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REVIEW 5 major objections 5 minor 95 references

By coupling the fast transport code RAPTOR with the inverse equilibrium solver FBT, a tokamak discharge can be simulated from its pulse schedule before it runs, and the resulting kinetic profiles yield improved coil-current predictions.

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2026-08-02 19:41 UTC pith:J6Y7TJRE

load-bearing objection Solid, honest engineering integration paper with a real 211-shot benchmark, but the operational payoff (better coil-current prediction) is under-proven because the benchmark uses post-shot density and there is no sensitivity analysis. the 5 major comments →

arxiv 2603.01210 v3 pith:J6Y7TJRE submitted 2026-03-01 physics.plasm-ph

Kinetic Equilibrium Prediction at TCV using RAPTOR and FBT

C. E. Contr\'e , A. Merle , O. Sauter , S. Van Mulders , R. Coosemans , G. Durr-Legoupil-Nicoud , F. Felici , O. F\'evrier
show 8 more authors
C. Heiss B. Labit A. Pau Y. Poels C. Venturini B. Vincent the TCV team the EUROfusion Tokamak Exploitation Team
This is my paper
classification physics.plasm-ph
keywords kinetic equilibrium predictionRAPTORFBTtokamak transportGrad-Shafranov equationcoil currentsH-mode transitionnegative triangularity
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper builds a pre-shot Kinetic Equilibrium Prediction workflow by coupling RAPTOR, a fast 1.5D transport solver, to FBT, an inverse free-boundary Grad-Shafranov solver. It aims to show that a full TCV discharge, from current ramp-up to ramp-down across L-mode, H-mode, and a variety of shapes, can be simulated before the shot using only the pulse schedule, given an estimate of the confinement quality factor H98(y,2) and the line-averaged density. The two codes relax to a self-consistent answer in a few iterations, and feeding the predicted pressure and current-function profiles into FBT changes the computed poloidal-field coil currents by tens to hundreds of amperes, most importantly improving estimates of internal inductance and normalized beta. If correct, this gives tokamak operators a physics-based equilibrium for shot preparation instead of hand-tuned polynomial profiles, and the paper backs it with statistical comparisons over 211 discharges and two full experimental validations.

Core claim

The paper's central claim is that the usual separation between pre-shot equilibrium preparation and transport prediction can be removed: RAPTOR's predicted p' and TT' profiles are used directly as the free functions in FBT's Grad-Shafranov solve, and iterating between the two codes produces self-consistent equilibria within a few minutes. With this coupling, the deliberately low poloidal beta and high q_a used in conventional FBT programming are corrected to realistic values, and the resulting feedforward PF coil currents differ from the standard preparation by tens to hundreds of amperes. In two experimentally repeated scenarios, the FBT-RAPTOR prepared shots kept the X-point closer to its

What carries the argument

The central object is the pair of free functions p'(psi) and TT'(psi) that enter the Grad-Shafranov equation. RAPTOR generates these profiles from the pulse schedule using a stiff logarithmic-gradient transport model whose pedestal gradients are controlled by a PI controller tied to H98(y,2) and line-averaged density; FBT solves the inverse free-boundary equilibrium with these profiles. The coupling loop iterates between the codes, seeding each FBT run with the previous plasma current distribution, until the FBT and RAPTOR profiles agree (alpha approximately 1), with residual systematic differences in poloidal beta below a few percent after two iterations.

Load-bearing premise

The whole prediction rests on pre-shot guesses of the confinement quality factor H98(y,2) and line-averaged density; the paper's ad-hoc density model is admitted to be insufficient for large-database validation, so the large benchmark uses post-shot experimental density, and the full pre-shot workflow is demonstrated on only two cases.

What would settle it

Run the fully predictive workflow, with no post-shot data, on a set of TCV discharges outside the 211-shot training set, using the ad-hoc density model and a literal reading of the H98 scaling; then compare the predicted beta_N, li3, and PF coil currents against LIUQE kinetic reconstructions and measured coil currents. The central claim would be falsified if the signed errors in beta_N or li3 are comparable to the correction the coupling is supposed to provide, or if shots prepared with the FBT-RAPTOR traces do not show measurably smaller X-point misalignment than shots prepared with the stand

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Tokamak operators can prepare feedforward PF coil traces from a physics-based equilibrium, reducing X-point and shape misalignment and lowering the risk of vertical displacement events.
  • The 211-shot benchmark indicates that a default parameter set predicts electron and ion stored energies within about 20% across a wide range of TCV scenarios, when the line-averaged density is known.
  • The extended H-mode threshold model lets confinement transitions be predicted automatically from the pulse schedule rather than assumed by the operator.
  • The coupled simulation runs in a few minutes per second of discharge, making pre-shot iteration feasible within the inter-shot latency at TCV.
  • More accurate internal inductance and normalized beta estimates give operators more realistic information about operational limits before a pulse begins.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Inference: If the line-averaged density prediction is improved, the same workflow could run fully autonomously across a much larger scenario space, moving the demonstration from two validated cases to routine operations.
  • Inference: The same p'/TT' handoff could be reused in a tight-coupling mode or in real-time kinetic reconstruction, extending the benefit of better internal profiles from pre-shot planning to post-shot analysis.
  • Inference: The coil-current corrections driven by the Shafranov shift suggest a sensitivity test: how strongly do the prepared currents and beta_N estimates change per unit change in the assumed H98(y,2)? The paper does not quantify this sensitivity.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper presents a Kinetic-Equilibrium Prediction (KEP) workflow for TCV, coupling the RAPTOR 1.5D transport solver with the FBT free-boundary inverse equilibrium solver. RAPTOR predicts full-discharge profiles of current, temperatures, and density from pulse-schedule information using a gradient-based transport model whose pedestal gradients are PI-controlled to match prescribed values of H98 and line-averaged density. The resulting p' and TT' profiles are fed to FBT, and the two codes are iterated to self-consistency. The paper reports convergence in a few iterations, a benchmark on 211 TCV discharges (using experimental line-averaged density for the large database), and two fully predictive experimental demonstrations (one PT-LSN H-mode, one upper-NT snowflake), claiming improved X-point alignment and stationarity when the KEP-computed feedforward coil currents are used.

Significance. If fully validated, KEP would be a practically valuable tool: it is fast (minutes per discharge), uses existing TCV infrastructure, and directly addresses a known weakness of standard FBT preparation—the use of simple polynomial p' and TT' profiles with operator-chosen beta_pol and l_i. The paper's strengths include a clear coupling scheme, a convergence demonstration (Fig. 5), a large-database transport benchmark, and two experimental tests showing improved X-point targeting. However, the significance of the benchmark is limited by the use of post-shot density in Sec. 3, and the headline operational benefit is not yet supported by a sensitivity analysis connecting input uncertainties (H98, n_e,l) to the claimed coil-current improvements. These are addressable with existing data and additional analysis, so the core idea is defensible but the current evidence is incomplete.

major comments (5)
  1. [Sec. 3 and Sec. 4] The 211-shot benchmark does not validate the fully predictive pre-shot workflow. Sec. 3 states that all simulations in that section use post-shot experimental n_e,l because the ad-hoc density model 'does not yet allow for validation over a large shot database'; the fully predictive workflow (including the n_e,l model) is demonstrated only for the two discharges of Sec. 4. The abstract and conclusion nonetheless present the 211-shot result as evidence for pre-shot simulation 'across a wide range of plasma shapes and scenarios'. Since n_e,l is one of the two user-supplied inputs, and Fig. 7 shows a ~20% n_e,l error at one time in one shot, the operational benefit of KEP is not yet established. Please (i) run the ad-hoc density model over the 211-shot database and report its error statistics, and (ii) add a propagation study of realistic H98 and n_e,l uncertainties (e.g., ±20% in n_e,l, H98
  2. [Sec. 2.1, Table 1, Figs. 10/A.3] The energy-content validation is largely prescribed by the input H98. The PI controller adjusts mu_Te to match H98 = tau_E/tau_scal_E (Sec. 2.1), with default H98 values in Table 1, so W_e is forced to track H98*P_loss*tau_scal. The within-20% agreement in Figs. 10, A.3 and A.4 is therefore mostly a test of the assumed H98 values and the ITER scaling, not an independent test of predictive transport. The equilibrium-relevant quantities—l_i, beta_pol/beta_N, and the profile shapes of p' and TT'—are not compared against reconstructions over the database; only single-shot examples are given (Figs. 13, 15). Please add a database-level comparison of predicted beta_pol and l_i (or beta_N) to LIUQE-KER/MER values, and report profile-shape errors separately from energy-content errors.
  3. [Sec. 2.1, Table 1] The NT transport parameters are selected in-sample. The text states that the NT parameters in Table 1 'were selected to match the set of discharges simulated in this study (41 NT shots, among the 211 shots presented in Section 3)'. Consequently, the good NT agreement in the 211-shot benchmark is not out-of-sample evidence for the 'wide range of plasma shapes and scenarios' claim. Please provide a cross-validation (e.g., train on a subset of NT shots and validate on the rest) or otherwise quantify the sensitivity of results to lambda_Te, lambda_Ti, lambda_ne and H98_NT. This is important because NT is one of the two headline scenarios in Sec. 4.
  4. [Eq. (2.8) and Sec. 2.1] The L–H transition model relies on ad-hoc prohibitive thresholds: alpha_l is 'set to a high prohibitive value' and NT access is inhibited by construction. The intermediate s-region of f_div is constrained by essentially one discharge (#82274, Fig. 11), and the paper itself notes that DN behavior 'remains uncertain'. Since transition timing sets the confinement regime (and hence the effective H98 used by the controller) in the fully predictive workflow, the sensitivity of KEP outputs to alpha_f, alpha_u, and the s-criterion should be quantified. At minimum, report the distribution of P_sep/P_LH and s for the benchmark and identify which shots lie in the sensitive region |s| <= 1.
  5. [Sec. 4, Figs. 14 and 17] The experimental demonstration of improved coil-current programming is based on very few discharges without quantitative error analysis. For #81882, one FBT-RAPTOR-prepared shot is compared with one standard shot; for NT-SF, two vs three shots are compared. Fig. 17 shows time traces but no uncertainties, no X-point-error metric, and no control for shot-to-shot variability. Since the Delta|I_a| corrections in Fig. 12 are tens to hundreds of amperes—the same order as the likely effect of input uncertainties—the claim that KEP 'improves the evaluation of coil currents' needs a quantitative metric, e.g., time-integrated X-point gap error for KEP vs standard preparations, and a comparison of the KEP correction amplitude to the propagated H98/n_e,l uncertainty.
minor comments (5)
  1. [Sec. 3 title] The section is titled 'Benchmark of the pre-shot prediction of 207 shots' but the text consistently says 211 shots; the mismatch should be corrected.
  2. [Fig. 14 caption] The caption refers to shot #83740 as the FBT-RAPTOR-prepared shot, while Sec. 4.1 text says #83940. Please reconcile.
  3. [Figs. 16 and 17] The caption of Fig. 16 and the text of Sec. 4.2 appear to swap the shot numbers for the initial FBT and FBT-RAPTOR groups relative to Fig. 17; please clarify which shots used which preparation.
  4. [Table 1] The two H98 columns are labeled 'H 98(y,2) e' and 'H 98(y,2)' with no explicit explanation in the caption; a sentence defining electron vs total confinement factor would help.
  5. [Sec. 2.1, line-averaged density model] The ad-hoc density rules (125 ms decay lifetime, +30% for NBI, +30% for H-mode) are TCV-specific; please state more explicitly that these are not intended as a general model and cite any prior use, to avoid overgeneralization.

Circularity Check

2 steps flagged

Energy-content and β_pol predictions are largely inherited from the assumed H98 input; benchmark uses post-shot density and NT parameters selected on validation shots, so the headline predictiveness is partially circular.

specific steps
  1. fitted input called prediction [Sec. 2.1, 'Transport and pedestal' (around Eq. 2.4 and Table 1)]
    "In this model, the value of the non-stiff electron temperature gradient µTe is modified by a PI controller to match a total confinement factor H=τE/τscal E , scaling the energy confinement time obtained during the discharge, τE =W/Ploss, with a data-driven scaling law τscal E . [...] Such simple model eliminates the need for complex pedestal physics, although it does require a starting hypothesis about the confinement quality."

    The controller is set to match W/Ploss = H98 τ_scal_E, so once H98, Ploss, and the ITER scaling input are specified, the total thermal energy W is algebraically fixed by the input H98. The paper's benchmark claim that We and Wi are 'predicted within 20%' (Sec. 3) with H98 = 0.7 (L-mode) and 1 (H-mode) is therefore mostly a test of the assumed H98 values, not an independent energy prediction. Since β_pol = (8/3) W_th / (µ0 R0 Ip^2), the normalized-pressure part of the FBT equilibrium—and the corresponding Shafranov-shift contribution to the PF coil correction—is likewise fixed by the input H98. The profile shapes and l_i evolution contain additional modeling, but the headline energy/β_pol result is by construction the input confinement factor.

  2. fitted input called prediction [Sec. 2.1, 'Extension of the gradient-based model to negative triangularity plasmas' / Sec. 3 benchmark]
    "These parameters, selected to match the set of discharges simulated in this study (41 NT shots, among the 211 shots presented in Section 3), are kept the same and constant across the whole database simulated in this work."

    The 211-shot benchmark is presented as validation 'across a wide range of plasma shapes and scenarios.' But the NT transport parameters in Table 1 were explicitly selected using 41 of those same 211 shots. For the NT subset, the 'prediction' is not out-of-sample: it checks how well parameters fit the data from which they were chosen. This is a calibration/validation overlap rather than a full definitional reduction, and the PT/H-mode portion of the database remains largely independent, but it inflates the apparent breadth of the validation.

full rationale

The derivation chain is: pulse schedule + assumed H98 + assumed or measured n_e,l → RAPTOR gradient-based model → p', TT' → FBT → coil currents. The clearest circular element is the H98 controller: because the pedestal gradient is adjusted until τE = W/Ploss equals H98 τ_scal_E, the energy content and hence β_pol are fixed by the input H98. The paper is transparent that H98 is a required input and admits the density model is not yet validated on the large database: 'All the simulations shown in this Section were performed providing the post-shot n_e,l from experiments together with the pulse schedule as the accuracy of the n_e,l ad-hoc model ... does not yet allow for validation over a large shot database.' Thus the 211-shot benchmark tests the transport model with an experimental input rather than the fully predictive KEP, and the fully predictive workflow is demonstrated only on two discharges in Sec. 4. In addition, the NT parameters were selected on 41 of the benchmark shots, making part of the claimed wide-range validation in-sample. Against this, the FBT-RAPTOR coupling itself has genuine independent content: l_i evolution from current diffusion, the profile-shape effect on the Shafranov shift and PF coil flux (Fig. A.1), and the two experimental demonstrations of improved X-point alignment/vertical control are not definitional consequences of H98. Self-citations to the gradient-based model ([19,25,58]) are used to justify a modeling choice but the model is tested against TCV data, so this is not a uniqueness-import or ansatz-via-citation chain. Overall, the energy-content and β_pol predictions partially reduce by construction to the input H98, while the coupling remains a real engineering contribution; hence the score is 6 rather than higher.

Axiom & Free-Parameter Ledger

7 free parameters · 7 axioms · 0 invented entities

The model is a prescribed-parameter engineering tool: H98, n_e,l ad-hoc trajectory, λ and boundary values, NBI absorption fractions, and α_LH are all inputs chosen by hand or fitted to the TCV database. No new physical entities are postulated.

free parameters (7)
  • H98(y,2) confinement factor (and H98_e) = L PT: 0.7; H PT: 1; L NT: 1 (total), H98_e 0.5-0.6 per scenario
    Prescribed per confinement mode/shape; the gradient-based model's PI controller forces profiles to match it, so the predicted thermal energy is approximately H98·P_loss·τ_scal. Also 'An electron confinement H98_e=0.6 is used for predicting the NT phase... based on previous discharges' (Sec. 4.2).
  • Logarithmic gradient parameters λ_Te, λ_Ti, λ_ne = L PT 3.2/3/2; H PT 2.3/2.5/1; L NT 3/3/3
    Table 1; for NT explicitly 'selected to match the set of discharges simulated in this study (41 NT shots...)' (Sec. 2.1), so these are fitted to the validation set.
  • Separatrix boundary values T_e|sep, T_i|sep, n_e|sep = 20/16/0.5 (L), 100/80/1 (H), etc.
    Table 1; kept constant per phase, chosen from typical experimental values.
  • α_LH geometry re-scaling factors (α_f=1, α_u=2, α_l prohibitive, NT prohibitive) = 1, 2, 'high prohibitive value', 'prohibitive factor' for NT
    Eq. 2.8-2.9; ad-hoc factor set from divertor geometry; NT H-mode access is inhibited by fiat: 'values of α_LH listed in Eq. 2.8 are increased by a prohibitive factor' (Sec. 2.1).
  • Line-averaged density ad-hoc model parameters = exponential lifetime 125 ms; +30% for NBI-1; +30% on H-mode
    Sec. 2.1 'Line-averaged density': 'set of ad-hoc rules built on the shot programming, which are based on empirical observations'.
  • NBI deposition/absorption parameters = 15% duct losses, 55% total absorption, Gaussian width w_dep=0.25, 60% power to ions
    Sec. 2.1 'General assumptions'; fixed empirical fractions; not derived from first principles.
  • Ion-electron temperature ratio γ expression coefficients = γ = min(0.4+0.5 n_e,l[5e19], 0.9); γ=1.6 with NBI
    Sec. 2.1 'Ion temperature'; ad-hoc scaling from TCV analyses.
axioms (7)
  • standard math Grad-Shafranov equilibrium and flux-surface averaged transport equations (Hinton-Hazeltine) are valid descriptions of TCV plasmas.
    Used implicitly in FBT (Eq. 2.13) and RAPTOR (Sec. 2.1, Eq. 2.1-2.2).
  • domain assumption The three-region stiff transport representation (flat core, stiff log-gradient, linear pedestal) in Eq. 2.4 holds across TCV L-, H-, PT- and NT-phases.
    Assumed by the gradient-based model; unifies L/H modes; not derived from turbulence theory.
  • ad hoc to paper L-H transition threshold is governed by the 2008 Martin scaling law rescaled by a continuous geometry factor f_div(s) (Eq. 2.8), with NT and limited phases inhibited by prohibitive α values.
    Introduced in Sec. 2.1; f_div(s) is continuous interpolation between favorable/unfavorable asymptotes; values α_l, α_NT are set to prohibitive values, not measured.
  • domain assumption No particle source (S_e=0), no convective/pinch heat flux terms, and constant Z_eff=1.5 across the discharge.
    Sec. 2.1 'General assumptions'; carbon impurity; affects density and heat transport equations.
  • domain assumption Core radiation can be neglected so P_sep = P_oh + P_aux - dW/dt, and dW/dt is neglected in threshold evaluation.
    Sec. 2.1 'L-H transitions'; 'As in many carbon devices, the core radiated power can be neglected [61]'; Fig. A.2 shows including radiation would change classification of shot #82547.
  • domain assumption Radial transport is only weakly dependent on equilibrium geometry, so a loose sequential coupling converges to the self-consistent solution within a few iterations.
    Sec. 2.3: 'rapid convergence can be explained by the quasi-unilateral nature of this coupling, as radial transport is only weakly sensitive to changes in geometry'; tested on examples, not proven.
  • domain assumption NBI heating can be represented by centered Gaussian deposition with fixed 55% absorption into thermal plasma and zero current drive.
    Sec. 2.1 'General assumptions'; omits NBI current drive, relevant for current profile.

pith-pipeline@v1.3.0-alltime-deepseek · 28034 in / 16871 out tokens · 156367 ms · 2026-08-02T19:41:01.536629+00:00 · methodology

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Cite this review

Pith. "Pith review of Kinetic Equilibrium Prediction at TCV using RAPTOR and FBT." pith.science (2026). https://pith.science/paper/J6Y7TJRE

@misc{pith2026260301210,
  author       = {Pith},
  title        = {Pith review of: Kinetic Equilibrium Prediction at TCV using RAPTOR and FBT},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J6Y7TJRE}},
  note         = {Machine review of arXiv:2603.01210}
}
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read the original abstract

We present results from a new Kinetic-Equilibrium Prediction (KEP) workflow and shot preparation for full TCV discharges, by coupling predict-first RAPTOR transport simulations with FBT inverse equilibrium calculations. RAPTOR is a 1.5D transport code which has been extensively used for plasma shot optimization and real-time modeling. We show that rapid pre-shot simulations can be performed directly using information from the pulse schedule across a wide range of plasma shapes and scenarios, given an estimate of the confinement quality factor H98(y,2) and line-averaged density. The resulting p' and TT' profiles are then provided to the pre-shot equilibrium computation performed by FBT - a static free-boundary solver routinely used at TCV - achieving convergence between the two codes in a few iterations. Finally, we show that this coupling, when integrated into the TCV shot preparation, improves the evaluation of the coil currents needed to match the target plasma shape; in particular providing an accurate estimate of critical quantities such as the internal inductance $l_i$ and normalized pressure $\beta_N$, giving more realistic information to tokamak operators about the expected pulse behavior and enabling them to adjust the plan correspondingly.

Figures

Figures reproduced from arXiv: 2603.01210 by A. Merle, A. Pau, B. Labit, B. Vincent, C. E. Contr\'e, C. Heiss, C. Venturini, F. Felici, G. Durr-Legoupil-Nicoud, O. F\'evrier, O. Sauter, R. Coosemans, S. Van Mulders, The EUROfusion Tokamak Exploitation Team, The TCV Team, Y. Poels.

Figure 1
Figure 1. Figure 1: Schematic overview of the Kinetic Equi￾librium Prediction (KEP) workflow. The pulse schedule is given as input to RAPTOR and FBT, loosely coupled to provide the active coil currents and pulse profiles predictively. These new feed￾forward currents traces are then integrated into the TCV preparation system to control the plasma shape and position with its 16 separately powered PF coils. Examples of #82030 (l… view at source ↗
Figure 2
Figure 2. Figure 2: Comparison of two Ti estimations, using Eq. 2.5 with H 98(y,2) e = 0.5 (in blue) and solving for the full ion heat equation with H98(y,2) = 1 (in red), with the measurements of 4 CXRS systems for shot #81882, and comparison with Te, in the L-mode ohmic phase (a) and in the NBI-heated H￾mode phase (b). These models yield similar results, both predicting the CXRS measurements within their error-bars, except … view at source ↗
Figure 3
Figure 3. Figure 3: Evolution of (a) Te, (b) ne and (c) Ti profiles before and after the onset of NBI. As the H-mode is triggered and reaches a QCE regime, the TS measurements (markers with error-bars) show a slightly better confinement (H 98(y,2) exp > 1) than the one modeled in RAPTOR (plain line), particularly shown in Ti . The experimental ne,l was used as input to this simulation. 5 [PITH_FULL_IMAGE:figures/full_fig_p00… view at source ↗
Figure 4
Figure 4. Figure 4: (a) Te, (b) ne and (c) Ti profiles of a negative triangularity (NT) shot. As the shaping decreases from δ = −0.37 to −0.30, the NBI-1 power is switched on and ramped up to 516kW, leading to an increase in density and total energy. With an H98(y,2) = 1 and taking the experimen￾tal ne,l, RAPTOR (plain line) matches the high confinement properties of the L-mode NT plasma, slightly over-estimating Te in the oh… view at source ↗
Figure 5
Figure 5. Figure 5: Result of the FBT-RAPTOR convergence for TCV shot #81882 after N iterations. α = p ′ FBT/p′ RAPTOR and ∆βpol = |β FBT pol − β RAPTOR pol |. The y-axis shows the distance between these quan￾tities and their final values at N=7. 2.3 FBT-RAPTOR coupling With no prior estimation of the kinetic evolution and non-inductive current drive, the right-hand side of the GSE remains under-determined. In this case, 8 [… view at source ↗
Figure 6
Figure 6. Figure 6: Plasma and coils contribution to the total poloidal flux in the H-mode phase ( [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of RAPTOR jΦ (a), p ′ (b) and T T′ (c) profiles at t = 2s using, for the line-averaged density, the pre-shot estimate (blue, green) shown in Fig. 16f, which is overestimated by about 20% at that time, or experimental post￾shot values (red), and using the predicted Ti (blue, red) or the electron-to-ion scaling (green), Eq.2.5; in black are the simple polynomial FBT profiles used in reference shot… view at source ↗
Figure 8
Figure 8. Figure 8: L–H transitions predicted by RAPTOR from the pulse schedule and experimental line￾averaged density using Eq. 2.6 for 51 SN discharges for which a transition was detected from experi￾ments by a confinement state classifier. Each sim￾ulation is time-aligned to the detection of either H-mode or D-mode (Dithering) by the classifier at t L−H. The vertical axis represents the number of simulations Nsim predicted… view at source ↗
Figure 9
Figure 9. Figure 9: Evolution of Te and ne profiles during the NBI-1 power ramp of (DN) shot #82274. A de￾lay occurs between the appearance of the H-mode pedestal (a, b) in TS measurements (markers with error-bars) and RAPTOR predicted profiles (solid line). (c) shows the evolution of s = σfav| δrsep δrthr |. This value is programmed to 0 in FBT (dashed line), to target a DN, LIUQE post-shot (solid line) shows a slightly unfa… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of the electron and ion energies predicted by RAPTOR with values reconstructed [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Illustration in black of the fdiv (solid) and f ∗ div (dashed), functions of s = σfav| δrsep δrthr |, Eqs. 2.8- 2.9. Each point corresponds to a time sample from 175 experiments having a diverted configuration, showing the dependence of the confinement state on the Psep/PLH ratio (computed from experimental data with the standard TCV analysis routines) and the s value (reconstructed with LIUQE) ; PLH, fro… view at source ↗
Figure 12
Figure 12. Figure 12: Distribution of differences in feedfor￾ward PF coil currents (in absolute value) com￾puted by FBT-RAPTOR for various TCV shots, ∆|Ia| = |Ia KEP| − |I 0 a |. Equilibria in early ramp￾up and late ramp-down phases, less sensitive to the coupling, are excluded from the distribution by se￾lecting times for which Ip > 20%I max p . PF coils are colored as in [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Overview of the KEP performed for TCV shot #81882. The pre-shot simulation (fully predictive [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: FBT X-point target and LIUQE shape reconstructions obtained in the H-mode phase for shot #83738, prepared with an initial FBT equilib￾rium with low-βpol and low-li3, and shot #83740, prepared with the FBT-RAPTOR correction triggering the transition to H-mode at t = 0.8s. The electron confinement quality H98 e is thus slowly increased from 0.4 to 0.5, while the target nel is in￾creased up to nel ≃ 7 1019m−… view at source ↗
Figure 15
Figure 15. Figure 15: Comparison of li3 and βpol reconstructed from LIUQE KER and LIUQE MER with and with￾out the DML measurement. 4.2 Negative-triangularity L-mode scenario with SF divertor target [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Overview of the KEP performed for TCV shot #83575. The pre-shot simulation (fully predictive [PITH_FULL_IMAGE:figures/full_fig_p018_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Evolution of βN , elongation κ and X-point locations for upper NT plasmas with snowflake divertor configuration for a series of shots prepared with the same programming but differ￾ent equilibrium preparation. Shots #[83946, 83947, 83950] (in red and orange), prepared with an initial FBT equilibrium with high βpol and li3, and shots #[83926, 83978] (in blue), prepared with the FBT￾RAPTOR corrections. charg… view at source ↗

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