Title

Quantitative Fitting of transport model parameters to experimental profiles

Presentation Type

Poster

Abstract

The HELCAT experiment is a cylindrical device in New Mexico that is being studied to better understand the interactions between turbulence and flows in an ionized argon gas. The Plasma Group at the University of Montana has developed a numerical transport model to simulate radial transport of heat and particles in the HELCAT experiment. In this work, the results of a quantitative comparison of experimental data and computational results from the 1D transport code are presented. While some of the input parameters to the transport code are measured in the experiment (such as the magnetic field strength), others are not measured and need to be constrained by comparison with experimental results. My work has been a computational effort to develop a method of determining which input parameters to the transport code result in the smallest differences with experimental results. This work presents a Python code that performs an optimization of the transport code input parameters in order to fit experimental measurements using a least squares fitting routine.

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Apr 12th, 3:00 PM Apr 12th, 4:00 PM

Quantitative Fitting of transport model parameters to experimental profiles

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The HELCAT experiment is a cylindrical device in New Mexico that is being studied to better understand the interactions between turbulence and flows in an ionized argon gas. The Plasma Group at the University of Montana has developed a numerical transport model to simulate radial transport of heat and particles in the HELCAT experiment. In this work, the results of a quantitative comparison of experimental data and computational results from the 1D transport code are presented. While some of the input parameters to the transport code are measured in the experiment (such as the magnetic field strength), others are not measured and need to be constrained by comparison with experimental results. My work has been a computational effort to develop a method of determining which input parameters to the transport code result in the smallest differences with experimental results. This work presents a Python code that performs an optimization of the transport code input parameters in order to fit experimental measurements using a least squares fitting routine.