A review of existing basic turbulence modeling approaches reveals the need for the development of unified turbulence models which can be used continuously as filter density function (FDF) or probability density function (PDF) methods, large eddy simulation (LES) or Reynolds-averaged Navier–Stokes (RANS) methods. It is then shown that such unified stochastic and deterministic turbulence models c...

5 Large eddy simulation (LES) is computationally extremely expensive for the investigation of wall-bounded turbulent flows at high Reynolds numbers. A way to reduce the computational cost of LES by orders of magnitude is to combine LES equations with Reynolds-averaged Navier-Stokes (RANS) equations used in the near-wall region. A large variety of such hybrid RANS-LES methods are currently in us...

The gas turbine presents significant challenges to any computational fluid dynamics techniques. The combination of a wide range of flow phenomena with complex geometry is difficult to model in the context of Reynolds-averaged Navier–Stokes (RANS) solvers. We review the potential for large eddy simulation (LES) in modelling the flow in the different components of the gas turbine during a practic...

The gas turbine presents significant challenges to any computational fluid dynamics techniques. The combination of a wide range of flow phenomena with complex geometry is difficult to model in the context of Reynolds-averaged Navier-Stokes (RANS) solvers. We review the potential for large eddy simulation (LES) in modelling the flow in the different components of the gas turbine during a practic...

The gas turbine presents significant challenges to any computational fluid dynamics techniques. The combination of a wide range of flow phenomena with complex geometry is difficult to model in the context of Reynolds-averaged Navier–Stokes (RANS) solvers. We review the potential for large eddy simulation (LES) in modelling the flow in the different components of the gas turbine during a practic...

Deep learning (DL)-based Reynolds stress with its capability to leverage values of large data can be used to close Reynolds-averaged Navier-Stoke (RANS) equations. Type I and Type II machine learning (ML) frameworks are studied to investigate data and flow feature requirements while training DL-based Reynolds stress. The paper presents a method, flow features coverage mapping (FFCM), to quantif...

Hybrid acoustic prediction methods have an important advantage over the current Reynolds averaged Navier-Stokes (RANS) based methods in that they only involve modeling of the relatively universal subscale motion and not the configuration dependent larger scale turbulence. Unfortunately, they are unable to account for the high frequency sound generated by the turbulence in the initial mixing lay...

the steger and warming flux vector splitting implicit scheme is used to numerically solve two dimensional reynolds averaged navier–stokes (rans) equations governing the vortex induced vibration of a flexible riser laterally supported by a spring and a damper. the k–ε model is used as turbulence model to simulate the turbulent flow in the wake of the riser. to update the new position of the rise...

An entirely new approach to the large-eddy simulation (LES) of high-speed compressible turbulent flows is presented. Subgrid scale stress models are proposed that are dimensionless functions of the computational mesh size times a Reynolds stress model. This allows a DNS to go continuously to an LES and then a Reynolds-averaged Navier-Stokes (RANS) computation as the mesh becomes successively mo...