arXiv subjects

Nikhila Kalia

Publications and source records attributed to Nikhila Kalia.

4 recordsLinked to original sources

arXiv2025

Realizability-Informed Machine Learning for Turbulence Anisotropy Mappings

Within the context of machine learning-based closure mappings for RANS turbulence modelling, physical realizability is often enforced using ad-hoc postprocessing of the predicted anisotropy tensor. In this study, we address the realizability issue via a new physics-based loss function that penalizes non-realizable results during training, thereby embedding a preference for realizable predictions into the model. Additionally, we propose a new framework for data-driven turbulence modelling which retains the stability and conditioning of optimal eddy viscosity-based approaches while embedding equivariance. Several modifications to the tensor basis neural network to enhance training and testing stability are proposed. We demonstrate the conditioning, stability, and generalization of the new framework and model architecture on three flows: flow over a flat plate, flow over periodic hills, and flow through a square duct. The realizability-informed loss function is demonstrated to significantly increase the number of realizable predictions made by the model when generalizing to a new flow configuration. Altogether, the proposed framework enables the training of stable and equivariant anisotropy mappings, with more physically realizable predictions on new data. We make our code available for use and modification by others. Moreover, as part of this study, we explore the applicability of Kolmogorov-Arnold Networks (KAN) to turbulence modeling, assessing its potential to address non-linear mappings in the anisotropy tensor predictions and demonstrating promising results for the flat plate case.

physics.flu-dyn↗

arXiv2025

Kolmogorov-Arnold Networks for Turbulence Anisotropy Mapping

This study evaluates the generalization performance and representation efficiency (parsimony) of a previously introduced Tensor Basis Kolmogorov-Arnold Network (TBKAN) architecture for data-driven turbulence modeling. The TBKAN framework replaces the multi-layer perceptron (MLP) used in either the standard or modified Tensor Basis Neural Network (TBNN) with a Kolmogorov-Arnold network (KAN), which significantly reduces the model complexity while providing a structure that potentially can be used with symbolic regression to provide a physical interpretability that is not available in a 'black box' MLP. While some prior work demonstrated TBKAN's feasibility for modeling a 'simple' flat plate boundary layer flow, this study extends the TBKAN architecture to model more complex benchmark flows, in particular, square duct and periodic hills flows which exhibit strong turbulence anisotropy, secondary motion, and flow separation and reattachment. A realizability-informed loss function is employed to constrain the model predictions, and, for the first time, TBKAN predictions are stably injected into the Reynolds-averaged Navier-Stokes equations to provide it a posteriori predictions of the mean velocity field.

physics.flu-dyn↗

arXiv2025

Bayesian Optimization of the GEKO Turbulence Model for Predicting Flow Separation Over a Smooth Surface

This paper applies Bayesian-optimization-RANS (turbo-RANS) to improve Reynolds-averaged Navier-Stokes (RANS) turbulence models for a converging-diverging channel, a case with adverse pressure gradients and flow separation. Using Bayesian optimization, the Generalized $k$-$ω$ (GEKO) model was calibrated by tuning $C_\text{SEP}$ and $C_\text{NW}$ with sparse direct numerical simulation (DNS) data at $Re = 12,600$. The calibration followed the Generalized Error Distribution-based Calibration Procedure (GEDCP), optimizing coefficients based on pressure recovery ($C_p$) and skin friction ($C_f$). The optimized model was evaluated beyond training data. Streamwise velocity ($U$) predictions at $Re = 12,600$ were compared to DNS to assess improvements in $C_p$ and $C_f$. To test robustness, comparisons were made against large-eddy simulation (LES) data at $Re = 20,580$ for velocity and skin friction. Results show that optimized GEKO (turbo-RANS) improves wall quantity predictions, particularly reattachment. Improved velocity profiles at both Reynolds numbers suggest Bayesian-optimized coefficients enhance adverse pressure gradient modeling. The model retains accuracy across different $Re$, showing turbo-RANS' potential in turbulence model corrections that generalize across flows. While skin friction predictions showed limited improvement due to constraints of two-equation models, this study highlights the role of machine learning-assisted RANS calibration in improving predictive accuracy for complex flows. The results suggest optimized coefficients from a single dataset can be applied across moderate $Re$ variations, improving turbo-RANS' applicability for turbulence model tuning.

physics.flu-dyn↗

arXiv2024

turbo-RANS: Straightforward and Efficient Bayesian Optimization of Turbulence Model Coefficients

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. In this work, we address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization. We introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse, or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, we describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure. We demonstrate that the performance of the $k$-$ω$ shear stress transport (SST) and Generalized $k$-$ω$ (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel. This work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. We propose a data-flexible objective function for the calibration target. Our open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+, and solver-agnostic templates for user application.

physics.flu-dyn↗