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N. M. Cao

Researcher at Massachusetts Institute of Technology

Publications -  28
Citations -  314

N. M. Cao is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Alcator C-Mod & Tokamak. The author has an hindex of 7, co-authored 25 publications receiving 194 citations. Previous affiliations of N. M. Cao include Courant Institute of Mathematical Sciences.

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Conceptual design study for heat exhaust management in the ARC fusion pilot plant

TL;DR: Sorbom et al. as mentioned in this paper extended the ARC pilot plant conceptual design study to explore options for managing ∼525 MW of fusion power generated in a compact, high field tokamak that is approximately the size of JET.
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Explaining Cold-Pulse Dynamics in Tokamak Plasmas Using Local Turbulent Transport Models.

TL;DR: This Letter shows that the steady-state profiles, the cold-pulse rise time, and disappearance at higher density as measured in these experiments are successfully captured by a recent local quasilinear turbulent transport model, demonstrating that the existence of nonlocal transport phenomena is not necessary for explaining the behavior and time scales of cold-Pulse experiments in tokamak plasmas.
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Investigation of the critical edge ion heat flux for L-H transitions in Alcator C-Mod and its dependence on B T

TL;DR: In this paper, the role of the edge ion heat flux for transitions from L-mode to H-mode in Alcator C-Mod is investigated. And the results are discussed from the point of view of the possible physics mechanism of the L-H transition.
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Validation of nonlinear gyrokinetic simulations of L- and I-mode plasmas on Alcator C-Mod

TL;DR: In this article, global, nonlinear, ion-scale GYRO simulations are able to match the experimental ion heat flux, but underpredict electron heat flux (at most radii), electron temperature fluctuations, and perturbative thermal diffusivity in both L- and I-mode.
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Inference of experimental radial impurity transport on Alcator C-Mod: Bayesian parameter estimation and model selection

TL;DR: In this article, a fully Bayesian approach for the inference of radial profiles of impurity transport coefficients is presented, which can handle complex parameter spaces with multiple possible solutions, offering great advantages in interpretative power and reliability with respect to previously demonstrated methods.