arXiv subjects

Orso Meneghini

Publications and source records attributed to Orso Meneghini.

3 recordsLinked to original sources

arXiv2026

Compact Experimental Negative TriAngUlarity Reactor (CENTAUR): A design study for a compact, affordable breakeven tokamak

This work presents the compact experimental negative triangularity reactor (CENTAUR), a low overnight cost, high-field tokamak, breakeven reactor design, achieving a predicted total fusion power of 40MW and scientific energy gain of 1.3. Ballooning stability calculations confirm that the device's pedestal is within the first stability regime, which is consistent with the expected ELM-free operation associated with negative triangularity (NT) plasmas. The geometry of the NT divertor allows for high fraction of radiated power (13.5$\%$) between the separatrix and plasma facing components. Heat transport modeling based on simulations of the edge region show heat loads into plasma facing components well below material limits. The magnet system employs rare-earth barium copper oxide (REBCO) high-temperature superconductors in 18 toroidal field coils, an hourglass-shaped central solenoid, and six poloidal field coils to support high-field ($B_0=10.9$ T) plasma confinement, shaping, and current drive. Neutronics analysis shows that a 12 cm $B_4C$ shield keeps superconducting magnet heating below the 33~K quench limit during 10 s, 40 MW DT pulses. With this shielding, the modeled fluence indicates HTS components can survive more than ten times the 3000-pulse design lifetime. Iteration of economic analysis in tandem with the technical design process allows CENTAUR to achieve its overnight cost goal of $\$$2B determined using a custom costing model that predicts a total overnight cost of $1.6$B$\pm0.2$B.

physics.plasm-ph↗

arXiv2025

TGLF-WINN: Data-Efficient Deep Learning Surrogate for Turbulent Transport Modeling in Fusion

The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulations requiring thousands of evaluations remain computationally expensive. Neural network (NN) surrogates offer accelerated inference with fully differentiable approximations that enable gradient-based coupling but typically require large training datasets to capture transport flux variations across plasma conditions, creating significant training burden and limiting applicability to expensive gyrokinetic simulations. We propose TGLF-WINN (Wavenumber-Informed Neural Network) with three key innovations: (1) principled feature engineering that reduces target prediction range, simplifying the learning task; (2) physics-guided wavenumber-resolved regularization to improve generalization under sparse data; and (3) Bayesian Active Learning (BAL) to strategically select training samples based on model uncertainty, reducing data requirements while maintaining accuracy. Feature tuning and wavenumber regularization together deliver a 12.5% relative RMSLE reduction over TGLF-NN on the full dataset; under sparse, unfiltered training (approximately 1/9 the full size) they yield an order-of-magnitude smaller RMSLE degradation than TGLF-NN, with the wavenumber-informed regularization imposing a physics-guided constraint on per-mode fluxes. Adding Bayesian Active Learning, TGLF-WINN matches TGLF-NN's full-data offline accuracy using only 25% of the training data, within 2.8% of TGLF-NN's full-data baseline and 4.3% of our own full-data result. A downstream flux-matching workflow further shows practicality: the NN surrogate gives a 45x speedup over TGLF with comparable reconstruction accuracy.

physics.plasm-ph↗

arXiv2023

Elevating zero dimensional global scaling predictions to self-consistent theory-based simulations

We have developed an innovative workflow, STEP-0D, within the OMFIT integrated modelling framework. Through systematic validation against the International Tokamak Physics Activity (ITPA) global H-mode confinement database, we demonstrated that STEP-0D, on average, predicts the energy confinement time with a mean relative error (MRE) of less than 19%. Moreover, this workflow showed promising potential in predicting plasmas for proposed fusion reactors such as ARC, EU-DEMO, and CFETR, indicating moderate H-factors between 0.9 and 1.2. STEP-0D allows theory-based prediction of tokamak scenarios, beginning with zero-dimensional (0D) quantities. The workflow initiates with the PRO-create module, generating physically consistent plasma profiles and equilibrium using the same 0D quantities as the IPB98(y,2) confinement scaling. This sets the starting point for the STEP (Stability, Transport, Equilibrium, and Pedestal) module, which further iterates between theory-based physics models of equilibrium, core transport, and pedestal to yield a self-consistent solution. Given these attributes, STEP-0D not only improves the accuracy of predicting plasma performance but also provides a path towards a novel fusion power plant (FPP) design workflow. When integrated with engineering and costing models within an optimization, this new approach could eliminate the iterative reconciliation between plasma models of varying fidelity. This potential for a more efficient design process underpins STEP-0D's significant contribution to future fusion power plant development.

physics.plasm-ph↗