This paper introduces a novel maximum likelihood approach to determine the local thermal transport coefficients belonging to diffusion and convection from excitation (perturbative) transport experiments. It extends previous work developed for linear (slab) geometry to cylindrical (toroidal) geometry for fusion reactors. The previous linear geometry approach is based on analytic solutions of the partial differential equation. However, for cylindrical geometries with convection the analytic solutions are confluent hypergeometric functions (CHFs) with complex valued arguments. Most numerical libraries do not support CHFs evaluation with complex valued arguments. Hence, this paper proposes the use of an ultra-fast transfer function evaluation based on sparse numerical solutions for the discretized partial differential equation. This solution is implemented in MATLAB © and incorporated in the frequency domain Maximum Likelihood Estimation framework. Consequently, transport coefficients can be estimated consistently when measurements are perturbed by coloured and spatially correlated noise.
Original languageEnglish
Pages (from-to)3220-3226
JournalProceedings of the IEEE Conference on Decision & Control
Publication statusPublished - Dec 2019
Event2019 IEEE Conference on Decision and Control - Acropolis Convention and Exhibition Center, Nice, France
Duration: 11 Dec 201913 Dec 2019

    Research areas

  • Clustering, digital signal processing, high-rate measurement problems, least squares (LS), multisplitting (MS), parallelization

ID: 53931256