Zone Model with Timber Lining

A Python implementation of a numerical zone model that simulates fire behaviour in an enclosure with timber-lined walls and ceiling. This model considers a single zone approach for analysing fire dynamics in compartments with combustible linings.

Features

Work in progress.

Installation

Work in progress.

Usage

from zone_model_1 import main_model_as_function

(
    output_time_arr,
    output_gas_temp_arr,
    output_char_depth_arr,
    output_charring_rate_arr,
    output_MLR_arr,
    output_HRR_wood_arr,
    output_HRR_total_arr,
    output_HRR_ext_arr,
    HRR_time_arr,
    HRR_hrr_arr,
) = main_model_as_function()

import matplotlib.pyplot as plt

plt.style.use('seaborn-v0_8-whitegrid')
fig, ax1 = plt.subplots()
ax1.plot(output_time_arr, output_gas_temp_arr, label='Gas')
ax1.set_ylabel('Temperature [$^o$C]')
ax1.minorticks_on()
fig.tight_layout()

fig, ax2 = plt.subplots()
ax2.plot(output_time_arr, output_char_depth_arr, label='Char')
ax2.set_xlabel('Time [min]')
ax2.set_ylabel('Char depth [mm]')
fig.tight_layout()

fig, ax3 = plt.subplots()
ax3.plot(output_time_arr, output_charring_rate_arr, label='Char')
ax3.set_xlabel('Time [min]')
ax3.set_ylabel('Charring rate [mm/min]')
fig.tight_layout()

fig, ax4 = plt.subplots()
ax4.plot(output_time_arr, output_MLR_arr, label='Char')
ax4.set_xlabel('Time [min]')
ax4.set_ylabel('MLR [kg/m$^2$/s]')
fig.tight_layout()

fig, ax5 = plt.subplots()
ax5.plot(output_time_arr, output_HRR_wood_arr, label='wood')
ax5.plot(HRR_time_arr, HRR_hrr_arr, label='contents')
ax5.plot(output_time_arr, output_HRR_total_arr, label='total internal')
ax5.plot(output_time_arr, output_HRR_ext_arr, label='total external')
ax5.set_xlabel('Time [min]')
ax5.set_ylabel('HRR [W]')
ax5.legend().set_visible(True)
ax5.set_xlim(0, 120)
fig.tight_layout()

plt.show()

Code Structure

todo

Mathematical Model

todo

Key Equations

todo

Dependencies

See pyproject.toml.

Validation

todo

Contributing

Please follow PEP 8 for naming except variable names from literature.

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

References

todo