Investigating Symbolic Regression for Bubble Departure Diameter Prediction in Subcooled Flow Boiling

Wed, 27 May 2026 00:00:00 +0000

Project details

Student: Samuel Williams
Programme: MEng Mechanical Engineering
Project type: Final-year dissertation
Academic year: 2025-26
Supervisor: Dr Omid Bidar

Overview

Accurate prediction of bubble departure diameter is important in heat flux partitioning models for subcooled flow boiling, where relatively small errors can propagate strongly into predicted evaporation and quenching heat fluxes. This project investigated symbolic regression as a route to developing computationally efficient, interpretable, closed-form expressions that bridge mechanistic boiling models and data-driven optimisation.

Samuel first implemented a deterministic mechanistic baseline in MATLAB, based on the Klausner force-balance model with Colombo and Fairweather improvements, and validated it against a multi-source database containing 378 experimental measurements. Symbolic regression was then applied using PySR in two complementary forms: a hybrid correction to the mechanistic baseline and a standalone correlation for bubble departure diameter. Leave-one-dataset-out cross-validation was used to assess generalisation beyond individual experimental datasets.

Key outcomes

The mechanistic baseline achieved mean absolute percentage errors between 21.0% and 32.4% across the validation data.
Both symbolic-regression approaches identified superheat-driven growth, cap condensation and flow-shear inertia as the dominant physical drivers.
The hybrid model reduced log-RMSE by approximately 15% relative to the mechanistic baseline.
The standalone correlation achieved a mean absolute percentage error of 27.8% on held-out vertical-upflow data, improving on the empirical correlations used for comparison.
The work demonstrated the potential of symbolic regression for producing transparent and physically interpretable closure models for subcooled flow boiling.

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.

Student-Projects | Omid Bidar

Investigating Symbolic Regression for Bubble Departure Diameter Prediction in Subcooled Flow Boiling

Project details

Overview

Key outcomes

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

Project details

Overview

Key outcomes