Data-driven turbulent heat flux modeling with inputs of multiple fidelity

TL;DR

This paper presents a data-driven thermal turbulence model that uses multi-fidelity data to improve robustness and generalization by detecting and adjusting to input data inconsistencies from different fidelity levels.

Contribution

It introduces a multi-fidelity training approach that enables the model to identify input fidelity levels and adapt predictions, enhancing robustness against data inconsistencies.

Findings

The model can distinguish between different fidelity levels in input data.

Multi-fidelity training improves robustness to model uncertainties.

Verification shows potential for flow simulations with uncertain momentum statistics.

Abstract

Data-driven RANS modeling is emerging as a promising methodology to exploit the information provided by high-fidelity data. However, its widespread application is limited by challenges in generalization and robustness to inconsistencies between input data of varying fidelity levels. This is especially true for thermal turbulent closures, which inherently depend on momentum statistics provided by low or high fidelity turbulence momentum models. This work investigates the impact of momentum modeling inconsistencies on a data-driven thermal closure trained with a dataset with multiple fidelity (DNS and RANS). The analysis of the model inputs shows that the two fidelity levels correspond to separate regions in the input space. It is here shown that such separation can be exploited by a training with heterogeneous data, allowing the model to detect the level of fidelity in its inputs and adjust its prediction accordingly. In particular, a sensitivity analysis and verification shows that such a model can leverage the data inconsistencies to increase its robustness. Finally, the verification with a CFD simulation shows the potential of this multi-fidelity training approach for flows in which momentum statistics provided by traditional models are affected by model uncertainties.