Non regression testing for the JOREK code

Non Regression Testing (NRT) aims to check if software modifications result in undesired behaviour. Suppose the behaviour of the application previously known, this kind of test makes it possible to identify an eventual regression, a bug. Improving and tuning a parallel code can be a time-consuming and difficult task, especially whenever people from different scientific fields interact closely. The JOREK code aims at investing Magnetohydrodynamic (MHD) instabilities in a Tokamak plasma. This paper describes the NRT procedure that has been tuned for this simulation code. Automation of the NRT is one keypoint to keeping the code healthy in a source code repository.Comment: No. RR-8134 (2012

Numerical study of tearing mode seeding in tokamak X-point plasma

A detailed understanding of island seeding is crucial to avoid (N)TMs and their negative consequences like confinement degradation and disruptions. In the present work, we investigate the growth of 2/1 islands in response to magnetic perturbations. Although we use externally applied perturbations produced by resonant magnetic perturbation (RMP) coils for this study, results are directly transferable to island seeding by other MHD instabilities creating a resonant magnetic field component at the rational surface. Experimental results for 2/1 island penetration from ASDEX Upgrade are presented extending previous studies. Simulations are based on an ASDEX Upgrade L-mode discharge with low collisionality and active RMP coils. Our numerical studies are performed with the 3D, two fluid, non-linear MHD code JOREK. All three phases of mode seeding observed in the experiment are also seen in the simulations: first a weak response phase characterized by large perpendicular electron flow velocities followed by a fast growth of the magnetic island size accompanied by a reduction of the perpendicular electron velocity, and finally the saturation to a fully formed island state with perpendicular electron velocity close to zero. Thresholds for mode penetration are observed in the plasma rotation as well as in the RMP coil current. A hysteresis of the island size and electron perpendicular velocity is observed between the ramping up and down of the RMP amplitude consistent with an analytically predicted bifurcation. The transition from dominant kink/bending to tearing parity during the penetration is investigated

Edge Localized Mode control by Resonant Magnetic Perturbations in tokamak plasmas

Dans les tokamaks, les instabilités nommées ELMs (pour ``Edge Localized Modes'') génèrent des relaxations quasi-périodiques du plasma, potentiellement néfastes pour le divertor d'ITER. Une méthode de contrôle des ELMs prévue pour ITER est l'application de Perturbations Magnétiques Résonantes (RMPs), capables de mitiger ou supprimer les ELMs dans les tokamaks existants. Afin d'améliorer la compréhension de l'interaction entre les ELMs, les RMPs et les écoulements du plasma et de réaliser des prédictions fiables pour ITER, la simulation non-linéaire des ELMs et des RMPs est réalisée avec le code de MHD réduite JOREK, en géométrie réaliste. Les effets bi-fluides diamagnétiques, la friction poloidale néoclassique, une source de rotation parallèle et les RMPs ont été ajoutés dans JOREK pour simuler la pénétration des RMP en prenant en compte la réponse cohérente du plasma. Dans un premier temps, la réponse du plasma aux RMPs (sans ELMs) est étudiée dans le cas des tokamaks JET, MAST et ITER, pour des paramètres réalistes. Ensuite, la dynamique cyclique des ELMs (sans RMPs) est modélisée pour la première fois en géométrie réaliste. La compétition entre la stabilisation du plasma par la rotation diamagnétique et sa déstabilisation par la source de chaleur induit la reconstruction cyclique du piédestal. Enfin la mitigation et la suppression des ELMs sont obtenues pour la première fois dans nos simulations. Le couplage non-linéaire des RMPs avec des modes instables du plasma induit une activité MHD continue à la place des violentes relaxations d'ELMs. Au-delà d'un seuil de perturbation magnétique, la suppression totale des ELMs est également observée.The growth of plasma instabilities called Edge Localized Modes (ELMs) in tokamaks results in the quasi-periodic relaxations of the edge plasma, potentially harmful for the divertor in ITER. One of the promising ELM control methods planned in ITER is the application of external resonant magnetic perturbations (RMPs), already efficient for ELM mitigation/suppression in current tokamak experiments. However a better understanding of the interaction between ELMs, RMPs and plasma flows is needed to make reliable predictions for ITER. In this perspective, non-linear modeling of ELMs and RMPs is done with the reduced MHD code JOREK, in realisitic geometry including the X-point and the Scrape-Off Layer. The two-fluid diamagnetic drifts, the neoclassical friction, a source of parallel rotation and RMPs have been implemented to simulate the RMP penetration consistently with the plasma response. As a first step, the plasma response to RMPs (without ELMs) is studied for JET, MAST and ITER realistic plasma parameters and geometry. Then the cyclic dynamics of the ELMs (without RMPs) is modeled for the first time in realistic geometry. After an ELM crash, the diamagnetic rotation is found to be instrumental to stabilize the plasma and to model the cyclic reconstruction and collapse of the plasma pressure profile. Last the ELM mitigation and suppression by RMPs is observed for the first time in modeling. The non-linear coupling of the RMPs with unstable modes is found to induce a continuous MHD activity in place of a large ELM crash, resulting in the mitigation of the ELMs. Over a threshold in magnetic perturbation, the full ELM suppression is also observed

Situation actuelle en France du contournement de la résistance Vf par Venturia inaequalis - propositions de méthodes de lutte adaptées.

National audienc

Non-linear MHD simulations of QH-mode DIII-D plasmas and implications for ITER high Q scenarios

International audienceIn nonlinear MHD simulations of DIII-D QH-mode plasmas it has been found that low n kink/peeling modes (KPMs) are unstable and grow to a saturated external kink mode. The features of the dominant saturated KPMs, which are localized toroidally by non-linear coupling of harmonics, such as mode frequencies, density fluctuations and their effect on pedestal particle and energy transport, are in good agreement with the observations of the Edge Harmonic Oscillation (EHO) typically present in DIII-D QH-mode experiments. The non-linear evolution of MHD modes with toroidal mode numbers n from 0 to 10, including both kink-peeling modes and ballooning modes, is investigated through MHD simulations by varying the pedestal current and pressure relative to the initial conditions of DIII-D QH-mode plasma. The edge current and pressure at the pedestal are key parameters for the plasma either saturating to a QH-mode regime or a ballooning mode dominant regime. The influence of E×B flow and its shears on QH-mode plasma has been investigated. The behavior of QH-mode with different flow shear shows E×B rotation has strong stabilization effects on the medium to high-n modes but destabilizing for n=2. The QH-mode extrapolation results of an ITER Q=10 plasma show that the pedestal currents are large enough to destabilize an n=1-5 kink/peeling mode, leading to a saturated kink-peeling mode

Non-linear MHD modeling of edge localized mode cycles and mitigation by resonant magnetic perturbations

International audienc

Dimensional reduction based on peak fitting of Raman micro spectroscopy data improves detection of prostate cancer in tissue specimens

Significance: Prostate cancer is the most common cancer among men. An accurate diagnosis of its severity at detection plays a major role in improving their survival. Recently, machine learning models using biomarkers identified from Raman micro-spectroscopy discriminated intraductal carcinoma of the prostate (IDC-P) from cancer tissue with a [Formula: see text] detection accuracy and differentiated high-grade prostatic intraepithelial neoplasia (HGPIN) from IDC-P with a [Formula: see text] accuracy. Aim: To improve the classification performance of machine learning models identifying different types of prostate cancer tissue using a new dimensional reduction technique. Approach: A radial basis function (RBF) kernel support vector machine (SVM) model was trained on Raman spectra of prostate tissue from a 272-patient cohort (Centre hospitalier de l’Université de Montréal, CHUM) and tested on two independent cohorts of 76 patients [University Health Network (UHN)] and 135 patients (Centre hospitalier universitaire de Québec-Université Laval, CHUQc-UL). Two types of engineered features were used. Individual intensity features, i.e., Raman signal intensity measured at particular wavelengths and novel Raman spectra fitted peak features consisting of peak heights and widths. Results: Combining engineered features improved classification performance for the three aforementioned classification tasks. The improvements for IDC-P/cancer classification for the UHN and CHUQc-UL testing sets in accuracy, sensitivity, specificity, and area under the curve (AUC) are (numbers in parenthesis are associated with the CHUQc-UL testing set): [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), [Formula: see text] (6%), [Formula: see text] ([Formula: see text]) with respect to the current best models. Discrimination between HGPIN and IDC-P was also improved in both testing cohorts: [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]). While no global improvements were obtained for the normal versus cancer classification task [[Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text])], the AUC was improved in both testing sets. Conclusions: Combining individual intensity features and novel Raman fitted peak features, improved the classification performance on two independent and multicenter testing sets in comparison to using only individual intensity features