Search

A new mechanism has been identified that explains the generation of Langmuir circulations. A wind-driven current in the presence of surface waves gives rise to an instability where the emerging circulations redistribute the turbulence in the cross-wind direction. The non-uniform eddy-viscosity locally changes the rate of momentum transfer from the wind to the shear current, producing a non-uniform velocity field. The interaction of this non-uniform velocity field with the surface waves, due to the Craik-Leibovich vortex force, amplifies the circulations and creates a feedback mechanism. The currently accepted CL2 model of instability assumes a constant eddy-viscosity. This paper presents a model which explains the generation of Langmuir circulations and its predictions of both spatial and time scales are in good agreement with experimental results. The modeling approach combines a perturbation method with a RANS turbulence model. Through parametric variation of the perturbation, the growth rate and spatial scales of the circulations are extracted from the simulations.

Surface ships operating in ocean waves are shown to generate transverse surface currents which persist long after the initial ship-induced currents have decayed. These surface currents are due to the formation of large-scale Langmuir-type circulations and are suggested to be the mechanism for the concentration of surface-active substance into streaks or bands, which are regularly seen in synthetic aperture radar imagery of the ocean surface. These circulations are generated by the Craik-Leibovich vortex force, which results from interaction of ship-induced current with ambient surface waves. Once generated, the circulations persist due to their large-scale nearly-inviscid nature. Numerical simulations of the wake behind a surface ship in calm, head, and following seas are presented and show good agreement with radar imagery from at-sea experiments. It is demonstrated that surface currents do not persist in the absence of surface waves, but can persist for tens of kilometers through the formation of Langmuir-type circulations. Since the circulations are driven by the cross product of the Stokes drift and the wake vorticity, the structure of the persistent wake is a function of relative heading of the ship with respect to the direction of surface-wave propagation., Comment: 30 pages, 20 figures

Feature identification is an important task in many fluid dynamics applications and diverse methods have been developed for this purpose. These methods are based on a physical understanding of the underlying behavior of the flow in the vicinity of the feature. Particularly, they rely on definition of suitable criteria (i.e. point-based or neighborhood-based derived properties) and proper selection of thresholds. For instance, among other techniques, vortex identification can be done through computing the Q-criterion or by considering the center of looping streamlines. However, these methods rely on creative visualization of physical idiosyncrasies of specific features and flow regimes, making them non-universal and requiring significant effort to develop. Here we present a physics-based, data-driven method capable of identifying any flow feature it is trained to. We use convolutional neural networks, a machine learning approach developed for image recognition, and adapt it to the problem of identifying flow features. The method was tested using mean flow fields from numerical simulations, where the recirculation region and boundary layer were identified in a two-dimensional flow through a convergent-divergent channel, and the horseshoe vortex was identified in three-dimensional flow over a wing-body junction. The novelty of the method is its ability to identify any type of feature, even distinguish between similar ones, without the need to explicitly define the physics (i.e. through development of suitable criterion and tunning of threshold). This provides a general method and removes the large burden placed on identifying new features. We expect this method can supplement existing techniques and allow for more automatic and discerning feature detection. The method can be easily extended to time-dependent flows, where it could be particularly impactful.

Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncertainties in modeled Reynolds stresses. Recently, Wang et al. demonstrated that machine learning can be used to improve the RANS modeled Reynolds stresses by leveraging data from high fidelity simulations (Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Physical Review Fluids. 2, 034603, 2017). However, solving for mean flows from the improved Reynolds stresses still poses significant challenges due to potential ill-conditioning of RANS equations with Reynolds stress closures. Enabling improved predictions of mean velocities are of profound practical importance, because often the velocity and its derived quantities (QoIs, e.g., drag, lift, surface friction), and not the Reynolds stress itself, are of ultimate interest in RANS simulations. To this end, we present a comprehensive framework for augmenting turbulence models with physics-informed machine learning, illustrating a complete workflow from identification of input features to final prediction of mean velocities. This work has two innovations. First, we demonstrate a systematic procedure to generate mean flow features based on the integrity basis for mean flow tensors. Second, we propose using machine learning to predict linear and nonlinear parts of the Reynolds stress tensor separately. Inspired by the finite polynomial representation of tensors in classical turbulence modeling, such a decomposition is instrumental in overcoming the ill-conditioning of RANS equations. Numerical tests demonstrated merits of the proposed framework., Comment: This manuscript is for substitution purpose of a previous arXiv submission (arXiv:1701.07102). That previous arXiv submission is no longer under consideration for any journal publication and is only for archive purpose

In the turbulence modeling community, significant efforts have been made to quantify the uncertainties in the Reynolds-Averaged Navier--Stokes (RANS) models and to improve their predictive capabilities. Of crucial importance in these efforts is the understanding of the discrepancies in the RANS modeled Reynolds stresses. However, to what extent these discrepancies can be predicted or whether they are completely random remains a fundamental open question. In this work we used a machine learning algorithm based on random forest regression to predict the discrepancies. The success of the regression--prediction procedure indicates that, to a large extent, the discrepancies in the modeled Reynolds stresses can be explained by the mean flow feature, and thus they are universal quantities that can be extrapolated from one flow to another, at least among different flows sharing the same characteristics such as separation. This finding has profound implications to the future development of RANS models, opening up new possibilities for data-driven predictive turbulence modeling., Comment: Part of the content of this article has been incorporated into "Physics informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data. Physical Review Fluids. 2(3), 034603, 1-22, 2017", which is also available at arXiv:1606.07987