Week 6 - Data analysis

AIM - Write a script that does data analysis.

Objectives - To ensure

1. Data visualizer

2. Compatability check

3. Basic performance calculation

Theory

This code takes takes the data from a user specified CONVERGE format file. It also checks the file for the correct format i.e. compatibility. If the file is not a CONVERGE output data format then the code will throw an error statement "This is not a valid CONVERGE output file.". Also, if the file format is correct but, there is no data present in the file then the code will prompt the user "No data present in the file".

If the format is correct and data is present then the code will implement following steps:-

1) Data extraction - Extracting data along with the column name and column number from the CONVERGE output file. Complete data from the file is stored in a two dimensional array 'data' The columns of data matrix/2D array contains the data for each individual parameter.

2) Data visualizer - Provide an "Input map" to user so that he/she can use numbers as an input to designate the variables to be ploted. Ask user if plot between any two variables is required. If input is 'y' representing 'yes' then, ask for the numbers representing the variables to be ploted (Refering input map). Ask the user if the plot is to be saved. Then, show the plot. Above step is under while loop and runs untill user gives an input 'n' to the question "Do you want to plot any other variable? (y/n): ".

3) Basic performance - Extract volume and pressure data from matrix 'data' and store in array 'volume' and 'pressure' respectively. Next, numerically integrate pressure and volume data using 'simps' function (Function for numerical integration by Simphson's rule) from scipy.integrate library. This integration is area under the P-V curve i.e. work done per cycle. Then, RPM of the engine at which the data was collected and fuel consumption in microgram per cycle is taken as user input. Next, the power produced by the engine at this RPM is calculated. Finally, the Specific fuel consumption is calculated and Area under the curve, Power output and Specific fuel consumption are displayed.

A=Area under the P-V curve=Work done per cycle (MJ)A=Area under the P-V curve=Work done per cycle (MJ)

Power output =A⋅n⋅106 WPower output =A⋅n⋅106 W

n=Number of cycles per secondn=Number of cycles per second

n=N2⋅60n=N2⋅60` for 4-stroke engine

n=N60n=N60 for 2-stroke engine

N = Rotations per min.N = Rotations per min.

SFC(Specific Fuel consumption)=Fuel consumed in kg/hrPower output in kW

Theoritical Background:

Data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.

Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses.

PROCEDURE:

1] Import the modules such as Math, matplotlib, numpy,scipy for performing their respective functions

2] For the purpose of plotting the curves between 2 quantities as per the user input, a line counter is initiated from 1. Now an "if" loop is iterated with the condition if '#' not in line all the numerical values present in the data file is now to be stored in the data array as string. Now another "if" loop is executed with the conditon that line counter greater than or equal to 6 all the data will be stored in the pressure and volume array respectively.

3] For Engine performance calculations, the area under the PV diagram has to be calculated and we are using Simpson method for calculating it.

4] Two arrays of name of parameters and their units are made as per the data file given and then the user has to choose from them to plot respective graphs. For this 2 inputs of the user are taken using the input command and data type is set as 'int'. The user is entering column number as input and results are generated.

Programming in Python:

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

#Compatibility check
fname = input("Enter the filename:..")

if fname == 'engine_data.out':
	print('file is valid')
else:
	print('file is invalid')

	exit()

def compatibility_check(a):

	if 'CONVERGE' not in a:

		print('Please provide converge output file to process')

		exit()

	else:

		print('Compatibility check is successful')

		pass

#try:
open('engine_data.out')


line_count = 1

# Creating an empty array to store the values of Volume and pressure respectively 
Volume =[]
pressure =[]

v = 8
p = 2

# Creating null array and storing all the values of data file in to it 

data =[]

for line in open('engine_data.out'):
	if '#' not in line:
	  data.append(line.split())
	  if line_count >= 6:
	  	Volume.append(float(line.split()[v-1]))
	  	pressure.append(float(line.split()[p-1]))
			
	line_count = line_count + 1

#plotting p-v diagram
plt.plot(Volume, pressure, linewidth = 2, color = 'b')
plt.xlabel('Volume')
plt.ylabel('pressure')
plt.title("p-v diagram of engine")
plt.grid()
plt.show()

area = simps(pressure,Volume)
print('Area under the curve using Simpsons Rule :' +str(area) + ' units')
print('Work done by the engine : ' + str(area) + ' MJ')
area = area * 1e6
print('Work done by engine : ' + str(area) + ' J')

	# Inputs 
	# Speed of an Engine 
rpm = 1500
rps = 1500/60
stroke_cycle = rps/2

	# Fuel consumption in micrograms per 1 stroke cycle 

fuel_consumption = 20

	# Engine specifications 
	# Number of cylinder
n = 1
	# 4 stroke engine
	# Calculation of power output and Specific fuel consumption 

power_output = n*area*stroke_cycle/(1000)
print('power output from engine : ' + str(power_output) + 'KW')
SFC = (fuel_consumption*pow(10,-6)*stroke_cycle*3600)/(power_output*1000)
print('Specific Fuel consumption, SFC = ' + str(SFC) + ' (kg/hr)/KW')
print('t')

	# Calculation of Area under PV diagram 
Area = np.trapz(pressure,Volume)
print('Area under curve using trapezoidal rule : ' + str(Area) + 'units')
print('Work done by the engine : ' + str(Area) + ' MJ' )
Area = Area * 1e6
print('Work done by engine : ' + str(Area) + ' J')
power_output = n*Area*stroke_cycle/(1000)
print('power output from the engine : ' + str(power_output) + 'KW')
SFC = (fuel_consumption*pow(10,-6)*stroke_cycle*3600)/(power_output*1000)
print('Specific Fuel consumption, SFC = ' + str(SFC) + ' (kg/hr)/KW')
print('t')

	# Transposing columns in to row matrix and converting them in to float type from string type
transpose = np.transpose(data)
float_f  = transpose.astype(np.float)
	# Array for header and their units 

parameters = ['Crank','Pressure','Maximum Pressure','Minimum Pressure','Mean Temperature','Maximum Temperature','Minimum Temperature','Volume','Mass','Density','Integrated HR','HR rate','Cp','Cv','gamma','Kinematic Viscosity','Dynamic Viscosity']
units = ['(DEG)','(Mpa)','(Mpa)','(Mpa)','(K)','(K)','(K)','pow(m,3)','(kg)','(kg/(pow(m,3)))','(J)','(J)','(J/time)','(J/kgK)','(J/kgK)','','pow(m,2)/s','(Ns/pow(m,2)']

# Providing user choice of picking 2 parameters for plotting 
print('Choose from following options and press enter key')
print('1 Crank \n 2 Pressure \n 3 Maximum Pressure \n 4 Minimum Pressure \n 5 Mean Temperature \n 6 Maximum Temperature \n 7 Minimum Temperature \n 8 Volume \n 9 Mass \n 10 Density \n 11 Integrated HR \n 12 HR rate \n 13 Cp \n 14 Cv \n 15 gamma \n 16 Kinematic Viscosity \n 17 Dynamic Viscosity')

X = int(input('Enter your choice on X-axis \n'))

Y = int(input('Enter your choice on Y-axis \n'))


# Plotting hr figure based on user choice 
plt.figure(1)
plt.plot(float_f[X-1],float_f[Y-1],linewidth=3)
plt.title(' plot for ' + parameters[Y-1]+ ' versus ' +parameters[X-1],color='k')
plt.xlabel(parameters[X-1] + ' ' +units[X-1])
plt.ylabel(parameters[Y-1] + ' ' +units[Y-1])
plt.grid()
plt.show()

PLOTS and RESULTS:

Conclusion

Hence , the data analysis was done and the all the questions were answered.