Feature extraction and reduced-order modelling of nitrogen plasma models using principal component analysis
| Autor: | Gianmarco Aversano, Axel Coussement, Alessandro Parente, Aurélie Bellemans |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Coupling
Chemistry reduction General Chemical Engineering Mécanique des fluides 05 social sciences Feature extraction Principal component analysis 050401 social sciences methods 02 engineering and technology Sciences de l'ingénieur Manifold Computer Science Applications Reduction (complexity) Total variation Dynamical study 020401 chemical engineering 0504 sociology 0204 chemical engineering Plasma flows Representation (mathematics) Biological system Rotation (mathematics) Physical interpretation |
| Zdroj: | Computers & Chemical Engineering Computers & chemical engineering |
| ISSN: | 0098-1354 |
| DOI: | 10.1016/j.compchemeng.2018.05.012 |
| Popis: | Principal component analysis has been presented in recent research as an accurate and efficient method to reduce the complex chemistry and kinetics of large reacting mechanisms. Following the reduction, the original variables are transformed and projected onto a set of independent, orthogonal variables maximizing the total variance in the system: the principal components. However, these new variables are difficult to interpret physically and may introduce instabilities in the low dimensional representation of the manifold. In the present paper we will show the benefits of coupling PCA to a rotation method: the interpretation of the principal components can be related back to the physics. The advantages of rotation are demonstrated on a PCA reduced model for modelling dissociation and excitation processes in nitrogen shock flows. This project has received funding from the European Unions Horizon 2020 re- search and innovation program under the Marie Sklodowska-Curie grant agreement No 643134. SCOPUS: ar.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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