1. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
Logical steps for the incorporation of chemical kinetics in internal combustion engine simulations

2. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
CPU time comparison of the adiabatic constant volume problem ODE functions using the SpeedCHEM package at different reaction mechanism dimensions [4,19–23]

3. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
Jacobian matrix sparsity pattern for the ERC multiChem [5] mechanism. Both axes represent the species indices in the reaction mechanism.

4. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
Sample schematic of the gridlike initialization procedure, in two dimensions. Points represent the dataset; diamond marks the initial cluster centers.

5. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
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

6. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
Local CO mass fractions on a vertical cut-plane at 2.0 deg after TDC, (bottom) full chemistry solution versus (top) high-dimensional clustering

7. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
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

8. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
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).

9. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
In-cylinder pressure trace predictions with different grid resolutions. Full chemistry solution (solid lines) versus high-dimensional clustering (dashed lines + marks).

10. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
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.

11. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
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

12. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
Reference engine sector mesh adopted for the current study

13. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
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).

14. Date of download: 11/9/2017
Copyright © ASME. All rights reserved.
From: Computationally Efficient Simulation of Multicomponent Fuel Combustion Using a Sparse Analytical Jacobian Chemistry Solver and High-Dimensional Clustering
J. Eng. Gas Turbines Power. 2014;136(9): doi: /
Figure Legend:
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.