Data-driven analysis of relight variability of jet fuels induced by turbulence

• Published in: Combustion and flame Vol. 225; pp. 453 - 467
• Main Authors: Hassanaly, Malik, Tang, Yihao, Barwey, Shivam, Raman, Venkat
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.03.2021; Elsevier BV
• Subjects: Aerodynamics; Aircraft; Aircraft engines; Algorithms; Aviation fuel; Clustering; Combustion chambers; Data clustering; Delay time; Discriminant analysis; Electric sparks; Entrainment; Failure analysis; Fluid dynamics; High-altitude relight; Ignition; Initial conditions; Jet engine fuels; Kernel-turbulence Interaction; Kernels; Large eddy simulation; Parameter uncertainty; Turbulence; Turbulent flow; Variability; Discriminant analysis; Kernel-turbulence Interaction; High-altitude relight; Large eddy simulation; Data clustering
• DOI: 10.1016/j.combustflame.2020.11.025
• ISSN: 0010-2180

For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. A larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.