15-19 April 2018
Paradise Point Resort & Spa
America/Los_Angeles timezone

14.3 A support vector regression method for efficiently determining neutral profiles from LIF data

19 Apr 2018, 10:30
2h 1m
Paradise Point Resort & Spa

Paradise Point Resort & Spa

1404 Vacation Rd, San Diego, CA 92109

Speakers

Dr Dustin M. Fisher (University of New Mexico) Deep Patel (University of New Mexico) Ralph F. Kelly (University of New Mexico) Mark Gilmore (University of New Mexico)

Description

A support vector regression (SVR) method is integrated with a collisional radiative (CR) model of the dual-source Helicon-Cathode (HelCat) linear plasma device to determine ArI profiles based on metastable-pumped LIF measurements. A machine learning approach to the CR model allows for an efficient exploration of the input parameter space and can incorporate probe measurement errors for inputs of electron density and temperature profiles that the CR model would normally be sensitive to. A training set is created for mapping ArI input profiles to metastable CR model outputs using shape preserving cubic Hermite interpolating polynomials. This method may be easily adapted to other LIF pumping schemes and may even be used to validate electron temperature and density plasma profiles if neutral or ion profiles are already known.

Primary author

Dr Dustin M. Fisher (University of New Mexico)

Co-authors

Deep Patel (University of New Mexico) Ralph F. Kelly (University of New Mexico) Mark Gilmore (University of New Mexico)

Presentation Materials

There are no materials yet.
Your browser is out of date!

Update your browser to view this website correctly. Update my browser now

×