7 research outputs found
Performance of hybrid turbulence models in OpenFOAM for numerical simulations of a confined backward-facing step flow at low Prandtl number
/To date, numerical simulation of complex turbulent flows with separation remains challenging. On the one hand,turbulence models in Reynolds-averaged Navier-Stokes (RANS) equations struggle with correctly representing turbulent momentum transfer in such flows, whereas turbulence-resolving techniques such as large-eddy simulations (LES) carry high computational cost on the other hand. Alternatively, hybrid RANS–LES turbulence models promise to deliver scale-resolving accuracy at acceptable computational cost, yet their accuracy remains highly dependent on simulation setup and flow conditions. Here, we investigate hybrid turbulence models readily available in OpenFOAM, and benchmark their performance to Reynolds-averaged approaches and turbulence-resolving high-fidelity reference data for a confined backward-facing step flow at low Prandtl number and relatively low Reynolds number. Although temperature is generally well predicted by all considered setups, a comparison between RANS and LES shows that turbulence resolution can increase the accuracy for the considered flow case. Results show that scale-adaptive simulation techniques do not produce resolved turbulence and fail to outperform the baseline Reynolds-averaged simulations for the considered case. In contrast, detached-eddy variants do resolve turbulence in the separated shear layer, yet some configurations suffer from modeled-stress depletion due to late development of resolved turbulence. A grid coarsening study compares the degradation of accuracy for each approach, showcasing robustness of the standard RANS approach and the good performance of full LES even at surprisingly coarse resolutions. For each grid, the best-performing setup was either a RANS or an LES approach, but never a hybrid turbulence model setup. Finally, a Reynolds-number sensitivity is presented, indicating that resolved turbulence development is promoted at higher Reynolds numbers, thus leading to setups more amenable to hybrid turbulence models.  
A Robust Data-Driven Model for Flapping Aerodynamics under different hovering kinematics
Flapping Wing Micro Air Vehicles (FWMAV) are highly manoeuvrable,
bio-inspired drones that can assist in surveys and rescue missions. Flapping
wings generate various unsteady lift enhancement mechanisms challenging the
derivation of reduced models to predict instantaneous aerodynamic performance.
In this work, we propose a robust CFD data-driven, quasi-steady (QS) Reduced
Order Model (ROM) to predict the lift and drag coefficients within a flapping
cycle. The model is derived for a rigid ellipsoid wing with different
parameterized kinematics in hovering conditions. The proposed ROM is built via
a two-stage regression. The first stage, defined as `in-cycle' (IC), computes
the parameters of a regression linking the aerodynamic coefficients to the
instantaneous wing state. The second stage, `out-of-cycle' (OOC), links the IC
weights to the flapping features that define the flapping motion. The training
and test dataset were generated via high-fidelity simulations using the overset
method, spanning a wide range of Reynolds numbers and flapping kinematics. The
two-stage regressor combines Ridge regression and Gaussian Process (GP)
regression to provide estimates of the model uncertainties. The proposed ROM
shows accurate aerodynamic predictions for widely varying kinematics. The model
performs best for smooth kinematics that generate a stable Leading Edge Vortex
(LEV). Remarkably accurate predictions are also observed in dynamic scenarios
where the LEV is partially shed, the non-circulatory forces are considerable,
and the wing encounters its own wake.Comment: submitted to Physics of Fluid
Temperature perturbation method to generate turbulent inflow conditions for LES/DNS simulations
An alternative approach to the classical velocity perturbations is applied to generate inflow turbulence for LES and DNS of wall bounded flows. The method consists in introducing random temperature perturbations that generate turbulence through local buoyancy effects. The cell perturbation method is implemented in the incompressible buoyant solver of OpenFOAM v2.3 ans tested on a plane channel flow at Rer = 395. The buoyancy force is locally increased within in active zone specifications and the governing Richardson number. Reynolds stresses are compared to reference results. The method appears to be simple and efficient, while only first-order statistics are required as input
Turbulent heat flux modelling in forced convection flows using artificial neural networks
Fluid dynamics of liquid metals plays a central role in new generation liquid metal cooled nuclear reactors, for which numerical investigations require the use of an appropriate thermal turbulence model for low Prandtl number fluids. Given the limitations of traditional modelling approaches and the increasing availability of high-fidelity data for this class of fluids, we propose a machine learning strategy for the modelling of the turbulent heat flux. A comprehensive algebraic mathematical structure is derived and physical constraints are imposed to ensure attractive properties promoting stability and realizability. The closure coefficients of the model are predicted by an Artificial Neural Network (ANN) which is trained with the available Direct Numerical Simulation (DNS) data at different Prandtl numbers. The validation shows that the ANN provides an accurate representation of the heat flux in a wide range of Prandtl numbers (Pr = 0.01–0.71) and compares well with other existing thermal closures. Nevertheless, simulations with different auxiliary models to compute the inputs of the ANN revealed a certain sensitivity of the data-driven formulation on the models used in combination with it. In particular, the ANN model strongly relies on the Reynolds stress anisotropy. Such a sensitivity limits the robustness of the model to the inaccuracies of the underline momentum field and the momentum turbulence model applied in combination with it
Physics-constrained machine learning for thermal turbulence modelling at low Prandtl numbers
Liquid metals play a central role in new generation liquid metal cooled nuclear reactors, for which numerical investigations require the use of appropriate thermal turbulence models for low Prandtl number fluids. Given the limitations of traditional modelling approaches and the increasing availability of high-fidelity data for this class of fluids, we propose a Machine Learning strategy for the modelling of the turbulent heat flux. A comprehensive algebraic mathematical structure is derived and physical constraints are imposed to ensure attractive properties promoting applicability, robustness and stability. The closure coefficients of the model are predicted by an Artificial Neural Network (ANN) which is trained with DNS data at different Prandtl numbers. The validity of the approach was verified through a priori and a posteriori validation for two and three-dimensional liquid metal flows. The model provides a complete vectorial representation of the turbulent heat flux and the predictions fit the DNS data in a wide range of Prandtl numbers (Pr=0.01-0.71). The comparison with other existing thermal models shows that the methodology is very promising