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

Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering

[+] Author and Article Information
Federico Perini, Anand Krishnasamy, Youngchul Ra, Rolf D. Reitz

Engine Research Center,
University of Wisconsin-Madison,
1500 Engineering Drive,
Madison, WI 53706

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 17, 2014; final manuscript received February 17, 2014; published online May 5, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(9), 091515 (May 05, 2014) (11 pages) Paper No: GTP-14-1103; doi: 10.1115/1.4027280 History: Received February 17, 2014; Revised February 17, 2014

The need for more efficient and environmentally sustainable internal combustion engines is driving research towards the need to consider more realistic models for both fuel physics and chemistry. As far as compression ignition engines are concerned, phenomenological or lumped fuel models are unreliable to capture spray and combustion strategies outside of their validation domains—typically, high-pressure injection and high-temperature combustion. Furthermore, the development of variable-reactivity combustion strategies also creates the need to model comprehensively different hydrocarbon families even in single fuel surrogates. From the computational point of view, challenges to achieving practical simulation times arise from the dimensions of the reaction mechanism, which can be of hundreds species even if hydrocarbon families are lumped into representative compounds and, thus, modeled with nonelementary, skeletal reaction pathways. In this case, it is also impossible to pursue further mechanism reductions to lower dimensions. central processing unit (CPU) times for integrating chemical kinetics in internal combustion engine simulations ultimately scale with the number of cells in the grid and with the cube number of species in the reaction mechanism. In the present work, two approaches to reduce the demands of engine simulations with detailed chemistry are presented. The first one addresses the demands due to the solution of the chemistry ordinary differential equation (ODE) system, and features the adoption of SpeedCHEM, a newly developed chemistry package that solves chemical kinetics using sparse analytical Jacobians. The second one aims to reduce the number of chemistry calculations by binning the computational fluid dynamics (CFD) cells of the engine grid into a subset of clusters, where chemistry is solved and then mapped back to the original domain. In particular, a high-dimensional representation of the chemical state space is adopted for keeping track of the different fuel components, and a newly developed bounding-box- constrained k-means algorithm is used to subdivide the cells into reactively homogeneous clusters. The approaches have been tested on a number of simulations featuring multicomponent diesel fuel surrogates and different engine grids. The results show that significant CPU time reductions, of about 1 order of magnitude, can be achieved without loss of accuracy in both engine performance and emissions predictions, prompting for their applicability to more refined or full-sized engine grids.

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Fig. 1

Logical steps for the incorporation of chemical kinetics in internal combustion engine simulations

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Fig. 2

CPU time comparison of the adiabatic constant volume problem ODE functions using the SpeedCHEM package at different reaction mechanism dimensions [4,19-23]

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Fig. 3

Jacobian matrix sparsity pattern for the ERC multiChem [5] mechanism. Both axes represent the species indices in the reaction mechanism.

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Fig. 4

Sample schematic of the gridlike initialization procedure, in two dimensions. Points represent the dataset; diamond marks the initial cluster centers.

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Fig. 5

Reference engine sector mesh adopted for the current study

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Fig. 6

Average in-cylinder pressure and apparent heat release rate comparison for the three cases considered, (solid lines) CHEMKIN versus (dashed lines + marks) SpeedCHEM chemistry solver

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Fig. 14

CPU time performance of the HDC algorithm at different grid resolutions. Full chemistry KIVA simulations (squares) versus clustered KIVA simulations (triangles). Speed-up factors refer to the CPU time spent on chemistry only.

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Fig. 13

In-cylinder pressure trace predictions at case 2 and case 3, grids 3 and 4. Full chemistry solution (solid lines) versus high-dimensional clustering (dashed lines + marks).

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Fig. 12

Local CO mass fractions on a vertical cut-plane at 2.0 deg after TDC, (bottom) full chemistry solution versus (top) high-dimensional clustering

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Fig. 11

Local NOx mass fractions on a vertical cut-plane, case 1, grid 4, 2.0 deg ATDC, (bottom) full chemistry solution versus (top) high-dimensional clustering

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Fig. 10

Local temperature distribution on a vertical cut-plane, case 1, grid 4, 2.0 deg ATDC, (bottom) full chemistry solution versus (top) high-dimensional clustering

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Fig. 9

Pollutant predictions at different grid resolutions: carbon monoxide (top), unburned hydrocarbons (center), nitrogen oxides (bottom). Full chemistry solution (solid lined) versus high-dimensional clustering (dashed lines + marks).

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Fig. 8

In-cylinder pressure trace predictions with different grid resolutions. Full chemistry solution (solid lines) versus high-dimensional clustering (dashed lines + marks).

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Fig. 7

CPU time comparison between KIVA simulations with detailed chemistry when either using Chemkin-II or SpeedCHEM as the chemistry solver. Values are reported for chemistry/fluid flow only parts.




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