Analysing the engine data geometry with data visualizer tool in Python

Objective: Task is 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. NOT TO BE DONE MANUALLY.

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

Brief:

In the field of automation, an automation engineer has to analyse all kinds of data in order to optimize the best values for the system. Here we have the engine geometery to compute the temperature, crank an all other elements to plot the ideal graph. And we are using file parsing method to get all the values. We have an .OUT data file where we get all the geometrey parameters and to proceed we have to wrte a script in python. Below is the image of DATA file we have.

Original Coding:

\"\"\"
Data analysis on engine geometery
\"\"\"
import matplotlib.pyplot as plt
import numpy as np
import math

# given all geometeric parametres

crank = []
pressure = []
max_press = []
min_press = []
mean_temp = []
max_temp = []
min_temp = []
volume = []
mass = []
density= []
inte_hr = []
hr_rate = []
c_p = []
c_v = []
gamma = []
kin_visc = []
dyn_visc = []

try:

	open(\'engine_data.out\')

# Compatablity check

except wrong_imput:
	print(\'File not recognized. Please provide a valid converge output file\')

for line in open(\'engine_data.out\'):
	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]))
		inte_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]))

# Basic performance calculation

# plotting of PV diagram and pressure vs crank graph
plt.figure(1)
plt.plot(volume, pressure, linewidth = 3)
plt.xlabel(\'Volume [m^3]\')
plt.ylabel(\'Pressure [bar]\')
plt.title(\'PV diagram\')
plt.grid()
plt.show()
plt.figure(2)
plt.plot(crank, pressure, linewidth = 3)
plt.xlabel(\'Crank angle [deg]\')
plt.ylabel(\'Pressure [bar]\')
plt.title(\'Crank vs Pressure diagram\')
plt.grid()
plt.show()

# calculating area under PV plot
area = np.trapz(pressure, volume)
print(\'Area under PV diagram (m^2)\', area)

# calculating power output
rpm = 1500 # assuming the data
rps = rpm/60
power = area*rps*1000
print(\'Power output of an engine (W) is\', power)

# calculating Specific Fuel Consumption(SFC)
sfc = (20*pow(10,-6)*3600/(0.08*power))
print(\'specific fuel consumption (g/kwh) is\', sfc)

Output:

Now here we have the plot of PV diagram where curve behaves in a strange way that can be shown as below.

And another graph represent the crank vs maximum pressure plot which as shown below.

We have one more ouput where we got results as area under the P-V diagram. P is in column 2 and V is in column 8, Power output of this engine. Assuming the RPM is 1500 and the engine consumed 20 micro grams of fuel (per 1 stroke cycle) having its specific fuel consumption in g/KWh.

Conclusion:

Through data analysis we have plotted the graph with the desirable requirements. With having the ideal graph as PV where curve behaves randomly.

We have calculated the area under the curve PV, Power generation that engine and Specific fuel consumption.

                                                        --FIN--