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

Abstract

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.

Surface chemistry models for GaAs epitaxial growth and hydride cracking using reacting flow simulations

“…Appendix A: Fluid solver A variable density low-Mach number solver based on the OpenFOAM framework is used 21 to model the flow inside the reactor. The solver has been successfully applied in several other studies 22,23 including configuration with reactive phase transformations such as the one considered here 24 . The low-Mach number assumption allows for the use larger time-Appendix F: Infinitely fast kinetic growth model An infinitely fast kinetic model is implemented following the hypothesis that the reactor operates in a transport-limited regime.…”

A self-similarity principle for the computation of rare event probability

“…Details of the simulation procedure and operating conditions are provided in [32]. Briefly, each simulation of a sparking event is conducted using the large eddy simulation (LES) procedure, with an initial flow field that is sampled from a well-developed homogeneous flow.…”

Uniform-in-phase-space data selection with iterative normalizing flows

“…This technique enables the efficient elimination of redundant data points from quiescent or otherwise unimportant regions when the phenomenon of interest is intermittent, effectively attempting an approximate phase-space sampling like the one proposed in the present work. However, IS via clustering is sensitive to the choice of the number of clusters, and there is no systematic way of deciding on an appropriate number of clusters (Barwey et al, 2019; Barwey et al, 2020; Hassanaly et al, 2021b). Furthermore, in practice, clustering tends to underrepresent rare data points (see Appendix A).…”