Python code for the DOE project

The highaltitude_light folder houses three files that contains datasets used in this project. The three files are:

1. The equi_ratio.npy file: This file contains the equivalence ratio of different fuels used in the highAltitudeRelightcode.ipynb jupyter notebook file.
2. The fuels_PHY_properties.npy file: This file contains the physical properties of the different fuels used in this project.
3. The fuels_CHEM_properties.npy file: This file contains the chemical properties of the fuel used in this project.

The lean_blow_out folder houses three files that contains datasets used in the lean_blow_out.ipynb jupyter notebook file. The three files are:

1. The lbo_300_450_550.npy file: This file contains the equivalence ratio at lean blow out for temperature 300K, 450K and 550K respectively for the different fuels used in this project.
2. The New_CHEM_properties.npy file: This file contains the chemical properties of fuels used in this project.
3. The New_Phy_properties.npy file: This file contains the physical properties of fuel used in this project.

The common.py Python file is a helper class containing methods that allow for easy plotting and result visualization.

The highAltitudeRelightcode.ipynb is a Jupyter notebook containing the Python code that performs the statistical analysis of fuel properties (physical and chemical) in relation to High Altitude Relight. The file contains plots that shows how each of the physical and chemical property affects high altitude relight. A simple linear Regression was used to get the relationship between each fuel property and high altitude relight. Random Forest Regressor was also used to rank how each fuel property affects high altitude relight prediction

The lean_blow_out.ipynb is a Jupyter notebook containing the Python code that performs the statistical analysis of fuel properties (physical and chemical) in relation to Lean blow out. The file contains plots that shows how each of the physical and chemical property affects Lean blow out. A simple linear Regression was used to get the relationship between each fuel property and Lean blow out.Random Forest Regressor was also used to rank how each fuel property affects Lean blow out prediction

The results of the analysis include the following:

1. How each physical property of fuel affects LBO and high altitude relight.
2. How each chemical property of fuel affects LBO and high altitude relight.
3. The relationship (correlation plot) among all the properties of the fuel.
4. The property of the fuel that strongly affects LBO and High altitude relight.

To visualize the LBO analysis, open the lean_blow_out.ipynb notebook and run all cells. To visualize the High altitude relight analysis, open the highAltitudeRelightcode.ipynb notebook and run all cells.