[School of Engineering PhD Scholarships] Data-driven modelling and active control of aerodynamic flow separation

About the project

About the Project Flow separation can cause substantial increases in aerodynamic drag and loss of performance in aircraft, UAVs and wind-energy devices, and its prediction and control remain major challenges in aerodynamics. Recent advances in data-driven dynamical systems, particularly Koopman operator theory and reduced-order modelling, provide new opportunities to construct tractable models of separated flows and use them for active control. This PhD project will exploit these advances to develop new data-driven models and control strategies that can be applied to a wide range of fluid-flow problems. A central part of the project will be the construction of explicit, highly interpretable, and scalable data-driven dynamical models describing how the flow evolves and responds to actuation. These models will support efficient closed-loop control strategies and determine the actuation required to achieve target aerodynamic outcomes. Modern machine-learning approaches, including neural operators or reinforcement learning, will also be considered where they provide useful alternatives or benchmarks. The main test case for this framework will be flow separation over airfoils and wings controlled using spinning surface elements, which provide a novel mechanism for modifying the near-wall flow and separated shear layer through variations in their rotational velocity. The primary objective of this demonstration will be to reduce drag and improve aerodynamic efficiency. The project will first study unsteady laminar flows, isolating the fundamental actuator-flow interaction mechanisms in a controlled setting, before progressing towards three-dimensional turbulent separated flows, where data-driven closed-loop control remains considerably more challenging. High-fidelity simulations will be performed using the open-source spectral-element solver Nek5000, generating accurate datasets for model development, validation and control design. A key focus will be assessing how well the developed models and controllers generalise across Reynolds numbers, angles of attack, inflow conditions and actuator settings. Such robustness is essential if data-driven flow-control methods are ultimately to move beyond individual operating conditions and be deployed in real-world applications. Expected outcomes include new methodologies for modelling and controlling separated flows, improved understanding of the interaction between active surface actuation and flow dynamics, and demonstrated improvements in aerodynamic performance, with potential implications for applications including UAV endurance and wind-energy efficiency. The student will be trained in computational fluid dynamics (CFD), flow physics, high-performance computing, data-driven dynamical systems, reduced-order modelling, optimisation and flow control, supported through regular supervision and integration within a research environment spanning CFD, turbulence and data-driven modelling. Applicants should have a strong background in fluid dynamics and good numerical and computational skills. Experience with CFD, Python, or data science/machine learning is desirable but not essential. However, enthusiasm for learning and implementing advanced computational methods on challenging engineering problems is particularly important. 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/Aerospace Engineering, Applied Mathematics, Computational Science (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in Mechanical/Aerospace Engineering, Applied Mathematics, Computational Science(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

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