Plasma position reflectometry (PPR) will be used for measuring the plasma position and shape in DEMO. To ensure the reliability, PPR systems are optimized for the expected range of plasmas foreseen during the operation. The study and optimization of PPR systems is a highly demanding computational task and the current approach to define the simulation setup is time-consuming and does not allow the study of a large number of configurations in useful time. In this article we automate the simulation process required for studying and optimizing PPR systems with the REFMUL family of full-wave FDTD codes. A general overview of the fundamental concepts of PPR systems is presented in the context of reflectometry simulations. Different solutions are proposed to produce realistic reflectometer models from CAD files, realistic plasma models, minimize the complexity of the input definition, manage the simulations in HPCs and analyze the simulation results of PPR systems. A new algorithm based in the In-phase and Quadrature (I/Q) detection scheme is developed to extract the phase derivative and the detected signal amplitude from the synthetic signals of a generic simulation. These techniques allow a brute-force approach to reflectometry problems, being essential for the study of reflectometry systems. The algorithms are validated using the 2017 DEMO baseline scenario to test more than 100 different reflectometers at different poloidal positions in the same toroidal section. The application of our methodology can be extended to different reflectometry techniques and other fields.
Authors
E. Ricardo, F. da Silva, Stéphane Heuraux, A. Silva, J. Santos
- Bibliographic Reference
- E. Ricardo, F. da Silva, Stéphane Heuraux, A. Silva, J. Santos. Simulation and data processing techniques to design optimized PPR systems on plasma fusion devices. Computer Physics Communications, 2024, 294, pp.108945. ⟨10.1016/j.cpc.2023.108945⟩. ⟨hal-04237328⟩
- DOI
- https://doi.org/10.1016/j.cpc.2023.108945
- European Project Title
- EUROfusion
- Funding
- ['“Fundac¸ão para a Ciência e a Tecnologia” through projects UIDB/50010/2020 and UIDP/50010/2020', 'This work has been carried out within the framework of the EUROfusion Consortium, funded by the European Unionvia the Euratom Research and Training Programme (Grant Agreement No. 101052200—EUROfusion).']
- HAL Collection
- ['CNRS - Centre national de la recherche scientifique', 'OpenAIRE', 'Université de Lorraine', 'Institut de Chimie du CNRS', 'Institut Jean Lamour', 'IJL - équipe plasmas de fusion']
- HAL Identifier
- 4237328
- Institution
- ['Instituto Superior Técnico, Universidade Técnica de Lisboa', 'Institut de Chimie - CNRS Chimie', 'Université de Lorraine']
- Laboratory
- ['Instituto de Plasmas e Fusão Nuclear [Lisboa]', 'Institut Jean Lamour']
- Published in
- France
Table of Contents
- Introduction 3
- Plasma position reflectometry systems 4
- The basics of plasma position reflectometry 4
- The reflectometer set-up and detection schemes 5
- The position error and the amplitude of the detected signal 7
- Notation to describe multi reflectometers systems 9
- Description of systems of multiple reflectometers 9
- Acquisition and data processing in PPR systems 11
- Multiple configurations of reflectometers 12
- Simulation of multi reflectometers systems 13
- The REFMUL* family of FDTD codes for reflectometry simulation 13
- Reflectometry simulation with FDTD codes 13
- The REFMUL* family of codes and the simulation setup 14
- Formulation of a high-level framework for multi-reflectometry simulations 15
- The need of a high-level framework for reflectometry simulation 15
- Formulation of a high-level framework for reflectometry simulations 15
- Identification of the main variables of the problem 17
- Automating the simulations 18
- Dependence of the problem variables on the main variables 18
- Defining the input data and generating the simulation lists 19
- Simulation scripts 20
- Producing realistic antenna setup and plasma models 21
- Simplifying the input data definition 23
- Automation of the data analysis for PPR systems 25
- Automation of the data analysis 25
- Automation of the signal time lag detection 26
- Automation of the IQ detection technique 27
- Testing the principle in a simulation with mirror 28
- Simulation with the plasma 30
- Comparison of the results with the manual data analysis 31
- Conclusions 33
- Signal injection 36
- Signal detection 37
- Baseline scenario density map 38
- Simulated models 39
- Fit parameters of the waveguide propagation function 39
