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IRIS
This work applies the coupled JINTRAC and QuaLiKiz-neural-network (QLKNN) model on the ohmic current ramp-up phase of a JET D discharge. The chosen scenario exhibits a hollow T-e profile attributed to core impurity accumulation, which is observed to worsen with the increasing fuel ion mass from D to T. A dynamic D simulation was validated, evolving j, n(e), T-e, T-i, n(Be), n(Ni), and n(W) for 7.25 s along with self-consistent equilibrium calculations, and was consequently extended to simulate a pure T plasma in a predict-first exercise. The light impurity (Be) accounted for Z(eff) while the heavy impurities (Ni, W) accounted for Prad. This study reveals the role of transport on the Te hollowing, which originates from the isotope effect on the electron-ion energy exchange affecting T-i. This exercise successfully affirmed isotopic trends from previous H experiments and provided engineering targets used to recreate the D q-profile in T experiments, demonstrating the potential of neural network surrogates for fast routine analysis and discharge design. However, discrepancies were found between the impurity transport behaviour of QuaLiKiz and QLKNN, which lead to notable T-e hollowing differences. Further investigation into the turbulent component of heavy impurity transport is recommended.
Predictive JET current ramp-up modelling using QuaLiKiz-neural-network
Ho A.;Citrin J.;Challis C. D.;Bourdelle C.;Casson F. J.;Garcia J.;Hobirk J.;Kappatou A.;Keeling D. L.;King D. B.;Koechl F.;Lerche E.;Maggi C. F.;J. Mailloux;N. Abid;K. Abraham;P. Abreu;O. Adabonyan;P. Adrich;V. Afanasev;M. Afzal;T. Ahlgren;L. Aho-Mantila;N. Aiba;M. Airila;M. Akhtar;R. Albanese;M. Alderson-Martin;D. Alegre;S. Aleiferis;A. Aleksa;A. G. Alekseev;E. Alessi;P. Aleynikov;J. Algualcil;M. Ali;M. Allinson;B. Alper;E. Alves;G. Ambrosino;R. Ambrosino;V. Amosov;E. Andersson Sunden;P. Andre;B. M. Angelini;C. Angioni;I. Antoniou;L. C. Appel;C. Appelbee;S. Aria;M. Ariola;G. Artaserse;W. Arter;V. Artigues;N. Asakura;A. Ash;N. Ashika a;V. Aslanyan;M. Astrain;O. As talos;D. Auld;F. Auriemma;Y. Austin;L. Avotina;E. Aymerich;A. Baciero;F. Bairaktaris;J. Balbin;L. Balbinot;I. Balboa;M. Balden;C. Balsha;N. Balsha;V. K. Bandaru;J. Banks;Yu. F. Baranov;C. Barcellona;A. Barnard;M. Barnard;R. Barnsley;A. Barth;M. Baruzzo;S. Barwell;M. Bassan;A. Batista;P. Batistoni;L. Baumane;B. Bauvir;L. Baylor;P. S. Beaumont;D. Beckett;A. Begolli;M. Beidler;N. Bekris;M. Beldishevski;E. Belli;F. Belli;É. Belonohy;M. Ben Yaala;J. Benayas;J. Bentley;H. Bergsaker;J. Bernardo;M. Bernert;M. Berry;L. Bertalot;H. Betar;M. Beurskens;S. Bickerton;B. Bieg;J. Bielecki;A. Bierwage;T. Biewer;R. Bilato;P. Blkov;G. Birkenmeier;H. Bishop;J. P. S. Bizzarro;J. Blackburn;P. Blanchard;P. Blatchford;V. Bobkov;A. Boboc;P. Bohm;T. Bohm;I. Bolshakova;T. Bolzonella;N. Bonanomi;D. Bonfiglio;X. Bonnin;P. Bonofiglo;S. Boocock;A. Booth;J. Booth;D. Borba;D. Borodin;I. Borodkina;C. Boulbe;C. Bourdelle;M. Bowden;K. Boyd;I. Boiicevic Mihalic;S. C. Bradnam;V. Braic;L. Brandt;R. Bravanec;B. Breizman;A. Brett;S. Brezinsek;M. Bri;K. Bromley;B. Brown;D. Brunetti;R. Buckingham;M. Buckley;R. Budny;J. Buermans;H. Bufferand;P. Buratti;A. Burgess;A. Buscarino;A. Busse;D. Butcher;E. de la Cal;G. Calabrò;L. Calacci;R. Calado;Y. Camenen;G. Canal;B. Cannas;M. Cappelli;S. Carcangiu;P. Card;A. Cardinali;P. Carman;D. Carnevale;M. 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Zlobinski;A. Zocco;A. Zohar;V. Zoita;S. Zoletnik;V. K. Zotta;I. Zoulias;W. Z ingmann;I. Zychor
2023-01-01
Abstract
This work applies the coupled JINTRAC and QuaLiKiz-neural-network (QLKNN) model on the ohmic current ramp-up phase of a JET D discharge. The chosen scenario exhibits a hollow T-e profile attributed to core impurity accumulation, which is observed to worsen with the increasing fuel ion mass from D to T. A dynamic D simulation was validated, evolving j, n(e), T-e, T-i, n(Be), n(Ni), and n(W) for 7.25 s along with self-consistent equilibrium calculations, and was consequently extended to simulate a pure T plasma in a predict-first exercise. The light impurity (Be) accounted for Z(eff) while the heavy impurities (Ni, W) accounted for Prad. This study reveals the role of transport on the Te hollowing, which originates from the isotope effect on the electron-ion energy exchange affecting T-i. This exercise successfully affirmed isotopic trends from previous H experiments and provided engineering targets used to recreate the D q-profile in T experiments, demonstrating the potential of neural network surrogates for fast routine analysis and discharge design. However, discrepancies were found between the impurity transport behaviour of QuaLiKiz and QLKNN, which lead to notable T-e hollowing differences. Further investigation into the turbulent component of heavy impurity transport is recommended.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/174035
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simulazione ASN
Il report seguente simula gli indicatori relativi alla propria produzione scientifica in relazione alle soglie ASN 2023-2025 del proprio SC/SSD. Si ricorda che il superamento dei valori soglia (almeno 2 su 3) è requisito necessario ma non sufficiente al conseguimento dell'abilitazione. La simulazione si basa sui dati IRIS e sugli indicatori bibliometrici alla data indicata e non tiene conto di eventuali periodi di congedo obbligatorio, che in sede di domanda ASN danno diritto a incrementi percentuali dei valori. La simulazione può differire dall'esito di un’eventuale domanda ASN sia per errori di catalogazione e/o dati mancanti in IRIS, sia per la variabilità dei dati bibliometrici nel tempo. Si consideri che Anvur calcola i valori degli indicatori all'ultima data utile per la presentazione delle domande.
La presente simulazione è stata realizzata sulla base delle specifiche raccolte sul tavolo ER del Focus Group IRIS coordinato dall’Università di Modena e Reggio Emilia e delle regole riportate nel DM 589/2018 e allegata Tabella A. Cineca, l’Università di Modena e Reggio Emilia e il Focus Group IRIS non si assumono alcuna responsabilità in merito all’uso che il diretto interessato o terzi faranno della simulazione. Si specifica inoltre che la simulazione contiene calcoli effettuati con dati e algoritmi di pubblico dominio e deve quindi essere considerata come un mero ausilio al calcolo svolgibile manualmente o con strumenti equivalenti.