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

Preview Only

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.
Loading next page...
 
/lp/aps_physical/reduced-order-kinetic-plasma-models-using-principal-component-analysis-Ym9s8yYfpK
Publisher
The American Physical Society
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

There are no references for this article.

Sorry, we don’t have permission to share this article on DeepDyve,
but here are related articles that you can start reading right now:

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve Freelancer

DeepDyve Pro

Price
FREE
$49/month

$360/year
Save searches from Google Scholar, PubMed
Create lists to organize your research
Export lists, citations
Access to DeepDyve database
Abstract access only
Unlimited access to over
18 million full-text articles
Print
20 pages/month
PDF Discount
20% off