L_std=L_mean*0.01; | ||
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rrand=normrnd(r_mean,r_std,[N,1]); | ||
Lrand=normrnd(L_mean,L_std,[N,1]); | ||
E = 200*10^9; | ||
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P_cr=(pi^3*E.*rrand.^4)./(16.*Lrand.^2); | ||
x1 = mean(P_cr) | ||
x2 = std(P_cr) | ||
P_test=1000*9.81/100; | ||
sum(P_cr<P_test) | ||
end |
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function [mean_buckle_load,std_buckle_load]=buckle_monte_carlo_2(E,r_mean,r_std,P_cr); | ||
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N=100; | ||
r_mean=0.01; | ||
r_std=.001; | ||
P_test=1000*9.81/100; | ||
sum(2.5<P_test) | ||
P_cr = 164.4154; | ||
rrand=normrnd(r_mean,r_std,[N,1]); | ||
E = 200*10^9; | ||
L = ((pi^3*E.*rrand.^4)./(16*P_cr)).^0.5; | ||
mean(L) | ||
end |
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