Question
In: Electrical Engineering
Write a MATLAB code to obtain the following. Keep your code commented whenever required. Copy your...
Write a MATLAB code to obtain the following. Keep your code commented whenever required. Copy your source code and command widow outcomes and screen shots of any plots in your solution.
Develop three functions for temperature-conversion.
- Create a function called F_to_K that converts and return temperatures in Fahrenheit to Kelvin and store results in ‘F_to_K2.txt’.
- Create a function called C_to_R that converts and return temperatures in Celsius to Rankine and store results in ‘C_to_R2.txt’.
- Create a function called C_to_F that converts and return temperatures in Celsius to Fahrenheit and store results in ‘C_to_F2.txt’.
Use the following equations to achieve these conversions.
1 degree Fahrenheit =255.928 Kelvin
1 degree Celsius =493.47 Rankine
1 degree Celsius =33.8 degrees Fahrenheit
- Write an appropriate MATLAB code to collectively call and test
these functions.
- It should import data from ‘Tempr2.txt’ and convey it to each of the above mentioned user defined functions. ‘Tempr2.txt’ is available on BlackBoard under the folder Final_Exam.
- It should display on the command window one conversion table for each case while displaying first 20 inputs and converted values.
- It should also display a table on the command window, which presents the mean, median, variance and standard deviation values according to the following Table.
|
Measurements |
Input Temperature |
Output of F_to_K |
Output of C_to_R |
Output of C_to_F |
|
Mean |
||||
|
Median |
||||
|
Variance |
||||
|
Standard Deviation |
Tempr2
70.000000 54.000000 -58.000000 -4.000000 -32.000000 17.000000 110.000000 -39.000000 102.000000 -18.000000 -28.000000 35.000000 40.000000 19.000000 -68.000000 -30.000000 -38.000000 -21.000000 -34.000000 26.000000 56.000000 -8.000000 33.000000 43.000000 12.000000 -9.000000 -22.000000 -53.000000 -9.000000 13.000000 -4.000000 16.000000 7.000000 39.000000 -27.000000 -1.000000 17.000000 -14.000000 19.000000 -84.000000 28.000000 28.000000 5.000000 -13.000000 -17.000000 -80.000000 60.000000 37.000000 -49.000000 -69.000000 -14.000000 39.000000 47.000000 32.000000 43.000000 93.000000 -80.000000 28.000000 2.000000 -32.000000 41.000000 5.000000 65.000000 -47.000000 -37.000000 27.000000 -39.000000 11.000000 -15.000000 60.000000 27.000000 -15.000000 -27.000000 -15.000000 125.000000 3.000000 -5.000000 -5.000000 81.000000 -82.000000 61.000000 -31.000000 -9.000000 39.000000 -42.000000 26.000000 1.000000 -65.000000 15.000000 34.000000 37.000000 -23.000000 4.000000 36.000000 32.000000 -14.000000 -59.000000 19.000000 21.000000 80.000000 -73.000000 -13.000000 27.000000 -0.000000 106.000000 -13.000000 -113.000000 41.000000 -65.000000 71.000000 25.000000 -12.000000 -44.000000 98.000000 20.000000 -19.000000 -30.000000 -4.000000 68.000000 101.000000 6.000000 26.000000 21.000000 -77.000000 12.000000 -20.000000 -16.000000 -3.000000 -140.000000 -32.000000 43.000000 11.000000 -21.000000 -12.000000 -49.000000 -30.000000 -24.000000 -4.000000 34.000000 4.000000 1.000000 -37.000000 54.000000 17.000000 33.000000 16.000000 45.000000 81.000000 -17.000000 32.000000 39.000000 5.000000 101.000000 -44.000000 -19.000000 9.000000 6.000000 -6.000000 -19.000000 46.000000 2.000000 -35.000000 91.000000 51.000000 79.000000 4.000000 101.000000 51.000000 -85.000000 -58.000000 34.000000 -1.000000 -2.000000 59.000000 90.000000 25.000000 11.000000 118.000000 4.000000 -18.000000 -25.000000 -32.000000 9.000000 -38.000000 -88.000000 -59.000000 27.000000 60.000000 65.000000 121.000000 -49.000000 73.000000 9.000000 -18.000000 -16.000000 49.000000 37.000000 -85.000000 56.000000 46.000000 -7.000000 -44.000000 -33.000000 -57.000000 2.000000 -14.000000 -11.000000 16.000000 -10.000000 -80.000000 -25.000000 -21.000000 -81.000000 49.000000 -103.000000 26.000000 -10.000000 -13.000000 37.000000 -10.000000 -6.000000 -39.000000 61.000000 55.000000 39.000000 32.000000 41.000000 40.000000 25.000000 37.000000 -30.000000 6.000000 33.000000 -31.000000 5.000000 -4.000000 67.000000 29.000000 60.000000 36.000000 55.000000 0.000000 11.000000 -26.000000 24.000000 159.000000 10.000000 3.000000 48.000000 -73.000000 61.000000 -37.000000 33.000000 19.000000 2.000000 -31.000000 26.000000 61.000000 39.000000 75.000000 -64.000000 6.000000 -63.000000 11.000000 32.000000 -33.000000 69.000000 -19.000000 36.000000 -12.000000 -33.000000 -32.000000 -68.000000 81.000000 -117.000000 -60.000000 -94.000000 -62.000000 -135.000000 35.000000 7.000000 -47.000000 -60.000000 -61.000000 -9.000000 -96.000000 25.000000 1.000000 -12.000000 38.000000 22.000000 -12.000000 7.000000 -2.000000 -26.000000 61.000000 65.000000 9.000000 -46.000000 -54.000000 18.000000 -8.000000 -36.000000 12.000000 -54.000000 22.000000 -75.000000 -118.000000 -5.000000 -44.000000 82.000000 74.000000 -26.000000 -43.000000 -6.000000 -11.000000 74.000000 -12.000000 65.000000 67.000000 131.000000 -14.000000 -19.000000 89.000000 -32.000000 -86.000000 9.000000 30.000000 -13.000000 -13.000000 -63.000000 91.000000 15.000000 -22.000000 25.000000 47.000000 -47.000000 16.000000 -19.000000 88.000000 13.000000 -51.000000 -1.000000 27.000000 110.000000 7.000000 34.000000 65.000000 30.000000 -38.000000 -3.000000 -28.000000 40.000000 82.000000 4.000000 58.000000 -30.000000 13.000000 -4.000000 87.000000 20.000000 43.000000 -12.000000 -27.000000 25.000000 -36.000000 -63.000000 -10.000000 85.000000 -15.000000 16.000000 -25.000000 -98.000000 5.000000 90.000000 -12.000000 -53.000000 -45.000000 23.000000 -16.000000 54.000000 -9.000000 -99.000000 -17.000000 41.000000 56.000000 69.000000 55.000000 59.000000 100.000000 82.000000 14.000000 -19.000000 -9.000000 40.000000 65.000000 -7.000000 81.000000 -31.000000 -18.000000 -14.000000 -28.000000 48.000000 -4.000000 98.000000 5.000000 98.000000 -116.000000 23.000000 19.000000 102.000000 18.000000 94.000000 100.000000 -26.000000 -55.000000 48.000000 -36.000000 -3.000000 -24.000000 10.000000 35.000000 41.000000 12.000000 -42.000000 -37.000000 20.000000 -104.000000 -54.000000 -73.000000 -11.000000 29.000000 123.000000 5.000000 -48.000000 -18.000000 28.000000 56.000000 -18.000000 5.000000 64.000000 16.000000 9.000000 -12.000000 -14.000000 58.000000 -22.000000 3.000000 -47.000000 35.000000 17.000000 27.000000 4.000000 -32.000000 1.000000 -59.000000 -12.000000 120.000000 89.000000 48.000000 27.000000 46.000000 24.000000 52.000000 65.000000 -46.000000 -5.000000 4.000000 -23.000000 -45.000000 69.000000 38.000000 -41.000000 -48.000000 26.000000 -29.000000 -55.000000 -7.000000 -37.000000 47.000000 120.000000 -80.000000 47.000000 26.000000 -37.000000 53.000000 -39.000000 -89.000000 -51.000000 34.000000 26.000000 55.000000 30.000000 68.000000 -95.000000 -10.000000 -13.000000 2.000000 58.000000 -29.000000 18.000000 -91.000000 -41.000000 52.000000 -82.000000 76.000000 -19.000000 31.000000 -54.000000 43.000000 5.000000 68.000000 -22.000000 -32.000000 -33.000000 104.000000 -11.000000 -75.000000 52.000000 6.000000 -13.000000 31.000000 49.000000 -27.000000 11.000000 -11.000000 -24.000000 29.000000 41.000000 -64.000000 61.000000 12.000000 -7.000000 -74.000000 -8.000000 2.000000 10.000000 -17.000000 51.000000 83.000000 67.000000 -7.000000 22.000000 25.000000 -26.000000 4.000000 -24.000000 8.000000 86.000000 -4.000000 -32.000000 74.000000 49.000000 -21.000000 -32.000000 76.000000 7.000000 14.000000 22.000000 -26.000000 59.000000 123.000000 -119.000000 15.000000 52.000000 -35.000000 9.000000 14.000000 -25.000000 40.000000 66.000000 16.000000 23.000000 -22.000000 61.000000 0.000000 27.000000 64.000000 -10.000000 -5.000000 10.000000 -6.000000 -58.000000 29.000000 -67.000000 -10.000000 -65.000000 70.000000 9.000000 29.000000 -8.000000 84.000000 -15.000000 63.000000 63.000000 -21.000000 26.000000 11.000000 111.000000 -45.000000 -74.000000 21.000000 -43.000000 -24.000000 22.000000 -60.000000 59.000000 26.000000 76.000000 -11.000000 51.000000 -51.000000 17.000000 61.000000 -27.000000 9.000000 87.000000 -34.000000 -51.000000 52.000000 4.000000 -50.000000 -40.000000 22.000000 17.000000 -13.000000 16.000000 33.000000 -32.000000 -11.000000 -31.000000 72.000000 -59.000000 86.000000 -23.000000 5.000000 53.000000 42.000000 -12.000000 49.000000 -54.000000 74.000000 63.000000 6.000000 25.000000 54.000000 -11.000000 32.000000 39.000000 24.000000 -66.000000 -12.000000 -94.000000 -14.000000 -34.000000 0.000000 24.000000 47.000000 -2.000000 7.000000 56.000000 70.000000 16.000000 48.000000 -31.000000 84.000000 -28.000000 -33.000000 32.000000 2.000000 -74.000000 59.000000 40.000000 87.000000 20.000000 40.000000 -53.000000 98.000000 124.000000 -14.000000 -30.000000 55.000000 -52.000000 13.000000 -2.000000 46.000000 -93.000000 11.000000 18.000000 -84.000000 -3.000000 36.000000 -59.000000 -10.000000 15.000000 -21.000000 84.000000 43.000000 -24.000000 22.000000 61.000000 17.000000 33.000000 37.000000 42.000000 -61.000000 -29.000000 -13.000000 -75.000000 -65.000000 23.000000 23.000000 51.000000 23.000000 -33.000000 4.000000 -31.000000 48.000000 -71.000000 48.000000 30.000000 44.000000 -24.000000 15.000000 3.000000 -71.000000 29.000000 27.000000 -32.000000 67.000000 -83.000000 -16.000000 -15.000000 -31.000000 -28.000000 29.000000 -39.000000 8.000000 -16.000000 -1.000000 102.000000 -22.000000 49.000000 7.000000 7.000000 -90.000000 56.000000 13.000000 -17.000000 7.000000 -0.000000 97.000000 -50.000000 -23.000000 18.000000 98.000000 18.000000 61.000000 43.000000 41.000000 69.000000 120.000000 12.000000 -10.000000 14.000000 -10.000000 -111.000000 -1.000000 13.000000 -13.000000 -6.000000 67.000000 9.000000 4.000000 -28.000000 -23.000000 -0.000000 11.000000 24.000000 87.000000 73.000000 -55.000000 93.000000 -44.000000 134.000000 15.000000 -17.000000 -96.000000 -15.000000 82.000000 -16.000000 -18.000000 -61.000000 -54.000000 -24.000000 23.000000 -98.000000 38.000000 -19.000000 45.000000 5.000000 -17.000000 30.000000 30.000000 106.000000 -60.000000 -30.000000 -56.000000 -24.000000 -20.000000 -17.000000 -18.000000 -21.000000 -7.000000 26.000000 61.000000 -13.000000 96.000000 49.000000 66.000000 -44.000000 -86.000000 21.000000 22.000000 -80.000000 -69.000000 23.000000 57.000000 73.000000 -41.000000 35.000000 36.000000 3.000000 -48.000000 -50.000000 42.000000 -79.000000 -10.000000 12.000000 60.000000 -13.000000 -65.000000 22.000000 -31.000000 94.000000 -38.000000 14.000000 107.000000 58.000000 4.000000 29.000000 125.000000 75.000000 -61.000000 14.000000 58.000000 -72.000000 19.000000 2.000000 19.000000 -35.000000 -86.000000 -49.000000 99.000000 -1.000000 -43.000000 78.000000 51.000000 -38.000000 59.000000 61.000000 -34.000000 32.000000 -14.000000 68.000000 -58.000000 -15.000000 46.000000 1.000000 34.000000 92.000000 -16.000000 -50.000000 83.000000 -17.000000 21.000000 -24.000000 -73.000000 -42.000000 71.000000 -16.000000 -3.000000 20.000000 64.000000 13.000000 -8.000000 -38.000000 34.000000 -99.000000 -54.000000 42.000000 27.000000 -59.000000 32.000000 28.000000 -54.000000 1.000000 85.000000 -20.000000 -62.000000 13.000000 -23.000000 -13.000000 26.000000 2.000000 -14.000000 -47.000000 -36.000000 -46.000000 -25.000000 93.000000 12.000000 -22.000000 -1.000000 17.000000 19.000000 21.000000 60.000000 -12.000000 23.000000 -26.000000 -88.000000 -24.000000 76.000000 -13.000000 56.000000 35.000000 -25.000000 101.000000 49.000000 50.000000 -40.000000 -27.000000 37.000000 65.000000 4.000000 91.000000 -1.000000 41.000000 5.000000 60.000000 -11.000000 3.000000 -70.000000 20.000000 9.000000 31.000000 22.000000 -28.000000 38.000000 136.000000 -27.000000 52.000000 -24.000000 -40.000000 -51.000000 34.000000 7.000000 49.000000 -5.000000 -54.000000 -5.000000 1.000000 36.000000 -56.000000 -87.000000 28.000000 -34.000000 -20.000000 33.000000 6.000000 13.000000 64.000000 58.000000 5.000000 -22.000000 -55.000000 21.000000 121.000000 57.000000 16.000000 -50.000000 -56.000000 -22.000000 -182.000000 26.000000 67.000000 11.000000 -11.000000 73.000000
Solutions
Expert Solution
%matlab
T=readtable('Tempr2.txt');
F_to_K=@(t) (t-32)*(5/9)+273.15;
C_to_R=@(t) t*(9/5) + 491.67;
C_to_F=@(t) t*(9/5) + 32;
t=T.Tempr2;
t_F_K=F_to_K(t);
t_C_R=C_to_R(t);
t_C_F=C_to_F(t);
T_F_K=table(t(1:20),t_F_K(1:20),'VariableNames',{'temperature_Fahrenheit','temperature_Kelvin'})
T_C_R=table(t(1:20),t_C_R(1:20),'VariableNames',{'temperature_Celsius','temperature_Rankine'})
T_C_F=table(t(1:20),t_C_F(1:20),'VariableNames',{'temperature_Celsius','temperature_Fahreheit'})
t_mean=mean(t);
t_median=median(t);
t_variance=var(t);
t_std=std(t);
t_F_K_mean=mean(t_F_K);
t_F_K_median=median(t_F_K);
t_F_K_variance=var(t_F_K);
t_F_K_std=std(t_F_K);
t_C_R_mean=mean(t_C_R);
t_C_R_median=median(t_C_R);
t_C_R_variance=var(t_C_R);
t_C_R_std=std(t_C_R);
t_C_F_mean=mean(t_C_F);
t_C_F_median=median(t_C_F);
t_C_F_variance=var(t_C_F);
t_C_F_std=std(t_C_F);
Measurements={'mean';'median';'variance';'std'};
input_temperature=[t_mean;t_median;t_variance;t_std];
output_F_K=[t_F_K_mean;t_F_K_median;t_F_K_variance;t_F_K_std];
output_C_R=[t_C_R_mean;t_C_R_median;t_C_R_variance;t_C_R_std];
output_C_F=[t_C_F_mean;t_C_F_median;t_C_F_variance;t_C_F_std];
Statistics=table(Measurements,input_temperature,output_F_K,output_C_R,output_C_F)
---------------------------------------
%result
>> m3
T_F_K =
temperature_Fahrenheit temperature_Kelvin
______________________ __________________
70 294.26
54 285.37
-58 223.15
-4 253.15
-32 237.59
17 264.82
110 316.48
-39 233.71
102 312.04
-18 245.37
-28 239.82
35 274.82
40 277.59
19 265.93
-68 217.59
-30 238.71
-38 234.26
-21 243.71
-34 236.48
26 269.82
T_C_R =
temperature_Celsius temperature_Rankine
___________________ ___________________
70 617.67
54 588.87
-58 387.27
-4 484.47
-32 434.07
17 522.27
110 689.67
-39 421.47
102 675.27
-18 459.27
-28 441.27
35 554.67
40 563.67
19 525.87
-68 369.27
-30 437.67
-38 423.27
-21 453.87
-34 430.47
26 538.47
T_C_F =
temperature_Celsius temperature_Fahreheit
___________________ _____________________
70 158
54 129.2
-58 -72.4
-4 24.8
-32 -25.6
17 62.6
110 230
-39 -38.2
102 215.6
-18 -0.4
-28 -18.4
35 95
40 104
19 66.2
-68 -90.4
-30 -22
-38 -36.4
-21 -5.8
-34 -29.2
26 78.8
Statistics =
Measurements input_temperature output_F_K output_C_R
output_C_F
____________ _________________ __________ __________ __________
'mean' 6.537 259 503.44 43.767
'median' 5 258.15 500.67 41
'variance' 2384.2 735.85 7724.6 7724.6
'std' 48.828 27.127 87.89 87.89
>>
--------------------
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