CFD Turbulence Modeling Overview
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Questions and Answers

What are the main points of concern regarding data-driven turbulence models?

Generalisability and the consistency of the a posteriori results.

What computational approach is used for the progressive improvement of turbulence models in this study?

Simulation-driven Bayesian optimisation with Kriging surrogates.

What is the objective of augmenting secondary-flow prediction capability in the linear eddy-viscosity model?

To enhance prediction without violating the original performance on canonical cases.

Match the turbulence modeling approaches with their definitions:

<p>RANS = Reynolds-averaged Navier–Stokes equations, preferred for industrial applications DNS = Direct numerical simulation, high-fidelity method for turbulence modeling LES = Large-eddy simulation, another high-fidelity approach for simulating turbulence</p> Signup and view all the answers

What type of models are obtained through progressive data-augmented explicit algebraic Reynolds stress models?

<p>PDA-EARSMs for k – ω SST.</p> Signup and view all the answers

What performance aspect is preserved when applying new models on channel flow cases?

<p>The successful performance of the original k - ω SST model.</p> Signup and view all the answers

The study reports that there is a significant improvement in the prediction of secondary flows and streamwise velocity with the new models.

<p>True</p> Signup and view all the answers

What optimization method is presented in the study for turbulence model improvement?

<p>Simulation-driven Bayesian optimisation with Kriging surrogates</p> Signup and view all the answers

What is the goal of augmenting the secondary-flow prediction capability?

<p>To maintain original model performance</p> Signup and view all the answers

The study focuses primarily on direct numerical simulation (DNS) methods for turbulence modeling.

<p>False</p> Signup and view all the answers

What are progressively data-augmented explicit algebraic Reynolds stress models (PDA-EARSMs) used for?

<p>To predict secondary flows in the k-ω SST model.</p> Signup and view all the answers

What findings were observed with the new turbulence models on unseen test cases?

<p>Significant improvement in secondary flows and streamwise velocity</p> Signup and view all the answers

Study Notes

RANS

  • Reynolds-averaged Navier–Stokes (RANS) equations are preferred over more computationally expensive methods like DNS and LES for industrial applications of CFD.
  • RANS relies on Reynolds stress tensor (RST) models to predict turbulence physics.

Data-Driven Turbulence Models

  • Generalisability and consistency of the a posteriori results are critical concerns in data-driven turbulence models.
  • This study progressively improves turbulence models using a simulation-driven Bayesian optimisation with Kriging surrogates.
  • The optimisation of the models is achieved through a multi-objective approach based on duct flow quantities.

PDA-EARSMs for 𝑘 − 𝜔 SST

  • This research aims to augment the secondary-flow prediction capability of the linear eddy-viscosity model 𝑘 − 𝜔 SST without compromising its performance on canonical cases like channel flow.
  • The study develops progressively data-augmented explicit algebraic Reynolds stress models (PDA-EARSMs) for the 𝑘 − 𝜔 SST model.
  • These PDA-EARSMs can predict secondary flows that the standard 𝑘 − 𝜔 SST model fails to predict while maintaining the original model's performance on channel flow.

Results

  • Numerical verification for unseen test cases demonstrates a significant improvement by PDA-EARSMs in predicting secondary flows and streamwise velocity.
  • The progressive approach enhances the performance of data-driven turbulence models for fluid flow simulation while preserving robustness and stability of the solver.

Prior Data-Driven Approaches

  • Existing research on the improvement of RANS turbulence models is focused on training models using high-fidelity RST data to improve predictions from empirical models.
  • Other studies aim to correct RST predictions or to modify existing empirical models.

Introduction

  • Reynolds-averaged Navier–Stokes (RANS) equations are preferred over DNS and LES for industrial CFD applications because of their robustness and computational speed.
  • RANS predicts turbulence physics through a Reynolds stress tensor (RST) model, influencing simulation results.
  • Despite the prevalence of RANS simulations, common empirical models have limitations.

Data-Driven Turbulence Models

  • Generalisability and consistency of results are crucial considerations for data-driven turbulence models.
  • This study enhances turbulence models using simulation-driven Bayesian optimisation with Kriging surrogates.
  • The optimisation is multi-objective, targeting duct flow quantities.

Aim of the Study

  • To augment the secondary-flow prediction capability of the 𝑘 − 𝜔 SST linear eddy-viscosity model without compromising its performance on canonical cases like channel flow.

Methodology

  • Progressively data-augmented explicit algebraic Reynolds stress models (PDA-EARSMs) for 𝑘 − 𝜔 SST are developed, enabling prediction of secondary flows missed by the standard model.
  • New models are tested on channel flow cases to ensure they retain the performance of the original 𝑘 − 𝜔 SST model.

Findings

  • Numerical verification is carried out on various test cases.
  • Unseen test cases demonstrate significant improvement in secondary flow and streamwise velocity predictions, highlighting the generalisability of the new models.

Conclusion

  • The study promotes the potential of a progressive approach for enhancing data-driven turbulence models while preserving solver robustness and stability.

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Description

Explore the fundamentals of turbulence modeling in Computational Fluid Dynamics (CFD), focusing on RANS equations, data-driven approaches, and the PDA-EARSMs for the k-ω SST model. This quiz covers key concepts and advancements in turbulence prediction methods, particularly for industrial applications.

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