Stochastic analysis and modelling of flow boiling

Project Overview

Flow boiling is a highly efficient heat transfer process critical to many modern energy applications, including electronics cooling and renewable energy technologies. However, its complex multiphase flow characteristics are inherently stochastic, limiting reliable prediction with current deterministic models. This project aims to develop next-generation stochastic models incorporating uncertainty quantification by combining advanced data-driven modelling and high-resolution experimental diagnostics.

What You Will Do

As a PhD researcher, you will use a state-of-the-art flow boiling facility equipped with optical diagnostics to collect time-resolved data. Responsibilities include developing automated image-processing algorithms to extract statistical characteristics, creating stochastic flow boiling models, and building uncertainty-aware artificial neural networks for predictive multiphase heat transfer modelling.

Expected Outcomes

The project will deliver validated stochastic models that rigorously quantify uncertainties in flow boiling processes. These models will enhance predictive accuracy for industrial thermal management and energy technologies. You will gain expertise at the intersection of thermal fluids, data science, and machine learning within a collaborative, industry-engaged research environment.

Why This Matters

Reliable predictive models of flow boiling are essential for advancing energy system efficiencies and thermal management technologies. By addressing the stochastic nature of multiphase flows, this research will enable better design and optimization of critical systems in electronics cooling and renewable energy applications, thereby supporting technological innovation and sustainability.