Stochastic analysis and modelling of flow boiling (Ref: FP-HZ-2026)

About the Project

Flow boiling is one of the most efficient heat transfer mechanisms and plays a critical role in modern energy systems, including electronics cooling, renewable energy, and advanced thermal management technologies. However, its practical deployment is often limited by the lack of reliable predictive models, due to the complex and inherently stochastic nature of multiphase flow phenomena.

This PhD project aims to develop next-generation, uncertainty-aware stochastic models for flow boiling, moving beyond traditional deterministic approaches. The research will combine advanced data-driven modelling with high-resolution experimental analysis.

You will work with an established flow boiling facility equipped with state-of-the-art optical diagnostics to generate and analyse time-resolved experimental data, providing a unique opportunity to bridge fundamental science with real-world engineering applications.

The successful candidate will join a dynamic, industry-collaborative research team and will develop expertise at the interface of thermal sciences, data science, and machine learning.

Key Responsibilities：

• Develop and implement automated image-processing algorithms to extract and quantify statistical characteristics of flow boiling processes
• Develop stochastic flow boiling models and rigorously quantify associated uncertainties
• Develop uncertainty-aware artificial neural network models for predictive modelling of multiphase heat transfer

Name of primary supervisor/CDT lead:
HUAYONG ZHAO h.zhao2@lboro.ac.uk
https://www.lboro.ac.uk/schools/meme/staff/huayong-zhao/

Names of secondary supervisors:
STEVEN KENNY, ED LONG

Entry requirements:
Applicants should hold, or expect to achieve, a first-class or high upper second-class (2:1) honours degree (or equivalent) in Mechanical Engineering, Physics, or a closely related discipline. A relevant Master’s degree or prior research experience in experimental and/or analytical multiphase thermofluids systems would be advantageous but not essential.

English language requirements:
Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/).

Funding information:
This PhD project is fully funded by a long-term industrial collaborator. The studentship is for 3 years and provides a tax-free stipend of £22,027 per annum for the duration of the studentship plus university tuition fees.

Bench fees required: No

Closing date of advert: 15 May 2026

Start date: July 2026, October 2026

Full-time/part-time availability: Full-time 3 years

Who is eligible to apply?: Both UK and International

How to apply:
All applications should be made online at http://www.lboro.ac.uk/study/apply/research/. Under programme name, select Mechanical, Electrical & Manufacturing Engineering. . Please quote the advertised reference number ‘FP-HZ-2026’ in your application under the ‘Finance’ section.
Applications must include a personal statement, up-to-date curriculum vitae (CV), details of two referees (one from your highest degree qualification), certified certificates and transcripts for all completed degree programmes, and a reference to the project ‘FP-HZ-2026’. Submission of a research proposal is not essential but may strengthen your application.
You are encouraged to contact the project supervisor to discuss your suitability and interest in the project: h.zhao2@lboro.ac.uk.

Project search terms:
chemical engineering, energy technologies, fluid mechanics, mechanical engineering, nuclear physics, heat transfer, boiling, data analytics, statistical analysis