[School of Engineering PhD Scholarships] Machine learning for violent free surface flows

About the project

About the Project Free-surface flows play a critical role in many environmental and renewable energy applications, from coastal protection and flood mitigation to offshore wind turbines and wave energy devices. Predicting these highly complex flows remains one of the major challenges in computational fluid dynamics (CFD) due to their strong nonlinearities, turbulent behaviour, air entrainment and violent impacts. This PhD project aims to develop a new generation of CFD tools that combine physics-based simulation with state-of-the-art machine learning to dramatically improve the accuracy and efficiency of free-surface flow predictions. The successful candidate will investigate how neural networks can be embedded directly within Lagrangian CFD methods to capture unresolved turbulence effects and multiphase flow physics that are traditionally too expensive to simulate in engineering practice. The research will use DualSPHysics, a leading open-source meshless CFD solver, together with high-fidelity OpenFOAM simulations employing Volume-of-Fluid (VOF) techniques. Machine learning models, including graph neural networks and other physics-informed approaches, will be trained using detailed numerical data and subsequently integrated into the Lagrangian solver. The resulting framework will provide improved predictions of impact pressures, air entrainment, turbulence effects and flow kinematics while maintaining practical computational costs. The project sits at the intersection of computational fluid dynamics, machine learning, high-performance computing and coastal/offshore engineering. The developed technology has the potential to enable engineering-grade simulations on desktop computers within hours, rather than requiring large-scale computing resources for days. The successful candidate will gain expertise in advanced CFD, machine learning, scientific software development, large-scale numerical simulations and data-driven modelling. Opportunities will exist to contribute to the internationally recognised DualSPHysics project, publish in leading journals, present at international conferences and collaborate with academic and industrial partners working in environmental and renewable energy applications. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in Mechanical, Civil, Aerospace or a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in Mechanical, Civil, Aerospace or a discipline directly relevant to the PhD (or international equivalent). This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

Research areas