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Mechanical Engineering
Academic Industrial Advisor
v1ghesse@ed.ac.uk
Mechanical Engineering
jcorney@ed.ac.uk
1.133 Sanderson Building
Mechanical Engineering
Materials and Processes
Lecturer in Digital Manufacture
s.tammaswilliams@ed.ac.uk
1.075 Sanderson Building
Mechanical Engineering
Materials and Processes
- MEng, Aerospace Engineering, The University of Manchester, 2010
- PhD, Advanced Metallic Systems, The University of Manchester, 2015
- PGCert, Academic Practice, LJMU, 2021
- Fellow of Advance HE (FHEA)
- Member of The Institute of Materials, Minerals & Mining (MIMMM)
Sam is currently course organiser for three courses:
- Additive and Computer Aided Manufacturing (PGEE11210)
- Digital Design and Manufacture Dissertation (PGEE11217)
- Digital Manufacture 5 (MECE11017)
He also provides supervision for BEng, MEng and MSc projects.
Chair in Renewable Energy Technology and Head of Research Institute
Alasdair.McDonald@ed.ac.uk
3.101 Faraday Building
Mechanical Engineering
Energy Systems
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Professor in the Institute for Energy Systems and Mechanical Engineering Discipline, School of Engineering, University of Edinburgh. Models and designs powertrains and generators for offshore wind turbines.
PhD in "Structural analysis of low speed, high torque electrical generators for direct drive renewable energy converters" from Edinburgh (2004-2008). This started me looking at the integrated electrical-magnetic-mechanical modelling and design of large electrical machines for offshore renewable energy.
During my PhD, I started work on a double-sided air-cored permanent magnet machine concept called "C-Gen". Ultimately this lead to a formation and spin-out of a company called NGenTec, where as a founder I worked as Chief Engineer, helping to develop linear, radial-flux and axial-flux variants.
In 2012, I returned to academia, as a lecturer in Wind Turbine Technology in the Department of Electronic and Electrical Engineer at the University of Strathclyde. Based in the EPSRC CDT in Wind Energy Systems, over the following years I was promoted to Senior Lecturer and then Reader in Wind Turbine Technology. During those years I developed interests in wind turbine powertrain modelling, design, optimisation, reliability and condition monitoring, always asking what technology will give the lowest cost of energy for offshore renewables.
In 2021, I rejoined Edinburgh, where I work in Electrical Power Conversion group as applied to Wind Energy and Offshore Renewable Energy.
My career publications can be found here (please scroll down to the very bottom to see the ones that no one has read) and my EPSRC-funded projects are here (email me for the long list of those that didn't get funded).
PhD, University of Edinburgh, 2008
MEng (Hons) in Integrated Electrical & Mechanical Engineering, University of Durham, 2004
Member of the Institution for Engineering Technology (IET), CEng
- Design of permanent magnet electrical machines for wind energy and offshore renewable energy
- Design for lightweight electrical machines
Honorary Fellow
nyu2@ed.ac.uk
nfo No Fixed Office
Mechanical Engineering
Materials and Processes
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Dr Nan Yu is a Senior Lecturer/Associate Professor at the University of Edinburgh, and the Deputy Director of MSc Digital Design and Manufacture.
Nan was trained at Cranfield University (PhD: plasma figuring of large optics), University College Dublin (UCD, as a postdoc in precision manufacturing of medical devices), and the European Organisation of Nuclear Research (CERN, as an Associate Scientist in precision alignment and metrology). He has established academic reputation in precision manufacturing and advanced plasma technologies as evidenced by over £ 1 M funding secured as PI, more than 50 papers in peer-reviewed journals and conferences, 4 paper awards and 10 invited/keynote talks. Nan receives the prestigious Marie Curie International Fellowship (2018-2020), Irish Research Council Fellowship (2020), and Royal Academy of Engineering Industrial Fellowship (2023-2024). He holds two visiting appointments at UCD (2021-2026) and Osaka University (2022-2024).
PgCAP in Higher Education (2023), Edinburgh
PhD in Precision Engineering (2017), Cranfield
MSc in Mechanical Manufacturing (2013), Harbin;
BSc in Mechanical Engineering (2011), Harbin;
2018 Professional Certificate of Entrepreneurial Educators, UCD, National University of Ireland;
2018 International Scientific Committee member of European Society for Precision Engineering and Nanotechnology (EUSPEN);
2019 Member of International Academy of Engineering and Technology (AET);
2021 Research Affiliate of International Academy of Production Engineering (CIRP);
2021 Member of EPSRC Early Career Forum in Manufacturing Research;
2022 Fellow of Royal Society for Art, Manufacture and Commerce (RSA);
2023 Fellow of Higher Education Academy (HEA)
1. Conceptual Design for Mechanical Engineers 3 (Course Organiser);
2. BEng Mechanical Engineering Project 4;
3. Digital Manufacturing 5;
4. Metrology in MSc Digital Design and Manufacture (Course Organiser)
5. MEng Mechanical Engineering Project 5
In the context of long-term monitoring applications, there are numerous structural states that exhibit similar behavior but cannot be generalized with a single model (whether data- or physics-based) due to the inherent time-variant nature of structural evolution (1). Addressing such scenarios necessitates methodologies with adaptable models that can capture the interdependencies between Environmental and Operational Variabilities (EOV) and Damage Sensitivity Features (DSF) at various stages of structural evolution. The challenge lies in determining when distinct structures can be considered pseudo-similar, thereby sharing the same underlying physical properties to better represent the dynamics and associated EOV dependencies (2). Similarly, the incorporation of physics-based models, with varying levels of fidelity, adds knowledge towards understanding structural changes, which is essential for incorporating interpretable constraints on DSF evolution.
In practice, there are two main Challenges: (i) Robust extraction of DSF for continuous monitoring which are insensitive to EOVs. These DSF should be interpretable during the entire evolution of the structural performance and they should be able to accommodate dimensionality and complexity reduction of their associated non-linear time-variant nature. (ii) And there is a need of developing measures to quantify and propagate uncertainty towards the estimation of future stages of the structure evolution.
Website: https://dgarciacava.github.io/
Contact: David Garcia Cava (david.garcia@ed.ac.uk)
(1) Garcia Cava D., Avedaño Valencia L. D., Movsessian A., Roberts C. and Tcherniak D., On explicit and implicit procedures to mitigate environmental and operational variabilities in data-driven structural health monitoring. Chapter in the Book on Structural Health Monitoring Based on Data Science Techniques. Structural Integrity, Springer, pp. 309-330, vol 21, Cury A., Ribeiro D., Ubertini F., Todd M.D. (eds). 2021 (DOI: doi.org/10.1007/978 − 3 − 030 − 81716 −9 15)
(2) Qu, K., Logan, A., Miller, M. and Garc´ıa Cava D., Multi-phase adaptive methodology for mitigating environmental and operational variability in slowly changing time-variant engineering structures. Mechanical Systems and Signal Processing, 229:112494 , 2025. (DOI: doi.org/10.1016/j.ymssp.2025.112494)
Minimum entry qualification- an Honours degree at 2:1 or above (or international equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Applications are particularly welcome from candidates expecting to receive a first-class degree in mechanical engineering, physics, applied mathematics or a closely related subject.
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere as well as sel-funded students.
Competition (EPSRC) funding may be available for an exceptional candidate. Link below for the further details.
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This project aims to investigate the capabilities of adaptive structures that change their geometry and mechanical properties to accommodate operational loading and damage management. The core objective of this project is to engineer an adaptive structure that adjusts to the prescribed loading conditions. This adaptation is achieved by integrating local structures that accommodate stiffness variations along the global structure.
The local structures will change their geometry and shape in response to the applied loads, resulting in emergent properties in the main global structure (1). Analytical modeling of the local-structures will provide understanding and control for stiffness tailoring, which will translate into desirable mechanical properties in the main structure. The connection between global properties and local-structure geometry changes aims to be achieved by understanding the relationships between geometric parameters and vibration response.
The geometric nonlinearity induced by the local-structures may cause amplitude-dependent nonlinear dynamic responses (2). Thus, understanding the underlying physics in the coupling between local and global structures, along with the vibration response of the global structure, aims to facilitate feedback to passively control the mechanical properties of the structure. Consequently, this dynamic response leads to continuous shape and geometry modifications within the structure, ultimately enhancing its capacity to accommodate specified loading requirements more effectively. The adaptive structures will benefit operability by maximising structural capacity during service. Interests on: Structural mechanics and dynamics, Stochastic modelling and Uncertainty quantification.
Website: https://dgarciacava.github.io/
Contact: David Garcia Cava (david.garcia@ed.ac.uk)(1) Sundararaman, V., O’Donnell, M.P., Chenchiah, I.V., Clancy, G. and Weaver, P.M., 2023. Stiffness tailoring in sinusoidal lattice structures through passive topology morphing using contact connections. Materials & Design, 226, p.111649.
(2) Zhao, B., Thomsen, H.R., Pu, X., Fang, S., Lai, Z., Van Damme, B., Bergamini, A., Chatzi, E. and Colombi, A., 2024. A nonlinear damped metamaterial: Wideband attenuation with nonlinear bandgap and modal dissipation. Mechanical Systems and Signal Processing, 208, p.111079.
Minimum entry qualification- an Honours degree at 2:1 or above (or international equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Applications are particularly welcome from candidates expecting to receive a first-class degree in mechanical engineering, physics, applied mathematics or a closely related subject.
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere as well as self-funded students. *Competition (EPSRC) funding may be available for an exceptional candidate but please note you must be a UK student or an EU student who has lived in the UK 3+ years
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We invite applications for a PhD position to advance flue gas cleaning technologies from industrial emissions, particularly in Energy-from-Waste (EfW) power plants. The EfW solutions incorporate advanced flue gas cleaning systems that notably reduce landfill waste, lower emissions, generate energy, and assist in material recovery, thus supporting a sustainable closed-loop circular economy. The appointed researcher will contribute to the EPSRC funded M2CLEAN project, which intends to thoroughly investigate the complex dynamics of particle interactions at various scales, including those between solid particles, liquid droplets, and gas phases. The research work will tackle key operational challenges such as regulating temperature and humidity, alongside optimising particle size and distribution to increase emission removal efficiency. The primary goal of this project is to create experimentally informed predictive models that detail these inter-particle interactions, enhancing understanding and efficiency of semi-dry flue gas cleaning systems.
The current Ph.D. position focuses on the experimental investigation of particle-droplet interactions under acoustic levitation. The project aims to foster a new understanding of the spatiotemporal scales and the controlling parameters of particles when subjected to varying temperature and humidity. The project offers hands-on experience in setting up multiphase experiments and using several state-of-the-art optical diagnostics, such as high-speed shadowgraphy, Particle Image Velocimetry (PIV), and Planar-Induced Fluorescence (PLIF).
The ideal candidate will be interested in fluid dynamics, reacting flows, and laser diagnostics and have programming experience in at least one language (e.g., Python, MATLAB, etc.). The selection process considers the comprehensive strength of the entire application, including the academic qualifications, personal statement, CV, and references. Ideally, candidates should have a strong background in fluid dynamics and the development of experimental methodologies.
The project includes close collaboration with TU Darmstadt, Germany and industrial partner Kanadevia INOVA, Zurich, Switzerland. This collaborative environment will offer the opportunity to learn from and contribute to a diverse team of experts.
The intended PhD start date is in beginning of April 2026. If a suitable candidate is found, this position may close earlier than the closing date.
Informal inquiries may be addressed to Dr Khushboo Pandey at kpandey@ed.ac.uk.
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline. The candidate should have a master’s degree in either Physics or Engineering.
Further information on English language requirements for EU/Overseas applicants.
Tuition Fees and stipend are avilable for Home/EU and International students.
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