< Terug naar vorige pagina


Sensitivity analysis of plasma edge code parameters through algorithmic differentiation

Tijdschriftbijdrage - Tijdschriftartikel

Anomalous radial transport coefficients, boundary conditions and reaction rates are among the main sources of uncertainty within plasma edge modeling. In principle, an analysis to determine the sensitivity of code results to changes in uncertain model parameters can be easily implemented through finite differences. However, this incurs in error accumulations and allows scanning only one parameter at a time, requiring a huge computational effort. Algorithmic Differentiation (AD) is a possible alternative to finite differences already applied to several transport codes in different research domains but not yet in plasma edge modeling. AD tools preprocess the source code, identifying elementary operations for which the differential form is well known, and producing anew version of the code that contains the additional derivative information. In this paper, the feasibility of applying AD to plasma edge codes is demonstrated using the TAPENADE tool on the SOLPS-ITER code. As a first preliminary step, the AD tool is applied in the so-called “forward” mode on the B2.5 plasma solver, adopting a fluid neutral approximation to obtain the sensitivities of the calculated quantities of interest on selected code parameters. The proof of principle is carried out by comparing the AD results with those evaluated with finite differences on an ITER H-only case. The sensitivities of the target peak heat load and maximum electron temperature with respect to anomalous radial transport coefficients and core input power are assessed. The comparison with finite differences results in a relative error lower than 10−6. This proves that, in a next step, AD can be exploited for an efficient and accurate sensitivity analysis in the framework of plasma edge simulations.
Tijdschrift: Nuclear Materials and Energy
ISSN: 2352-1791
Volume: 18
Pagina's: 6 - 11
BOF-publication weight:1
Authors from:Higher Education