The ignition temperature of the aluminium copper oxide (Al-CuO) thermite was measured using differential thermal analysis (DTA) at a scan rate of 50 °C/min in an inert nitrogen atmosphere. Thermite reactions are difficult to start as they require very high temperatures for ignition, e.g. for the Al-CuO thermite comprising micron particles it is ca. 940 °C. It was found that the ignition temperature is significantly reduced when the binary Si-Bi2O3 system is used as sensitiser. Further improvement is achieved when nano-sized particles are used. For the composition CuO + Al + Bi2O3 + Si (65.5:14.5:16:4 wt %), when all components except the aluminium fuel are nano-sized, the observed ignition temperature is reduced to ca. 615 °C and results in a thermal runaway. / Dissertation (MSc)--University of Pretoria, 2011. / Chemical Engineering / unrestricted

An accumulated combustible dust layer on some hot process equipment such as dryers or hot bearings can be ignited and result in fires when the hot surface temperature is sufficiently high. The ASTM E 2021 test procedure is often used to determine the Hot Surface Minimum Ignition Temperature for a half inch deep layer of a particular dust material. This test procedure was used in this thesis to study possible effects of combustible liquid (such as lubricating oil) and powder additives in the dust layer as well as air flow effects. The following combustible dusts were used: paper dust from a printing press, Arabic gum powder, Pittsburgh seam coal, and brass powder. To develop an improved understanding of the heat transfer, and oxygen mass transfer phenomena occurring in the dust layer, additional instrumentation such as a second thermocouple in the dust layer, an oxygen analyzer and gas sampling line, and an air velocity probe were used in at least some tests. Hot Surface Minimum Ignition temperatures were 220oC for Pittsburgh seam coal, 360oC for paper dust, 270¡Ãƒâ€° for Arabic gum powder, and > 400oC for brass powder. The addition of 5-10 weight percent stearic acid powder resulted in significantly lower ignition temperature of brass powder. When combustible liquids were added to the dust layer, the ignition temperatures did not decrease regardless of the liquids¡¯ ignitibility because the liquids seemed to act as heat absorbents. Although air velocity on the order of 1 cm/s did not affect test results, much larger air velocities did affect the results. With 33 cm/s downward airflow at the elevation of the surface of the layer, Pittsburgh seam coal was not ignited at 230¡Ãƒâ€° which was 10¡Ãƒâ€° higher than the 220¡Ãƒâ€° hot surface ignition temperature without airflow. Based on the results and data from the additional instrumentations, modifications of the ASTM E2021 test procedure are recommended.

Current fire-spread models are based largely on empirical correlations based on fires burning through dead pine needles. There is a need to increase the accuracy of modeling wildfires in live vegetation. This project investigates the quantitative and qualitative ignition characteristics of eight live fuels, four from southern California (manzanita, scrub oak, ceanothus, and chamise) and four from Utah (canyon maple, gambel oak, big sagebrush, and Utah juniper). Individual leaves were observed as they were exposed to hot gases from a flat flame burner. The broadleaf species from both California and Utah had noticeable surface changes during the ignition process. All fresh samples showed a color change on the leaf surface from a light dusty color to a dark wet color. This is likely due to the melting of the waxy protective layer. Samples of scrub oak, manzanita, ceanothus, canyon maple, and gambel oak at moderate moisture contents (50 to 75%) exhibited bubbling under the leaf surface. Liquid droplets were observed on the surface of Manzanita samples at moisture contents near 75%, while bursting was observed on the surface at moisture contents near 100%. This bursting is due to evaporation of the moisture inside the leaf causing internal pressures to exceed the surface strength of the leaf. Ignition was defined as the time when the first visible gaseous flame was observed near the leaf surface. Measurements of the time to ignition and the temperature at ignition were performed for all broadleaf species. A large degree of scatter was observed in the quantitative ignition data, due largely to variations in leaf thickness and moisture content. Time to ignition was found to correlate with sample thickness and the mass of moisture in the sample. Ignition temperature was constant for varying moisture mass but appeared to increase with thickness. The burning time, defined as the duration of a visible flame near the leaf, was found to correlate roughly with leaf mass. Several types of correlations were made to describe ignition temperature and ignition time as a function of leaf thickness and mass of moisture.

The current research is aimed at investigating the explosion behaviour of hazardous materials in relation to particle size. The materials of study are titanium powders having size distributions in both the micron- and nano-size ranges with nominal size distributions: -100 mesh, -325 mesh, ?20 ?m, 150 nm, 60-80 nm, and 40-60 nm. The explosibility parameters investigated explosion severity and explosion likelihood for both size ranges of titanium. Tests include, maximum explosion pressure (Pmax), maximum rate of pressure rise ((dP/dt)max), minimum explosible concentration (MEC), minimum ignition energy (MIE), minimum ignition temperature (MIT) and dust inerting using nano-titanium dioxide. ASTM protocols were followed using standard dust explosibility test equipment (Siwek 20-L explosion chamber, MIKE 3 apparatus, and BAM oven). The explosion behaviour of the micron-size titanium has been characterized to provide a baseline study for the nano-size testing, however, nano-titanium dust explosion research presented major experimental challenges using the 20-L explosion chamber.

<div>Hot surface ignition is a safety design concern for serval industries including mining, aviation, automotive, boilers, and maritime applications. Bleed air ducts, exhaust pipes, combustion liners, and machine tools that are operated at elevated temperatures may be a source of ignition that needs to be accounted for during design. An apparatus for the measurements of minimum hot surface ignition temperature (MHSIT) of 3 aviation fluids (Jet-A, Hydraulic Oil (MIL-PRF-5606) and Lubrication Oil (MIL-PRF-23699)) has been developed. This study expands a widely utilized database of values of MHSIT. The study will expand the current range of design parameters including air temperature, crossflow velocity, fluid temperature, global equivalence ratio, injection method, and the effects of pressure. The expanded data are utilized to continue the development of a physics-anchored data dependent system and machine learning model for the estimation of MHSIT.</div><div><br></div><div>The aviation industry, including Rolls Royce, currently use a database of MHSIT values resulting from experiments conducted in 1988 at the Air Force Research Laboratory (AFRL) within the Wright Patterson Air Force Base in Dayton, OH. Over the three decades since these experiments, the range of operating conditions have significantly broadened in most applications including high performance aircraft engines. For example, the cross-stream air velocities (V) have increased by a factor of two (from ~3.4 m/s to ~6.7 m/s). Expanding the known database to document MHSIT for a range of fuel temperatures (TF), air temperatures (TA), pressure (P) and air velocities (V) is of great interest to the aviation industry. MHSIT data for current aviation fluids such as Jet-A and MIL-PRF-23699 (lubrication oil) and their relation to the design parameters have recently been under investigation in a generic experimental apparatus. </div><div><br></div><div>The current work involves utilization of this generic experimental apparatus to further the understanding of MHSIT through the investigation of intermediate air velocities, global equivalence ratios, injection method, and the effects of pressure. This study investigates the effects of air velocity in a greater degree of granularity by utilizing 0.6 m/s increments. This is done to capture the uncertainty seen in MHSIT values above 3.0 m/s. Furthermore, this study also expands the understanding of the effects of injection method on the MHSIT value with the inclusion of spray injected lubrication oil (MIL-PRF-23699) and stream injected Jet-A. The effects of global equivalence ratio are examined for spray injected Jet-A by modulating the aviation fluid injection rate and the crossflow air velocity in tandem. </div><div><br></div><div>During previous experimental campaigns, it was found that MHSIT did not monotonically increase with crossflow air velocity as previously believed. This new finding inspired a set of experiments that found MHSIT in crossflow to have four proposed ignition regimes: conduction, convective cooling, turbulent mixing, and advection. The current study replicates the results from the initial set of experiments at new conditions and to determine the effects of surface temperature on the regimes. </div><div><br></div><div>The MHSIT of flammable liquids depends on several factors including leak type (spray or stream), liquid temperature, air temperature, velocity, and pressure. ASTM standardized methods for ignition are limited to stagnant and falling drops downward (autoignition) at atmospheric pressure (ASTM E659, ASTM D8211, and ASTM E1491) and at pressures from 218 to 203 kPa (ASTM G72). Past studies have shown that MHSIT decreases with increasing pressure, but the available databases lack results of extensive experimental investigation. Therefore, such data for pressures between 101 to 203 kPa are missing or inadequate. As such the generic experimental apparatus was modified to produce the 101 to 203 kPa air duct pressure levels representative of a typical turbofan engine. </div><div><br></div><div>Machine learning (ML) and deep learning (DL) have become widely available in recent years. Open-source software packages and languages have made it possible to implement complex ML based data analysis and modeling techniques on a wide range of applications. The application of these techniques can expedite existing models or reduce the amount of physical lab investigation time required. Three data sets were utilized to examine the effectiveness of multiple ML techniques to estimate experimental outcomes and to serve as a substitute for additional lab work. To achieve this complex multi-variant regressions and neural networks were utilized to create estimating models. The first data sets of interest consist of a pool fire experiment that measured the flame spread rate as a function of initial fuel temperature for 8 different fuels, including Jet-A, JP-5, JP-8, HEFA-50, and FT-PK. The second data set consists of hot surface ignition data for 9 fuels including 4 alternative piston engine fuels for which properties were not available. The third data set is the MHSIT data generated by the generic experimental apparatus during the investigations conducted to expand the understanding of minimum hot surface ignition temperatures. When properties were not available multiple imputation by chained equations (MICE) was utilized to estimate fluid properties. Training and testing data sets were split up to 70% and 30% of the respective data set being modeled. ML techniques were implemented to analyze the data and R-squared values as high as 92% were achieved. The limitation of machine learning models is also discussed along with the advantages of physics-based approaches. The current study has furthered the application of ML in combustion through use of the MHSIT database.</div>