Research Papers: Internal Combustion Engines

Simulation of Combustion Recession After End-of-Injection at Diesel Engine Conditions

[+] Author and Article Information
Dorrin Jarrahbashi

School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: dorrin.jarrahbashi@me.gatech.edu

Sayop Kim

School of Aerospace Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: sayopkim@gatech.edu

Caroline L. Genzale

School of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: caroline.genzale@me.gatech.edu

Contributed by the IC Engine Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 15, 2017; final manuscript received February 21, 2017; published online April 25, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(10), 102804 (Apr 25, 2017) (8 pages) Paper No: GTP-17-1065; doi: 10.1115/1.4036294 History: Received February 15, 2017; Revised February 21, 2017

Recent experimental observations show that lifted diesel flames tend to propagate back toward the injector after the end-of-injection (EOI) under conventional high-temperature conditions. The term “combustion recession” has been adopted to reflect this process dominated by “auto-ignition” reactions. This phenomenon is closely linked to the EOI entrainment wave and its impact on the transient mixture–chemistry evolution upstream of the lift-off length. A few studies have explored the physics of combustion recession with experiments and simplified modeling, but the details of the chemical kinetics and convective–diffusive transport of reactive scalars and the capability of engine computational fluid dynamics (CFD) simulations to accurately capture them are mainly unexplored. In this study, highly resolved numerical simulations have been employed to explore the mixing and combustion of a diesel spray after the EOI and the influence of modeling choices on the prediction of these phenomena. The simulations are centered on a temperature sweep around the engine combustion network (ECN) spray-A conditions, from 800 to 1000 K, where different combustion recession behaviors are observed experimentally. Reacting spray simulations are performed via openfoam, using a Reynolds-averaged Navier–Stokes (RANS) approach with a traditional Lagrangian–Eulerian coupled formulation. Two reduced chemical kinetics models for n-dodecane are used to evaluate the impact of low-temperature chemistry and mechanism formulation on predictions of combustion recession behavior. Observations from the numerical simulations are consistent with recent findings that a two-stage auto-ignition sequence drives the combustion recession process. Simulations with two different chemical mechanisms indicate that low-temperature chemistry reactions drive the likelihood of combustion recession.

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Fig. 2

Ignition delay time validation for Cai and Yao reduced mechanisms compared with experiment at different temperatures

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Fig. 3

LOL validation for Cai and Yao mechanisms compared with experiment at different temperatures

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Fig. 4

Ignition delay time versus temperature with Cai and Yao mechanisms based on constant pressure batch reactor calculations: (a) p = 2.0 MPa, φ = 1 compared with experiment and (b) p = 6.0 MPa, φ = 1, 3

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Fig. 5

Temperature contours (K) after EOI for case 2 and Cai mechanism: (a) t = 1.70 ms, (b) t = 1.78 ms, (c) t = 1.84 ms, and (d) t = 1.90 ms

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Fig. 6

Contours of OH mass fraction after EOI for case 2 and Cai mechanism: (a) t = 1.84 ms and (b) t = 1.90 ms

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Fig. 7

Chemiluminescence images of spray A [5,23] at 900, i.e., case 2 (left) compared with temperature contours (K) from RANS simulations after the start-of-ramp-down (right) (color online)

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Fig. 10

Heat-release rate downstream of the nozzle along the spray axis during quasi-steady and EOI: (a) Cai and (b) Yao mechanisms

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Fig. 11

Axial gas-phase velocity versus axial distance downstream of the injector for case 2 at steady-state and after EOI: (a) Cai and (b) Yao mechanisms

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Fig. 12

Equivalence ratio versus axial distance downstream of the injector for case 2 at steady-state and EOI: (a) Cai and (b) Yao mechanisms

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Fig. 1

(a) Predicted liquid length and (b) predicted vapor penetration rate using different grid densities compared with experiments

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Fig. 8

LOL versus time for cases 1–3

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Fig. 9

Temperature contours (K) after EOI for case 2 using Yao mechanism: (a) t = 1.84 ms and (b) t = 1.90 ms




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