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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.…”
A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertainty is the validation in accuracy of reduced turbulent transport models. Drift-reduced Braginskii two-fluid theory is one such set of reduced equations that has for decades simulated boundary plasmas in experiment, but significant questions exist regarding its predictive ability. To this end, using a novel physics-informed deep learning framework, we demonstrate the first ever direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling with good overall agreement found in magnetized helical plasmas at low normalized pressure. This framework is readily adaptable to experimental and astrophysical environments, and presents a new technique for the numerical validation and discovery of reduced global plasma turbulence models.
“…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.…”
A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertainty is the validation in accuracy of reduced turbulent transport models. Drift-reduced Braginskii two-fluid theory is one such set of reduced equations that has for decades simulated boundary plasmas in experiment, but significant questions exist regarding its predictive ability. To this end, using a novel physics-informed deep learning framework, we demonstrate the first ever direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling with good overall agreement found in magnetized helical plasmas at low normalized pressure. This framework is readily adaptable to experimental and astrophysical environments, and presents a new technique for the numerical validation and discovery of reduced global plasma turbulence models.
“…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%
“…To compare the gyrokinetic and two-fluid theories as directly as possible, the toroidal simulations are analyzed at 𝑧 = 𝐿 𝑧 /3 where no applied sources are present. Further, beyond the inclusion of appropriate collisional drifts and sources, this technique is generalizable to boundary plasmas with multiple ions and impurities present due to the quasi-neutrality assumptions underlying the two-fluid theory [171].…”
Section: Machine Learning Fluid Theory (Again)mentioning
“…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].…”
A collisional gyro-fluid model is presented. The goal of the model is edge and scrape-off layer turbulence. The emphasize in the model derivation heavily lies on ”implementability” with today’s numerical methods. This translates to an avoidance of infinite sums, strongly coupled equations in time and intricate elliptic operator functions. The resulting model contains the four moments density, parallel momentum, perpendicular pressure and parallel energy and is closed by a polarisation equation and parallel Ampere law. The central ingredient is a collisional long-wavelength closure that relies on a drift-fluid gyro-fluid correspondence principle. In this way the extensive literature on fluid collisions can be incorporated into the model including sources, plasma-neutral interactions and scattering collisions. Even though this disregards the characteristic finite Larmor radius terms in the collisional terms the resulting model is at least as accurate as the corresponding drift-fluid model in these terms. Furthermore, the model does enjoy the benefits of an underlying variational principle in an energy-momentum theorem and an inherent symmetry in moment equations with regards to multiple ion species. Consistent particle drifts as well as finite Larmor radius corrections and high amplitude effects in the advection and polarization terms are further characteristics of the model. Extensions and improvements like short-wavelength expressions, a trans-collisional closure scheme for the low-collisionality regime or zeroth order potential must be added at a later stage.
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