Abstract
An approach for the determination of principal components using nonlinear principal component analysis (NLPCA) is proposed in the context of turbulent combustion. NLPCA addresses complex data sets where the contours of the inherent principal directions are curved in the original manifold. Thermo-chemical scalars' statistics are reconstructed from the optimally derived moments. The tabulation of the scalars is then implemented, using artificial neural networks (ANN). The approach is implemented on numerical data generated for the stand-alone one-dimensional turbulence (ODT) simulation of hydrogen autoignition in a turbulent jet with preheated air. It is found that 2 nonlinear principal components are sufficient to capture thermo-chemical scalars' profiles. For some of the scalars, a single principal component reasonably captures the scalars' profiles as well.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4622-4633 |
| Number of pages | 12 |
| Journal | International Journal of Hydrogen Energy |
| Volume | 39 |
| Issue number | 9 |
| DOIs | |
| State | Published - Mar 18 2014 |
| Externally published | Yes |
Bibliographical note
Funding Information:The work was supported by the National Science Foundation Computational Mathematics Program under grant DMS-1217200 .
Keywords
- Autoignition
- Nonlinear principal component analysis
- Turbulent combustion
