Challenge

Week 6 - Data analysis

GG Sivaprasad1 Dec 2024

Q) Take a look at the data analysis video before you attempt this challenge.

Your task is to write a script that does the following.

Data visualizer (100 points)

Your script should take column numbers as the input and plot the respective columns as separate images
Each file should be saved by the name of the column
The plot labels should be extracted from the file. If, I request for a plot between column 1 (crank angle) and column 8(volume), then the label information should automatically be extracted and must appear as labels in the plots. THIS SHOULD NOT BE DONE MANUALLY

Compatability check (30 points)

You code should exit gracefully, if a non-compatible file is provided as an input. It should say something like "File not recognized. Please provide a valid CONVERGE output file"

Basic performance calculation (50 points)

Calculate the area under the P-V diagram. P is in column 2 and V is in column 8.
Calculate the power output of this engine. Assume that RPM is 1500
This engine consumed 20 micro grams of fuel (per 1 stroke cycle). Calculate its specific fuel consumption.

Ans:

Engine data analysis is a crucial aspect of understanding and optimizing the performance of internal combustion engines. By examining various parameters recorded during engine operation, such as crank angle, cylinder pressure, temperature, and volume, engineers can gain insights into the engine's behavior, efficiency, and areas for improvement.

Importance of Engine Data Analysis

Performance Optimization: Analyzing engine data helps in fine-tuning the engine for maximum efficiency and power output. By understanding the relationship between different parameters, adjustments can be made to improve fuel efficiency and reduce emissions.

Fault Diagnosis: Regular monitoring and analysis of engine data can help in early detection of potential issues. This proactive approach can prevent major failures and extend the engine's lifespan.

Design Improvements: Insights gained from data analysis can inform the design of future engine models, leading to innovations and enhancements in engine technology.

Compliance and Testing: Ensuring that engines meet regulatory standards for emissions and performance is another critical application of engine data analysis. It helps in verifying compliance and conducting rigorous testing.

Python code:

import sys

import math

import numpy as np

import matplotlib.pyplot as plt

crank = []

pressure = []

max_pres = []

min_pres = []

mean_temp = []

max_temp = []

min_temp = []

volume = []

mass = []

density = []

integrated_hr = []

hr_rate = []

c_p = []

c_v = []

gamma = []

kin_visc = []

dyn_visc = []

line_count = 1

# File path

file_path = 'engine_data.out'

# Check file compatibility using if-else

with open(file_path, 'r') as file:

first_line = file.readline()

if not first_line.startswith('#'):

print("File not recognized. Please provide a valid CONVERGE output file.")

sys.exit()

for line in open('engine_data.out'):

if '#' not in line:

crank.append(float(line.split()[0])) #Crank Angle

pressure.append(float(line.split()[1])) #Cylinder Pressure

max_pres.append(float(line.split()[2])) #Maximum Pressure

min_pres.append(float(line.split()[3])) #Minimum Pressure

mean_temp.append(float(line.split()[4])) #Mean Temperature

max_temp.append(float(line.split()[5])) #Maximum Temperature

min_temp.append(float(line.split()[6])) #Minimum Temperature

volume.append(float(line.split()[7])) #Cylinder Volume

mass.append(float(line.split()[8])) #Cylinder Mass

density.append(float(line.split()[9])) #Cylinder Density

integrated_hr.append(float(line.split()[10])) #Integrated Heat Release

hr_rate.append(float(line.split()[11])) #Heat Release Rate

c_p.append(float(line.split()[12]))#Specific Heat at Constant Pressure

c_v.append(float(line.split()[13])) #Specific Heat at Constant Volume

gamma.append(float(line.split()[14])) #Gamma

kin_visc.append(float(line.split()[15])) #Kinematic Viscosity

dyn_visc.append(float(line.split()[16])) #Dynamic Viscosity

# We convert the input from string to float so that

#we can do all arithemtic operations

line_count = line_count+1

# Initialize column labels

data_labels = [

"Crank Angle", "Cylinder Pressure", "Maximum Pressure", "Minimum Pressure",

"Mean Temperature", "Maximum Temperature", "Minimum Temperature", "Cylinder Volume",

"Cylinder Mass", "Cylinder Density", "Integrated Heat Release", "Heat Release Rate",

"Specific Heat at Constant Pressure", "Specific Heat at Constant Volume",

"Gamma", "Kinematic Viscosity", "Dynamic Viscosity",]

grouped_data=[crank, pressure, max_pres, min_pres, mean_temp, max_temp,

min_temp, volume, mass, density, integrated_hr, hr_rate, c_p, c_v, gamma,

kin_visc, dyn_visc ]

print(""""Plotting of Engine data, choose any two number from below:

1) Crank Angle 2)Cylinder Pressure 3) Maximum Pressure 4) Minimum Pressure

5) Mean Temperature 6) Maximum Temperature 7) Minimum Temperature 8) Cylinder Volume

9) Cylinder Mass 10) Cylinder Density 11) Integrated Heat Release 12) Heat Release Rate

13) Specific Heat at Constant Pressure 14) Specific Heat at Constant Volume

15) gamma 16) Kinematic Viscosity 17) Dynamic Viscosity """)

# Ask the user to enter a number

user_input_1=input('enter the first number:')

user_input_2=input('enter the second number:')

# Convert the input from string to float so that we can do all arithemtic operations

numb_1 = int(user_input_1)-1

numb_2 = int(user_input_2)-1

if 0 < numb_1 < 18 and 0 < numb_2 < 18:

x_data=grouped_data[numb_1]

y_data=grouped_data[numb_2]

plt.figure(figsize=(8, 6))

plt.plot(x_data,y_data)

plt.title("ENGINE DATA PLOT")

plt.xlabel(data_labels[numb_1])

plt.ylabel(data_labels[numb_2])

plt.show()

else:

print("Invalid entry")

sys.exit()

# Performance Calculations

work_done = np.trapz(pressure, volume) #area under the curve

# Calculate power output

RPM = 1500

power_output = (work_done * RPM / 120) #considering its a 4 stroke

# Calculate specific fuel consumption

fuel_consumption = 20e-6 # 20 micro grams per stroke

specific_fuel_consumption = (fuel_consumption * 60) / power_output # Convert to grams per kilowatt-hour

print(f"Area under the P-V diagram: {work_done}")

print(f"Power output: {power_output} Watts")

print(f"Specific fuel consumption: {specific_fuel_consumption} grams/kWh")

Explanation:

1) File compatibility is first checked, by checking if the provided file follows a basic pattern seen in our engine data output file.

2) An empty array of all the componets are first created.

3) The script checks if the file starts with a header line (indicated by #). If not, it prints an error message and exits. It reads data from the file, skipping header lines and extracting values into various lists (e.g., crank, pressure, volume).

4) All the empty arrays are appended with the corresponding values from engine data file.

5)We created data_labels and grouped_data, this is mainly done for plotting purpose. using this we can automate the plot so that we need not manually create individual plot.

6) The script prompts the user to select two columns by their indices for plotting. It generates a plot using matplotlib based on the selected columns and labels it automatically using predefined column labels.

7) Finally performance calculation is carried out

Results:

Run 1:

first number inputted: 5

second number inputted: 6