Improved profile fitting and quantification of uncertainty in experimental measurements of impurity transport coefficients using Gaussian process regression
| Autor: | Chilenski, Mark Alan, Greenwald, Martin J., Marzouk, Youssef M., Howard, Nathaniel Thomas, White, Anne E., Rice, John E., Walk, John R., Jr., Walk Jr., John R. |
|---|---|
| Přispěvatelé: | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology. Department of Nuclear Science and Engineering, Massachusetts Institute of Technology. Plasma Science and Fusion Center, Chilenski, Mark Alan, Greenwald, Martin J., Marzouk, Youssef M., Howard, Nathaniel Thomas, White, Anne E., Rice, John E., Walk Jr., John R. |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | MIT web domain |
| ISSN: | 1741-4326 0029-5515 |
| Popis: | The need to fit smooth temperature and density profiles to discrete observations is ubiquitous in plasma physics, but the prevailing techniques for this have many shortcomings that cast doubt on the statistical validity of the results. This issue is amplified in the context of validation of gyrokinetic transport models (Holland et al 2009 Phys. Plasmas 16 052301), where the strong sensitivity of the code outputs to input gradients means that inadequacies in the profile fitting technique can easily lead to an incorrect assessment of the degree of agreement with experimental measurements. In order to rectify the shortcomings of standard approaches to profile fitting, we have applied Gaussian process regression (GPR), a powerful non-parametric regression technique, to analyse an Alcator C-Mod L-mode discharge used for past gyrokinetic validation work (Howard et al 2012 Nucl. Fusion 52 063002). We show that the GPR techniques can reproduce the previous results while delivering more statistically rigorous fits and uncertainty estimates for both the value and the gradient of plasma profiles with an improved level of automation. We also discuss how the use of GPR can allow for dramatic increases in the rate of convergence of uncertainty propagation for any code that takes experimental profiles as inputs. The new GPR techniques for profile fitting and uncertainty propagation are quite useful and general, and we describe the steps to implementation in detail in this paper. These techniques have the potential to substantially improve the quality of uncertainty estimates on profile fits and the rate of convergence of uncertainty propagation, making them of great interest for wider use in fusion experiments and modelling efforts. United States. Dept. of Energy. Office of Fusion Energy Sciences (Award DE-FC02-99ER54512) United States. Dept. of Energy. Office of Science (Contract DE-AC05-06OR23177) United States. Dept. of Energy. Office of Advanced Scientific Computing Research (Award DE-SC0007099) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|