<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/17161193 |
| Date | 11 December 2023 |
| Creators | Luke N Dillard (11825048) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/EXPERIMENTS_DATA_ANALYSIS_AND_MACHINE_LEARNING_APPLIED_TO_FIRE_SAFETY_IN_AIRCRAFT_APPLICATIONS/17161193 |
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