Predicting uncertainty in turbulence simulation: a multi-fidelity approach to economise computation

The protocol clearly defines falsifiable predictions with quantitative bounds (e.g., a 10-fold reduction in high-fidelity runs, St uncertainty within 20% of Monte Carlo) and specifies statistical null hypotheses, which is commendable for rigorous testing.

The hypothesis clearly identifies a practical need (systematic UQ in LES) and proposes a concrete solution (transfer of the QUEENS framework) that leverages mature multi-fidelity surrogate methods, aligning with current trends in UQ for computational fluid dynamics.

The multi-fidelity Bayesian UQ approach is a promising direction for reducing the computational cost of LES uncertainty quantification, as it exploits correlations between different fidelity levels.

There is a clear market need in the aerospace, energy, and automotive sectors, where LES is employed for design and certification, and where the quantification of uncertainty is critical for safety and performance. Companies such as Airbus, Rolls-Royce, and Siemens Energy are investing in UQ for CFD.

Strong methodological novelty: the transfer of a multi-fidelity Bayesian UQ framework to LES is timely and addresses a critical need for systematic error propagation in computational fluid dynamics.