Multiphase Flows | Omid Bidar

Evaluations of boiling heat flux partitioning and bubble parameter models using a 0-D framework

Wed, 10 Jun 2026 00:00:00 +0000

XBOIL

Wed, 10 Jun 2026 00:00:00 +0000

Overview

XBOIL is an open-source MATLAB framework for rapidly and systematically evaluating heat flux partitioning formulations and bubble-dynamics sub-models. Its zero-dimensional, point-averaged formulation isolates boiling-model behaviour from turbulence, interfacial transport, mesh and numerical uncertainties associated with full computational fluid dynamics simulations.

Capabilities

Independently select heat flux partitioning formulations and bubble-parameter sub-models.
Exhaustively evaluate thousands of model combinations in milliseconds per configuration.
Compare predictions against pool- and flow-boiling experimental databases.
Investigate model compatibility, sensitivity and generalisability across operating conditions.
Extend the modular framework with new closures from the literature.

XBOIL was developed for the study Evaluations of boiling heat flux partitioning and bubble parameter models using a 0-D framework, published in the International Journal of Heat and Mass Transfer.

View XBOIL on GitHub

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.

Multiphase Flows | Omid Bidar

Evaluations of boiling heat flux partitioning and bubble parameter models using a 0-D framework

XBOIL

Overview

Capabilities

Related research

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

Project details

Overview

Key outcomes