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Alexei Pankin

Researcher at Lehigh University

Publications -  57
Citations -  1973

Alexei Pankin is an academic researcher from Lehigh University. The author has contributed to research in topics: Tokamak & Pedestal. The author has an hindex of 21, co-authored 56 publications receiving 1799 citations. Previous affiliations of Alexei Pankin include Oak Ridge National Laboratory & Science Applications International Corporation.

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The tokamak Monte Carlo fast ion module NUBEAM in the National Transport Code Collaboration library

TL;DR: The NUBEAM module as mentioned in this paper is a comprehensive computational model for Neutral Beam Injection (NBI) in tokamaks, which is used to compute power deposition, driven current, momentum transfer, fueling, and other profiles.
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Pedestal stability comparison and ITER pedestal prediction

TL;DR: In this paper, a new predictive model for the pedestal height and width (EPED1) was developed by self-consistently combining a simple width model with peeling-ballooning stability calculations.
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Analysis of pedestal plasma transport

TL;DR: In this article, an H-mode edge pedestal plasma transport benchmarking exercise was undertaken for a single DIII-D pedestal, where profiles for the edge plasma were obtained from Thomson and charge exchange recombination data averaged over the last 20% of the average 33.53 ms repetition time between type I edge localized modes.
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Burning plasma projections using drift wave transport models and scalings for the h-mode pedestal

TL;DR: In this article, the GLF23 and multi-mode core transport models are used along with models for the H-mode pedestal to predict the fusion performance for the International Thermonuclear Experimental Reactor, Fusion Ignition Research Experiment, and IGNITOR tokamak designs.
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Improved understanding of physics processes in pedestal structure, leading to improved predictive capability for ITER

TL;DR: In this article, a model for pedestal pressure height has been proposed for a range in pressure of a factor of 20, which is found to be an extrapolation of 3 beyond the existing data set, with some areas needing further work also being identified.