Real-time estimation of the electron temperature profile in DIII-D by leveraging neural-network surrogate models
S. Morosohk and E. Schuster
Contributions to Plasma Physics 2023, e202200153
Abstract
|
|
Control of both the magnitude and the shape of tokamak profiles will be
necessary to achieve stable, high-performance plasmas. In order to reject
disturbances in real time, feedback-control algorithms rely on accurate
real-time knowledge of the plasma state. When diagnostics alone are
insufficient, because they are limited in number or their measurements
are too noisy, observers can be used to combine diagnostic data with a
response model to provide a better estimation of different plasma properties.
An observer has been developed to estimate the electron temperature profile
in real time using both diagnostic data from the Thomson scattering system
and a model based on the electron heat transport equation describing the
evolution of the electron temperature profile. Neural network surrogate
models are leveraged to help improve the overall model prediction while
staying within computation time constraints for real-time use. The observer
algorithm is shown in offline tests to produce smooth profiles that are
consistent with both the diagnostic data and the electron heat transport
equation. When implemented into the real-time plasma control system, this
observer will provide valuable information on the electron temperature
profile to many potential feedback-control applications.