No abstract

The Large Eddy Simulation (LES)/three-dimensional Conditional Moment Closure (3D-CMC) model with detailed chemistry and finite-volume formulation is employed to simulate a swirl-stabilized non-premixed flame with local extinction. The results demonstrate generally good agreement with the measurements concerning velocity, flame shape, and statistics of flame lift-off, but the penetration of fuel jet into the recirculation zone is under-predicted possibly due to the over-predicted swirl velocities in the chamber. Localized extinctions are seen in the LES, in agreement with experiment. The local extinction event is shown by very low heat release rate and hydroxyl mass fraction and reduced temperature, and is accompanied by relatively high scalar dissipation. In mixture fraction space, CMC cells with strong turbulence-chemistry interaction and local extinction show relatively large fluctuations between fully burning and intermediate distributions. The probability density functions of conditional reactedness, which shows how far the conditionally-filtered scalars are from reference fully burning profiles, indicate that for CMC cells with local extinction, some reactive scalars demonstrate pronounced bimodality while for those cells with strong reactivity the PDFs are very narrow.

Large Eddy Simulations of two-phase flames with the Conditional Moment Closure combustion model have been performed for flow conditions corresponding to stable and blow-off regimes in a swirl n-heptane spray burner. In the case of stable flame (i.e. low air velocity), the predicted mean and r.m.s. velocities and the location and shape of the flame agree reasonably well with experiment. In particular, the presence of localised extinctions is captured in agreement with experiment. Using model constants previously calibrated against piloted jet methane flames (Sandia F) with localised extinction, we obtain that at the experimentally determined blow-off velocity of the swirling spray flame, the predicted flame also blows off, demonstrating that the LES-CMC approach can capture the global extinction point in a realistic configuration.

Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations.