Reduced-order kinetic plasma models using principal component analysis: Model formulation and manifold sensitivity

Reduced-order kinetic plasma models using principal component analysis: Model formulation and... Plasma flows involve hundreds of species and thousands of reactions at different time scales, resulting in a very large set of governing equations to solve. Simulating large reacting systems in nonequilibrium plasma mixtures remains a challenge with the currently available computational resources. Principal component analysis (PCA) offers a general and rather simple and automated method to reduce large kinetic mechanisms by principal variable selection. This work shows how to adapt and apply the PCA-scores technique, which has its origin in the combustion field, to a collisional-radiative model. We have successfully applied this technique to argon plasmas, reducing the set of governing equations by more than 90%, leading to an important speed-up of the calculation and a reduction of computational cost. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Physical Review Fluids American Physical Society (APS)

Reduced-order kinetic plasma models using principal component analysis: Model formulation and manifold sensitivity

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Reduced-order kinetic plasma models using principal component analysis: Model formulation and manifold sensitivity

Abstract

Plasma flows involve hundreds of species and thousands of reactions at different time scales, resulting in a very large set of governing equations to solve. Simulating large reacting systems in nonequilibrium plasma mixtures remains a challenge with the currently available computational resources. Principal component analysis (PCA) offers a general and rather simple and automated method to reduce large kinetic mechanisms by principal variable selection. This work shows how to adapt and apply the PCA-scores technique, which has its origin in the combustion field, to a collisional-radiative model. We have successfully applied this technique to argon plasmas, reducing the set of governing equations by more than 90%, leading to an important speed-up of the calculation and a reduction of computational cost.
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Publisher
American Physical Society (APS)
Copyright
Copyright © ©2017 American Physical Society
eISSN
2469-990X
D.O.I.
10.1103/PhysRevFluids.2.073201
Publisher site
See Article on Publisher Site

Abstract

Plasma flows involve hundreds of species and thousands of reactions at different time scales, resulting in a very large set of governing equations to solve. Simulating large reacting systems in nonequilibrium plasma mixtures remains a challenge with the currently available computational resources. Principal component analysis (PCA) offers a general and rather simple and automated method to reduce large kinetic mechanisms by principal variable selection. This work shows how to adapt and apply the PCA-scores technique, which has its origin in the combustion field, to a collisional-radiative model. We have successfully applied this technique to argon plasmas, reducing the set of governing equations by more than 90%, leading to an important speed-up of the calculation and a reduction of computational cost.

Journal

Physical Review FluidsAmerican Physical Society (APS)

Published: Jul 24, 2017

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