M. E. Young

Bio: M. E. Young is an academic researcher. The author has contributed to research in topics: Large eddy simulation & Reynolds stress equation model. The author has an hindex of 1, co-authored 1 publications receiving 33 citations.

Comparative assessment of les and URANS for flow over a cylinder at a Reynolds number of 3900

Abstract: Numerical simulations utilising turbulence models based on the Reynolds Averaged Navier Stokes (RANS) equations generally exhibit poor performance in predicting separated flow around cylinders. This paper assesses potential improvements offered by the three-dimensional unsteady RANS and Large Eddy Simulation (LES) methodologies in replicating the flow around a cylinder at a Reynolds number, based on diameter, of 3900. The performance is assessed against corresponding experimental data and two-dimensional unsteady RANS turbulence simulations.

A novel iterative direct-forcing immersed boundary method and its finite volume applications

Abstract: We present a novel iterative immersed boundary (IB) method in which the body force updating is incorporated into the pressure iterations. Because the body force and pressure are solved simultaneously, the boundary condition on the immersed boundary can be fully verified. The computational costs of this iterative IB method is comparable to those of conventional IB methods. We also introduce an improved body force distribution function which transfers the body force in the discrete volume of IB points to surrounding Cartesian grids totally. To alleviate the demanding computational requirements of a full-resolved direct numerical simulation, a wall-layer model is presented. The accuracy and capability of the present method is verified by a variety of two- and three-dimensional numerical simulations, ranging from laminar flow past a cylinder and a sphere to turbulent flow around a cylinder. The improvement of the iterative IB method is fully demonstrated and the influences of different body force distribution strategies is discussed.

Simulation of the flow around a circular cylinder at Re=3900 with Partially-Averaged Navier-Stokes equations

Abstract: This study employs Partially-Averaged Navier-Stokes (PANS) equations to simulate the flow around a smooth circular cylinder at Reynolds number 3900. It intends to evaluate the importance of discretization and modelling errors on the accuracy of this mathematical model. Furthermore, the study addresses the effect of the physical resolution, or fraction of turbulence kinetic energy being modelled fk, on the predictions accuracy. To this end, Validation exercises are carried out using five different values of fk which range from typical values for well-resolved Scale-Resolving Simulations (fk ≤ 0.25) to Reynolds-Averaged Navier-Stokes equations ( f k = 1.00 ). Naturally, these exercises require the evaluation of numerical errors, i.e. Verification studies. Consequently, and taking advantage of the ability of PANS to enable the distinction between discretization and modelling errors, spatial and temporal grid refinement studies are carried out to assess the magnitude of the discretization error, as well as its dependence on fk. The outcome confirms the ability of PANS, in combination with fk f k = 1.00 . However, the reduction of fk tends to increase the model dependence on the spatial and temporal resolution. It is demonstrated that similarly to the effect of the spatial and temporal grid resolution on the magnitude of the numerical error, the modelling error diminishes with the physical resolution (fk → 0). The convergence of the predictions with fk is also illustrated.

Deep Neural Networks for Nonlinear Model Order Reduction of Unsteady Flows

Abstract: Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit multiple complex phenomena both in time and space Reduced Order Modeling (ROM) of fluid flows has been an active research topic in the recent decade with the primary goal to decompose complex flows to a set of features most important for future state prediction and control, typically using a dimensionality reduction technique In this work, a novel data-driven technique based on the power of deep neural networks for reduced order modeling of the unsteady fluid flows is introduced An autoencoder network is used for nonlinear dimension reduction and feature extraction as an alternative for singular value decomposition (SVD) Then, the extracted features are used as an input for long short-term memory network (LSTM) to predict the velocity field at future time instances The proposed autoencoder-LSTM method is compared with non-intrusive reduced order models based on dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) Moreover, an autoencoder-DMD algorithm is introduced for reduced order modeling, which uses the autoencoder network for dimensionality reduction rather than SVD rank truncation Results show that the autoencoder-LSTM method is considerably capable of predicting fluid flow evolution, where higher values for coefficient of determination $R^{2}$ are obtained using autoencoder-LSTM compared to other models

Scrutinizing URANS in Shedding Flows: The Case of Cylinder in Cross-Flow in the Subcritical Regime

Abstract: To unravel the widespread perception that the RANS (Reynolds-averaged Navier-Stokes) concept is unreliable in predicting the dynamics of separated flows, we assessed the performance of two RANS closure levels, the linear eddy-viscosity (LEVM) and the second-moment (Reynolds stress, RSM) approaches in a massively separated generic flow over a bluff body. Considered is the canonical, zero-turbulence, cross-flow over an infinite cylinder with reference to our LES and the available DNS and experiments at two Reynolds numbers, Re = 3.9 × 103 and 1.4 × 105, both within the sub-critical regime with laminar separation. Both models capture successfully the vortex shedding frequency, but the low frequency modulations are detected only by the RSM. At high Reynolds numbers the RSM is markedly superior to the LEVM showing very good agreement with the LES and experimental data. The RSM, accounting naturally for the stress anisotropy and phase lag between the stress and strain eigenvectors, is especially successful in reproducing the growth rate of the turbulent kinetic energy in the initial shear layer which proved to be crucial for accurate prediction of the separation-induced transition. A scrutiny of the unsteady RANS (URANS) stress terms based on the conditional phase-averaged LES data shows a remarkable similarity of the normalized coherent and stochastic (modeled) stress components for the two Reynolds numbers considered. The mixed (cross) correlations, while non-negligible at the low Re number, diminish fast relative to the stochastic ones with increasing Reynolds number and, in the whole, are not significant to undermine the URANS concept and its applicability to high Re flows of industrial relevance.

Toward a Higher Order Unsteady Finite Volume Solver Based on Reproducing Kernel Methods

Abstract: During the last decades, research efforts are headed to develop high order methods on CFD and CAA to reach most industrial applications (complex geometries) which need, in most cases, unstructured grids. Today, higher-order methods dealing with unstructured grids remain in infancy state and they are still far from the maturity of structured grids-based methods when solving unsteady cases. From this point of view, the development of higher order methods for unstructured grids become indispensable. The finite volume method seems to be a good candidate, but unfortunately it is difficult to achieve space flux derivation schemes with very high order of accuracy for unsteady cases. In this paper we propose, a high order finite volume method based on Moving Least Squares approximations for unstructured grids that is able to reach an arbitrary order of accuracy on unsteady cases. In order to ensure high orders of accuracy, two strategies were explored independently: (1) a zero-mean variables reconstruction to enforce the mean order at the time derivative and (2) a pseudo mass matrix formulation to preserve the residuals order.