Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

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Stability And Transition In Shear Flows

Fuel effects on lean blow-out in a realistic gas turbine combustor

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equation, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a nonintrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly nonintrusive since any prior information about the underlying governing equations is not required for generating the reduced order model. Our a posteriori analysis shows that the proposed data-driven approach is remarkably accurate and can be used as a robust predictive tool for nonintrusive model order reduction of complex fluid flows.In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various DNN architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equation, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a nonintrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered trul...

A deep learning enabler for nonintrusive reduced order modeling of fluid flows

Confined short turbulent swirling premixed and non-premixed methane and heptane spray flames stabilized on an axisymmetric bluff body in a square enclosure have been examined close to the blow-off limit and during the extinction transient with OH* chemiluminescence and OH-PLIF operated at 5 kHz. The comparison of flames of different canonical types in the same basic aerodynamic field allows insights on the relative blow-off behaviour. The flame structure has been examined for conditions increasingly closer to blow-off. The premixed flame was seen to change from a cylindrical shape at stable burning condtions, with the flame brush closing across the flow at conditions close to blow-off. The PLIF images show that for the gaseous non-premixed flame, holes appear along the flame sheet with increasing frequency as the blow-off condition is approached, while the trend is less obvious for the spray flame. Non-premixed and spray flames showed randomly-occurring lift-off, which is further evidence of localised extinction. The mean lift-off height increased with increasing fuel jet velocity and decreased with increasing air velocity and approaches zero (i.e. the flame is virtually attached) just before the blow-off condition, despite the fact that more holes were evident in the flame sheet as extinction was approached. It was found that the average duration of the blow-off event, when normalised with the characteristic flow time d/U
 b
 (d being the bluff-body diameter and U
 b
 the bulk velocity) was in the range 9–38 with the spray flame extinction lasting a shorter time than the gaseous flames. Finally, it was found that correlations based on a Damkohler number collapse the blow-off velocity data for all flames with reasonable accuracy. The results can help the development of advanced turbulent combustion models.

A Comparison of the Blow-Off Behaviour of Swirl-Stabilized Premixed, Non-Premixed and Spray Flames

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.

/pdf/les-cmc-of-blow-off-in-a-liquid-fueled-swirl-burner-59nv2aadq3.pdf

LES/CMC of Blow-off in a Liquid Fueled Swirl Burner

Ignition in an aircraft gas turbine combustion chamber is simulated using Large Eddy Simulation (LES) in conjunction with the filtered probability density function (pdf) equation approach, which is solved using the Eulerian stochastic field method. The LES-pdf methodology is used for both dispersed (liquid) and gas phases. The liquid phase is described using a Lagrangian formulation whilst an Eulerian approach is employed for the gas phase. The spark energy deposition was mimicked by a distributed energy source term added to the enthalpy equation. Unsuccessful and successful ignition sequences have been simulated and the results suggest that spark ‘size’ is an important parameter in the ignition of kerosene fuelled combustion chambers.

Artur Tyliszczak

Papers

LES/CMC of Blow-off in a Liquid Fueled Swirl Burner

Large Eddy Simulation of Spark Ignition in a Gas Turbine Combustor

A high-order compact difference algorithm for half-staggered grids for laminar and turbulent incompressible flows

A new approach to sub-grid surface tension for LES of two-phase flows

High-order compact difference algorithm on half-staggered meshes for low Mach number flows