Mori-Zwanzig projection operator method as a statistical correlation analysis of time-series data
S. Maeyama, M. Misawa, T.-H. Watanabe
Department of Physics, Nagoya University, Nagoya 464-8602, Japan
e-mail: smaeyama@p.phys.nagoya-u.ac.jp
Mori-Zwanzig projection operator method is a mathematical method developed in non-equilibrium statistical physics [1]. A key is a...
Physics understanding of fusion plasmas has made significant advances, but further progress towards steady-state operation is challenged by a host of kinetic and MHD instabilities. Alfvén eigenmodes (AE) are a class of mixed kinetic and MHD instabilities that are important to identify and control because they can reduce confinement and potentially damage machine components. In the present...
ITER already has a catalogue of around 2000 simulations stored in IMAS format. This catalogue will grow much larger as we approach the initial operational phase of the experiment. Alongside the simulation data are other catalogues including ITER machine description. To make this data useful to the community requires making it FAIR (findable, accessible, interoperable, and reusable). The...
The fusion community and ITER need reliable software tools to be included in data reconstructions and validation chains. One way to provide such tools is via open-source solutions developed using industry-standard quality processes like version-control, continuous integration, unit testing etc.
Such open-source libraries, are usually developed jointly by communities of developers and users,...
The ITER Integrated Modeling and Analysis Suite (IMAS) is utilized in this research to develop a generalized approach to transport model implementation in the TRANSP code [1]. Similar to the efforts in the European Transport Simulator (ETS) [2], the transport models in TRANSP will communicate with all other components through the Interface Data Structures (IDSs) that are defined in the IMAS...
Currently, largely for historical reasons, almost all fusion experiments are using their own tools to manage and store measured and processed data as well as their own ontology. Thus, very similar functionalities (data storage, data access, data model documentation, cataloguing and browsing of metadata) are often provided differently depending on experiment. The overall objective of the...
E-mail：jxw19@mail.ustc.edu.cn
The mismatch of the operating parameters of the NBI (Neutral Beam Injector) ion source of the nuclear fusion experimental device will lead to the instability of the plasma in NBI ion source, and maybe cause breakdown in the ion source, which will limit the operation of the NBI long pulse and high power. At present, the main method to mitigate the equipment...
Reduced models of SOLPS-ITER plasma edge simulations are deployed in the time-dependent model predictive control of upstream and downstream divertor conditions. Virtual main ion and impurity gas puffs actuate the simulated tokamak boundary based on predictions obtained from the dynamic mode decomposition (DMD) and the Sparse Identification of Nonlinear Dynamics (SINDy) data-driven techniques....
Gaussian Process Regression (GPR) is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. Most fusion researchers to date have utilized a different GPR kernel for each tokamak...
Digital twins capable of predicting plasma evolution ahead of plasma progression within a Tokamak is a crucial tool required for real-time plasma intervention and control. Considering speed and scale required, quite often these have to be purely data conditioned models as opposed to being physics conditioned, making data selection a vital component of model efficacy. However, as we move to the...
Tractable and accurate prediction of tokamak plasma temperature, density and rotation profiles is vital for interpretation and preparation of current-day fusion experiments, optimization of plasma scenarios, and designing future devices. Predictions can be made using so-called integrated modelling suites, a collection of codes describing different physical processes integrated together to...
Gyrokinetic codes are the essential tools to predict and understand turbulent transport in magnetically confined fusion plasmas. They calculate the time evolution of the perturbed distribution function in the five-dimensional phase space. It often takes more than a few days to finish their runs and an enormous amount of calculation data is generated per run. When such codes are used to...
UTILISING CLOUD RESOURCES TO PERFORM MONTE-CARLO BASED UNCERTAINTY QUANTIFICATION OF FUSION SIMULATIONS
Stanislas Pamela
UKAEA
Abingdon, UK
Email: Stanislas.Pamela@UKAEA.uk
James Buchanan
UKAEA
Abingdon, UK
Email: James.Buchanan@UKAEA.uk
Andrew Lahiff
UKAEA
Abingdon, UK
Email. Andrew.Lahiff@UKAEA.uk
Abstract
The increased...
Controlling impurity ions is a critical requirement for a fusion reactor. Impurity accumulation in the core region dilutes fuel and radiates away power through line emission and enhanced Bremsstrahlung. However, a proper amount of impurity is likely to be required in the edge region in order to mitigate the heat load on the plasma facing components.
The transport properties of impurity ions...
Magnetic confinement fusion experiments generate large quantities of complex data. At a basic level, the data reflects the state of the machine and plasma, enabling a safe and reliable operation of the device, i.e. well within the design limits of the machine and compatible with the scientific goals of the experiment. Depending on the requirements, different analysis techniques are needed to...
ASTI, a data assimilation system for integrated simulation of fusion plasma, is being developed to analyze, predict, and control the fusion plasma behavior. ASTI employs the ensemble Kalman filter (EnKF) and smoother (EnKS) as data assimilation methods. The integrated transport simulation code for helical fusion plasmas, TASK3D, is employed as the system model in the data assimilation...
Fluid based scrape-off layer transport codes such as UEDGE are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiment. Determining uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. We leverage recent work in the area of...
The well-known IPB98 scaling law for the energy confinement in tokamak H-mode plasmas has recently been revised. A considerably larger data set was used for estimating the scaling, including data from devices with fully metallic walls (JET and ASDEX Upgrade). In order to facilitate comparison with IPB98, the new scaling was estimated using a simple power-law model. Like its predecessor, the...
An example for complementarity between plasma physic and data-driven research will be reported. It is the application of the information criterion (either Akaike or Bayesian) in the field of the statistics to the data obtained in the fusion research. A particular example described in the paper is the trials being conducted by utilizing the thermal diffusivity database in the Large Helical...
Disruption predictors based on traditional machine learning have been very successful in present day devices but have shown some fundamental limitations in the perspective of the next generation of Tokomaks, such as ITER and DEMO. In particular, even the most performing require an unrealistic number of examples to learn, tend to become obsolete very quickly and cannot easily cope with new...
In metallic devices, the occurrence of disruptions is particularly difficult to predict because of the nonlinear interactions between various effects, such as neoclassical convection of impurities, centrifugal forces, rotation, profile hollowness and MHD modes, just to name a few. While efforts to develop physics based plasma simulators are continuing, data driven predictors, based on machine...
High bandwidth fluctuation diagnostics capture the fast plasma dynamics of drift wave turbulence and Alfven/MHD instabilities on µs timescales. Fluctuation diagnostics coupled with high throughput compute accelerators, such as field programmable gate arrays (FPGAs), introduce new capabilities for real-time characterization, prediction, and control of fast plasma dynamics. Real-time...
Recently, a linear disruption predictor was installed in the JET real-time network for mitigation purposes. From a mathematical point of view, the predictor is based on computing centroids of disruptive examples and non-disruptive examples in a two-dimensional space. This is the reason of calling it centroid method (CM). It uses a single signal: the mode lock normalised to the plasma current....
The general model of learning from data assumes that the examples are drawn independently from a fixed but unknown probability distribution function (PDF). In any system that generates a continuous data flow (data streaming setting), the PDF may change as the data are streaming. It is important to note that the new PDF is also unknown. Such changes in the data may convey interesting...
A real-time disruption predictor based on deep learning method is implemented into the Plasma Control System (PCS) of HL-2A. This upgrade consists of four parts:
1. The Data Acquisition System (DAS) of HL-2A is updated to provide real-time signals to PCS
2. The disruption prediction algorithm proposed in reference 1 is adjusted to reach a higher calculation speed.
3. The PCS, which is...
The locked mode amplitude is one of the most commonly used signals for disruption prediction in tokamaks. On the JET baseline scenario, our results suggest that the simple application of a threshold on that signal yields a disruption predictor with more than 95% accuracy. It is well-known that mode locking is one of the main disruption causes at JET; however, it is often too late to avoid a...
Disruption prediction has made rapid progress in recent years especially deep learning-based methods. Most of the current deep learning method use the raw or slightly progressed diagnostic data as the inputs. As deep learning is an end-to-end machine learning method, it requires little feature engineering. It can extract features from data if given enough. However, the diagnostic systems in...
Tokamak wall protection systems are becoming a key asset for fusion machine operation, as internal plasma facing components (main wall, divertor) have evolved to high-tech actively cooled metallic walls. Tokamak (and Stellerator) walls are the thermal power sink, transfer 10-100 MW to the cooling system and heat up to temperatures of 1000-2000 K during plasma operation. These protection...
The analysis of thermal events on the components of fusion reactors is of major importance, both from a machine protection and from a science standpoints. This analysis, which can be conducted using infrared cameras placed inside the reactor [1], ought to be transferred from human operators to automatized processes because of the quantity of data involved and the need for real-time analysis....
In steady-state fusion devices like Wendelstein 7-X (W7-X), the active control of heat loads is mandatory to attain long-plasma operation. An intelligent feedback control system that mitigates the risk of overheating is required to avoid a premature plasma termination by the safety system. To keep the plasma within the safe operational limits of the plasma facing components, the feedback...
Between-shots and real-time actuator trajectory planning will be critical to achieving high performance scenarios and reliable, disruption-free operation in present-day tokamaks, ITER, and future fusion reactors. Such tools require models that are both accurate enough to facilitate useful decision making and fast enough to enable optimization algorithms to meet between-shots and real-time...
The TGLF model is a quasi-linear model of transport driven by gyrokinetic turbulence. A reduced velocity space moment linear eigensolver is used, which is calibrated to first principles linear calculations. The saturation rule for the intensity of the fluctuations is fit to 3D spectra of nonlinear gyrokinetic simulations with the CGYRO code. TGLF is never fit to experiment so that it can be...
By means of shortening the execution cost of Gyro-Landau Extended Fluid Code (ExFC), a recurrent neural network (RNN) based surrogate model has been raised to forecast data on the next time step using initial values given by ExFC. The model has been structured as a sequence-to-sequence model which implemented with the well-known Gated Recurrent Unit (GRU) with a residual connection. By using a...
Multi-channel fluctuation diagnostics capture the spatial patterns of high-bandwidth plasma dynamics. The paper reports on an effort to develop machine learning (ML) models for the real-time identification of edge-localized-mode (ELM) events and the turbulence properties of confinement regimes using the 2-dimensional Beam Emission Spectroscopy (BES) system at DIII-D. The 64-channel BES system...
At large nuclear fusion experiments, plasma discharges are assessed through measurements collected with several diagnostic devices. Each instrument collects data generated in different physics processes: a consistent and efficient exploitation of the information contained in each different data source regarding a few common plasma parameters can be achieved with Bayesian inference. In Bayesian...
Bayesian inference provides the ideal framework for modelling complex systems made of multiple heterogeneous measurements. Independent generative models of plasma diagnostic measurements can be implemented in a modular way and used to carry out Bayesian inference within a single consistent Bayesian model which relates few common plasma parameters to different kinds of observations....
It is reported about the prior analysis in design and tomography analysis of soft X-ray (SXR) diagnostic on Keda Torus eXperiment (KTX). The tomographic KTX aims at studying three dimensional effect in reversed field pinch with high plasma current, particularly in quasi-single-helicity (QSH) states. In order to reflect the experimental constraints and QSH configuration, the Bayesian...
Recent progress in harnessing novel machine learning (ML) / artificial intelligence (AI) algorithms to enhance EFIT equilibrium reconstruction for fusion data analysis and real-time applications is presented. This includes development of a ML-enhanced Bayesian framework to automate and maximize information from measurements and Model-Order-Reduction (MOR)-based ML models to efficiently guide...
Infrared (IR) thermography system is a key diagnostic in fusion devices to monitor the Plasma Facing Components. Nevertheless, both the qualitative and quantitative analysis (i.e. the hot spot detection and the surface temperature measurement) are challenging due to the presence of disturbance phenomena as variable emissivity and multiple reflections in fully metallic environment. To do so, a...
The measuring conditions in Magnetic Confinement Fusion (MCF) devices are complex. Original diagnostic data of interferometer systems could be unreliable due to electromagnetic interference, mechanical vibration, and hardware failures. Obtaining accurate density profiles, which are reconstructed without the influence of incorrect data, are beneficial to the reliable feedback control of density...
Recently, an advanced plasma current tomography has been constructed on EAST, which combines Bayesian probability theory and neural networks. It is different from the previous current tomography using CAR prior. Here, CAR prior is replaced with Advanced Squared Exponential kernel function (ASE) prior. It can solve the deficiencies on CAR prior, where the calculated core current is lower than...
Causality is a crucial aspect of human understanding and therefore one would expect that it would play a major role in science and particularly in statistical inference. On the contrary, traditional statistical and machine learning tools cannot distinguish between correlation and causality. This lack of discrimination capability can have catastrophic consequences for both understanding and...
The next generation of Tokamaks and the future reactor will be operated relying much more on feedback control than present day machines. The control of macroscopic instabilities, such as Sawteeth and ELMs, will be essential. In this perspective, various pacing experiments have been indeed successfully carried out in many devices in the framework of scenario optimisation. Unfortunately, many...
In fusion devices, as in many other experiments, time series are the typical form of the signals produced by the measuring systems. The detection of causality between time series is therefore of great interest, since it can give a unique contribution to the understanding, modelling, and prediction of phenomena still not fully understood. However, detecting and quantifying the causal influence...
In many fields of the natural sciences, from biology to physics, information tools are acquiring more and more importance. For the analysis of information transfer between time series in particular, the use of the transfer entropy is spreading. A typical application is synchronization experiments, which involve coupled quantities, a “target” and a “source”, with quasi-periodic behaviours. On...
Plasma filaments – or blobs – are large, coherent structures in the Scrape-Off-Layer (SOL) of fusion devices which can significantly contribute to heat and particle transport out of the plasma. Electric probe arrangements are a standard tool for investigating plasma filaments in the SOL of magnetic fusion experiments. In the Wendelstein 7-X (W7-X) stellarator, recent work has characterized...
Over the last years, Bayesian Analysis became a standard method in plasma physics for a common plasma parameter profile determination and mathematical correct error analysis [1-3], evaluating data measured by various diagnostics.
This paper gives an overview of established as well as recently deployed physics models within the Minerva Bayesian analysis framework [2] for a wide range of...
When preparing the operation of a magnetic confinement fusion device, estimating the heat-load distribution on plasma-facing components expected during operation presents a critical issue. Due to the relevance of this problem, most magnetic confinement fusion laboratories can be expected to develop their own device-specific approaches to predict this heat-load distribution.
This...
Reflectometry will be used in ITER to measure the electron density profile and to provide key information of the density fluctuations. There are two reflectometry systems, one at the high magnetic field side (HFS) and one at the low field side (LFS).
The synthetic diagnostic (SD) for both reflectometry systems is being developed, with a goal of modelling the reflected signals for ITER...
The development of Synthetic Diagnostics for ITER is essential to optimise the design of the diagnostics, to develop the necessary control algorithms utilising them, and to perform specific physics studies, including integrated data analysis, for each phase of the ITER Research Plan. The work will involve the standardised approach of the Integrated and Modelling & Analysis Suite (IMAS) with...
The electron cyclotron emission (ECE) diagnostic is a well-established and robust instrument for the localized measurement of the electron temperature $T_\mathrm{e}$. It measures the microwave spectrum radiated by the plasma. The measured microwave intensity can be calibrated to directly deliver $T_\mathrm{e}$. The location of the measurement is determined by finding the position on the line...
Integrated Data Analysis (IDA) allows to infer plasma quantities like electron density using heterogeneous data sources. Essential is forward modelling with physically reasonable models to the data space for probabilistic evaluation. The paper presents the progress in extending the set of AUG's diagnostics for electron density profile inference by a microwave reflectometry system.
For swept...
This paper summarizes the development of software tools to detect the approach to the L-H transition in ITER PFPO (Pre-Fusion Power Operation) scenarios. In particular, it describes to what extent the physical phenomena associated with the L-H transition can be characterized using the set of diagnostics available in PFPO. The H-mode is associated with the development of an edge transport...
Previous analyses show that various Alfven Eigenmodes (AEs) can be partially unstable in ITER: energetic particles (EPs), such as fusion-born alpha-particles or neutral beam ions are energetic enough to resonantly interact with these weakly damped plasma waves. Due to the sensitivity of the AEs’ properties on the background kinetic profiles, an automated analysis method is required to study...
While experiments on fusion plasmas produce high-dimensional data time series with
ever increasing magnitude and velocity, turn-around times for analysis of this
data have not kept up. For example, many data analysis tasks are often performed
in a manual, ad-hoc manner some time after an experiment. In this article we
introduce the DELTA framework that facilitates near real-time...
“Fusion Cloud” is a new concept to realize an interdisciplinary data analysis platform primarily based on fusion experiments and numerical modeling data across academic institutions and universities in Japan. Not only for the next-generation fusion experiment’s diagnostics and operations, such as in ITER and JT-60SA, but also for enabling the fusion demo reactor designs, some standard...
TOWARDS IMPLEMENTING THE FAIR4FUSION OPEN DATA BLUEPRINT
MICHAL K. OWSIAK
Poznan Supercomputing and Networking Center – Institute of Bioorganic Chemistry, PAS
Poznan, Poland
Email: michal.owsiak@man.poznan.pl
STASINOS KONSTANTOPOULOS
Institute of Informatics and Telecommunications - National Centre for Scientific Research "Demokritos"
Email: konstant@iit.demokritos.gr
Aghia...
The European fusion research activities have over the last decades generated a vast and varied set of data. Even if a survey of the generated data is restricted to EUROFusion, the implementation of the fusion research programme under the EU Horizon 2020 framework programme, the volume and diversity of the data that need to be catalogued and reviewed make the task of organizing and making the...
A suggestion tool for experimental data analysis and tokamak proposal validation was developed using machine learning methods. The bidirectional LSTM neural network model was used, and experimental data from Experimental Advanced Superconducting Tokamak (EAST) campaign 2010-2020 discharges were used as the model training data set. Compared to our previous works (Chenguang Wan et al 2021 Nucl....
"Data provenance" is critical to establishing repeatable and integrated modeling and analysis workflow (e.g. IMAS). Most of the physical modules in the workflow are published in source code. The configuration parameters and compilation environment during the module construction process can affect their output. The provenance information generated by traditional workflow management tools only...
