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

On the Combination of Large Eddy Simulation and Phenomenological Soot Modeling to Calculate the Smoke Index From Aero-Engines Over a Large Range of Operating Conditions

[+] Author and Article Information
J. Lamouroux

Safran Helicopter Engines,
Bordes 64510, France
e-mail: jean.lamouroux@safrangroup.com

S. Richard

Safran Helicopter Engines,
Bordes 64510, France
e-mail: stephane.richard@safrangroup.com

Q. Male

Safran Helicopter Engines,
Bordes 64510, France
e-mail: quentin.male@cerfacs.com

G. Staffelbach

Toulouse 31100, France
e-mail: gabriel.staffelbach@cerfacs.com

A. Dauptain

Toulouse 31100, France
e-mail: antoine.dauptain@cerfacs.com

A. Misdariis

Toulouse 31100, France
e-mail: antony.misdariis@cerfacs.com

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, 2017; final manuscript received March 24, 2018; published online June 19, 2018. Editor: David Wisler.

J. Eng. Gas Turbines Power 140(10), 101501 (Jun 19, 2018) (8 pages) Paper No: GTP-17-1353; doi: 10.1115/1.4039940 History: Received July 13, 2017; Revised March 24, 2018

Nowadays, models predicting soot emissions are neither able to describe correctly fine effects of technological changes on sooting trends nor sufficiently validated at relevant operating conditions to match design office quantification needs. Yet, phenomenological descriptions of soot formation, containing key ingredients for soot modeling exist in the literature, such as the well-known Leung et al. model (Combust Flame 1991). However, when blindly applied to aeronautical combustors for different operating conditions, this model fails to hierarchize operating points compared to experimental measurements. The objective of this work is to propose an extension of the Leung model over a wide range of condition relevant of gas turbines operation. Today, the identification process can hardly be based on laboratory flames since few detailed experimental data are available for heavy-fuels at high pressure. Thus, it is decided to directly target smoke number values measured at the engine exhaust for a variety of combustors and operating conditions from idling to take-off. A large eddy simulation approach is retained for its intrinsic ability to reproduce finely unsteady behavior, mixing, and intermittency. In this framework, The Leung model for soot is coupled to the thickened flame model (TFLES) for combustion. It is shown that pressure-sensitive laws for the modeling constant of the soot surface chemistry are sufficient to reproduce engine emissions. Grid convergence is carried out to verify the robustness of the proposed approach. Several cases are then computed blindly to assess the prediction capabilities of the extended model.

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Fig. 5

Typical Smoke number at the outlet of an engine as function of engine power over its whole range of operating conditions. Error bars indicate the confidence margin of experimental values.

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Fig. 4

Scatter plot of ω˙YSoot as function of mixture fraction Z

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Fig. 3

Top: Instantaneous distribution of YSoot (left) and ω˙YSoot (right). Bottom: temporal average of soot production (left) and destruction (right) along an axial cut of the reference combustion chamber.

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Fig. 2

Scatter plot of temperature as function of mixture fraction Z

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Fig. 1

Instantaneous distribution of static temperature along an axial cut of the reference combustion chamber

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Fig. 6

Volume-integrated ω˙YSoot and production–destruction integrated ratio as function of engine power for four operating conditions

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Fig. 7

RTDF and OTDFmax for the three computed meshes on configuration 1. Error bars indicate a statistical 95% confidence margin of temporal-mean convergence of OTDFmax levels.

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Fig. 8

Volume-integrated ω˙YSoot and production–destruction integrated ratio as function of mesh resolution

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Fig. 9

Calibration of a modeling constant for two configurations as function of combustion chamber inlet pressure. Two calibration laws depending on pressure are depicted.

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Fig. 12

Simulation Smoke numbers as function of experimental Smoke numbers

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Fig. 13

Density functions of ω˙YSoot contributions as function of mixture fraction bins for configuration 1 and 3 (left. Density functions of YO2 contributions as function of mixture fraction bins for configuration 1 and 3 (right).

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Fig. 14

Density functions of YSoot contributions as function of normalized axial distance from the combustion chamber baseplate

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Fig. 10

Smoke numbers at engines outlets for three configurations as function of combustion chamber inlet pressure. Symbols depict both experimental and numerical values.

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Fig. 11

Simulation and experimental Smoke numbers for all configurations reported in the paper

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Fig. 15

Density functions of ω˙YSoot contributions as function of normalized axial distance. The color legend is given Fig. 13.



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