Overview of JET results for optimising ITER operation

The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (a) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under Grant Agreement No. 633053.Peer ReviewedArticle signat per 1223 autors/autores: J. Mailloux1, N. Abid1, K. Abraham1, P. Abreu2, O. Adabonyan1, P. Adrich3, V. Afanasev4, M. Afzal1, T. Ahlgren5, L. Aho-Mantila6, N. Aiba7, M. Airila6, M. Akhtar1, R. Albanese8, M. Alderson-Martin1, D. Alegre9, S. Aleiferis10, A. Aleksa1, A.G. Alekseev11, E. Alessi12, P. Aleynikov13, J. Algualcil14, M. Ali1, M. Allinson1, B. Alper1, E. Alves2, G. Ambrosino8, R. Ambrosino8, V. Amosov15, E.Andersson Sunden16, P. Andrew13, B.M. Angelini17, C. Angioni18, I. Antoniou1, L.C. Appel1, C. Appelbee1, S. Aria1, M. Ariola8, G. Artaserse17, W. Arter1, V. Artigues18, N. Asakura7, A. Ash1, N. Ashikawa19, V. Aslanyan20, M. Astrain21, O. Asztalos22, D. Auld1, F. Auriemma23, Y. Austin1, L. Avotina24, E. Aymerich25, A. Baciero9, F. Bairaktaris26, J. Balbin27, L. Balbinot23, I. Balboa1, M. Balden18, C. Balshaw1, N. Balshaw1, V.K. Bandaru18, J. Banks1, Yu.F. Baranov1, C. Barcellona28, A. Barnard1, M. Barnard1, R. Barnsley13, A. Barth1, M. Baruzzo17, S. Barwell1, M. Bassan13, A. Batista2, P. Batistoni17, L. Baumane24, B. Bauvir13, L. Baylor29, P.S. Beaumont1, D. Beckett1, A. Begolli1, M. Beidler29, N. Bekris30,31, M. Beldishevski1, E. Belli32, F. Belli17, É. Belonohy1, M. Ben Yaala33, J. Benayas1, J. Bentley1, H. Bergsåker34, J. Bernardo2, M. Bernert18, M. Berry1, L. Bertalot13, H. Betar35, M. Beurskens36, S. Bickerton1, B. Bieg37, J. Bielecki38, A. Bierwage7, T. Biewer29, R. Bilato18, P. Bílková39, G. Birkenmeier18, H. Bishop1, J.P.S. Bizarro2, J. Blackburn1, P. Blanchard40, P. Blatchford1, V. Bobkov18, A. Boboc1, P. Bohm39, T. Bohm41, I. Bolshakova42, T. Bolzonella23, N. Bonanomi18, D. Bonfiglio23, X. Bonnin13, P. Bonofiglo43, S. Boocock1, A. Booth1, J. Booth1, D. Borba2,30, D. Borodin44, I. Borodkina39,44, C. Boulbe45, C. Bourdelle27, M. Bowden1, K. Boyd1, I.Bozicevic Mihalic46, S.C. Bradnam1, V. Braic47, L. Brandt48, R. Bravanec49, B. Breizman50, A. Brett1, S. Brezinsek44, M. Brix1, K. Bromley1, B. Brown1, D. Brunetti1,12, R. Buckingham1, M. Buckley1, R. Budny, J. Buermans51, H. Bufferand27, P. Buratti17, A. Burgess1, A. Buscarino28, A. Busse1, D. Butcher1, E.de la Cal9, G. Calabrò52, L. Calacci53, R. Calado2, Y. Camenen54, G. Canal55, B. Cannas25, M. Cappelli17, S. Carcangiu25, P. Card1, A. Cardinali17, P. Carman1, D. Carnevale53, M. Carr1, D. Carralero9, L. Carraro23, I.S. Carvalho2, P. Carvalho2, I. Casiraghi56, F.J. Casson1, C. Castaldo17, J.P. Catalan14, N. Catarino2, F. Causa12, M. Cavedon18, M. Cecconello16, C.D. Challis1, B. Chamberlain1, C.S. Chang43, A. Chankin18, B. Chapman1,57, M. Chernyshova58, A. Chiariello8, P. Chmielewski58, A. Chomiczewska58, L. Chone59, G. Ciraolo27, D. Ciric1, J. Citrin60, Ł. Ciupinski61, M. Clark1, R. Clarkson1, C. Clements1, M. Cleverly1, J.P. Coad1, P. Coates1, A. Cobalt1, V. Coccorese8, R. Coelho2, J.W. Coenen44, I.H. Coffey62, A. Colangeli17, L. Colas27, C. Collins29, J. Collins1, S. Collins1, D. Conka24, S. Conroy16, B. Conway1, N.J. Conway1, D. Coombs1, P. Cooper1, S. Cooper1, C. Corradino28, G. Corrigan1, D. Coster18, P. Cox1, T. Craciunescu63, S. Cramp1, C. Crapper1, D. Craven1, R. Craven1, M.Crialesi Esposito48, G. Croci56, D. Croft1, A. Croitoru63, K. 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Zychor3 // 1 United Kingdom Atomic Energy Authority, Culham Science Centre, Abingdon, Oxon, OX14 3DB, United Kingdom of Great Britain and Northern Ireland 2 Instituto de Plasmas e Fusao Nuclear, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal 3 National Centre for Nuclear Research (NCBJ), 05-400 Otwock-Swierk, Poland 4 Ioffe Physico-Technical Institute, 26 Politekhnicheskaya, St Petersburg 194021, Russia 5 University of Helsinki, PO Box 43, FI-00014 University of Helsinki, Finland 6 VTT Technical Research Centre of Finland, PO Box 1000, FIN-02044 VTT, Finland 7 National Institutes for Quantum and Radiological Science and Technology, Naka, Ibaraki 311-0193, Japan 8 Consorzio CREATE, Via Claudio 21, 80125 Napoli, Italy 9 Laboratorio Nacional de Fusión, CIEMAT, Madrid, Spain 10 NCSR ‘Demokritos’ 153 10, Agia Paraskevi Attikis, Greece 11 NRC Kurchatov Institute, 1 Kurchatov Square, Moscow 123182, Russia 12 Institute for Plasma Science and Technology, CNR, via R. Cozzi 53, 20125 Milano, Italy 13 ITER Organization, Route de Vinon-sur-Verdon, CS 90 046, 13067 Saint Paul Lez Durance Cedex, France 14 Universidad Nacional de Educacion a Distancia, Dept Ingn Energet, Calle Juan del Rosal 12, E-28040 Madrid, Spain 15 Troitsk Insitute of Innovating and Thermonuclear Research (TRINITI), Troitsk 142190, Moscow Region, Russia 16 Department of Physics and Astronomy, Uppsala University, SE-75120 Uppsala, Sweden 17 Dip.to Fusione e Tecnologie per la Sicurezza Nucleare, ENEA C. R. Frascati, via E. Fermi 45, 00044 Frascati (Roma), Italy 18 Max-Planck-Institut für Plasmaphysik, D-85748 Garching, Germany 19 National Institute for Fusion Science, Oroshi, Toki, Gifu 509-5292, Japan 20 MIT Plasma Science and Fusion Center, Cambridge, MA 02139, United States of America 21 Universidad Politécnica de Madrid, Grupo I2A2, Madrid, Spain 22 Centre for Energy Research, POB 49, H-1525 Budapest, Hungary 23 Consorzio RFX, Corso Stati Uniti 4, 35127 Padova, Italy 24 University of Latvia, 19 Raina Blvd., Riga, LV 1586, Latvia 25 Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi 09123 Cagliari, Italy 26 National Technical University of Athens, Iroon Politechniou 9, 157 73 Zografou, Athens, Greece 27 CEA, IRFM, F-13108 Saint Paul Lez Durance, France 28 Dipartimento di Ingegneria Elettrica Elettronica e Informatica, Università degli Studi di Catania, 95125 Catania, Italy 29 Oak Ridge National Laboratory, Oak Ridge, TN 37831, TN, United States of America 30 EUROfusion Programme Management Unit, Culham Science Centre, Culham, OX14 3DB, United Kingdom of Great Britain and Northern Ireland 31 Karlsruhe Institute of Technology, PO Box 3640, D-76021 Karlsruhe, Germany 32 General Atomics, PO Box 85608, San Diego, CA 92186-5608, United States of America 33 Department of Physics, University of Basel, Switzerland 34 Fusion Plasma Physics, EECS, KTH Royal Institute of Technology, SE-10044 Stockholm, Sweden 35 Institut Jean Lamour, U

Overview of JET results for optimising ITER operation

The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (α) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER

New H-mode regimes with small ELMs and high thermal confinement in the Joint European Torus

New H-mode regimes with high confinement, low core impurity accumulation, and small edge-localized mode perturbations have been obtained in magnetically confined plasmas at the Joint European Torus tokamak. Such regimes are achieved by means of optimized particle fueling conditions at high input power, current, and magnetic field, which lead to a self-organized state with a strong increase in rotation and ion temperature and a decrease in the edge density. An interplay between core and edge plasma regions leads to reduced turbulence levels and outward impurity convection. These results pave the way to an attractive alternative to the standard plasmas considered for fusion energy generation in a tokamak with a metallic wall environment such as the ones expected in ITER.& nbsp;Published under an exclusive license by AIP Publishing

Spectroscopic camera analysis of the roles of molecularly assisted reaction chains during detachment in JET L-mode plasmas

The roles of the molecularly assisted ionization (MAI), recombination (MAR) and dissociation (MAD) reaction chains with respect to the purely atomic ionization and recombination processes were studied experimentally during detachment in low-confinement mode (L-mode) plasmas in JET with the help of experimentally inferred divertor plasma and neutral conditions, extracted previously from filtered camera observations of deuterium Balmer emission, and the reaction coefficients provided by the ADAS, AMJUEL and H2VIBR atomic and molecular databases. The direct contribution of MAI and MAR in the outer divertor particle balance was found to be inferior to the electron-atom ionization (EAI) and electron-ion recombination (EIR). Near the outer strike point, a strong atom source due to the D+2-driven MAD was, however, observed to correlate with the onset of detachment at outer strike point temperatures of Te,osp = 0.9-2.0 eV via increased plasma-neutral interactions before the increasing dominance of EIR at Te,osp < 0.9 eV, followed by increasing degree of detachment. The analysis was supported by predictions from EDGE2D-EIRENE simulations which were in qualitative agreement with the experimental observations

Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision

Shattered pellet injection experiments at JET in support of the ITER disruption mitigation system design

A series of experiments have been executed at JET to assess the efficacy of the newly installed shattered pellet injection (SPI) system in mitigating the effects of disruptions. Issues, important for the ITER disruption mitigation system, such as thermal load mitigation, avoidance of runaway electron (RE) formation, radiation asymmetries during thermal quench mitigation, electromagnetic load control and RE energy dissipation have been addressed over a large parameter range. The efficiency of the mitigation has been examined for the various SPI injection strategies. The paper summarises the results from these JET SPI experiments and discusses their implications for the ITER disruption mitigation scheme

The role of ETG modes in JET-ILW pedestals with varying levels of power and fuelling

We present the results of GENE gyrokinetic calculations based on a series of JET-ITER-like-wall (ILW) type I ELMy H-mode discharges operating with similar experimental inputs but at different levels of power and gas fuelling. We show that turbulence due to electron-temperature-gradient (ETGs) modes produces a significant amount of heat flux in four JET-ILW discharges, and, when combined with neoclassical simulations, is able to reproduce the experimental heat flux for the two low gas pulses. The simulations plausibly reproduce the high-gas heat fluxes as well, although power balance analysis is complicated by short ELM cycles. By independently varying the normalised temperature gradients (omega(T)(e)) and normalised density gradients (omega(ne )) around their experimental values, we demonstrate that it is the ratio of these two quantities eta(e) = omega(Te)/omega(ne) that determines the location of the peak in the ETG growth rate and heat flux spectra. The heat flux increases rapidly as eta(e) increases above the experimental point, suggesting that ETGs limit the temperature gradient in these pulses. When quantities are normalised using the minor radius, only increases in omega(Te) produce appreciable increases in the ETG growth rates, as well as the largest increases in turbulent heat flux which follow scalings similar to that of critical balance theory. However, when the heat flux is normalised to the electron gyro-Bohm heat flux using the temperature gradient scale length L-Te, it follows a linear trend in correspondence with previous work by different authors

Testing a prediction model for the H-mode density pedestal against JET-ILW pedestals

The neutral ionisation model proposed by Groebner et al (2002 Phys. Plasmas 9 2134) to determine the plasma density profile in the H-mode pedestal, is extended to include charge exchange processes in the pedestal stimulated by the ideas of Mahdavi et al (2003 Phys. Plasmas 10 3984). The model is then tested against JET H-mode pedestal data, both in a 'standalone' version using experimental temperature profiles and also by incorporating it in the Europed version of EPED. The model is able to predict the density pedestal over a wide range of conditions with good accuracy. It is also able to predict the experimentally observed isotope effect on the density pedestal that eludes simpler neutral ionization models

Comparing pedestal structure in JET-ILW H-mode plasmas with a model for stiff ETG turbulent heat transport

A predictive model for the electron temperature profile of the H-mode pedestal is described, and its results are compared with the pedestal structure of JET-ILW plasmas. The model is based on a scaling for the gyro-Bohm normalized, turbulent electron heat flux qe/qe,gB resulting from electron temperature gradient (ETG) turbulence, derived from results of nonlinear gyrokinetic (GK) calculations for the steep gradient region. By using the local temperature gradient scale length L-Te in the normalization, the dependence of q(e)/q(e,g)B on the normalized gradients R/L-Te and R/(Lne) can be represented by a unified scaling with the parameter eta(e) = L-ne/L-Te, to which the linear stability of ETG turbulence is sensitive when the density gradient is sufficiently steep. For a prescribed density profile, the value of R/L-Te determined from this scaling, required to maintain a constant electron heat flux qe across the pedestal, is used to calculate the temperature profile. Reasonable agreement with measurements is found for different cases, the model providing an explanation of the relative widths and shifts of the T-e and n(e) profiles, as well as highlighting the importance of the separatrix boundary conditions. Other cases showing disagreement indicate conditions where other branches of turbulence might dominate.This article is part of a discussion meeting issue "H-mode transition and pedestal studies in fusion plasmas'

Performance Comparison of Machine Learning Disruption Predictors at JET

Reliable disruption prediction (DP) and disruption mitigation systems are considered unavoidable during international thermonuclear experimental reactor (ITER) operations and in the view of the next fusion reactors such as the DEMOnstration Power Plant (DEMO) and China Fusion Engineering Test Reactor (CFETR). In the last two decades, a great number of DP systems have been developed using data-driven methods. The performance of the DP models has been improved over the years both for a more appropriate choice of diagnostics and input features and for the availability of increasingly powerful data-driven modelling techniques. However, a direct comparison among the proposals has not yet been conducted. Such a comparison is mandatory, at least for the same device, to learn lessons from all these efforts and finally choose the best set of diagnostic signals and the best modelling approach. A first effort towards this goal is made in this paper, where different DP models will be compared using the same performance indices and the same device. In particular, the performance of a conventional Multilayer Perceptron Neural Network (MLP-NN) model is compared with those of two more sophisticated models, based on Generative Topographic Mapping (GTM) and Convolutional Neural Networks (CNN), on the same real time diagnostic signals from several experiments at the JET tokamak. The most common performance indices have been used to compare the different DP models and the results are deeply discussed. The comparison confirms the soundness of all the investigated machine learning approaches and the chosen diagnostics, enables us to highlight the pros and cons of each model, and helps to consciously choose the approach that best matches with the plasma protection needs