Towards the realization on JET of an integrated H-mode scenario for ITER

Ongena, J.; Monier‐Garbet, P.; Suttrop, W.; Andrew, P.; Bécoulet, M.; Budny, R.; Corre, Y.; Cordey, G.; Dumortier, P.; Eich, T.; Garzotti, L.; Hillis, D.L.; Hogan, J.; Ingesson, L. C.; Jachmich, S.; Joffrin, E.; Lang, P. T.; Loarte, A.; Lomas, P. J.; Maddison, G.; McDonald, D. C.; Messiaen, A.; Nave, M. F. F.; Saibene, G.; Sartori, R.; Sauter, O.; Strachan, J.; Unterberg, B.; Valovič, M.; Voitsekhovitch, I.; Hellermann, M. von; Alper, B.; Baranov, Y.; Beurskens, M.; Bonheure, G.; Brzozowski, J.; Bucalossi, J.; Brix, M.; Charlet, M.; Coffey, I.; Baar, M. de; Vries, P. de; Giroud, C.; Gowers, C.; Hawkes, N.; Jackson, G.L.; Jupén, C; Kallenbach, A.; Koslowski, H. R.; Lawson, K.; Mantsinen, M.; Matthews, G. F.; Milani, F.; Murakami, M.; Murari, A.; Neu, R.; Parail, V.; Podda, S.; Puiatti, M. E.; Rapp, J.; Righi, E.; Sartori, F.; Sarazin, Y.; Staebler, A.; Stamp, M.; Telesca, G.; Valisa, M.; Weyssow, Boris; Zastrow, K.‐D.; Contributors, Efda‐Jet

doi:10.1088/0029-5515/44/1/015

Cited by 47 publications

(30 citation statements)

References 40 publications

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“…With regard to future lines of investigation, in terms of applications, BIC H and AIC H can be used in various disciplines not only for model selection but also for exploration of the operational space using genetic algorithms. One case is point is Magnetic Confinement Nuclear Fusion [9,10], in which the model versions of AIC and BIC are already extensively implemented [11][12][13][14]. One application, which can certainly profit form the proposed new version of the indicators, is the identification of the most appropriate solution of difficult inverse problems such as the reconstruction of the magnetic fields and the tomographies [15][16][17][18][19].…”

Section: Discussionmentioning

confidence: 99%

On the Use of Entropy to Improve Model Selection Criteria

Murari

Peluso

Cianfrani

et al. 2019

Entropy

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The most widely used forms of model selection criteria, the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), are expressed in terms of synthetic indicators of the residual distribution: the variance and the mean-squared error of the residuals respectively. In many applications in science, the noise affecting the data can be expected to have a Gaussian distribution. Therefore, at the same level of variance and mean-squared error, models, whose residuals are more uniformly distributed, should be favoured. The degree of uniformity of the residuals can be quantified by the Shannon entropy. Including the Shannon entropy in the BIC and AIC expressions improves significantly these criteria. The better performances have been demonstrated empirically with a series of simulations for various classes of functions and for different levels and statistics of the noise. In presence of outliers, a better treatment of the errors, using the Geodesic Distance, has proved essential.

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Section: Discussionmentioning

confidence: 99%

On the Use of Entropy to Improve Model Selection Criteria

Murari

Peluso

Cianfrani

et al. 2019

Entropy

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“…Under these conditions, they have always proved to have much better convergence properties in all respects. As a consequence, the proposed new versions of the selection criteria are expected to be deployed quite systematically in various fields of complex science, ranging from high-temperature plasmas [16][17][18][19][20][21][22][23] to atmospheric physics [24,25]. Another interesting application could be found in the regularization of recent tomographic inversion methods [26][27][28].…”

Section: Discussionmentioning

confidence: 99%

Upgrading Model Selection Criteria with Goodness of Fit Tests for Practical Applications

Rossi

Murari

Gaudio

et al. 2020

Entropy

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The Bayesian information criterion (BIC), the Akaike information criterion (AIC), and some other indicators derived from them are widely used for model selection. In their original form, they contain the likelihood of the data given the models. Unfortunately, in many applications, it is practically impossible to calculate the likelihood, and, therefore, the criteria have been reformulated in terms of descriptive statistics of the residual distribution: the variance and the mean-squared error of the residuals. These alternative versions are strictly valid only in the presence of additive noise of Gaussian distribution, not a completely satisfactory assumption in many applications in science and engineering. Moreover, the variance and the mean-squared error are quite crude statistics of the residual distributions. More sophisticated statistical indicators, capable of better quantifying how close the residual distribution is to the noise, can be profitably used. In particular, specific goodness of fit tests have been included in the expressions of the traditional criteria and have proved to be very effective in improving their discriminating capability. These improved performances have been demonstrated with a systematic series of simulations using synthetic data for various classes of functions and different noise statistics.

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“…This shot was run as a long duration, high density experiment to study density peaking, as described in Refs. [28,29], and is included in the ITER Profile Database [30]. Radial profiles of the shaping parameters and the ion and electron temperatures and densities from TRANSP analysis [31][32][33] of the data are shown in Fig.…”

Section: Jet-based Plasma Equilibriummentioning

confidence: 99%

Effects of Plasma Shaping on Nonlinear Gyrokinetic Turbulence

Belli¹,

Hammett

Dorland

2008

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The effects of flux surface shape on the gyrokinetic stability and transport of tokamak plasmas are studied using the GS2 code [M. Kotschenreuther, G. Rewoldt, and W.M. Tang, Comput. Phys. Commun. 88, 128 (1995); W. Dorland, F. Jenko, M. Kotschenreuther, and B.N. Rogers, Phys. Rev. Lett. 85, 5579 (2000)]. Studies of the scaling of nonlinear turbulence with shaping parameters are performed using analytic equilibria based on interpolations of representative shapes of the Joint European Torus (JET) [P.H. Rebut and B.E. Keen, Fusion Technol. 11, 13 (1987)]. High shaping is found to be a stabilizing influence on both the linear ion-temperature-gradient (ITG) instability and the nonlinear ITG turbulence. For the parameter regime studied here, a scaling of the heat flux with elongation of χ ∼ κ −1.5 or κ −2.0 , depending on the triangularity, is observed at fixed average temperature gradient. While this is not as strong as empirical elongation scalings, it is also found that high shaping results in a larger Dimits upshift of the nonlinear critical temperature gradient due to an enhancement of the Rosenbluth-Hinton residual zonal flows.

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Towards the realization on JET of an integrated H-mode scenario for ITER

Cited by 47 publications

References 40 publications

On the Use of Entropy to Improve Model Selection Criteria

On the Use of Entropy to Improve Model Selection Criteria

Upgrading Model Selection Criteria with Goodness of Fit Tests for Practical Applications

Effects of Plasma Shaping on Nonlinear Gyrokinetic Turbulence

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