Optimal energy transfer in nonlinear systems

About the project

Design and understanding of nonlinear models is required for optimal performance and for accurate reproduction of dynamical behavior. One of the intriguing phenomenona in nonlinear systems is Targeted Energy Transfer (TET), where the goal is to transfer energy within a nonlinear system between subsystems.

The theory of linear dynamical systems is well-developed in the context of tuned mass dampers, in contrast to nonlinear systems, where often individual nonlinear mechanisms are considered with a model-specific. A majority of these approaches rely on perturbation theory, for weak nonlinearities over finite time.

TET is a nonlinear alternative that can also be generalised to multi-degree-of-freedom systems (MDOF). In the traditional TET formulation, the nonlinearities are given for all degrees of freedom, for instance of cubic order, and the energy-transfer subsystem is tuned to an optimal set of parameters (coefficients) to mitigate undesirable dynamics of the primary system.

Based on the existing publications this leaves the following gaps in the TET’s state of the art:

To address these gaps the project will combine machine learning optimization algorithms, such as Surrogate Optimization (SO) and methods in nonlinear dynamics.

• The typical approximate methods used to analyse TET are not applicable to fully nonlinear effects and are infeasible for necessary MDOF systems.
• A given nonlinearity in the system may not be optimal, and traditional methods do not extend to exploring the full range of potential nonlinear mechanisms.
• There is no established framework for fast identification of the optimal nonlinear system for efficient TET from purely experimental data.

Dr Daniil Yurchenko

• Nonlinear and Stochastic Dynamics; Stability and Bifurcation; Energy Harvesting; Nonlinear Vibration Mitigation;

Professor Tim Waters

• Wave propagation in structures
• Vibration control
• Vibration based structural health monitoring

Entry requirements

This project will require a student with:

• a UK 2:1 honours degree, or its international equivalent, in mechanical engineering or applied mathematics degree
• excellent programming skills in one of the engineering languages (Matlab, Python, Julia) to explore nonlinear dynamical systems and optimisation algorithms
• great mathematical skills
• excellent communication skills

Fees and funding

For UK students, tuition fees and a stipend of £16,062 tax-free each year for up to 3.5 years

How to apply

Apply now

You need to:

Applications should include:

• choose programme type (Research), 2022/23, Faculty of Physical Sciences and Engineering
• choose PhD Engineering & Environment (Full time)
• add name of the supervisor (Doctor Daniil Yurchenko) in section 2
• research proposal
• your cv (resumé)
• 2 reference letters
• degree transcripts to date

Faculty of engineering and physical sciences

Email: feps-pgr-apply@soton.ac.uk

If you wish to discuss any details of the project informally, please contact Doctor Daniil Yurchenko, Dynamics Research Group. Email: d.yurchenko@soton.ac.uk Tel: +44 (0) 2380 59406

Research areas