Challenge

Week 6 - Data analysis

JJaswanth Sai Padugupadu7 Oct 2024

AIM: To perform the data analysis of a given data by programming in Python

Theoretical 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.

The four types of data analysis are:

Descriptive Analysis.
Diagnostic Analysis.
Predictive Analysis.
Prescriptive Analysis.

The data file given consist of various parameters of an engine. We have to calculate the below parameters

1] Work done by the engine is obtained by calculating the area under the P-V diagram

2] Power output of the engine is given as below

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 condition 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.

Program coding:

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:

Conculsion:

The Python program ran successfully and the results have been obtained.

Week 6 - Data analysis

Ship work like this
in your own course.

Talk to a career counsellor

Ship work like thisin your own course.

Talk to a career counsellor

Ship work like this
in your own course.