Offshore Wind Turbine Vibration Suppression and Load Alleviation

Supervisors: Professor Jason Zheng Jiang (z.jiang@bristol.ac.uk), Dr Tom Hill (tom.hill@bristol.ac.uk), Professor Simon Neild (simon.neild@bristol.ac.uk)

This PhD project will focus on using advanced control methods (passive, semi-active or active) for offshore wind turbine (OWT) vibration suppression and load alleviation. The current trends of increasing turbine size and locating OWTs to deeper water result in important vibration issues to be solved. To this end, this project will develop advanced control methodologies to suppress OWT vibrations, so as to extend service life and reduce Levelized Energy Cost (LEC). The focus of this project will be on mitigating vibrations in one or more of the following OWT components, gear box, nacelle, blade, tower as well as floating wind turbine platforms. Both numerical and experimental work will be carried out during the project. Mathematical computing software Matlab, and industrial software FAST and/or Romax wind will be used. The PhD student will not only have the opportunity to build a wide range of skills including vibration control theory, mechanical and aerodynamic modelling & simulation, but also gain experience of working with relevant industrial partners.

NVH Performance Enhancement for Electric Vehicles

Supervisors: Professor Jason Zheng Jiang (z.jiang@bristol.ac.uk), Professor Simon Neild (simon.neild@bristol.ac.uk)

For electric vehicles, new challenges such as low masking noise and non-standard driveline vibration modes arise, which can potentially be solved by using high performance vibration absorption systems. This project aims to effectively reduce the noise transmitted to the chassis, and as a result enhance the noise, vibration and harshness (NVH) performance. Through the project, the PhD student will build solid skills in wide range of dynamics and control theory, mechanical modelling & simulation. We also anticipate the student will work together with industrial partners (vibration absorber manufacturers and OEM companies). This project is part of a wider body of work under the Digitwin project (http://digitwin.ac.uk/) across 6 UK institutions (University of Sheffield, Bristol, Cambridge, Liverpool, Southampton & Swansea). The student will have the opportunity to give presentations at regular Digitwin project meetings.

Funding: EPSRC Doctoral Training Partnership (DTP) is available for this project.

Novel liquid-based vibration control devices

Supervisor: Dr Brano Titurus (Brano.Titurus@bristol.ac.uk)

This research aims to develop, theoretically and experimentally, a new class of controllable liquid-based devices for vibration mitigation in aerospace applications.

Titurus, Branislav. Generalized Liquid-Based Damping Device for Passive Vibration Control. AIAA Journal 56, no. 10 (2018): 4134-4145.

Damping for aeroelastic tailoring in wings and blades

Supervisor: Dr Brano Titurus (Brano.Titurus@bristol.ac.uk)

Increasing the flight stability margins and enabling higher performance lifting surfaces through embedded dynamic damping is the main focus of this project.

Szczyglowski, Christopher P., Simon Neild, Brano Titurus, Jason Z. Jiang, and Etienne Coetzee. Passive Gust Load Alleviation In a Truss-Braced Wing Using an Inerter-Based Device. In 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, p. 1958. 2018.

Titurus, Branislav. Vibration control in a helicopter with semi-active hydraulic lag dampers. Journal of Guidance, Control, and Dynamics 36, no. 2 (2013): 577-588.

A combined experimental and numerical investigation on nonlinear whirl flutter

Supervisors: Dr Djamel Rezgui (Djamel.rezgui@bristol.ac.uk) and Dr Brano Titurus (brano.titurus@bristol.ac.uk)

The dynamic interaction between rotating and stationary structures in the presence of nonlinearity and uncertainty (structural, material, aerodynamic, etc.)  is a complex problem. In rotorcraft, one of the current problem is the poor prediction of the whirl flutter instabilities for tiltrotor [1] and novel multi-rotor configurations. This project aims to investigate the nonlinear dynamics of the coupled rotating-stationary system for the case of the tiltrotor aircraft, through a combined experimental and numerical (bifurcation theory) approach.

[1] Mair C, Rezgui D, Titurus B, Nonlinear stability analysis of whirl flutter in a tiltrotor rotor-nacelle system, ERF 2017 - 43rd European Rotorcraft Forum, 12-15 September 2017, Milan, Italy

Design for performance and dynamics of novel electrical Rotary Wing UAVs

Supervisors: Dr Djamel Rezgui (Djamel.Rezgui@bristol.ac.uk) and Professor Dorian Jones (Dorian.Jones@bristol.ac.uk)

Drones and Unmanned Aircraft vehicles (UAVs) are now extensively considered by major manufactures and operators in a wide range of civilian and military applications. Of a particular interest are those of multi-rotor configurations, which are considered as the future mobility vehicles as in the “Air Taxi” or “Personal Flying Car” concepts. This project aims to investigate the complex design strategies and tools of the future electric multi-rotor UAV’s, from performance and dynamics aspects using advanced efficient modelling and analysis tools.

Design and Modelling of Electromagnetic Vibration Suppression Devices

Supervisors: Professor Simon Neild (Simon.Neild@bristol.ac.uk) and Professor Jason Jiang (z.jiang@bristol.ac.uk)

This project will focus on vibration suppression via electromagnetic devices. We will use a general passive mechanical controller to replace the conventional spring/damper system and optimise it to show the potential benefits. An electromagnetic device will then be built and tested in the context of the structure it will be deployed in. This will be achieved using hybrid testing, where the structure is modelled and the device is physically tested with real-time coupling between the two to emulate the dynamics of the full system.

Model updating for nonlinear dynamic structures

Supervisors: Dr Tom Hill (tom.hill@bristol.ac.uk) and Professor Simon Neild (Simon.Neild@bristol.ac.uk)

Engineers often model complex mechanical structures using Finite-Element (FE) models. However, due to uncertainties in the design, modelling and manufacturing processes, the results of these FE models do not always match experimental measurements from the real structures. To correct for this, certain features of these FE models can be updated, using experimental data, so that they better reflect the true structure. For traditional structures, which exhibit linear behaviour, this model updating process is well-established; however, many modern, high-performance structures exhibit nonlinear behaviour. Existing model updating processes cannot account for nonlinearity, creating a significant bottleneck that prevents engineers from improving the performance of modern structures and operating beyond the linear regime.

This PhD project will investigate new methods that allow FE models to be tuned, so that their dynamic behaviour matches the nonlinear phenomena seen in real structures. These methods will be developed and tested using relatively simple structures, so a detailed knowledge of FE software is not required; instead, it will require a strong grasp of dynamics and numerical analysis using software such as Matlab. This project will be associated with a larger collaboration “Digital Twins for Improved Dynamic Design” – http://digitwin.ac.uk/. This £5M grant brings together six leading universities and ten major industrial partners and will provide the opportunity to regularly meet and work with these collaborators.

Numerical modelling of impact and friction

Supervisors: Dr Robert Szalai (r.szalai@bristol.ac.uk) and Professor Alan Champneys

This project is part of a research theme investigating dynamic behaviour of frictional contact. The project will focus on numerical simulation of frictional contact. Simulation of frictional contact is problematic, because most methods predict non-unique solutions. These numerical methods are also badly conditioned due to the multiple time and length-scales present in the problem. In contrast, theory tells us that the continuum contact problem has unique solutions. This means that there is room for improvement. A recent result [1] addresses this issue in an analytical setting and for point contact only. There is now a rigorous model reduction technique that retains uniqueness and other essential qualitative features of continuum contact problems. The task is therefore to extend this new method so that it can be implemented in numerical schemes. 

The main task is to adapt a finite element, boundary element or collocation method using the rigorous model reduction technique [1]. The project does not aim to implement the method in a full-featured finite element software, instead we will take a semi-analytical approach and focus on simple examples, initially. We will start with a classical problem, when an elastic rod hits a rigid surface so that impact and friction needs to be considered simultaneously. (This is one representation of Painleve's paradox.) Further tasks involve extending the method to surface-surface contact to study how frictional contact ruptures. 

[1] R. Szalai, Model reduction of infinite dimensional piecewise-smooth systems, https://arxiv.org/abs/1509.08040 [2] O. Ben-David, G. Cohen, J. Fineberg The Dynamics of the Onset of Frictional Slip, Science, 330(6001), pp. 211-214, (2010)

Identification of reduced models of mechanical systems from vibration data

Supervisor: Dr Robert Szalai (r.szalai@bristol.ac.uk)

In order to gain understanding of the dynamics of a mechanical system it is useful to have a reduced order model. The best reduced models are exact: they describe the motion exactly for a specific set of initial conditions. For other initial conditions the dynamics will exponentially tend to the solution described by the reduced model. Finding such reduced models is equivalent to finding invariant manifolds with certain properties. In a recent paper [1] we have shown that it is possible to find such unique reduced models from experimental data. We have used the most general model that was constructed using a least squares method. This model then was analysed using analytical techniques. It is however desirable that the model being identified has low number of parameters, which is possible to achieve if the fitting and analysis of the model is carried out in one step. The task in the PhD project is to develop this method with the minimum number of parameters required to obtain a reduced model. In order to test the method, we will use various experimental data and data from finite element simulations to test the method.

 [1] R. Szalai, D Ehrhardt, G. Haller. Nonlinear model identification and spectral submanifolds for multi-degree-of-freedom mechanical vibrations. Proc. R. Soc. A 2017 473 20160759   

Computational flight mechanics - analysis of control law sensitivity for aircraft control and active load alleviation

Supervisors: Professor Mark Lowenberg (M.Lowenberg@bristol.ac.uk) and Professor Simon Neild (Simon.Neild@bristol.ac.uk)

Aircraft controllers are typically designed using linear techniques at a number of operating points, and combined into a gain-scheduled (nonlinear) controller.  The design requirements are usually based on linear design techniques and include frequency response criteria: the assumptions involved in applying these in the presence of significant nonlinearity and uncertainty (such as aircraft flying near their envelope limits) may be questionable.  This project aims to exploit numerical continuation techniques to explore this problem, including periodically-forcing the system to extract nonlinear frequency responses.  The study will consider sensitivity of the periodically-forced system stability to perturbation/uncertainty and aims to provide a new perspective on the stability and robustness of control law design where the dynamics is especially nonlinear.

The initial focus will be on rigid aircraft models under nominal and off-nominal design conditions (such as in the vicinity of upset, loss-of-control), with an extension to incorporate aeroelastic influences and active load control if feasible.

Funding restrictions: none (note: studentship funding needs to be sought for this project).

Experimental flight dynamics – multi-DOF dynamic wind tunnel testing of aeroelastic models

Supervisors: Professor Mark Lowenberg (M.Lowenberg@bristol.ac.uk), Professor Simon Neild (Simon.Neild@bristol.ac.uk) and Dr Djamel Rezgui (Djamel.Rezgui@bristol.ac.uk)

The 5-DOF ‘manoeuvre rig’ has been developed at the University of Bristol to investigate the aerodynamics and flight mechanics of model aircraft motions, driven by on-board control surfaces or externally via the rig, that involve nonlinear and unsteady flow phenomena.  To date, only rigid models have been tested.  This PhD will extend the use of the rig to complement previous work on modelling, analysis and testing of flexible wings that exhibit nonlinear behaviour.  The objective is to develop a new experimental approach to the study of dynamics of coupled wing-plus-airframe aeroelastics in the presence of nonlinearity.  It will incorporate multi-degree-of-freedom dynamic testing with compensation of rig effects to allow physical simulation of highly flexible air vehicles in flight, and will also involve modelling and numerical analysis of the coupled systems.

Funding restrictions: none (note: studentship funding needs to be sought for this project).

Hybrid modelling and nonlinear dynamics in aircraft design

Supervisors: Professor David Barton (david.barton@bristol.ac.uk), Professor Mark Lowenberg (m.lowenberg@bristol.ac.uk)

There is a drive towards high-efficiency and high-performance aircraft. This requires lighter and more fuel-efficient structural designs, which are often more flexible than traditional rigid designs. This flexibility can result in disastrous nonlinear behaviour; for example, the destruction of the NASA Helios prototype. This project seeks to develop new approaches to nonlinear behaviour in aircraft and so enable a new generation of low-carbon aircraft.

Within our well-equipped test facilities at the University of Bristol, Prof Mark Lowenberg and colleagues have developed a manoeuvre rig based on a scale model of a Hawk trainer aircraft. This 5 degree-of-freedom model provides an ideal test bed for investigating nonlinear aerodynamic behaviour. To explore the complex nonlinear dynamics, Prof David Barton has developed a range of experimental techniques known as Control-Based Continuation (CBC) that can track the onset of instabilities as system parameters change (e.g., the onset of flutter at a critical airspeed, Lee et al, 2023). As such, CBC can be used to investigate behaviour in the physical system that would have previously been out of reach. The combination of the manoeuvre rig and CBC opens up many possibilities for exploitation.

The data generated from these experiments is ideal for building new models and can be used in combination with scientific machine learning to create hybrid models: high-fidelity models that combine physics-based modelling with data-driven approaches. These models will then enable further design work to either mitigate or take advantage of the nonlinear behaviour in the experiment.

The overall aims are threefold:

• To generate industrially-relevant insights from the manoeuvre rig.
• To extend CBC to multi-degree-of-freedom systems, with application to other engineered structures.
• To develop a hybrid modelling approach for aerospace systems that combines machine learning with physics-based models.

See other projects with Professor Barton.

Funding restrictions: none (note: studentship funding needs to be sought for this project).

Online learning for tactile robotics

Supervisors: Professor David Barton (david.barton@bristol.ac.uk), Professor Nathan Lepora (n.lepora@bristol.ac.uk)

Machine learning (ML) equips robots with the ability to learn from and adapt to new environments, enhancing autonomy and efficiency in complex tasks. However, many ML approaches rely on extensive data for training. This heavy reliance on large volumes of data can hamstring a robot’s ability to function in dynamic real-world conditions—imagine a robotic assistant in a home struggling to recognize new types of objects because it was trained on a limited set of household items. Such data dependency limits the robots’ effectiveness in diverse real-world applications. We seek to overcome this problem in the context of tactile robotics by exploiting generative online learning, enabling robots to learn ‘in the moment’ and adapt swiftly to the unpredictability of real-world tasks.

Online learning is where new data is continuously integrated into a model as a task is being performed. As such, the performance of the robot increases over time. Joint with Prof Nathan Lepora, I have previously demonstrated a generative online learning approach (Learning to live life on the edge) that uses predictions of the tactile sensor output to track the edges of objects. This has been implemented on a robot arm and a quadruped robot.

Significant improvements are needed enable this generative online learning approach to be used on both a broader suite of tasks (not just edge following) and within a higher dimensional space (not just a plane). This will require non-trivial generalisation of the current machine learning approach (a Gaussian Process-Latent Variable Model) and exploration of other computationally efficient generative methods. Insights into the geometry of the tasks of interest are likely to be key to creating an effective approach.

This approach to online learning has the potential to greatly expand the use of tactile sensors and enable them to be used in novel contexts, bringing us closer to building robots that can both touch and feel, and interact safely with the environment around them.

See other projects with Professor Barton.

Funding restrictions: none (note: studentship funding needs to be sought for this project).

Surrogate modelling and machine learning for electrical power systems

Supervisors: Professor David Barton (david.barton@bristol.ac.uk), Dr Ian Laird

Designing new devices, particularly in electrical power conversion for renewable energy, is often challenging because of constraints on mass, volume, and cost. Designers must innovate within these boundaries, making trade-offs to meet specifications without compromising performance. This PhD project will employ surrogate modelling and machine learning to improve the efficiency of design processes.

Power electronic device design involves choosing from a limited library of existing parts as well as dealing with behaviors across timescales, from nano-second switching to bulk behaviour over several seconds. Commercial simulation tools, such as Plex, offer accuracy but lack the computational speed needed for quick iterative design. Collaborating with Dr. Ian Laird, who brings extensive experience in power system design optimisation, this project aims to provide the fundamental research developments needed to create rapid design tools.

The project will focus on developing surrogate models using reservoir computing techniques to accelerate simulations for components like modular multi-level converters (MMCs), crucial in renewable energy systems. These models will enable faster design iteration, optimising systems to meet specific application constraints, such as those for offshore wind turbines. Key challenges include modelling the discrete switching of electrical components and the wide range of dynamic timescales. Addressing these challenges is essential for capturing sudden changes and complex interactions, with potential applications extending beyond power systems to fields like synthetic biology and electromagnetic sensing.

The anticipated result is a tool for rapidly designing optimized electrical power systems, streamlining resource use and cost in system deployment. The research has the potential to yield significant publications with broad impacts across multiple disciplines.

See other projects with Professor Barton.

Funding restrictions: none (note: studentship funding needs to be sought for this project).

Nonlinear aeroelastic wing benchmark evaluation and modeling

Supervisors: Professor Mark Lowenberg (m.lowenberg@bristol.ac.uk), Professor Simon Neild and Professor Jonathan Cooper

This PhD offers the opportunity to participate in research that the University has been carrying out with industry on aeroelastic behaviour of high aspect ratio wings. It will involve the comparison of computational modeling methods - developed at the University to account for the geometric and other nonlinear effects in highly flexible wings - and experimental results. The latter were obtained from a unique test campaign recently conducted in an industrial wind tunnel. Please see here for further information: phd-benchmark-wing-evaluation-and-modeling (PDF, 88kB)