Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Coherent structures in Dissipative Particle Dynamics simulations of the transition to turbulence in compressible shear flows

We present simulations of coherent structures in compressible flows near the transition to turbulence using the Dissipative Particle Dynamics (DPD) method. The structures we find are remarkably consistent with experimental observations and DNS simulations of incompressible flows, despite a difference in Mach number of several orders of magnitude. The bifurcation from the laminar flow is bistable and shifts to higher Reynolds numbers when the fluid becomes more compressible. This work underlines the robustness of coherent structures in the transition to turbulence and illustrates the ability of particle-based methods to reproduce complex non-linear instabilities.Comment: 4 pages, 5 figure

Fast Magnetic Reconnection and Energetic Particle Acceleration

Our numerical simulations show that the reconnection of magnetic field becomes fast in the presence of weak turbulence in the way consistent with the Lazarian and Vishniac (1999) model of fast reconnection. We trace particles within our numerical simulations and show that the particles can be efficiently accelerated via the first order Fermi acceleration. We discuss the acceleration arising from reconnection as a possible origin of the anomalous cosmic rays measured by Voyagers.Comment: 11 pages, 9 figures, submitted to Planetary and Space Scienc

Joint PDF modelling of turbulent flow and dispersion in an urban street canyon

The joint probability density function (PDF) of turbulent velocity and concentration of a passive scalar in an urban street canyon is computed using a newly developed particle-in-cell Monte Carlo method. Compared to moment closures, the PDF methodology provides the full one-point one-time PDF of the underlying fields containing all higher moments and correlations. The small-scale mixing of the scalar released from a concentrated source at the street level is modelled by the interaction by exchange with the conditional mean (IECM) model, with a micro-mixing time scale designed for geometrically complex settings. The boundary layer along no-slip walls (building sides and tops) is fully resolved using an elliptic relaxation technique, which captures the high anisotropy and inhomogeneity of the Reynolds stress tensor in these regions. A less computationally intensive technique based on wall functions to represent boundary layers and its effect on the solution are also explored. The calculated statistics are compared to experimental data and large-eddy simulation. The present work can be considered as the first example of computation of the full joint PDF of velocity and a transported passive scalar in an urban setting. The methodology proves successful in providing high level statistical information on the turbulence and pollutant concentration fields in complex urban scenarios.Comment: Accepted in Boundary-Layer Meteorology, Feb. 19, 200

Perpendicular Ion Heating by Low-Frequency Alfven-Wave Turbulence in the Solar Wind

We consider ion heating by turbulent Alfven waves (AWs) and kinetic Alfven waves (KAWs) with perpendicular wavelengths comparable to the ion gyroradius and frequencies smaller than the ion cyclotron frequency. When the turbulence amplitude exceeds a certain threshold, an ion's orbit becomes chaotic. The ion then interacts stochastically with the time-varying electrostatic potential, and the ion's energy undergoes a random walk. Using phenomenological arguments, we derive an analytic expression for the rates at which different ion species are heated, which we test by simulating test particles interacting with a spectrum of randomly phased AWs and KAWs. We find that the stochastic heating rate depends sensitively on the quantity epsilon = dv/vperp, where vperp is the component of the ion velocity perpendicular to the background magnetic field B0, and dv (dB) is the rms amplitude of the velocity (magnetic-field) fluctuations at the gyroradius scale. In the case of thermal protons, when epsilon << eps1, where eps1 is a constant, a proton's magnetic moment is nearly conserved and stochastic heating is extremely weak. However, when epsilon > eps1, the proton heating rate exceeds the cascade power that would be present in strong balanced KAW turbulence with the same value of dv, and magnetic-moment conservation is violated. For the random-phase waves in our test-particle simulations, eps1 is approximately 0.2. For protons in low-beta plasmas, epsilon is approximately dB/B0 divided by the square root of beta, and epsilon can exceed eps1 even when dB/B0 << eps1. At comparable temperatures, alpha particles and minor ions have larger values of epsilon than protons and are heated more efficiently as a result. We discuss the implications of our results for ion heating in coronal holes and the solar wind.Comment: 14 pages, 5 figures, submitted to Ap

CFD-based process optimization of a dissolved air flotation system for drinking water production

Dissolved air flotation (DAF) has received more attention recently as a separation technique in both drinking water as well as wastewater treatment. However, the process as well as the preceding flocculation step is complex and not completely understood. Given the multiphase nature of the process, fluid dynamics studies are important to understand and optimize the DAF system in terms of operation and design. The present study is intended towards a comprehensive computational analysis for design optimization of the treatment plant in Kluizen, Belgium. Setting up the modelling framework involving the multiphase flow problem is briefly discussed. 3D numerical simulations on a scaled down model of the DAF design were analysed. The flow features give better confidence, but the flocs escape through the outlet still prevails which is averse to the system performance. In order to improve the performance and ease of maintenance, design modifications have been proposed by using a perforated tube for water extraction and are found to be satisfactory. The discussion is further reinforced through validating the numerical model against the experimental findings for stratified flow conditions

Self-similar transport processes in a two-dimensional realization of multiscale magnetic field turbulence

We present the results of a numerical investigation of charged-particle transport across a synthesized magnetic configuration composed of a constant homogeneous background field and a multiscale perturbation component simulating an effect of turbulence on the microscopic particle dynamics. Our main goal is to analyze the dispersion of ideal test particles faced to diverse conditions in the turbulent domain. Depending on the amplitude of the background field and the input test particle velocity, we observe distinct transport regimes ranging from subdiffusion of guiding centers in the limit of Hamiltonian dynamics to random walks on a percolating fractal array and further to nearly diffusive behavior of the mean-square particle displacement versus time. In all cases, we find complex microscopic structure of the particle motion revealing long-time rests and trapping phenomena, sporadically interrupted by the phases of active cross-field propagation reminiscent of Levy-walk statistics. These complex features persist even when the particle dispersion is diffusive. An interpretation of the results obtained is proposed in connection with the fractional kinetics paradigm extending the microscopic properties of transport far beyond the conventional picture of a Brownian random motion. A calculation of the transport exponent for random walks on a fractal lattice is advocated from topological arguments. An intriguing indication of the topological approach is a gap in the transport exponent separating Hamiltonian-like and fractal random walk-like dynamics, supported through the simulation.Comment: 10 pages (including cover page), 7 figures, improved content, accepted for publication in Physica Script