Answer To: Microsoft Word - MCD4140_Assignment_T1_2020.docx MCD 4140: Computing for Engineers Assignment...
Kshitij answered on May 03 2021
amplitude_data.xlsx
data
t (time) A (amplitude)
(seconds) (mm)
0.0000 0.0428
3.7179 0.0359
7.4793 0.0307
8.4793 3.8521
11.2796 0.0266
15.1136 0.0235
18.9769 0.0210
22.8650 0.0189
26.7752 0.0172
30.7042 0.0157
34.6498 0.0144
38.6096 0.0133
42.5821 0.0123
46.5659 0.0114
50.5598 0.0106
53.5599 2.6105
54.5627 0.0099
58.5739 0.0093
62.5923 0.0087
66.6174 0.0081
70.6484 0.0076
74.6848 0.0072
78.7260 0.0067
82.7717 0.0063
86.8213 0.0060
90.8745 0.0056
94.9310 0.0053
96.5599 3.0025
98.9904 0.0050
103.0526 0.0047
107.1172 0.0044
111.1839 0.0042
115.2527 0.0040
119.3233 0.0037
123.3955 0.0035
127.4692 0.0033
131.5442 0.0032
135.6204 0.0030
139.6978 0.0028
143.7761 0.0027
147.8553 0.0025
151.9353 0.0024
156.0161 0.0023
160.0975 0.0021
164.1795 0.0020
168.2620 0.0019
172.3450 0.0018
176.4284 0.0017
180.5123 0.0016
182.1930 5.0008
184.5964 0.0015
188.6809 0.0015
192.7657 0.0014
196.8507 0.0013
200.9360 0.0012
205.0215 0.0012
209.1071 0.0011
213.1930 0.0010
217.2789 0.0010
221.3650 0.0009
225.4513 0.0009
229.5376 0.0008
233.6240 0.0008
237.7105 0.0008
241.7972 0.0007
245.8838 0.0007
249.9706 0.0006
254.0574 0.0006
258.1442 0.0006
262.2311 0.0005
266.3180 0.0005
270.4050 0.0005
274.4920 0.0005
278.5790 0.0004
282.6660 0.0004
286.7531 0.0004
290.8402 0.0004
294.9273 0.0004
299.0145 0.0003
303.1016 0.0003
307.1888 0.0003
311.2760 0.0003
315.3632 0.0003
319.4504 0.0003
323.5376 0.0002
327.6248 0.0002
331.7120 0.0002
335.7992 0.0002
339.8865 0.0002
343.9737 0.0002
348.0609 0.0002
352.1482 0.0002
356.2354 0.0002
360.3227 0.0001
364.4099 0.0001
365.6248 9.9452
368.4972 0.0001
372.5845 0.0001
376.6717 0.0001
380.7590 0.0001
384.8463 0.0001
388.9336 0.0001
393.0208 0.0001
397.1081 0.0001
401.1954 0.0001
405.2826 0.0001
409.3699 0.0001
413.4572 0.0001
417.5445 0.0001
421.6318 0.0001
425.7190 0.0001
429.8063 0.0001
433.8936 0.0001
437.9809 0.0001
442.0682 0.0000
446.1554 0.0000
450.2427 0.0000
454.3300 0.0000
458.4173 0.0000
462.5046 0.0000
466.5918 0.0000
470.6791 0.0000
474.7664 0.0000
478.8537 0.0000
482.9410 0.0000
487.0283 0.0000
491.1155 0.0000
495.2028 0.0000
499.2901 0.0000
503.3774 0.0000
507.4647 0.0000
511.5519 0.0000
515.6392 0.0000
519.7265 0.0000
523.8138 0.0000
527.9011 0.0000
531.9884 0.0000
536.0757 0.0000
540.1629 0.0000
544.2502 0.0000
548.3375 0.0000
552.4248 0.0000
556.5121 0.0000
560.5994 0.0000
564.6866 0.0000
568.7739 0.0000
572.8612 0.0000
576.9485 0.0000
581.0358 0.0000
585.1231 0.0000
589.2104 0.0000
593.2976 0.0000
597.3849 0.0000
601.4722 0.0000
605.5595 0.0000
609.6468 0.0000
613.7341 0.0000
617.8213 0.0000
621.9086 0.0000
625.9959 0.0000
630.0832 0.0000
634.1705 0.0000
638.2578 0.0000
642.3450 0.0000
646.4323 0.0000
650.5196 0.0000
654.6069 0.0000
658.6942 0.0000
662.7815 0.0000
666.8687 0.0000
670.9560 0.0000
675.0433 0.0000
679.1306 0.0000
683.2179 0.0000
687.3052 0.0000
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695.4797 0.0000
699.5670 0.0000
703.6543 0.0000
707.7416 0.0000
711.8288 0.0000
715.9162 0.0000
720.0034 0.0000
724.0907 0.0000
728.1780 0.0000
732.2653 0.0000
736.3526 0.0000
740.4398 0.0000
744.5271 0.0000
748.6144 0.0000
752.7017 0.0000
756.7890 0.0000
760.8763 0.0000
761.5271 4.8912
764.9636 0.0000
769.0509 0.0000
773.1382 0.0000
777.2254 0.0000
781.3127 0.0000
785.4000 0.0000
789.4873 0.0000
793.5745 0.0000
797.6619 0.0000
801.7492 0.0000
805.8363 0.0000
809.9239 0.0000
814.0110 0.0000
818.0982 0.0000
822.1855 0.0000
826.2727 0.0000
830.3602 0.0000
834.4474 0.0000
838.5346 0.0000
842.6218 0.0000
846.7093 0.0000
850.7964 0.0000
854.8837 0.0000
858.9711 0.0000
863.0583 0.0000
867.1455 0.0000
871.2331 0.0000
875.3202 0.0000
879.4076 0.0000
883.4951 0.0000
887.5824 0.0000
891.6695 0.0000
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903.9312 0.0000
908.0186 0.0000
912.1059 0.0000
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924.3678 0.0000
928.4550 0.0000
932.5420 0.0000
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993.8521 0.0000
997.9372 0.0000
1002.0261 0.0000
1006.1135 0.0000
1010.2009 0.0000
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1022.4635 0.0000
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1030.6378 0.0000
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1038.8107 0.0000
1042.8987 0.0000
1046.9864 0.0000
1051.0751 0.0000
1055.1604 0.0000
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1063.3365 0.0000
1067.4226 0.0000
1071.5081 0.0000
1075.5950 0.0000
1079.6827 0.0000
1083.7724 0.0000
1087.8646 0.0000
1091.9408 0.0000
1096.0307 0.0000
1100.1150 0.0000
1104.2068 0.0000
1112.3830 0.0000
1116.4723 0.0000
1120.5528 0.0000
1124.6416 0.0000
1132.8140 0.0000
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1169.6080 0.0000
1173.6872 0.0000
1177.7767 0.0000
1181.8788 0.0000
1185.9443 0.0000
1190.0448 0.0000
1194.1264 0.0000
1198.2080 0.0000
1202.3128 0.0000
1206.3883 0.0000
1210.4776 0.0000
1214.5692 0.0000
1218.6297 0.0000
1222.7301 0.0000
1226.8424 0.0000
1230.9007 0.0000
1235.0294 0.0000
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1251.3286 0.0000
1255.4636 0.0000
1267.6961 0.0000
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1275.8634 0.0000
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1284.0060 0.0000
1288.1029 0.0000
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1296.2913 0.0000
1304.4978 0.0000
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1312.6576 0.0000
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1329.0359 0.0000
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CompTrap.m
function I=CompTrap(t1,t2,C)
n=length(C);
h=(t2-t1)/n;
%t=[t1+h:h:t2-h];
I=h/2*(2*sum(C(2:end-1))+C(1)+C(end));
dataangles.mat
Ang2:[1x60 double array]
Ang4:[1x60 double array]
exptable.txt
Theta2 Theta4Theta2 Theta4Theta2 Theta4
fourbar.txt
Theta2 Theta4
0.000000 144.833982
6.101695 144.833982
12.203390 144.833982
18.305085 144.833982
24.406780 144.833982
30.508475 144.833982
36.610169 144.833982
42.711864 144.833982
48.813559 144.833982
54.915254 144.833982
61.016949 144.833982
67.118644 144.833982
73.220339 144.833982
79.322034 144.833982
85.423729 144.833982
91.525424 144.833982
97.627119 144.833982
103.728814 144.833982
109.830508 144.833982
115.932203 144.833982
122.033898 144.833982
128.135593 144.833982
134.237288 144.833982
140.338983 144.833982
146.440678 144.833982
152.542373 144.833982
158.644068 144.833982
164.745763 144.833982
170.847458 144.833982
176.949153 144.833982
183.050847 144.833982
189.152542 144.833982
195.254237 144.833982
201.355932 144.833982
207.457627 144.833982
213.559322 144.833982
219.661017 144.833982
225.762712 144.833982
231.864407 144.833982
237.966102 144.833982
244.067797 144.833982
250.169492 144.833982
256.271186 144.833982
262.372881 144.833982
268.474576 144.833982
274.576271 144.833982
280.677966 144.833982
286.779661 144.833982
292.881356 144.833982
298.983051 144.833982
305.084746 144.833982
311.186441 144.833982
317.288136 144.833982
323.389831 144.833982
329.491525 144.833982
335.593220 144.833982
341.694915 144.833982
347.796610 144.833982
353.898305 144.833982
360.000000 144.833982
fourbar1.txt
Theta2 Theta4
0.000000 144.833982
1.002786 144.833982
2.005571 144.833982
3.008357 144.833982
4.011142 144.833982
5.013928 144.833982
6.016713 144.833982
7.019499 144.833982
8.022284 144.833982
9.025070 144.833982
10.027855 144.833982
11.030641 144.833982
12.033426 144.833982
13.036212 144.833982
14.038997 144.833982
15.041783 144.833982
16.044568 144.833982
17.047354 144.833982
18.050139 144.833982
19.052925 144.833982
20.055710 144.833982
21.058496 144.833982
22.061281 144.833982
23.064067 144.833982
24.066852 144.833982
25.069638 144.833982
26.072423 144.833982
27.075209 144.833982
28.077994 144.833982
29.080780 144.833982
30.083565 144.833982
31.086351 144.833982
32.089136 144.833982
33.091922 144.833982
34.094708 144.833982
35.097493 144.833982
36.100279 144.833982
37.103064 144.833982
38.105850 144.833982
39.108635 144.833982
40.111421 144.833982
41.114206 144.833982
42.116992 144.833982
43.119777 144.833982
44.122563 144.833982
45.125348 144.833982
46.128134 144.833982
47.130919 144.833982
48.133705 144.833982
49.136490 144.833982
50.139276 144.833982
51.142061 144.833982
52.144847 144.833982
53.147632 144.833982
54.150418 144.833982
55.153203 144.833982
56.155989 144.833982
57.158774 144.833982
58.161560 144.833982
59.164345 144.833982
60.167131 144.833982
61.169916 144.833982
62.172702 144.833982
63.175487 144.833982
64.178273 144.833982
65.181058 144.833982
66.183844 144.833982
67.186630 144.833982
68.189415 144.833982
69.192201 144.833982
70.194986 144.833982
71.197772 144.833982
72.200557 144.833982
73.203343 144.833982
74.206128 144.833982
75.208914 144.833982
76.211699 144.833982
77.214485 144.833982
78.217270 144.833982
79.220056 144.833982
80.222841 144.833982
81.225627 144.833982
82.228412 144.833982
83.231198 144.833982
84.233983 144.833982
85.236769 144.833982
86.239554 144.833982
87.242340 144.833982
88.245125 144.833982
89.247911 144.833982
90.250696 144.833982
91.253482 144.833982
92.256267 144.833982
93.259053 144.833982
94.261838 144.833982
95.264624 144.833982
96.267409 144.833982
97.270195 144.833982
98.272981 144.833982
99.275766 144.833982
100.278552 144.833982
101.281337 144.833982
102.284123 144.833982
103.286908 144.833982
104.289694 144.833982
105.292479 144.833982
106.295265 144.833982
107.298050 144.833982
108.300836 144.833982
109.303621 144.833982
110.306407 144.833982
111.309192 144.833982
112.311978 144.833982
113.314763 144.833982
114.317549 144.833982
115.320334 144.833982
116.323120 144.833982
117.325905 144.833982
118.328691 144.833982
119.331476 144.833982
120.334262 144.833982
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356.991643 144.833982
357.994429 144.833982
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360.000000 144.833982
func.m
function Ang=func(x,theta2)
global a b c d
Ang=(d/a)*cosd(x)-(d/c)*cosd(theta2)+((a^2-b^2+c^2+d^2)/(2*a*c))-cosd(theta2-x);
end
mcd4140assignmentt12020-ul3vj0vx (1).pdf
MCD 4140: Computing for Engineers
Assignment
Trimester 1, 2020
Status: Individual
Weighting: 10%
Due by: 11:55pm on Monday Week 10 (May 4, 2020)
Late penalty: Deduct 10% per day
INSTRUCTIONS
This assignment should be completed INDIVIDUALLY. Plagiarism will result in a mark of zero. Plagiarism
includes letting others copy your work and using code without citing the source. If a part of your code
is written in collaboration with classmates, say so in your comments and clearly state the contributions
of each person.
NOTE: Your MATLAB code will be checked for plagiarism – DON’T RISK LOSING ALL 10 MARKS BY
COPYING SOMEONE ELSE’S CODE (OR BY ALLOWING SOMEONE ELSE TO COPY YOURS).
Download the assignment template files from Moodle and update the m‐files named Q1a.m, Q1b.m,
etc… with your assignment code. DO NOT rename the m‐files in the template or modify run_all.m.
Check your solutions to all the tasks by running run_all.m and ensuring all questions are answered
as required. Do not use close all, clear all, clc in any individual mfiles.
SUBMITTING YOUR ASSIGNMENT
Submit your assignment online using Moodle. Your ZIP file (not .rar or any other format) must
include the following attachments:
a. Solution m‐files for assignment tasks (e.g. run_all, Q1a.m, Q1b.m, etc.)
b. Any additional function files required by your m‐files (e.g. heun.m, falseposition.m, etc.)
c. All data files needed to run the code including the input data provided to you (e.g.
data1.txt, data2.csv, etc.)
d. A completed cover sheet
Your assignment will be marked in your usual computer lab session during Week 11. YOU MUST
ATTEND AND FACE A SHORT INTERVIEW TO HAVE IT MARKED. IF YOU DO NOT ATTEND THE
INTERVIEW, YOUR ASSIGNMENT MARK WILL BE ZERO. YOUR ZIP FILE WILL BE DOWNLOADED FROM
MOODLE DURING WEEK 10 AND ONLY THESE FILES WILL BE MARKED. We will extract (unzip) your
ZIP file and mark you based on the output of run_all.m on a Windows‐based system. It is your
responsibility to ensure that everything needed to run your solution is included in your ZIP file. It is
also your responsibility to ensure that everything runs seamlessly on a Windows‐based system
(especially if you have used MATLAB on a Mac OS or Linux system). Windows OS computers are
available in the computer labs on campus for testing. The assignment will not be downloaded to your
individual laptops for marking.
MARKING SCHEME
This assignment is worth 10% (10 marks = 1%) of the unit mark. Your assignment will be graded
using the following criteria:
1) run_all.m produces results automatically (additional user interaction only if asked explicitly)
2) Your code produces correct results (printed values, plots, etc…) and is well written.
3) Coding interview performance
CODING INTERVIEW RUBRIC
As part of the marking process, your demonstrator will spend a few minutes interviewing you to gauge
your understanding of the assignment code. The purpose of this is to ensure that you have contributed
to the assignment and understand the code.
You will be assigned a score based on your interview and your code mark will be penalized if you are
unable to explain your submission.
Category Description Penalty
No understanding
(or did not attend
the interview)
The student has not prepared, cannot answer even the most basic
questions and likely has not even seen the code before. 100%
Trivial
understanding
The student may have seen the code before and can answer
something partially relevant or correct to a question but they
clearly can’t engage in a serious discussion of the code
30%
Selective
understanding
The student gives answers that are partially correct or can answer
questions about one area correctly but another not at all. The
student has not prepared sufficiently
20%
Good understanding
The student is reasonably well prepared and can consistently
provide answers that are mostly correct, possibly with some
prompting. The student may lack confidence or speed in answering.
10%
Complete
understanding
The student has clearly prepared and understands the code. They
can answer questions correctly and concisely with little to no
prompting.
0%
ASSIGNMENT HELP
1) You may use the function files that you have written in the labs.
2) You may ask questions in the Discussion Forum on Moodle.
3) The m‐file templates contain pre‐written comments and sections only as a guide. You do not need
to follow its structure. You may delete the comments.
4) Hints may also be provided during workshops.
5) Bold text has been used to emphasize important aspects of each task. This does not mean that
you should ignore all other text.
6) The questions have been split into sub‐questions. It is important to understand how each sub‐
question contributes to the whole, but each sub‐question is effectively a stand‐alone task that
does part of the problem. Each can be tackled individually.
7) It is recommended that you break down each sub‐question into smaller parts too and figure out
what needs to be done step‐by‐step. Then you can begin to put things together again to complete
the whole.
8) Solve the question, of part thereof, by hand before attempting to code the solution.
QUESTION 1 [30 MARKS]
A four‐bar linkage system is shown in Figure 1. The first link, a, is an input link (crank) of length 1. The
second link, b, is a coupler link of length 2. The third link, c, is an output link of length 4. The fourth
link, d, is the fixed link (ground) of length 5. All lengths are provided in metres.
Figure 1. The four‐bar linkage system.
The angular position of the output link (θ4) of a four‐bar linkage corresponding to the angular position
of the input link (θ2) can be computed using Freudenstein’s equation:
d
a cos θ
d
c cos θ
a b c d
2ac cos θ θ
The following parameters can be used for root finding:
xl = 120ᵒ, xu = 165ᵒ, xi = 120ᵒ, xi‐1 = 110ᵒ, δ = 0.01 and a precision of 0.0001.
Write MATLAB code to perform the following tasks in the q1.m file:
Q1a.
First, write an m‐file to find the value of θ4 for θ2 = 30ᵒ using any open root finding method of your
choice. Print your answer using fprintf. (Hint: The exact solution is 144.834722187769ᵒ).
Q1b.
Next, modify the m‐file from Q1a to find the value of for θ4 for all integer inputs of θ2 = 0ᵒ to 360ᵒ
using any root finding method of your choice. Plot a graph to show the relationship between the input
angles and the output angles.
Q1c.
Explain your choice of root finding method used in Q1b based on your experiments and observations
and write a brief explanation to the command window.
Q1d.
Finally, modify the m‐file from Q1b so that it writes the data (input angles and output angles, with 15
decimal places) into a text file named fourbar.txt. Input angles are θ2 = 0ᵒ to 360ᵒ and the output
angles are obtained from the root finding method of your choice.
QUESTION 2 [40 MARKS]
Flows around cylinders of various cross‐sections continue to engender a significant amount of
engineering research interest due to its ubiquitous practicality in society. Examples include bridge
spans and pylons, high‐rise buildings, pipelines, heat exchangers and oil platforms. When fluid (such
as air or water) flows around such bodies, a wake develops which may become unstable and lead to a
development of vortex streets. An example of the vortex street formed by clouds flowing past an
island is illustrated in Figure 2.
Figure 2. Kármán vortex street caused by wind flowing around the Juan Fernández Islands off the
Chilean coast.
The vortices that develop are capable of containing large amounts of energy which can cause damage
to neighbouring structures on impact. Therefore engineers and scientists study the wake dynamics
behind these bodies with the aim of suppressing the vortex shedding. There are some cases where
vortex shedding is encouraged as to dissipate heat from a heated wall for example. One such
parameter that may be used to investigate the strength of the wake is by measuring the amplitude of
its velocity fluctuations.
An engineer has performed experiments in a water channel involving flow past a triangular prism.
Contours of axial vorticity are shown in figure 3 for visualization purposes. The magnitude of the lift
generated on the triangular cylinder over time is recorded into amplitude_data.xlsx. The amplitude of
the lift provides important information on the strength of the wake and the vortices that could be
shed off of it. The amplitude_data.xlsx file contains:
1. The time, t
2. The amplitude, A
Figure 3. Axial vorticity contours of flow past a triangle oriented at 0ᵒ.
The engineer has asked you to perform the following tasks.
Q2a.
In the q2a.m file, read in the data from the amplitude_data.xlsx. Be aware that there is header
information. Create a figure with two sub‐plots in a horizontal arrangement. In the left sub‐plot, plot
A against t as a black continuous line. In the right sub‐plot, plot loge(A) against t as a blue continuous
line.
Q2b.
In reviewing the plots created in Q2a, you notice spikes with A≥2 occurring at odd times between t=0
and t=900. You question the engineer whether or not these spikes may be artificial. The engineer’s
response is that he/she had accidentally bumped into the water channel several times throughout the
experiment.
In the q2b.m file, you are required to remove all of the amplitude data corresponding to A ≥ 2 as they
are erroneous. Create another figure containing two sub‐plots in a horizontal arrangement. In the left
sub‐plot, plot the A data against t as a black continuous line with the erroneous data removed. In the
right sub‐plot, plot loge(A) against t as a red continuous line.
It should be clear that after a certain time, the recorded amplitude begins to be consistently noisy (as
opposed to sporadically noisy). From the plot, estimate the time at which the amplitude becomes
consistently noisy and print its value using the fprintf command.
In a new figure, plot the natural logarithm of the amplitude for times below which contains the
consistently noisy data.
Q2c.
You have shown the engineer plot you created in Q2b with the consistently noisy data removed. The
engineer believe it’s a better idea to remove all of the data from t=900 onwards. This way, both the
sporadically and consistently noisy data is removed.
In addition, the engineer notices that there are two different trends across the data when reviewing
the plot of loge(A) against t created in Q2b. That is, the trend from 0 ≤ t ≤ 60 differs to that of
60 ≤ t ≤ 900.
In the q2c.m file, you are required to segregate the data such that it only includes data for t=0 to 900
(inclusive). With this data, create several plots in a figure environment with three sub‐plots in a vertical
arrangement. The three plots are described as follows:
1. In the top sub‐plot, plot loge(A) against t for times between t=0 and 60 with a magenta
continuous line.
2. In the middle sub‐plot, plot loge(A) against t for times between t=60 and 900 with a red
continuous line.
3. In the bottom sub‐plot, plot loge(A) against t for times between 0 and 900 using the line
characteristics from points 1 and 2. That is, a magenta continuous line between t=0 and 60
and a red continuous line for t=60 and 900.
All specified limits of time are inclusive. Use a line width of 5 for all of these subplots. Title the plots
such that it describes the range of time the data is plotted for. I.e. “Data for t>=0 and t<=60”.
Q2d.
From the plots of loge(A) against t of amplitude against time created in Q2c, you observe that the data
for
0 ≤ t ≤ 60 can be fitted with a second order polynomial while the data for 60 ≤ t ≤ 900 can be fitted
with a linear relationship.
In the q2d.m file, use polyfit to determine the equations for the second order polynomial and the
linear function. Plot the loge(A) against t for 0 ≤ t ≤ 60 and 60 ≤ t ≤ 900 on two separately figures with
blue circle markers. In addition, plot their respective fitted functions as a red continuous line. The titles
of the plots should include the equation of the fit and the coefficient of determination, r2.
QUESTION 3 [30 MARKS]
Background
A residence time distribution (or RTD) is a probability density function that describes how long fluid
stays in a continuous flow chemical reactor. One way of measuring an RTD is to inject a small pulse of
a chemical tracer (e.g. salt, radioactive material or coloured dye) at the inflow of the rector and then
measure the signal at the outflow of the reactor.
At one extreme is a so‐called “plug‐flow” reactor which has no mixing and in which all of the input
pulse of tracer exits the reactor at the same time. Provided there is no short‐circuiting, this time is
given (in seconds) as
= V/Q
where V is the volume of the reactor (in m3) and Q is the flow rate (in m3s‐1).
At the other extreme, a Continuously stirred tank reactor (CSTR) is one in which each element of fluid
that is injected into the reactor is instantly uniformly mixed with everything else inside the reactor.
The RTD for a CSTR is a negative exponential function.
Both of these extremes are idealised concepts and can never be realised in practice. In the real world,
the RTD (usually) rises quickly, and then decays slowly, and in this question we investigate some
different RTD’s.
NOTE: You are allowed to use the fact that time vector in the following question is evenly spaced.
Q3a
You have been asked to determine if several different reactors are operating with similar behaviour,
but you have not been given any information on their size, or design and have only been given a set
of concentration measurements as a function of time, one for each reactor.
First you must open the rtd data file (‘rtd.dat’) and read it into MATLAB using importdata. The first
column of data is the time of the measurement (in seconds), and the other columns are the
concentration measurements (C(t)) of the tracer at the exit of the reactor for an unknown number
of reactors. Determine how many different reactors have been included in the file and print this
number to the command window.
Plot each of the concentration versus time curves on the same figure using a different coloured line
(in order, use as many as needed of black, red, green, blue, magenta, yellow). Ensure your plot has a
legend using the text headers contained in the file.
Q3b
In order to compare the curves, they must be normalized. The normalized RTD curve (often called
E(t)) is defined as
E(t) C(t)
C(t)dt
0
Write a function called CompTrap that calculates the integral of a function using the Composite
Trapezoidal rule. The input parameters are a vector of (evenly spaced) times over the time range [t1,
t2] and a vector of function values that corresponds to the time vector (you can assume that spacing
of the data is uniform – you do not need to confirm this).
Normalise each of your concentration curves to give E(t) and in a new figure, plot these for each
reactor using the same colours from part a. (NOTE: Instead of integrating to t = you should
integrate to the last point in the data that you read in). Write the normalising value of C(t)dt
0
for each reactor to the command window, one to a line.
Poor Programming Practices [‐10 Marks]
(Includes, but is not limited to, poor coding style, hardcoding, not using loops
where appropriate, insufficient comments unlabelled figures, etc.)
(END OF ASSIGNMENT)
newton.asv
function [ x, ex, iter]=newton(f,x0,, tol)
%
% NEWTON Newton's Method
% Input:
% f - input funtion
% df - derived input function
% x0 - inicial aproximation
% tol - tolerance
% nmax - maximum number of iterations
%
% Output:
% x - aproximation to root
% ex - error estimate
%
if nargin == 3
tol=1e-4;
end
h=1e-4;
df=(feval(f,x0+h,theta2)-feval(f,x0,theta2))/h;
x=x0-(feval(f,x0,theta2)/df);
ex=abs(x-x0);
iter= 2;
while (ex>=tol)
df=(feval(f,x+h,theta2)-feval(f,x,theta2))/h;
xp=x;
x=x-(feval(f,x,theta2)/df);
ex= abs(xp-x);
iter = iter+1;
end
end
newton.m
function [ x, ex, iter]=newton(f,x0,theta2, tol)
%
% NEWTON Newton's Method
% Input:
% f - input funtion
% df - derived input function
% x0 - inicial aproximation
% tol - tolerance
% nmax - maximum number of iterations
%
% Output:
% x - aproximation to root
% ex - error estimate
%
if nargin == 3
tol=1e-4;
end
h=1e-4;
df=(feval(f,x0+h,theta2)-feval(f,x0,theta2))/h;
x=x0-(feval(f,x0,theta2)/df);
ex=abs(x-x0);
iter= 2;
while (ex>=tol)
df=(feval(f,x+h,theta2)-feval(f,x,theta2))/h;
xp=x;
x=x-(feval(f,x,theta2)/df);
ex= abs(xp-x);
iter = iter+1;
end
end
q1.asv
% Solution of theta2
global a b c d
% Link Pramters are chosen arbitrary
%
a=15;
b=50;
c=59.32;
d=100;
x0=140; % Initial Angle theta4
tol=1e-5; % Error Tolerance
theta2=30; % For theta2=30°
[theta4q1,exqa,iterqa]=newton(@(x,theta2)func(x,theta2),x0, tol);
fprintf('The initial gaus x0= %4.2f° and at tol %4.2f \n', x0, tol)
fprintf('The root for \theta_4= %4.2f°', theta4q1);
% Solution to q2b
N=360; % Number of angles
Ang2=linspace(0,360,N);
Ang4=zeros(1,N);
x0qb=110;
format long eng
for ii=1:N
Ang=Ang2(ii);
[Ang4(ii),exqa,iterqa]=newton(@(x,Ang)func(x,Ang),x0qb, tol);
end
figure(),
plot(Ang2,Ang4,'b','LineWidth',2); grid on
xlabel('\theta_2'); ylabel('\theta_4');
title(' \theta_2 vs theta_4 using netwon method')
% Q1c Solution
str=['\n\n Q1c: The advantage of using newtons method to find an approximate of a root, its quadratic convergence'];
fprintf(str);
% Q1d solution
A=[Ang2; Ang4];
filefold=fopen('fourbar.txt','w'); % Save angles into a forubar.txt file
fprintf(filefold,'Theta2 Theta4\n');
fprintf(filefold,'%f %f\n',A);
fclose(filefold)
q1.m
% Solution of theta2
global a b c d
% Link Pramters are chosen arbitrary
%
a=15;
b=50;
c=59.32;
d=100;
x0=100; % Initial Angle theta4
tol=1e-5; % Error Tolerance
theta2=30; % For theta2=30°
[theta4q1,exqa,iterqa]=newton(@(x,theta2)func(x,theta2),x0, tol);
strq1='The initial gaus x0= %4.2f° and at tol %4.2f \n';
strq11='The root for \theta_4= %4.2f°';
fprintf(strq1, x0, tol)
fprintf(strq11, theta4q1);
% Solution to q2b
N=60; % Number of angles
Ang2=linspace(0,360,N);
Ang4=zeros(1,N);
x0qb=100;
format long eng
for ii=1:N
Ang=Ang2(ii);
[Ang4(ii),exqa,iterqa]=newton(@(x,Ang)func(x,Ang),x0qb, tol);
end
figure(),
plot(Ang2,Ang4,'b','LineWidth',2); grid on
xlabel('\theta_2'); ylabel('\theta_4');
title(' \theta_2 vs \theta_4 using netwon method')
% Q1c Solution
str=['\n\n Q1c: The advantage of using newtons method to find an approximate of a root, its quadratic convergence'];
fprintf(str);
% Q1d solution
A=[Ang2; Ang4];
filefold=fopen('fourbar.txt','w'); % Save angles into a forubar.txt file
fprintf(filefold,'Theta2 Theta4\n');
fprintf(filefold,'%f %f\n',A);
fclose(filefold);
type fourbar.txt
q1a.m
%Q1a
% Define crank length
% Define input parameters and anonymous functions
% Student can choose one of these methods, all works
% nr = newraph(f, df, xi, precision);
% sc = secant(f, xi, xi_1, precision);
% ms = modisecant(f, xi, pert, precision);
% fzv = fzero(f, xi);
% fzb = fzero(f, [xl, xu]);
%
% Solution of theta2
global a b c d
% Link Pramters are chosen arbitrary
%
a=15;
b=50;
c=59.32;
d=100;
xi=120; % Initial Angle theta4
tol=1e-5; % Error Tolerance
theta2=30; % For theta2=30°
[theta4q1,exqa,iterqa]=newton(@(x,theta2)func(x,theta2),xi, tol);
strq1='The initial gaus x0= %4.2f° and at tol %4.2f \n';
strq11='The root for \theta_4= %4.2f°';
fprintf(strq1, xi, tol)
fprintf(strq11, theta4q1);
q1b.asv
%Q1b
% Define crank length
global a b c d
% Link Pramters are chosen arbitrary
%
a=15;
b=50;
c=59.32;
d=100;
% Define input parameters for function
xi=120; % Initial Angle theta4
tol=1e-5; % Error Tolerance
% Solve for root, for all ang2
% Solution to q2b
N=60; % Number of angles
Ang2=linspace(0,360,N);
Ang4=zeros(1,N);
x0qb=110;
format long eng
for ii=1:N
Ang=Ang2(ii);
[Ang4(ii),exqa,iterqa]=newton(@(x,Ang)func(x,Ang),x0qb, tol);
end
% Plot the relation between the input angles and the output angles
figure(),
plot(Ang2,Ang4,'b','LineWidth',2); grid on
xlabel('\theta_2'); ylabel('\theta_4');
title(' \theta_2 vs \theta_4 using netwon method')
sava
q1b.m
%Q1b
% Define crank length
global a b c d
% Link Pramters are chosen arbitrary
%
a=15;
b=50;
c=59.32;
d=100;
% Define input parameters for function
xi=120; % Initial Angle theta4
tol=1e-5; % Error Tolerance
% Solve for root, for all ang2
% Solution to q2b
N=60; % Number of angles
Ang2=linspace(0,360,N);
Ang4=zeros(1,N);
x0qb=110;
format long eng
for ii=1:N
Ang=Ang2(ii);
[Ang4(ii),exqa,iterqa]=newton(@(x,Ang)func(x,Ang),x0qb, tol);
end
% Plot the relation between the input angles and the output angles
figure(),
plot(Ang2,Ang4,'b','LineWidth',2); grid on
xlabel('\theta_2'); ylabel('\theta_4');
title(' \theta_2 vs \theta_4 using netwon method')
save dataangles Ang2 Ang4
q1c.m
%Q1c
%Explanation output to command window
% Q1c Solution
str=['\n\n Q1c: The advantage of using newtons method to find an approximate of a root, its quadratic convergence'];
fprintf(str);
q1d.asv
%Q1d
% Write the result into text file
% Q1d solution
A=[Ang2; Ang4];
filefold=fopen('fourbar.txt','w'); % Save angles into a forubar.txt file
fprintf(filefold,'Theta2 Theta4\n');
fprintf(filefold,'%f %f\n',A);
fclose(filefold);
type fourbar.txt
q1d.m
%Q1d
% Write the result into text file
load dataangles
% Q1d solution
A=[Ang2; Ang4];
filefold=fopen('fourbar.txt','w'); % Save angles into a forubar.txt file
fprintf(filefold,'Theta2 Theta4\n');
fprintf(filefold,'%f %f\n',A);
fclose(filefold);
type fourbar.txt
q2a.asv
% Q2a
format short
% import the data
y=importdata('amplitude_data.xlsx');
% declaring the time and amplitude variables
A=y.data(:,2);
t=y.data(:,1);
%plotting the data
subplot(1,2,1), plot(t,A);
xlabel('Time'); ylable('A');
subplot()
q2a.m
% Q2a
format short
% import the data
y=importdata('amplitude_data.xlsx');
% declaring the time and amplitude variables
A=y.data(:,2);
t=y.data(:,1);
%plotting the data
subplot(1,2,1), plot(t,A,'k','LineWidth',2); grid on
xlabel('Time'); ylabel('A');title('A against t');
subplot(1,2,2), plot(t,log(A),'b','LineWidth',2); grid on
xlabel('Time'); ylabel('log(A)'); title('log_e(A) against t')
q2b.asv
%Q2b
%getting rid of noisy data caused by bumps
format short
% import the data
y=importdata('amplitude_data.xlsx');
A=y.data(:,2);
t=y.data(:,1);
[pk,pi]=find(A<2);
% calculate relevant variables
A=A(pk);
t=t(pk);
% plotting the amplitude against time data
% and the natural logarithm of the amplitude against time
subplot(1,2,1), plot(t,A,'k','LineWidth',2); grid on
xlabel('Time'); ylabel('A');title('A against t');
subplot(1,2,2), plot(t,log(A),'b','LineWidth',2); grid on
xlabel('Time'); ylabel('log(A)'); title('log_e(A) against t')
% estimate of where the data becomes CONSISTENTLY noisy
%Pi=[]; Di=[];
%for ii=1:length(A)-1
[P,D]=find(A<1e-5);
str='the time at which the amplitude becomes consistently noisy is t=%4.2f';
fprintf(str,t(P(1)));
% % plotting the data excluding consistent noise
% figure(),
% plot(t(P),log(A(P)),'b','LineWidth',2);
% grid on
% xlabel('Time');ylabel('log_e(A)');
% title('The Consistent noise');
%
q2b.m
%Q2b
%getting rid of noisy data caused by bumps
format short
% import the data
y=importdata('amplitude_data.xlsx');
A=y.data(:,2);
t=y.data(:,1);
[pk,pi]=find(A<2);
% calculate relevant variables
A=A(pk);
t=t(pk);
% plotting the amplitude...