A comprehensive modeling procedure for estimating statistical properties of forced ignition

Tang, Yihao; Hassanaly, Malik; Raman, Venkat; Sforzo, Brandon; Seitzman, Jerry M.

doi:10.1016/j.combustflame.2019.04.045

Cited by 28 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The reaction rate obtained from HR calculations allows for the initiation of reactions even starting from a zero C. As the kernel moves through the flow field, entrainment of cold outer flow reduces H, thereby transitioning to the FPVA region in the tabulation. Additional details of the hybrid tabulation construction are available in [28]. The tabulation for the different fuels is obtained based on the HyChem family of chemistry mechanisms [30][31][32][33].…”

Section: Simulation Configuration and Computational Detailsmentioning

confidence: 99%

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

Hassanaly

Tang

Barwey

et al. 2021

Combustion and Flame

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Simulation Configuration and Computational Detailsmentioning

confidence: 99%

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

Hassanaly

Tang

Barwey

et al. 2021

Combustion and Flame

Self Cite

View full text Add to dashboard Cite

show abstract

“…First, consider the regular Rosin-Rammler distribution where with the following cumulative volume density function, 12)…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Low-order modeling of high-altitude relight of jet engine combustors

Oliveira

Sitte

Zedda

et al. 2021

International Journal of Spray and Combustion Dynamics

View full text Add to dashboard Cite

A physics-based, low-order ignition model is used to assess the ignition performance of a kerosene-fueled gas-turbine combustor under high-altitude relight conditions. The ignition model used in this study is based on the motion of virtual flame particles and their extinction according to a Karlovitz number criterion, and a stochastic procedure is used to account for the effects of spray polydispersity on the flame’s extinction behavior. The effects of large droplets arising from poor fuel atomization at sub-idle conditions are then investigated in the context of the model parameters and the combustor’s ignition behavior. For that, a Reynolds-averaged Navier-Stokes simulation of the cold flow in the combustor was performed and used as an input for the ignition model. Ignition was possible with a Sauter mean diameter (SMD) of 50 μm, and was enhanced by increasing the spark volume. Although doubling the spark volume at larger SMDs (75 and 100 μm) resulted in the suppression of short-mode failure events, ignition was not achieved due to a reduction of the effective flammable volume in the combustor. Overall, a lower ignition probability is obtained when using the stochastic procedure for the spray, which is to be expected due to the additional detrimental effects associated with poor spray atomisation and high polydispersity.

show abstract

“…More recently, large eddy simulations have been used to successfully reproduce ignition sequences in flows of increasing complexity and industrial relevance [9][10][11][12][13][14][15][16][17][18]. For the purpose of design, there have been many studies investigating low-order models capable of predicting ignition probabilities using time-averaged and unsteady non-reacting flow fields [19][20][21][22][23][24], exhibiting various degrees of success over different flow configurations.…”

Section: Introductionmentioning

confidence: 99%

Using adjoint-based optimization to enhance ignition in non-premixed jets

et al. 2021

View full text Add to dashboard Cite

Gradient-based optimization is used to reliably and optimally induce ignition in three examples of laminar non-premixed mixture configurations. Using time-integrated heat release as a cost functional, the non-convex optimization problem identified optimal energy source locations that coincide with the stoichiometric local mixture fraction surface for short optimization horizons, while for longer horizons, the hydrodynamics plays an increasingly important role and a balance between flow and chemistry features determines non-trivial optimal ignition locations. Rather than identifying a single optimal ignition location, the results of this study show that there may be several equally good ignition locations in a given flow configuration.

show abstract

A comprehensive modeling procedure for estimating statistical properties of forced ignition

Cited by 28 publications

References 43 publications

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

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

Low-order modeling of high-altitude relight of jet engine combustors

Using adjoint-based optimization to enhance ignition in non-premixed jets

Contact Info

Product

Resources

About