Data Analysis of an IC engine Simulation using PYTHON

I. Introduction :

In this project, we use a data file obtained during an Internal Combustion engine simulation using Converge software. The simulation creates a large amount of data amounting to thousands of lines and to analyse such data we create a Data visualizer tool, scripting it in python. Since the data produces a number of properties of an Ic engine, this tool helps us visualize exactly the property we need, its behaviour and allows the flexibility to perform calculations on this data set with individual properties.

Data file from Converge software : engine_data.out

II. Data Visualizer :

• Python Script :

"""
	File parsing in python 

	Code by : Aadil Shaikh 
"""

import matplotlib.pyplot as plt
import numpy as np
import math

line_count = 1
crank = []
pressure = []
max_press = []
min_press = []
mean_temp = []
max_temp = []
min_temp = []
mass = []
density = []
temperature = []
volume=[]
integrated_hr=[]
hr_rate = []
c_p = []
c_v = []
gamma = []
kin_visc = []
dyn_visc = []
column_no=[]
properties=[]
units=[]


data_file = input('Type the file name : ')

try: 
	file_data = open(data_file,'r')

	# File parsing starts here
	for line in file_data:

		if line_count is 2:
			for i in range(2,18):
				column_no.append(line.split()[i])
			#print(column_no)

		if line_count is 3:
			for k in range(0,18):
				properties.append(line.split()[k])
			#print(properties)

		if line_count is 4:
			for p in range(0,18):
				units.append(line.split()[p])
			#print(units)

		
		line_count = line_count + 1

		if '#' not in line:
			crank.append(float(line.split()[0]))
			pressure.append(float(line.split()[1]))
			max_press.append(float(line.split()[2]))
			min_press.append(float(line.split()[3]))
			mean_temp.append(float(line.split()[4]))
			max_temp.append(float(line.split()[5]))
			min_temp.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]))
			c_p.append(float(line.split()[12]))
			c_v.append(float(line.split()[13]))
			gamma.append(float(line.split()[14]))
			kin_visc.append(float(line.split()[15]))
			dyn_visc.append(float(line.split()[16]))

	column_nos = np.array([[0],crank,pressure,max_press,min_press,mean_temp,max_temp,min_temp,volume,mass,density,integrated_hr,hr_rate,c_p,c_v,gamma,kin_visc,dyn_visc])

	print('n Choose Simulation parameters to Generate plot :  ')
	print('n 1. Crank n 2. Pressure n 3. max_press n 4. min_press n 5. mean_temp n 6. max_temp n 7. min_temp n 8. volume n 9. mass n 10. density n 11. integrated_hr n 12. hr_rate n 13. c_p n 14. c_v n 15. gamma n 16. kin_visc n 17. dyn_visc')
	x_value = int(input('n Choose parameter on X-axis : '))
	y_value = int(input('n Choose parameter on Y-axis : '))


	plt.plot(column_nos[x_value],column_nos[y_value],color="red",linewidth = 2)
	plt.xlabel(str(properties[x_value]) + ' ' + str(units[x_value]))
	plt.ylabel(str(properties[y_value]) + ' ' + str(units[y_value]))
	plt.title(str(properties[x_value]) + ' '+'vs'+ ' ' + properties[y_value])
	plt.savefig(str(properties[x_value]) + ' '+'vs'+ ' ' + properties[y_value])
	plt.show()


	if x_value == 2 and y_value == 8 or x_value == 8 and y_value == 2:

		rpm = 1500
		fuel_consumed_perstroke = 20 #micrograms
		N = np.trapz(column_nos[x_value],column_nos[y_value])
		p = (2*math.pi*rpm*N)/(60)
		sfc = ((fuel_consumed_perstroke*rpm)*(pow(10,-6))*(3600))/(p*1000*60)

	plt.fill_between(column_nos[x_value],column_nos[y_value],linewidth = 2 , color = 'red' , alpha = 0.35)
	plt.show()

	print('n Engine Performance Calculation')
	print('n Area under the pV plot = ', N , 'N-m')
	print('n Power output = ', p, 'MW')
	print('n sfc = ',sfc, 'g/kW-h')	


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

finally: 
	print("n code exiting")

1. The Data visualizer script asks for a converge output file name, and starts appending all the Column Numbers which represent different thermodynamic properties of the IC engine simulation. Then the Property header names and units.

2. The data is in a column format, hence we append all the values of the data as per each column in its respective property after splitting it.

3. The array of all properties is created using numpy module, to call each property data set according to its column number inside the data.

4. Then the program asks for the user to input in X and Y axis column number, representing the property needed to visualize.

5.

6. Once the user inputs the property required to be visualized, the program generates its plot and extracts the Property names, units & its title automatically from the header information and saves the Plot by its name.

6. Few plots Generated :

7 .

8.

9.

III. Performance Calculation :

Demonstration of how this data set can be utilized in perfomance calculation from these properties . Here, the calculation of Area under the PV diagram, Power output and specific fuel consumption of the engine is calculated.

The code has been modfied to give the performance results only when selecting the properties - Pressue and volume in either order.

Assumptions :

1500 rpm, Fuel consumed per stroke = 20 micrograms

IV. Compability check :

Code gives an error when non-compatible file is provided as an input and exits.

keyword - PYTHON, PYTHON-BASICS, CFD, IC-ENGINE-CFD