Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression

Journal Article · Tue Nov 15 00:00:00 EST 2016 · Review of Scientific Instruments

DOI:https://doi.org/10.1063/1.4960069· OSTI ID:22596484

Shabbir, A. ^[1]; Hornung, G. ^[1]

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1. Department of Applied Physics, Ghent University, B-9000 Ghent (Belgium)

Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.

• OSTI ID: 22596484
• Journal Information: Review of Scientific Instruments, Vol. 87, Issue 11; Other Information: (c) 2016 Author(s); Country of input: International Atomic Energy Agency (IAEA); ISSN 0034-6748
• Country of Publication: United States
• Language: English

Related Subjects

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
70 PLASMA PHYSICS AND FUSION TECHNOLOGY
COMPARATIVE EVALUATIONS
CONFINEMENT TIME
DISTRIBUTION
ERRORS
GEODESICS
LEAST SQUARE FIT
REGRESSION ANALYSIS
SCALING LAWS
TOKAMAK DEVICES