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Volume 8 Issue 4
Jul.  2023
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Article Contents
Rossi R., Gelfusa M., Craciunescu T., Spolladore L., Wyss I., Peluso E., Vega J., Maggi C. F., Mailloux J., Maslov M., Murari A., . A systematic investigation of radiation collapse for disruption avoidance and prevention on JET tokamak[J]. Matter and Radiation at Extremes, 2023, 8(4): 046903. doi: 10.1063/5.0143193
Citation: Rossi R., Gelfusa M., Craciunescu T., Spolladore L., Wyss I., Peluso E., Vega J., Maggi C. F., Mailloux J., Maslov M., Murari A., . A systematic investigation of radiation collapse for disruption avoidance and prevention on JET tokamak[J]. Matter and Radiation at Extremes, 2023, 8(4): 046903. doi: 10.1063/5.0143193

A systematic investigation of radiation collapse for disruption avoidance and prevention on JET tokamak

doi: 10.1063/5.0143193
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  • Corresponding author: a)Author to whom correspondence should be addressed: gelfusa@ing.uniroma2.it
  • Received Date: 2023-01-20
  • Accepted Date: 2023-05-07
  • Available Online: 2023-07-01
  • Publish Date: 2023-07-01
  • To produce fusion reactions efficiently, thermonuclear plasmas have to reach extremely high temperatures, which is incompatible with their coming into contact with material surfaces. Confinement of plasmas using magnetic fields has progressed significantly in the last years, particularly in the tokamak configuration. Unfortunately, all tokamak devices, and particularly metallic ones, are plagued by catastrophic events called disruptions. Many disruptions are preceded by anomalies in the radiation patterns, particularly in ITER-relevant scenarios. These specific forms of radiation emission either directly cause or reveal the approaching collapse of the configuration. Detecting the localization of these radiation anomalies in real time requires an innovative and specific elaboration of bolometric measurements, confirmed by visible cameras and the inversion of sophisticated tomographic algorithms. The information derived from these measurements can be interpreted in terms of local power balances, which suggest a new quantity, the radiated power divided by the plasma internal energy, to determine the criticality of the plasma state. Combined with robust indicators of the temperature profile shape, the identified anomalous radiation patterns allow determination of the sequence of macroscopic events leading to disruptions. A systematic analysis of JET campaigns at high power in deuterium, full tritium, and DT, for a total of almost 2000 discharges, proves the effectiveness of the approach. The warning times are such that, depending on the radiation anomaly and the available actuators, the control system of future devices is expected to provide enough notice to enable deployment of effective prevention and avoidance strategies.
  • Conflict of Interest
    The authors have no conflicts to disclose.
    Author Contributions
    R. Rossi: Conceptualization (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Software (equal); Supervision (equal); Validation (equal); Visualization (equal). M. Gelfusa: Data curation (equal); Investigation (equal); Methodology (equal); Project administration (equal); Resources (equal); Supervision (equal); Writing – original draft (equal); Writing – review & editing (equal). T. Craciunescu: Formal analysis (equal); Investigation (equal); Writing – review & editing (equal). L. Spolladore: Data curation (equal); Investigation (equal); Supervision (equal); Visualization (equal); Writing – review & editing (equal). I. Wyss: Data curation (equal); Software (equal); Writing – review & editing (equal). E. Peluso: Data curation (equal); Investigation (equal); Software (equal); Writing – review & editing (equal). J. Vega: Investigation (equal); Project administration (equal); Writing – review & editing (equal). C. F. Maggi: Investigation (supporting); Project administration (supporting); Writing – review & editing (equal). J. Mailloux: Investigation (supporting); Project administration (supporting); Writing – review & editing (equal). M. Maslov: Investigation (supporting); Project administration (supporting); Writing – review & editing (equal). A. Murari: Conceptualization (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Supervision (equal); Writing – original draft (equal); Writing – review & editing (equal).
    The data that support the findings of this study are available from EUROfusion Consortium. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the authors upon reasonable request and with the permission of EUROfusion Consortium.
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  • [1]
    F. Chen, An Indispensable Truth: How Fusion Power Can Save the Planet (Springer Science & Business Media, 2011).
    [2]
    J. Wesson, Tokamaks, 3rd ed. (Oxford Clarendon Press, Oxford, 2004).
    [3]
    T. Hender et al., Nucl. Fusion 47, S128–S202 (2007).10.1088/0029-5515/47/6/S03
    [4]
    [5]
    E. J. Strait et al., Nucl. Fusion 59, 112012 (2019).10.1088/1741-4326/ab15de
    [6]
    T. Ravensbergen, M. van Berkel, A. Perek et al., “Real-time feedback control of the impurity emission front in tokamak divertor plasmas,” Nat. Commun. 12, 1105 (2021).10.1038/s41467-021-21268-3
    [7]
    C. Angiolini et al., “Tungsten transport in JET H-mode plasmas in hybrid scenario, experimental observations and modelling,” Nucl. Fusion 54, 083028 (2014).10.1088/0029-5515/54/8/083028
    [8]
    G. A. Rattá, J. Vega, A. Murari, G. Vagliasindi, M. F. Johnson, and P. C. de Vries, JET EFDA Contributors, Nucl. Fusion 50, 025005 (2010).10.1088/0029-5515/50/2/025005
    [9]
    A. Murari et al., “On the transfer of adaptive predictors between different devices for both mitigation and prevention of disruptions,” Nucl. Fusion 60, 056003 (2020).10.1088/1741-4326/ab77a6
    [10]
    J. Vega, A. Murari, S. Dormido-Canto et al., “Disruption prediction with artificial intelligence techniques in tokamak plasmas,” Nat. Phys. 18, 741 (2022).10.1038/s41567-022-01602-2
    [11]
    L. Piron et al., “Progress in preparing real-time control schemes for Deuterium-Tritium operation in JET,” Fusion Eng. Des. 166, 112305 (2021).10.1016/j.fusengdes.2021.112305
    [12]
    M. Lehnen et al., Nucl. Fusion 51, 123010 (2011).10.1088/0029-5515/51/12/123010
    [13]
    T. Craciunescu, E. Peluso, A. Murari, and M. Gelfusa, “Maximum likelihood bolometric tomography for the determination of the uncertainties in the radiation emission on JET TOKAMAK,” Rev. Sci. Instrum. 89, 053504 (2018).10.1063/1.5027880
    [14]
    A. Murari et al., “Investigating the thermal stability of highly radiative discharges on JET with a new tomographic method,” Nucl. Fusion 60(4), 046030 (2020).10.1088/1741-4326/ab7536
    [15]
    [16]
    R. Mariano et al., “Acceleration of an algorithm based on the maximum likelihood bolometric tomography for the determination of uncertainties in the radiation emission on JET using heterogeneous platforms,” Appl. Sci. 12(13), 6798 (2022).10.3390/app12136798
    [17]
    A. Pau et al., “A first analysis of JET plasma profile-based indicators for disruption prediction and avoidance,” IEEE Trans. Plasma Sci. 46(7), 2691–2698 (2018).10.1109/tps.2018.2841394
    [18]
    I. Wyss, A. Murari, L. Spolladore, E. Peluso, M. Gelfusa, P. Gaudio, and R. Rossi, “Comparison of a fast low spatial resolution inversion method and peaking factors for the detection of anomalous radiation patterns and disruption prediction,” Fusion Eng. Des. 193, 113625 (2023).10.1016/j.fusengdes.2023.113625
    [19]
    C. L. Lawson and R. J. Hanson, Solving Least Squares Problems (SIAM, 1995).
    [20]
    T. Craciunescu, G. Bonheure, V. Kiptily, A. Murari, S. Soare, I. Tiseanu, and V. Zoita, “The maximum likelihood reconstruction method for JET neutron tomography,” Nucl. Instrum. Methods Phys. Res., Sect. A 595, 623–630 (2008).10.1016/j.nima.2008.07.145
    [21]
    T. Craciunescu, G. Bonheure, V. Kiptily, A. Murari, I. Tiseanu, and V. Zoita, “A comparison of four reconstruction methods for JET neutron and gamma tomography,” Nucl. Instrum. Methods Phys. Res., Sect. A 605, 374–383 (2009).10.1016/j.nima.2009.03.224
    [22]
    L. A. Shepp and Y. Vardi, “Maximum likelihood reconstruction for emission tomography,” IEEE Trans. Med. Imaging 1(2), 113–122 (1982).10.1109/TMI.1982.4307558
    [23]
    K. Lange and K. Carson, “EM reconstruction algorithms for emission and transmission tomography,” J. Comput. Assisted Tomogr. 8, 306–316 (1984).
    [24]
    Y. Li, “Noise propagation for iterative penalized-likelihood image reconstruction based on Fisher information,” Phys. Med. Biol. 56(4), 1083–1103 (2011).10.1088/0031-9155/56/4/013
    [25]
    E. Peluso, T. Craciunescu, A. Murari, P. Carvalho, and M. Gelfusa, “A comprehensive study of the uncertainties in bolometric tomography on JET using the maximum likelihood method,” Rev. Sci. Instrum. 90, 123502 (2019).10.1063/1.5119441
    [26]
    E. Peluso, T. Craciunescu, M. Gelfusa, A. Murari, P. J. Carvalho, and P. Gaudio, “On the effects of missing chords and systematic errors on a new tomographic method for JET bolometry,” Fusion Eng. Des. 146, 2124 (2019).10.1016/j.fusengdes.2019.03.120
    [27]
    E. Peluso, M. Gelfusa, T. Craciunescu, L. Martellucci, P. Gaudio, and A. Murari, “Dealing with artefacts in JET iterative bolometric tomography using masks,” Plasma Phys. Controlled Fusion 64, 045013 (2022).10.1088/1361-6587/ac4854
    [28]
    M. Gelfusa, T. Craciunescu, E. Peluso, L. Giacomelli, V. Kiptily, C. Reux, G. Szepesi, and A. Murari, “A maximum likelihood tomographic method applied to JET gamma ray emission during the current quench,” Fusion Eng. Des. 168(3), 112637 (2021).10.1016/j.fusengdes.2021.112637
    [29]
    L. Spolladore, R. Rossi, I. Wyss, P. Gaudio, A. Murari, and M. Gelfusa, “Detection of MARFEs using visible cameras for disruption prevention,” Fusion Eng. Des. 190, 113507 (2023).10.1016/j.fusengdes.2023.113507
    [30]
    R. Rossi et al., “Development of robust indicators for the identification of electron temperature profile anomalies and application to JET,” Plasma Phys. Controlled Fusion 64(4), 045002 (2022).10.1088/1361-6587/ac4d3b
    [31]
    A. Bhattacharyya, “On a measure of divergence between two multinomial populations,” Indian J. Stat. 7(4), 401–406 (1946).
    [32]
    F. C. Schueller et al., “Disruptions in tokamaks,” Plasma Phys. Controlled Fusion 37, A135 (1995).10.1088/0741-3335/37/11A/009
    [33]
    M. Greenwald, J. L. Terry, S. M. Wolfe, S. Ejima, M. G. Bell, S. M. Kaye, and G. H. Neilson, “A new look at density limits in tokamaks,” Nucl. Fusion 28(12), 2199–2207 (1988).10.1088/0029-5515/28/12/009
    [34]
    G. A. Rattá et al., “An advanced disruption predictor for JET tested in a simulated real-time environment,” Nucl. Fusion 50, 025005 (2010).10.1088/0029-5515/50/2/025005
    [35]
    C. D. Challis et al., Nucl. Fusion 60, 086008 (2020).10.1088/1741-4326/ab94f7
    [36]
    G. Pucella et al., “Onset of tearing modes in plasma termination on JET: The role of temperature hollowing and edge cooling,” Nucl. Fusion 61, 046020 (2021).10.1088/1741-4326/abe3c7
    [37]
    B. Lipschultz et al., “Marfe: An edge plasma phenomenon,” Nucl. Fusion 24, 977 (1984).10.1088/0029-5515/24/8/002
    [38]
    J. F. Drake, “Marfes: Radiative condensation in tokamak edge plasma,” Phys. Fluids 30, 2429 (1987).10.1063/1.866133
    [39]
    G. A. Rattá, J. Vega, A. Murari, D. Gadariya, and JET Contributors, “PHAD: A phase-oriented disruption prediction strategy for avoidance, prevention, and mitigation in JET,” Nucl. Fusion 61, 116055 (2021).10.1088/1741-4326/ac2637
    [40]
    A. R. Field et al., “The impact of felling and W radiation on the performance of high-power, ITER-baseline scenario plasma in JET-ILW,” Plasma Phys. Controlled Fusion 63, 095013 (2021).10.1088/1361-6587/ac1567
    [41]
    [42]
    L. Piron, D. Van Eester, D. Frigione, L. Garzotti, P. J. Lomas, M. Lennholm, F. Rimini, F. Auriemma, M. Baruzzo, P. J. Carvalho, D. R. Ferreira, A. R. Field, K. Kirov, Z. Stancar, C. I. Stuart, and D. Valcarcel, “Radiation control in deuterium, tritium and deuterium-tritium JET baseline plasmas - part,” Fusion Eng. Des 193, 113634 (2023).10.1016/j.fusengdes.2023.113634
    [43]
    E. Lerche et al., “ICRH for core impurity mitigation in JET-ILW,” AIP Conf. Proc. 1689, 030002 (2015).10.1063/1.4936467
    [44]
    M. Kong et al., “Physics-based control of neoclassical tearing modes on TCV,” Plasma Phys. Controlled Fusion 64, 044008 (2022).10.1088/1361-6587/ac48be
    [45]
    D. R. Ferreira, P. J. Carvalho, and H. Fernandes, “Deep learning for plasma tomography and disruption prediction from bolometer data,” IEEE Trans. Plasma Sci. 48, 36–45 (2019).10.1109/TPS.2019.2947304
    [46]
    D. R. Ferreira et al., Fusion Eng. Des. 164, 112179 (2021).10.1016/j.fusengdes.2020.112179
    [47]
    D. R. Ferreira et al., “Deep learning for the analysis of disruption precursors based on plasma tomography,” Fusion Sci. Technol. 76(8), 901–911 (2020).10.1080/15361055.2020.1820749
    [48]
    A. Murari et al., “Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees,” Nucl. Fusion 48, 035010 (2008).10.1088/0029-5515/48/3/035010
    [49]
    C. Rea et al., “Disruption prediction investigations using machine learning tools on DIII-D and Alcator C-Mod,” Plasma Phys. Controlled Fusion 60, 084004 (2018).10.1088/1361-6587/aac7fe
    [50]
    A. Murari et al., “Adaptive predictors based on probabilistic SVM for real-time disruption mitigation on JET,” Nucl. Fusion 58, 056002 (2018).10.1088/1741-4326/aaaf9c
    [51]
    C. Rea, K. J. Montes, K. G. Erickson, R. S. Granetz, and R. A. Tinguely, “A real-time machine learning-based disruption predictor in DIII-D,” Nucl. Fusion 59, 096016 (2019).10.1088/1741-4326/ab28bf
    [52]
    A. Murari, M. Lungaroni, M. Gelfusa, E. Peluso, J. Vega, and JET Contributors, “Adaptive learning for disruption prediction in non-stationary conditions,” Nucl. Fusion 59, 086037 (2019).10.1088/1741-4326/ab1ecc
    [53]
    A. Murari, R. Rossi, M. Lungaroni, M. Baruzzo, and M. Gelfusa, “Stacking of predictors for the automatic classification of disruption types to optimize the control logic,” Nucl. Fusion 61, 036027 (2021).10.1088/1741-4326/abc9f3
    [54]
    A. Murari et al., “Investigating the physics of tokamak global stability with interpretable machine learning tools,” Appl. Sci. 10, 6683 (2020).10.3390/app10196683
    [55]
    E. Peluso et al., “Alternative detection of n = 1 modes slowing down on ASDEX Upgrade,” Appl. Sci. 10(21), 7891 (2020).10.3390/app10217891
    [56]
    M. E. Puiatti et al., “Radiation pattern and impurity transport in argon seeded ELMy H-mode discharges in JET,” Plasma Phys. Controlled Fusion 44, 1863–1878 (2002).10.1088/0741-3335/44/9/305
    [57]
    L. Piron et al., “Radiation control in tritium and deuterium-tritium JET baseline plasmas—Part II,” Fusion Eng. Des. 192, 113695 (2023).10.1016/j.fusengdes.2023.113695
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