A newly developed three-dimensional electrostatic fluid model solving continuity and current closure equations aims to study phenomena that generate ionospheric turbulence. The model is spatially discretized using a pseudo-spectral method with full Fourier basis functions and evolved in time using a four-stage, fourth-order Runge Kutta method. The 3D numerical model is used here to investigate the behavior and evolution of ionospheric plasma clouds. This problem has historically been used to study the processes governing the evolution of the irregularities in the F region of the ionosphere. It has been shown that these artificial clouds can become unstable and structure rapidly (i.e., cascade to smaller scales transverse to the ambient magnetic field). The primary mechanism which causes this structuring of ionospheric clouds is the E×B, or the gradient drift instability (GDI). The persistence and scale sizes of the resulting structures cannot be fully explained by a two-dimensional model. Therefore, we suggest here that the inclusion of three-dimensional effects is key to a successful interpretation of mid-latitude irregularities, as well as a prerequisite for a credible simulation of these processes. We investigate the results of 2D and 3D nonlinear simulations of the GDI and secondary Kelvin–Helmholtz instability (KHI) in plasma clouds for three different regimes: highly collisional (≈200 km), collisional (≈300 km), and inertial (≈450 km). The inclusion of inertial effects permits the growth of the secondary KHI. For the three different regimes, the overall evolution of structuring of plasma cloud occurs on longer timescales in 3D simulations. The inclusion of three-dimensional effects, in particular, the ambipolar potential in the current closure equation, introduces an azimuthal “twist“ about the axis of the cloud (i.e., the magnetic field B). This azimuthal “twist” is observed in the purely collisional regime, and it causes the perturbations to have a non-flute-like character (k‖≠0). However, for the 3D inertial simulations, the cloud rapidly diffuses to a state in which the sheared azimuthal flow is substantially reduced; subsequently, the cloud becomes unstable and structures, by retaining the flute-like character of the perturbations (k‖=0).
Four-field Hamiltonian fluid closures of the one-dimensional Vlasov–Poisson equation
We consider a reduced dynamics for the first four fluid moments of the one-dimensional Vlasov–Poisson equation, namely, fluid density, fluid velocity, pressure, and heat flux. This dynamics depends on an equation of state to close the system. This equation of state (closure) connects the fifth-order moment—related to the kurtosis in velocity of the Vlasov distribution—with the first four moments. By solving the Jacobi identity, we derive an equation of state, which ensures that the resulting reduced fluid model is Hamiltonian. We show that this Hamiltonian closure allows symmetric homogeneous equilibria of the reduced fluid model to be stable.
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- Award ID(s):
- 2108788
- NSF-PAR ID:
- 10362743
- Date Published:
- Journal Name:
- Physics of Plasmas
- Volume:
- 29
- Issue:
- 10
- ISSN:
- 1070-664X
- Page Range / eLocation ID:
- 102101
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Context. Magnetic reconnection plays a fundamental role in plasma dynamics under many different conditions, from space and astrophysical environments to laboratory devices. High-resolution in situ measurements from space missions allow naturally occurring reconnection processes to be studied in great detail. Alongside direct measurements, numerical simulations play a key role in the investigation of the fundamental physics underlying magnetic reconnection, also providing a testing ground for current models and theory. The choice of an adequate plasma model to be employed in numerical simulations, while also compromising with computational cost, is crucial for efficiently addressing the problem under study. Aims. We consider a new plasma model that includes a refined electron response within the “hybrid-kinetic framework” (fully kinetic protons and fluid electrons). The extent to which this new model can reproduce a full-kinetic description of 2D reconnection, with particular focus on its robustness during the nonlinear stage, is evaluated. Methods. We perform 2D simulations of magnetic reconnection with moderate guide field by means of three different plasma models: (i) a hybrid-Vlasov-Maxwell model with isotropic, isothermal electrons, (ii) a hybrid-Vlasov-Landau-fluid (HVLF) model where an anisotropic electron fluid is equipped with a Landau-fluid closure, and (iii) a full-kinetic model. Results. When compared to the full-kinetic case, the HVLF model effectively reproduces the main features of magnetic reconnection, as well as several aspects of the associated electron microphysics and its feedback onto proton dynamics. This includes the global evolution of magnetic reconnection and the local physics occurring within the so-called electron-diffusion region, as well as the evolution of species’ pressure anisotropy. In particular, anisotropy-driven instabilities (such as fire-hose, mirror, and cyclotron instabilities) play a relevant role in regulating electrons’ anisotropy during the nonlinear stage of magnetic reconnection. As expected, the HVLF model captures all these features, except for the electron-cyclotron instability.more » « less
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The relative velocities and positions of monodisperse high-inertia particle pairs in isotropic turbulence are studied using direct numerical simulations (DNS), as well as Langevin simulations (LS) based on a probability density function (PDF) kinetic model for pair relative motion. In a prior study (Rani et al. , J. Fluid Mech. , vol. 756, 2014, pp. 870–902), the authors developed a stochastic theory that involved deriving closures in the limit of high Stokes number for the diffusivity tensor in the PDF equation for monodisperse particle pairs. The diffusivity contained the time integral of the Eulerian two-time correlation of fluid relative velocities seen by pairs that are nearly stationary. The two-time correlation was analytically resolved through the approximation that the temporal change in the fluid relative velocities seen by a pair occurs principally due to the advection of smaller eddies past the pair by large-scale eddies. Accordingly, two diffusivity expressions were obtained based on whether the pair centre of mass remained fixed during flow time scales, or moved in response to integral-scale eddies. In the current study, a quantitative analysis of the (Rani et al. 2014) stochastic theory is performed through a comparison of the pair statistics obtained using LS with those from DNS. LS consist of evolving the Langevin equations for pair separation and relative velocity, which is statistically equivalent to solving the classical Fokker–Planck form of the pair PDF equation. Langevin simulations of particle-pair dispersion were performed using three closure forms of the diffusivity – i.e. the one containing the time integral of the Eulerian two-time correlation of the seen fluid relative velocities and the two analytical diffusivity expressions. In the first closure form, the two-time correlation was computed using DNS of forced isotropic turbulence laden with stationary particles. The two analytical closure forms have the advantage that they can be evaluated using a model for the turbulence energy spectrum that closely matched the DNS spectrum. The three diffusivities are analysed to quantify the effects of the approximations made in deriving them. Pair relative-motion statistics obtained from the three sets of Langevin simulations are compared with the results from the DNS of (moving) particle-laden forced isotropic turbulence for $St_{\unicode[STIX]{x1D702}}=10,20,40,80$ and $Re_{\unicode[STIX]{x1D706}}=76,131$ . Here, $St_{\unicode[STIX]{x1D702}}$ is the particle Stokes number based on the Kolmogorov time scale and $Re_{\unicode[STIX]{x1D706}}$ is the Taylor micro-scale Reynolds number. Statistics such as the radial distribution function (RDF), the variance and kurtosis of particle-pair relative velocities and the particle collision kernel were computed using both Langevin and DNS runs, and compared. The RDFs from the stochastic runs were in good agreement with those from the DNS. Also computed were the PDFs $\unicode[STIX]{x1D6FA}(U|r)$ and $\unicode[STIX]{x1D6FA}(U_{r}|r)$ of relative velocity $U$ and of the radial component of relative velocity $U_{r}$ respectively, both PDFs conditioned on separation $r$ . The first closure form, involving the Eulerian two-time correlation of fluid relative velocities, showed the best agreement with the DNS results for the PDFs.more » « less
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Summary This article presents a nonlinear closed‐loop active flow control (AFC) method, which achieves asymptotic regulation of a fluid flow velocity field in the presence of actuator uncertainty and sensor measurement limitations. To achieve the result, a reduced‐order model of the flow dynamics is derived, which utilizes proper orthogonal decomposition (POD) to express the Navier‐Stokes equations as a set of nonlinear ordinary differential equations. The reduced‐order model formally incorporates the actuation effects of synthetic jet actuators (SJA). Challenges inherent in the resulting POD‐based reduced‐order model include (1) the states are not directly measurable, (2) the measurement equation is in a nonstandard mathematical form, and (3) the SJA model contains parametric uncertainty. To address these challenges, a sliding mode observer (SMO) is designed to estimate the unmeasurable states in the reduced‐order model of the actuated flow field dynamics. A salient feature of the proposed SMO is that it formally compensates for the parametric uncertainty inherent in the SJA model. The SMO is rigorously proven to achieve local finite‐time estimation of the unmeasurable state in the presence of the parametric uncertainty in the SJA. The state estimates are then utilized in a nonlinear control law, which regulates the flow field velocity to a desired state. A Lyapunov‐based stability analysis is provided to prove local asymptotic regulation of the flow field velocity. To illustrate the performance of the proposed estimation and AFC method, comparative numerical simulation results are provided, which demonstrate the improved performance that is achieved by incorporating the uncertainty compensator.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0102418
