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Research Papers

# Implementation of Detailed Chemistry Mechanisms in Engine Simulations

[+] Author and Article Information
Prithwish Kundu

Argonne National Laboratory,
Lemont, IL 60439
e-mail: pkundu@anl.gov

Muhsin M. Ameen

Argonne National Laboratory,
Lemont, IL 60439
e-mail: mameen@anl.gov

Chao Xu

University of Connecticut,
Storrs, CT 06269
e-mail: chao.xu@uconn.edu

Umesh Unnikrishnan

Argonne National Laboratory,
Lemont, IL 60439
e-mail: umesh.aero@gatech.edu

Tianfeng Lu

University of Connecticut,
Storrs, CT 06269
e-mail: tlu@engr.uconn.edu

Sibendu Som

Argonne National Laboratory,
Lemont, IL 60439
e-mail: ssom@anl.gov

Manuscript received July 17, 2018; final manuscript received August 8, 2018; published online October 16, 2018. Editor: Jerzy T. Sawicki.

J. Eng. Gas Turbines Power 141(1), 011026 (Oct 16, 2018) (10 pages) Paper No: GTP-18-1498; doi: 10.1115/1.4041281 History: Received July 17, 2018; Revised August 08, 2018

## Abstract

The stiffness of large chemistry mechanisms has been proved to be a major hurdle toward predictive engine simulations. As a result, detailed chemistry mechanisms with a few thousand species need to be reduced based on target conditions so that they can be accommodated within the available computational resources. The computational cost of simulations typically increases super-linearly with the number of species and reactions. This work aims to bring detailed chemistry mechanisms within the realm of engine simulations by coupling the framework of unsteady flamelets and fast chemistry solvers. A previously developed tabulated flamelet model (TFM) framework for nonpremixed combustion was used in this study. The flamelet solver consists of the traditional operator-splitting scheme with variable coefficient ordinary differential equation (ODE) solver (VODE) and a numerical Jacobian for solving the chemistry. In order to use detailed mechanisms with thousands of species, a new framework with the Livermore solver for ODEs in sparse form (LSODES) chemistry solver and an analytical Jacobian was implemented in this work. Results from 1D simulations show that with the new framework, the computational cost is linearly proportional to the number of species in a given chemistry mechanism. As a result, the new framework is 2–3 orders of magnitude faster than the conventional variable coefficient ODE (VODE) solver for large chemistry mechanisms. This new framework was used to generate unsteady flamelet libraries for n-dodecane using a detailed chemistry mechanism with 2755 species and 11,173 reactions. The engine combustion network (ECN) spray A experiments, which consist of an igniting n-dodecane spray in turbulent, high-pressure engine conditions are simulated using large eddy simulations (LES) coupled with detailed mechanisms. A grid with 0.06 mm minimum cell size and 22 ×106 peak cell count was implemented. The framework is validated across a range of ambient temperatures against ignition delay and liftoff lengths (LOLs). Qualitative results from the simulations were compared against experimental OH and CH2O planar laser-induced fluorescence (PLIF) data. The models are able to capture the spatial and temporal trends in species compared to those observed in the experiments. Quantitative and qualitative comparisons between the predictions of the reduced and detailed mechanisms are presented in detail. The main goal of this study is to demonstrate that detailed reaction mechanisms (∼1000 species) can now be used in engine simulations with a linear increase in computation cost with number of species during the tabulation process and a small increase in the 3D simulation cost.

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## Figures

Fig. 1

Central processing unit time per time-step as a function of number of species in a mechanism for different chemistry solvers

Fig. 2

Total computational cost of generating flamelet libraries for different mechanisms as a function of the number of species

Fig. 3

Coupling of the TFM approach with the CFD code

Fig. 4

Transient flame development for the two mechanisms are shown using the OH mass fraction contours. The black iso-line represents the stoichiometric line.

Fig. 5

CH2O and OH formation versus time in the domain for both mechanisms

Fig. 6

Flame LOL evolution for the two mechanisms compared against experimental measurements

Fig. 7

Formation of CO (secondary Y axis) and C2H2 in the domain as a function of time for the different mechanisms

Fig. 8

Ignition delay versus ambient temperature conditions for different chemistry mechanisms and compared against the experimental data

Fig. 9

Flame liftoff versus ambient temperature conditions for different chemistry mechanisms and compared against the experimental data

Fig. 10

Flame structures at different ambient temperature conditions predicted by the two mechanisms. The OH mass fraction contours (left) and temperature contours (right) are shown.

Fig. 11

Single shot CH2O PLIF images from experiments are compared against model predictions from the detailed and reduced mechanisms for different times. The ambient conditions are for the baseline spray A case with an ambient temperature of 900 K and 15% ambient O2 concentration. The PLIF images are based on false color images.

Fig. 12

Ensemble-averaged OH PLIF data at the baseline spray A conditions (left) compared with the results from the detailed mechanism (center) and the reduced mechanism (right). The LES results are obtained by ensemble averaging over 3 realizations and azimuthal averaging over 128 planes.

## Errata

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