Exploring epistemic quantification uncertainty in turbulent flow for multi-element aerofoils

Thu, 01 Sep 2022 00:00:00 +0000

Project details

Student: Zhenting Kong
Programme: Second-year BEng Mechanical Engineering
Scheme: Sheffield Undergraduate Research Experience (SURE)
Period: Summer 2022
Supervisor: Omid Bidar

Overview

This project explored epistemic uncertainty in Reynolds-averaged Navier-Stokes (RANS) predictions for single- and multi-element aerofoils. Zhenting used the open-source SU2 computational fluid dynamics suite and its EQUiPS uncertainty-quantification capability to generate uncertainty bounds using eigenspace perturbations of the turbulence model.

The study compared an S809 single-element aerofoil with the MDA30P3ON multi-element configuration across several angles of attack. Simulations were run on the University of Sheffield’s ShARC high-performance computing system, with ParaView and MATLAB used for post-processing and analysis.

Key outcomes

Uncertainty increased substantially at higher angles of attack, where flow separation and other complex physics become more important.
At lower angles of attack, the perturbed RANS predictions produced much narrower uncertainty bounds.
Regions of high predictive uncertainty were identified as candidate locations for informative sensor placement.
The project produced simulation data and analysis that contributed to further research on turbulence-model uncertainty and data assimilation.

DAFoam - Field Inversion and Machine Learning

Mon, 27 Jun 2022 00:00:00 +0000

My contribution

DAFoam is an established open-source discrete-adjoint framework built on OpenFOAM and developed by the MDO Lab community. I did not create the overall DAFoam codebase. My contribution was to first implement its Field Inversion and Machine Learning (FIML) capability, enabling adjoint-based field inversion for data-driven augmentation of Reynolds-averaged Navier-Stokes turbulence models.

This work introduced the capability to infer spatial corrective fields from reference data and supported objectives based on full velocity fields, velocity profiles, surface pressure, skin friction and aerodynamic force coefficients. It established DAFoam as an open-source tool for field-inversion-based turbulence-model development and subsequent machine-learning workflows.

The implementation was presented in the paper An open-source adjoint-based field inversion tool for data-driven RANS modelling and has since been extended by the wider DAFoam development community.