Title :
Phenomenological modeling of plasma transport via stochastic filtering
Author :
Xu, Chao ; Ou, Yongsheng ; Schuster, Eugenio
Abstract :
The accuracy of first-principles predictive models for the evolution of plasma profiles is sometimes limited by the lack of understanding of the plasma transport phenomena. It is possible then to develop approximate transport models for the prediction of the plasma dynamics which are consistent with the available diagnostic data. This data-driven approach, usually referred to as phenomenological modeling, arises as an alternative to the more classical theory-driven approach. In this work we propose a stochastic filtering approach based on an extended Kalman filter to provide real-time estimates of poorly known or totally unknown transport coefficients. We first assume that plasma dynamics can be governed by tractable models obtained by first principles. However, the transport parameters are considered unknown and to-be-estimated. These estimates will be based solely on input-output diagnostic data and limited understanding of the transport physics. Numerical methods (e.g., finite differences) can be used to discretize the PDE models both in space and time to obtain finite-dimensional discrete-time state-space representations. The system states and to-be-estimated parameters are then combined into an augmented state vector. The resulting nonlinear state-space model is used for the design of an extended Kalman filter that provides real-time estimations not only of the system states but also of the unknown transport coefficients. Simulation results demonstrate the effectiveness of the proposed method for a benchmark transport model in cylindrical coordinates.
Keywords :
Kalman filters; control engineering computing; estimation theory; feedback; filtering theory; finite difference methods; partial differential equations; physics computing; plasma diagnostics; plasma transport processes; stochastic processes; Kalman filter; PDE models; benchmark transport model; data-driven approach; feedback plasma control system; finite difference method; finite-dimensional discrete-time state-space representations; first-principles predictive models; nonlinear state-space model; numerical methods; partial differential equations; phenomenological model; plasma diagnostic; plasma dynamics; plasma transport phenomena; real-time estimations; stochastic filtering approach; Accuracy; Filtering; Finite difference methods; Physics; Plasma diagnostics; Plasma transport processes; Predictive models; Real time systems; State estimation; Stochastic processes;
Conference_Titel :
Fusion Engineering, 2009. SOFE 2009. 23rd IEEE/NPSS Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-2635-5
Electronic_ISBN :
978-1-4244-2636-2
DOI :
10.1109/FUSION.2009.5226465