COEB1013 Programming for Engineers Project Semester 1 2025/26 | UNITEN

COEB1013 Project 

INSTRUCTIONS TO CANDIDATES:

1. Give all your file names as PP_StudentID / SP_StudentID.
2. Add the last THREE (3) digits of your student ID at the end of any created variables.
3. Submit all files (i.e. .ipynb, .m, .mat, .csv, .pdf, .slx) via BRIGHTEN. Submissions via other platforms after the due date will not be accepted.

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Question 1: Python Programming

A civil/mechanical engineering team evaluates the efficiency of water pumps used in a distribution network. Each pump operates at different flow rates, head levels, and power consumptions. You are required to analyse the dataset, detect performance patterns, and identify the most efficient pump model.

Dataset

Save the following example as pump_performance_XXX.csv:

Tasks

1. Load the Data:o Import the CSV file using Pandas.
   o Fill in the missing values in the Data
   o Convert numerical columns into NumPy arrays for further calculations.
2. Analyse the Data:

Using Numpy/Pandas:

o Compute the average efficiency of all pumps.
o Identify the highest-efficiency pump.
o Calculate the specific energy consumption (SEC) for each pump:

𝑃𝑜𝑤𝑒𝑟 (𝑘𝑊) 𝑆𝐸𝐶 = 𝐹𝑙𝑜𝑤 𝑅𝑎𝑡𝑒 (𝑚₃/ℎ)

o Identify the pump with the lowest SEC.

3. Visualize the Data:

Create two plots:

o Bar chart of pump efficiencies (highlight the highest-efficiency pump in a different colour).
o Scatter plot showing relationship between flow rate and power consumption.

4. Save Output o Save a summary table to pump_analysis_summary_XXX.csv, containing:o Best performing pump
   o Lowest SEC pump
   o Average efficiency
   o Ranking of pumps by efficiency
5. Flowcharto Draw a flowchart that outlines the steps your program takes.
   o Attach the flowchart in your submission
6. Short Discussion
   o Explain your findings in 2–3 paragraphs within the notebook.

Question 2: Simulink

Project Title:

Modeling and Simulation of a Traffic Light System with Vehicle Queue Dynamics Using Simulink

Scenario:

You are assigned to model a smart intersection equipped with a traffic light system and vehicle queue detector. The objective is to simulate how the queue length changes over time depending on the green-red cycle.

System Description:

o Vehicles arrive at the intersection at a rate λ (vehicles/min).
o When the light turns green, vehicles leave at rate μ (vehicles/min).
o Queue length dynamics:

o Queue cannot be negative (use Saturation block).

Tasks:

1. Build Simulink Model Include:o A Signal Builder / Repeating Sequence block to define a traffic signal (RED-GREEN cycle).
   o A Switch block to select the correct rate (λ or λ − μ).
   o An Integrator block to compute queue length Q(t).
   o A Saturation block (minimum = 0).

2. Parameter Requirements Students must define:o Vehicle arrival rate, λ
   o Departure rate, μ
   o Cycle time (Tred, Tgreen)
   o Initial queue length
3. Simulationo Run for at least 5 full signal cycles.
   o Display queue length using Scope.
   o Add a Display block showing maximum queue length.

4. Flowcharto Draw a flowchart that shows:
   o Parameter definition
   o Traffic signal generation
   o dQ/dt selection logic (Switch)
   o Integration process
   o Output display
   o The flowchart must be included in the written report.

6. Extensiono Add a simple controller that adjusts green time automatically when queue > threshold.
   o Compare results with/without controller.
7. Short Report (3–5 pages) Should include:o Objective
   o Flowchart/system diagram
   o Model description
   o Parameter justification
   o Simulation results
   o Discussion & conclusion

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PROJECT GUIDELINE

A. General guideline

1. Submission Platform: BRIGHTEN

2. Due Date: (Insert date and time here)

3. File Naming Convention:

• Python Project: PP_StudentID.ipynb, PP_StudentID.csv
• Simulink Project: SP_StudentID.slx, SP_StudentID_Report.pdf

4. Individual assignment

5. Academic Integrity:

• Code and models must be written/developed by the student.
• Discussion is allowed, but direct copying is not permitted.
• Any external references must be acknowledged.

B. Python

1. Project title and student information

2. Student Instruction:

i. Draw the flowchart for the program
ii. Create data in Excel and save the data as CSV format
iii. Write a Python program
iv. Task to be completed in notebook

a. Load the data to Colab notebook
b. Perform data analysis
c. Generate bar chart and scatter plot
d. Summary output:

o Create a DataFrame summarizing pump model, efficiency, SEC, rank by efficiency
o Save as pump_analysis_summary_XXX.csv

e. Short Written Interpretation (in the notebook)
In the Text Cell, provide 2–3 short paragraphs explaining:

o Which pump is most efficient and why.
o How SEC relates to efficiency.
o Any observed trend between flow rate and power.

3. Mark distribution:

C. Simulink

1. Project title
2. Design Simulink model that simulates the queue length of vehicles at a signalised intersection subject to a red–green cycle.

System Summary

• Vehicle arrival rate: λ vehicles/min.
• Vehicle departure rate during green: 𝜇 vehicles/min.
• Queue length Q(t) evolves as:

𝑑𝑄       𝜆 − 𝜇,        𝑖𝑓 𝑔𝑟𝑒𝑒𝑛 𝑙𝑖𝑔ℎ𝑡 𝑖𝑠 𝑂𝑁

= {

𝑑𝑡               𝑥,        𝑖𝑓 𝑟𝑒𝑑 𝑙𝑖𝑔ℎ𝑡 𝑖𝑠 𝑂𝑁

• Queue length cannot be negative: Q(t) ≥ 0

 Tasks

(a) Parameter Definition Choose and clearly state:

• • 𝜆: arrival rate (e.g. 6 vehicles/min)
  • 𝜇: departure rate when green (e.g. 12 vehicles/min)
  • Signal cycle:
    o 𝑇𝑔𝑟𝑒𝑒𝑛 (e.g. 30 s)
    o 𝑇𝑟𝑒𝑑 (e.g. 40 s)
  • Initial queue length 𝑄(0).

(b) Simulink Model Construction Model components required:
i. Traffic Signal Generator
– Use Repeating Sequence (or Pulse Generator) to create a signal:
o Output = 1 when GREEN
o Output = 0 when RED

ii. Arrival Rate Block
– Constant block with value λ (in vehicles/s – convert from vehicles/min).

• iii. Departure Rate Block
  – Constant block with value 𝜇 (vehicles/s).

iv. Logic for dQ/dt

• • Use a Switch block:
  • Input 1: lambda – mu (for green)
  • Input 3: lambda (for red)
  • Control input: traffic light signal (1 = green, 0 = red)

v. Integrator

• • Integrate dQ/dt to obtain Q(t).

vi. Saturation

• • Minimum = 0, Maximum = a reasonable upper limit (e.g. 100 vehicles).

vii. Scopes and Displays

• • Scope to plot Q(t) versus time.
  • Display to show maximum queue length via a MinMax block or by postprocessing.

(c) Simulation Settings

• • Total simulation time: at least 5 complete cycles.
  • Choose an appropriate solver (e.g. ode45) and step size.

(d) Extension

• • Add a simple feedback controller:
  • If Q(t) exceeds a threshold (e.g. 25 vehicles), automatically extend green time in the next cycle.
  • Show comparative plots:
  • Without control
  • With control

3. Simulink Block Diagram – Guide
   (a) Traffic Signal Subsystem

• Block: Repeating Sequence
• Time–value pairs:
  o (0 s, 1) … (T_green, 1) → GREEN o (T_green, 0) … (T_green + T_red, 0) → RED
• Set sample time appropriately; set Time offset to 0; Final value repeated over period.

(b) Arrival and Departure Path

• • Block: Constant (lambda_s) – vehicles/s.
  • Block: Constant (mu_s) – vehicles/s.
  • Block: Sum1 (lambda_s – mu_s).
  • Block: Sum2 (optional, for extensions).

(c) Switch Logic for dQ/dt

• • Block: Switch o Input 1: lambda_s – mu_s (green condition).
    o Input 3: lambda_s (red condition). o Control: output from Traffic Signal (1 or 0).
    o Threshold: 0.5.

(d) Queue Dynamics

• Block: Integrator ➡️ output is Q(t).
• Block: Saturation ➡️ min = 0, max = 100.
• Connect Switch output ➡️ Integrator ➡️

(e) Outputs

• • Block: Scope → input = Q(t).
  • Block: To Workspace (optional) for Q(t).
  • Block: MinMax or post-processing to find peak queue

4. Report Template (3-5 pages)

(a) Title Page

(b) Objective & System Overview

• 1–2 short objectives.
• Brief description of an urban traffic signal and queueing.

(c) Model Description

• Explanation of:
  o  Arrival and departure rates
  o Traffic signal representation
  o  Differential equation of queue length

• Insert block diagram screenshot.

(d) Parameters and Assumptions

• Table with:
  oParameter
  o Value
  o Unit
  o Justification/source

(e) Simulation Results

• Plots of Q(t) over time.
• Comparison of before vs after control.

(f) Discussion

• Comment on:
  o How green/red times affect queue length.
  o Effect of increasing/decreasing μ or λ.
  o Implications for traffic engineering.

(g) Conclusion

• 3-4 bullet points summarising key findings.

5. Mark distribution and rubric

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