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A hybrid numerical‐analytical approach, based on recent developments in the generalized integral transform technique, is presented for the solution of a class of non‐linear transient convection‐diffusion problems. The original partial differential equation is integral transformed into a denumerable system of coupled non‐linear ordinary differential equations, which is numerically solved for the transformed potentials. The hybrid analysis convergence is illustrated by considering the one‐dimensional non‐linear Burgers equation and numerical results are presented for increasing truncation orders of the infinite ODE system.

The numerical onset of parasitic and spurious artefacts in the vicinity of uid interfaces with surface tension is an important and well-recognised problem with respect to the accuracy and numerical stability of interfacial ow simulations. Issues of particular interest are spurious capillary waves, which are spatially underresolved by the computational mesh yet impose very restrictive time-step requirements, as well as parasitic currents, typically the result of a numerically unbalanced curvature evaluation. We present an arti cial viscosity model to mitigate numerical artefacts at surface-tension-dominated interfaces without adversely a ecting the accuracy of the physical solution. The proposed methodology computes an additional interfacial shear stress term, including an interface viscosity, based on the local ow data and uid properties that reduces the impact of numerical artefacts and dissipates underresolved small scale interface movements. Furthermore, the presented methodology can be readily applied to model surface shear viscosity, for instance to simulate the dissipative e ect of surface-active substances adsorbed at the interface. The presented analysis of numerical test cases demonstrates the e cacy of the proposed methodology in diminishing the adverse impact of parasitic and spurious interfacial artefacts on the convergence and stability of the numerical solution algorithm as well as on the overall accuracy of the simulation results

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The present work is devoted to the development and implementation of a computational framework to perform numerical simulations of low Mach number turbulent flows over complex geometries. The algorithm under consideration is based on a classical predictor-corrector time integration scheme that employs a projection method for the momentum equations. The domain decomposition strategy is adopted for distributed computing, displaying very satisfactory levels of speed-up and efficiency. The Immersed Boundary Methodology is used to characterize the presence of a complex geometry. Such method demands two separate grids: An Eulerian, where the transport equations are solved with a Finite Volume, second order discretization and a Lagrangian domain, represented by a non-structured shell grid representing the immersed geometry. The in-house code developed was fully verified by the Method of Manufactured Solutions, in both Eulerian and Lagrangian domains. The capabilities of the resulting computational framework are illustrated on four distinct cases: a turbulent jet, the Poiseuille flow, as a matter of validation of the implemented Immersed Boundary methodology, the flow over a sphere covering a wide range of Reynolds numbers, and finally, with the intention of demonstrating the applicability of Large Eddy Simulations -LES -in an industrial problem, the turbulent flow inside an industrial fan.

Multispecies mixing processes play an important role in many engineering, biological, and environmental applications. Since simulating mixing flows can be useful to understand its physics and to study industrial issues, this work aims to develop the basis of a methodology able to simulate the physics of multiple-species mixing flows, using a hybrid large eddy simulation/Lagrangian filtered density function (FDF) method on an adaptive, block-structured mesh. A computational model of notional particles transport on a distributed processing environment is built using a parallel Lagrangian map. This map connects the Lagrangian information with the Eulerian framework of the in-house code MFSim, in which transport equations are solved. The Lagrangian composition FDF method, through the Monte Carlo technique, performs algebraic calculations over an ensemble of notional particles and provides composition fields statistically equivalent to those obtained by finite volume numerical solution of partial differential equations. Finally, to maintain high accuracy in the system of stochastic differential equations solver when an adaptive mesh refinement environment is used, a methodology for ensuring mass conservation is developed to preserve at least the statistical moments up to order two, even in the case of annihilation or cloning of a large number of notional particles in one time step, ensuring the applicability of Lagrangian FDF methods in dynamically adaptive grid refinement.