Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects, , for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value of has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST)

Exploration of the Equilibrium and Stability Properties of Spherical Tokamaks and Projection for MAST-U

In preparation for high fusion plasma performance operation of the newly operating spherical tokamak MAST-U, the equilibrium and stability properties of plasmas in the MAST database, as well as projections for MAST-U, are explored. The disruption event characterization and forecasting (DECAF) code is utilized to map disruptions in MAST, particularly with regard to vertical displacement events. Loss of vertical stability control was not found to be common in MAST, providing reassurance for MAST-U operation. MAST equilibria were reconstructed with magnetic diagnostics, adding kinetic diagnostics, or finally also adding magnetic pitch angle data. The reconstructions work well for MAST and the procedures are set up for MAST-U, including determination of the plasma current in the first MAST-U discharges. A 3D wall model of MAST-U has been constructed in the VALEN code, indicating that significant toroidal currents may be induced in the conducting structure. Rotation measurements may also be included in the reconstructions, and a test with the FLOW code of a rotating MAST plasma indicates a modest shift of the pressure contours off of the magnetic flux surfaces may be expected. Unstable resistive wall modes (RWMs) may constrain the performance of high pressure MAST-U plasmas. A machine learning (ML) assisted algorithm for stability calculation developed for the NSTX spherical tokamak has been applied to MAST plasmas. Improvements and expansion of the ML techniques continue, including semi-supervised learning techniques and a detection algorithm for unstable RWMs. Finally, projections of MAST-U plasma stability have been performed, indicating that a region of high pressure operational space exists in which the new passive stabilization plates act to stabilize ideal kink modes and RWMs may be stabilized by kinetic effects or active control

Overview of NSTX Upgrade initial results and modelling highlights

The National Spherical Torus Experiment (NSTX) has undergone a major upgrade, and the NSTX Upgrade (NSTX-U) Project was completed in the summer of 2015. NSTX-U first plasma was subsequently achieved, diagnostic and control systems have been commissioned, the H-mode accessed, magnetic error fields identified and mitigated, and the first physics research campaign carried out. During ten run weeks of operation, NSTX-U surpassed NSTX record pulse-durations and toroidal fields (TF), and high-performance similar to 1 MA H-mode plasmas comparable to the best of NSTX have been sustained near and slightly above the n = 1 no-wall stability limit and with H-mode confinement multiplier H-98y,H-2 above 1. Transport and turbulence studies in L-mode plasmas have identified the coexistence of at least two ion-gyro-scale turbulent micro-instabilities near the same radial location but propagating in opposite (i.e. ion and electron diamagnetic) directions. These modes have the characteristics of ion-temperature gradient and micro-tearing modes, respectively, and the role of these modes in contributing to thermal transport is under active investigation. The new second more tangential neutral beam injection was observed to significantly modify the stability of two types of Alfven eigenmodes. Improvements in offline disruption forecasting were made in the areas of identification of rotating MHD modes and other macroscopic instabilities using the disruption event characterization and forecasting code. Lastly, the materials analysis and particle probe was utilized on NSTX-U for the first time and enabled assessments of the correlation between boronized wall conditions and plasma performance. These and other highlights from the first run campaign of NSTX-U are described

Learning from Conect4children: A Collaborative Approach towards Standardization of Disease-Specific Paediatric Research Data

The conect4children (c4c) initiative was established to facilitate the development of new drugs and other therapies for paediatric patients. It is widely recognized that there are not enough medicines tested in all relevant ages of the paediatric population. To overcome this, it is imperative that clinical data from different sources are interoperable and can be pooled for larger post-hoc studies. c4c has collaborated with the Clinical Data Interchange Standards Consortium (CDISC) to develop the cross-cutting data resources that build on existing CDISC standards, in an effort to standardize paediatric data. The natural next step was an extension to disease-specific data items. c4c brought together several existing initiatives and resources relevant to disease-specific data and to analyse their use for standardizing disease-specific data in clinical trials. Several case studies that combined disease-specific data from multiple trials have demonstrated the need for disease-specific data standardization. We identified three relevant initiatives. These include European Reference Networks, European Joint Programme on Rare Diseases, and Pistoia Alliance. Other resources reviewed were: National Cancer Institute Enterprise Vocabulary Services, CDISC standards, pharmaceutical company-specific data dictionaries, Human Phenotype Ontology, Phenopackets, Unified Registry for Inherited Metabolic Disorders, Orphacodes, Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP) and Observational Medical Outcomes Partnership. The collaborative partners associated with these resources were also reviewed briefly. A plan of action focussed on collaboration was generated for standardizing disease-specific paediatric clinical trial data. A paediatric data standards multistakeholder and multi-project user group was established to guide the remaining actions– FAIRification of metadata, a Phenopackets pilot with RDCA-DAP, applying Orphacodes to case report forms of clinical trials, introducing CDISC standards into European Reference Networks, testing of the CDISC Pediatric User Guide using data from the mentioned resources and organization of further workshops and educational materials

Process monitoring for plastics injection molding

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993 and Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1993.Includes bibliographical references (leaves 196-197).by Daniel John Berkery.M.S