Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Liquid Fuel Composition Effects on Forced, Nonpremixed Ignition

[+] Author and Article Information
Brandon Sforzo

Ben T. Zinn Combustion Laboratory,
Guggenheim School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0150
 e-mail: brandon.sforzo@gatech.edu

Hoang Dao, Sheng Wei, Jerry Seitzman

Ben T. Zinn Combustion Laboratory,
Guggenheim School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332-0150

1Corresponding author.

2Present address: Aerojet Rocketdyne, Redmond, WA 98052.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received July 13, 2016; final manuscript received July 14, 2016; published online October 11, 2016. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(3), 031509 (Oct 11, 2016) (8 pages) Paper No: GTP-16-1335; doi: 10.1115/1.4034502 History: Received July 13, 2016; Revised July 14, 2016

The effects of jet fuel composition on ignition probability have been studied in a flowfield that is relevant to turbine engine combustors, but also fundamental and conducive to modeling. In the experiments, a spark kernel is ejected from a wall and propagates transversely into a crossflow. The kernel first encounters an air-only stream before transiting into a second, flammable (premixed) stream. The two streams have matched velocities, as verified by hot-wire measurements. The liquid fuels span a range of physical and chemical kinetic properties. To focus on their chemical differences, the fuels are prevaporized in a carrier air flow before being injected into the experimental facility. Ignition probabilities at atmospheric pressure and elevated crossflow temperature were determined from optical measurements of a large number of spark events, and high-speed imaging was used to characterize the kernel evolution. Eight fuel blends were tested experimentally; all exhibited increasing ignition probability as equivalence ratio increased, at least up to the maximum value studied (∼0.8). Statistically significant differences between fuels were measured that have some correlation with fuel properties. To elucidate these trends, the forced ignition process was also studied with a reduced-order numerical model of an entraining kernel. The simulations suggest ignition is successful if sufficient heat release occurs before entrainment of colder crossflow fluid quenches the exothermic oxidation reactions. As the kernel is initialized in air, it remains extremely lean during the initial entrainment of the fuel–air mixture; thus, richer crossflows lead to quicker and higher exothermicity.

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Fig. 1

Schematic of the experimental flow facility. Air supplied to a common plenum is divided into upper and lower flows by a movable splitter plate. The plate was fixed at 6.35 mm for the current work and the igniter was raised 3.18 mm above the test section floor.

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Fig. 2

Schematic of the fuel and air delivery flow path to the experimental test section

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Fig. 3

Velocity profiles, vertically traversing along the midplane of the facility at two downstream distances from the splitter plate. The x-direction is streamwise and y-direction is vertical.

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Fig. 4

Schlieren image sequence of the ignition kernel ejecting into the crossflow at times after the discharge. Flow is from left to right at v = 6 m/s and the horizontal line denotes the splitter height.

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Fig. 5

Emission of growing flame following an ignition event. Edge tracking was used to determine ignition success.

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Fig. 6

Reduced-order reactor model implemented in cantera

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Fig. 7

Volumetric growth of the spark kernel following discharge

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Fig. 8

Binned ignition probabilities for each fuel. The bottom right axes show the ignition probabilities of each fuel relative to the baseline fuel (A-2) at ϕ=0.75, based on the quadratic model for each fuel.

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Fig. 9

Actual-by-predicted plot depicting the fidelity of the polynomial model to capture variability in the data. The line represents perfect matching of prediction to data.

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Fig. 10

Tornado plot listing the model parameters in their order of model significance. The lines indicate an α = 0.05 significance level for the t-ratio.

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Fig. 11

Area growth of chemiluminescence signals for three fuels. The “C1 shifted” signal has been advanced by 1.33 ms.

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Fig. 12

Temperature evolution of the plasma (dashed) and four ignition simulations at varying ϕ. Input conditions were Ti = 420 K and τtransit = 90 μs.

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Fig. 13

Composition evolution for a failed ignition case (dashed, ϕ=0.8) and a successful ignition (solid, ϕ=0.9) in the cantera simulation. Both cases were run with Ti = 420 K and τtransit = 90 μs.

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Fig. 14

Boundary between cases that fail for all input ϕ values and those that have cases with ignition success, as plotted for input temperatures and transit times




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