Related papers: Physics-informed machine learning techniques for e…
Recently, a new probabilistic "data fusion" framework based on Bayesian principles has been developed on JET and W7-AS. The Bayesian analysis framework folds in uncertainties and inter-dependencies in the diagnostic data and signal…
The impact of turbulent fluctuations on the forces exerted by a fluid on a towed spherical particle is investigated by means of high-resolution direct numerical simulations. The measurements are carried out using a novel scheme to integrate…
The magnetorotational instability (MRI) is a fundamental mechanism determining the macroscopic dynamics of astrophysical accretion disks. In collisionless accretion flows around supermassive black holes, MRI-driven plasma turbulence…
Accurate characterization of subsurface heterogeneity is challenging but essential for applications such as reservoir pressure management, geothermal energy extraction and CO$_2$, H$_2$, and wastewater injection operations. This challenge…
In the full-orbit particle simulations of energetic particle transport in plasmas, the plasma turbulence is typically described as a homogeneous superposition of linear Fourier modes. The turbulence evolution is, however, typically a…
To rigorously model fast ions in fusion plasmas, a non-Maxwellian equilibrium distribution must be used. In the work, the response of high-energy alpha particles to electrostatic turbulence has been analyzed for several different tokamak…
In this study, global nonlinear electromagnetic gyrokinetic simulations are conducted to investigate turbulence in the Internal transport barrier (ITB) region of the EAST tokamak discharge with weakly reversed magnetic shear. Linear…
Despite a cost-effective option in practical engineering, Reynolds-averaged Navier-Stokes simulations are facing the ever-growing demand for more accurate turbulence models. Recently, emerging machine learning techniques are making…
A specific set of dimensionless plasma and turbulence parameters is introduced to characterize the nature of turbulence and its dissipation in weakly collisional space and astrophysical plasmas. Key considerations are discussed for the…
A recent thrust in turbulence closure modeling research is to incorporate machine learning (ML) elements, such as neural networks, for the purpose of enhancing the predictive capability to a broader class of flows. Such a turbulence closure…
The quasilinear premise is a hypothesis for the modeling of plasma turbulence in which the turbulent fluctuations are represented by a superposition of randomly-phased linear wave modes, and energy is transferred among these wave modes via…
It is often asserted or implicitly assumed, without justification, that the results of two-dimensional investigations of plasma turbulence are applicable to the three-dimensional plasma environments of interest. A projection method is…
Azimuthal flow is driven in the edge of the Large Plasma Device (LAPD) [W. Gekelman, {\itshape et. al}, Rev. Sci. Instr. {\bfseries 62}, 2875 (1991)] through biasing a section of the vacuum vessel relative to the plasma source cathode. As…
Boundary plasma physics plays an important role in tokamak confinement, but is difficult to simulate in a gyrokinetic code due to the scale-inseparable nonlocal multi-physics in magnetic separatrix and open magnetic field geometry. Neutral…
In this study, eXplainable Artificial Intelligence (XAI) methods are applied to analyze flow fields obtained through PIV measurements of an axisymmetric turbulent jet. A convolutional neural network (U-Net) was trained to predict velocity…
Real-time simulation of elastic structures is essential in many applications, from computer-guided surgical interventions to interactive design in mechanical engineering. The Finite Element Method is often used as the numerical method of…
This paper presents a novel methodology for the direct numerical modeling and simulation of turbulent flows. The kinetic model equation is firstly extended to turbulent flow with the account of coupled evolution of kinetic, thermal, and…
The fast-ion (FI) on turbulent transport is one of the key topics of magnetic confinement fusion. This work focus on the impact of FI pressure gradients on turbulence in a high-\b{eta} plasma scenario using gyrokinetic simulations. Linear…
We derive a reduced model for the electrostatic turbulence in a Tokamak edge, when dealing with a resistive plasma and neglecting the spatial gradient of the background density which triggers the linear drift wave response. The obtained…
A physics-informed neural network is presented for poroelastic problems with coupled flow and deformation processes. The governing equilibrium and mass balance equations are discussed and specific derivations for two-dimensional cases are…