Mechanical Engineering - Theses
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ItemData-driven large eddy simulation modelling in natural convection( 2022)Natural convection is a commonly occurring heat-transfer problem in many industrial flows and its prediction with conventional large eddy simulations (LES) at higher Rayleigh numbers using progressively coarser grids leads to increasingly inaccurate estimates of important performance indicators, such as Nusselt number (Nu). Thus, to improve the heat transfer predictions, we utilize Gene Expression Programming (GEP) to develop sub-grid scale (SGS) stress and SGS heat-flux models simultaneously for LES. With that as the focus, in the present study, two geometrically distinct natural convection cases are considered to develop and generalize turbulence models. The Rayleigh-Benard Convection (RBC) is used to develop models, while the Concentric Horizontal Annulus (CHA) is used to test the model generalization. An in-house compressible solver, HiPSTAR, for simulating natural convection flows for low Mach number problems is benchmarked against the experiments and Direct Numerical Simulations (DNS) results. Subsequently, HiPSTAR is used to run simulations for the RBC and CHA configurations and the generated DNS database is then used to train and assess LES models. The models’ development starts with RBC, where the fluid is in a cubic box with the bottom wall as the hot wall and the top wall as the cold wall. The alignment between different basis functions and the Gaussian-filtered SGS stress and SGS heat flux is used to determine the most suitable training framework. The trained models in isotropic form, by utilizing the norm of the grid cell as the length scales demonstrate good performance in the bulk region, but less improved performance in the near wall region. It is shown, that for LES of wall-bounded flow, the GEP models in anisotropic form, i.e. using different grid length scales for the different spatial directions, are required to obtain generalized models suitable for different regions. Consequently, the a-priori results demonstrate a significant improvement in the prediction of both instantaneous and mean quantities for a wide range of filter widths. However, developing accurate LES models that generalize well to complex geometries poses a challenge, particularly for data-driven methods. Thus, in the next stage, machine-learned closure models with embedded geometry independence are proposed, where the subgrid-scale (SGS) stress and heat-flux models developed by using Gene Expression Programming (GEP) are built in the computational space. The CHA case is chosen to develop and generalize the models. Subsequently, the formulation between the SGS closures, the total, and the resolved large-scale turbulent stress and heat flux is derived in the compressible LES context. The a-priori results show that the GEP models developed in computational space significantly improve both the SGS stress and SGS heat-flux prediction while being robust to complex flows. Similarly, the a-posteriori results demonstrate that the GEP models perform better than the wall-adapting local eddy-viscosity (WALE) model in the prediction of the mean SGS stress and the SGS heat-flux. The data-driven approach for turbulence model development presented clearly offers promising geometry independence for LES in the prediction of SGS stress and heat flux.