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2020
DOI: 10.1063/1.5140522
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Collisional multispecies drift fluid model

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Cited by 17 publications
(26 citation statements)
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“…To compare the gyrokinetic and two-fluid theories as directly as possible, we analyze the toroidal simulations at z = L z /3 where no applied sources are present. We further remark that, beyond the inclusion of appropriate collisional drifts and sources, this technique is quite robust and generalizable to boundary plasmas with multiple ions and impurities present due to the quasi-neutrality assumptions underlying the two-fluid theory [75]. This turbulence diagnostic analysis technique is hence easily transferable, which permits its systematic application across magnetic confinement fusion experiments where the underlying physical model governing turbulent transport is consistent.…”
Section: Machine Learning Fluid Theorymentioning
confidence: 95%
“…To compare the gyrokinetic and two-fluid theories as directly as possible, we analyze the toroidal simulations at z = L z /3 where no applied sources are present. We further remark that, beyond the inclusion of appropriate collisional drifts and sources, this technique is quite robust and generalizable to boundary plasmas with multiple ions and impurities present due to the quasi-neutrality assumptions underlying the two-fluid theory [75]. This turbulence diagnostic analysis technique is hence easily transferable, which permits its systematic application across magnetic confinement fusion experiments where the underlying physical model governing turbulent transport is consistent.…”
Section: Machine Learning Fluid Theorymentioning
confidence: 95%
“…The plasma is further assumed to be magnetized, collisional, and quasineutral with the perpendicular fluid velocity given by E × B, diamagnetic, and ion polarization drifts. This chap- ions and impurities are present in the experimental plasma due to quasi-neutrality underlying the electron fluid theory in the machine learning framework [171].…”
Section: The Experimental Calculationmentioning
confidence: 99%
“…Merging partial observational data of 𝑛 𝑒 and 𝑇 𝑒 along with physical laws in the form of partial differential equations governing the time-dependent evolution of 𝑛 𝑒 and 𝑇 𝑒 sufficiently constrains the set of admissible solutions for the previously unknown nonlinear mappings the neural networks ultimately learn. It is also quite general: due to quasineutrality, no significant adjustments are necessary to generalize the technique when multiple ions and impurities may be present in boundary plasmas beyond the inclusion of appropriate collisional drifts and sources in multi-species plasmas [171,131]. This deep learning technique for diagnosing turbulent fields is hence easily transferable which permits its systematic application across magnetic confinement fusion experiments whereby the underlying physical model fundamental to the turbulent transport is consistent.…”
Section: Machine Learning Fluid Theorymentioning
confidence: 99%
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“…The result is that practical implementations of gyro-fluid models largely ignore collisions and plasma neutral interactions altogether [4,5,6,7]. Drift-fluid models are preferred for that purpose even though these models do not share many advantages of gyro-fluid models: finite Larmor radius corrections, consistent particle drifts, an energy and momentum theorem based on variational methods in the underlying gyro-kinetic model and an inherent symmetry in moment equations with regards to multiple ion species [8,9,10,11].…”
Section: Introductionmentioning
confidence: 99%