Fire Safety Journal

The data sets contain the raw data (i.e. gas temperatures and temperatures measured by Thin Skin Calorimeters) pertaining to the experimental work done in this paper. Additionally, processed data is also presented (i.e. calculated heat fluxes) alongside heat flux curves as used in the manuscript.

This Dataset was created based on Remote Sensing data to predict the occurrence of wildfires, it contains Data related to the state of crops (NDVI: Normalized Difference Vegetation Index), meteorological conditions (LST: Land Surface Temperature) as well as the fire indicator “Thermal Anomalies”. All three parameters were collected from MODIS (Moderate Resolution Imaging Spectroradiometer), an instrument carried on board the Terra platform. The collected data went through several preprocessing techniques before building the final Dataset. The experimental Dataset is considered as a case study to illustrate what can be done at larger scales. The Data contains parameters with high influence of wildfires occurrence collected using remote sensing. The Dataset is composed of four columns, the first three columns are NDVI, LST, and Thermal Anomalies and the fourth column represents the corresponding class (fire or no_fire), the Dataset contains 804 rows: 386 instances of the class “fire” and 418 instances of the class “no fire” with 418 rows. Each row contains the collected data and its class. The data were downloaded from the official website of NASA's Land Processes Distributed Active Archive Center (LP DAAC), and then we preprocessed them using multiple preprocessing techniques to remove noises and correct inconsistencies, and finally extracting useful information. The study area is composed of multiple zones located in the center of Canada. The surface of this area is approximately 2 million hectares. These zones differ in their size, burn period, date of burn and extent. We have chosen to apply the experiment in a big region of Canada's forests because it is known for its high rate of wildfires and also for the availability of fire information (start and end fire date, cause of fire and the surface of the burned area in hectares), these information were acquired from The Canadian Wild-land Fire Information System (CWFIS) which creates daily fire weather and fire behavior maps year-round and hot spot maps throughout the forest fire season

Videos of flame spread over 8-mm and 9-mm thick research wires with copper (Cu) and stainless steel (SS) cores and low-density polyethylene (LDPE). The wire is positioned vertically and horizontally under various opposed flow velocity in the wind tunnel. The flame spread rate is measured from the video.

These datasets contain supplementary material for the article " Experimental data about the evacuation of preschool children from nursery schools, Part II: Movement characteristics and behaviour " accepted to Fire Safety Journal on April 16, 2023 (DOI 10.1016/j.firesaf.2023.103797). The article presents experimental data sets on the evacuation movement and behaviour of preschool children observed during 15 evacuation drills in 10 nursery schools in the Czech Republic involving 970 children (3-7 years of age) and 87 staff members. In the presented spreadsheets, raw experimental data on speed-density and flow-density relationships are provided separately for corridors, straight staircases (flights, landings, and entire staircase), and doorways. The movement travel speed ('Speed') is expressed in [m·s-1], specific flow ('Flow') in [pers·s-1·m-1], density variable is expressed in the units of [pers·m−2] ('Density1') and [m−2·m−2] ('Density2'). Observations made for the different age groups of children are distinguished by letters: 'J' – Junior, 'S' – Senior, 'S+' - Senior+, 'M' – Mixed. In speed-density data sets, observations for walking children are denoted as 'W', for running children as 'R' (e.g., 'JW' – Junior walking). Speed-density data points that were calculated by excluding waiting times of children (only in the spreadsheets for corridors and landings of straight staircases) are marked 'M' after age group denotation (e.g., 'SWM-Speed' – modified speed data points for Senior walking children).

The experimental results from the article: Simulation of a premixed explosion of gas vented during Li-ion Battery Failure. Each csv file contains the four pressure readings, the flame front, and flame velocity for one experiment. Fuel composition and fuel-air equivalence ratio are also included in the csv file.

mech2Foam is a Python code for generating gas mixture specific input parameters needed for XiFoam simulations. For the thermopysical properties, the code generates NASA polynomial coefficients (Cp, H, S) and sutherland coefficients for dynamic gas viscosity. The laminar burning velocity are modeled by the Gülder equation and the code generates Gülder coefficients for fuel and oxidizer. Other CFD codes that can use the same input data can utilize this code.

mech2Foam is a Python code for generating gas mixture specific input parameters needed for XiFoam simulations. For the thermopysical properties, the code generates NASA polynomial coefficients (Cp, H, S) and sutherland coefficients for dynamic gas viscosity. The laminar burning velocity are modeled by the Gülder equation and the code generates Gülder coefficients for fuel and oxidizer. Other CFD codes that can use the same input data can utilize this code.

The experimental results from the article: Simulation of a premixed explosion of gas vented during Li-ion Battery Failure. Each csv file contains the four pressure readings, the flame front, and flame velocity for one experiment. Fuel composition and fuel-air equivalence ratio are also included in the csv file.

The file attached contains a complete set of experimental data from carbon-rich soil self-heating ignition cubic basket experiments for a range of soil inorganic content (IC) ranging from 3% to 86%. The experiments were carried out in a thermostatically controlled oven with thermocouples for measuring the ambient and soil temperatures. The data reported includes the dates of experiments, volume of soil baskets being tested, oven ambient temperature, inorganic content present in the sample, bulk density of the soil and if the sample ignited or not. This data is in support of the journal paper: F. Restuccia, X. Huang, G. Rein, Self-ignition of Natural Fuels: Can Wildfires of Carbon-Rich Soil Start by Self-heating?, Fire Safety Journal 2017, http://doi.org/10.1016/j.firesaf.2017.03.052.