Week 6 - Data analysis

Aim:

To write a script that does the following:

Data visualizer

• 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.

Compatability check

• 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

• 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.

Python code: (with explanation)

#Week 6 - Data Analysis

import matplotlib.pyplot as plt 
import numpy as np
from scipy.integrate import simps


# 1. Data visualizer

#creating empty array for all 17 parameters
crank = []
pressure = []
max_pressure = []
min_pressure = []
mean_temperature = []
max_temperature = []
min_temperature = []
volume = []
mass = []
density = []
integrated_HR = []
HR_rate = []
Cp = []
Cv = []
gamma = []
kinematic_viscosity = []
dynamic_viscosity = []


line_count1 = 1

for line in open('engine_data.out'):	#appending values into each of the 17 empty arrays after extracting the data from the data file.
	if '#' not in line:
		crank.append(float(line.split()[0]))
		pressure.append(float(line.split()[1]))
		max_pressure.append(float(line.split()[2]))
		min_pressure.append(float(line.split()[3]))
		mean_temperature.append(float(line.split()[4]))
		max_temperature.append(float(line.split()[5]))
		min_temperature.append(float(line.split()[6]))
		volume.append(float(line.split()[7]))
		mass.append(float(line.split()[8]))
		density.append(float(line.split()[9]))
		integrated_HR.append(float(line.split()[10]))
		HR_rate.append(float(line.split()[11]))
		Cp.append(float(line.split()[12]))
		Cv.append(float(line.split()[13]))
		gamma.append(float(line.split()[14]))
		kinematic_viscosity.append(float(line.split()[15]))
		dynamic_viscosity.append(float(line.split()[16]))
		
		line_count1 = line_count1 + 1


#creating a numpy array to store all the values under each of the 17 parameters
all_values = np.array([crank, pressure, max_pressure, min_pressure, mean_temperature, max_temperature, min_temperature, 
	volume, mass, density, integrated_HR, HR_rate, Cp, Cv, gamma, kinematic_viscosity, dynamic_viscosity])

col_names = []	#creating an empty array for colum names

line_count2 = 1	

for row in open('engine_data.out'):		#using for loop to append values into the empty array col_names after splitting them.
	if line_count2 == 3:
		col_names.append(row.split())
	line_count2 = line_count2 + 1

col_labels = col_names[0]	#extracting the required list/ array from the col_names and storing them in col_labels.


#Plotting each of the 17 columns/ parameters as separate images with plot labels extracted from the given data file and plots saved by the name of the columns.
for col_no in range(1, 18):
	plt.figure(col_no)
	plt.plot(all_values[col_no - 1])
	plt.xlabel('Number of entries')
	plt.ylabel(col_labels[col_no])
	plt.savefig(col_labels[col_no] + '.png')
	plt.show()


#For plotting two parameters
option = int(input('To generate a plot between two parameters, press "2"\n'))	#obtaining input from the user

if option == 2:
	sub_option1 = int(input('Choose your parameter for x-axis: \n1 - Crank \n2 - Pressure \n3 - Maximum pressure \n4 - Minimum pressure \n5 - Temperature \n6 - Maximum temperature \n7 - Minimum temperature \n8 - Volume \n9 - Mass \n10 - Density \n11 - Integrated HR \n12 - HR rate \n13 - Cp \n14 - Cv \n15 - Gamma \n16 - Kinematic Viscosity \n17 - Dynamic Viscosity \n'))
	
	sub_option2 = int(input('Choose your parameter for y-axis: \n1 - Crank \n2 - Pressure \n3 - Maximum pressure \n4 - Minimum pressure \n5 - Temperature \n6 - Maximum temperature \n7 - Minimum temperature \n8 - Volume \n9 - Mass \n10 - Density \n11 - Integrated HR \n12 - HR rate \n13 - Cp \n14 - Cv \n15 - Gamma \n16 - Kinematic Viscosity \n17 - Dynamic Viscosity \n'))
	
	#Plotting the requested graph
	plt.figure()
	plt.plot(all_values[sub_option1 - 1], all_values[sub_option2 - 1])
	plt.xlabel(col_labels[sub_option1])
	plt.ylabel(col_labels[sub_option2])
	plt.show()


# 2. Basic performance calculation

#Area under the P-V diagram
p_col = 2
v_col = 8

area = simps(all_values[p_col-1], all_values[v_col-1])
print('The area under the P-V diagram is: ' + str(area*pow(10,3)) + ' KJ')

#Power output in KW
rpm = 1500
power = area*pow(10,6) * rpm / 9549 	#power in KiloWatts
print('Power output of the engine at 1500 rpm is: ' + str(power) + ' KW')

#Specific fuel consumption in g/J
sfc = (20*pow(10,-6) * rpm * 60) / (power*pow(10,3))
print('Specific fuel consumption is: ' + str(sfc) + ' g/J')

#3. Compatibility check

#Note: I would like to know how this is done.

Results:

Data Visualizer:

Single parameter plots:

Two parameter plot (example - PV):

Basic performance calculation:

• The area under the P-V diagram is: 0.5005864474262364 KJ
• Power output of the engine at 1500 rpm is: 78.63437754103619 KW
• Specific fuel consumption is: 2.2890751555331518e-05 g/J