Division of Mechanical Engineering, Robotics and Design
The Division of Mechanical Engineering – research impact
The Division of Mechanical Engineering, Robotics and Design combines a wide array of engineering activities in the areas of control, fire safety engineering, neuromechanics of locomotion, nuclear reactor modelling, computational mechanics, renewable energy, heat transfer and refrigeration, multi-bodied systems dynamics, soft robotics and design.
The Division has around 30 research active members of staff with international reputation operating at the forefront of science. The Division is running numerous competitively funded projects and has established research centres on the pursuit to develop tomorrow’s technologies.
The Centre for Advanced Robotics @ Queen Mary (ARQ)
QMUL has made a significant investment in the development of robotics research activities and central to this initiative was the establishment of the Centre for Advanced Robotics @ Queen Mary (ARQ). The research within the Centre encompasses a wide range of activities in the general field of intelligent robotics. The team has a long-standing expertise in developing sensors (e.g measuring forces, positions) and embedding those with robot effectors such as robot hands and manipulators. A range of sensor hardware solutions have been developed over the years and integrated with many devices, including robot hands for in-hand manipulation of everyday objects (EU Project HANDLE), robotic devices for minimally invasive surgery (EU Project STIFF-FLOP), haptic probes for intraoperative diagnosis of soft tissue (Project funded by the Guy’s and St Thomas Trust), multi-robot arm systems for ultrasound monitoring of the human foetus (in the framework of project iFIND at St Thomas Hospital, King’s College London funded by Welcome Trust/EPSRC), development of miniaturised sensing and robotics systems for cardiac catheterisation (in collaboration with the Medical Engineering Centre (MEC) at St Thomas Hospital, King’s College London funded by Wellcome Trust/EPSRC) and humanoid robots that can interact, communicate and collaborate with people (EU Project Poeticon++).
The centre also focuses on the development of novel methodologies for advanced sensor signal interpretation and classification, and human robot interaction. Spanning a range of techniques in artificial neural networks and machine learning, including deep learning (e.g. deep variational autoencoders), probabilistic reasoning (e.g. Bayesian Networks) and estimation (e.g. sequential Monte Carlo methods), As well as more traditional model-based approaches, the team is developing methods to allow humans to experience the sensation of robots and to interact through communication (both verbal and non-verbal) and collaboration. For example, we have developed methodologies for humans to experience the touching of objects experienced by a tele-operated robot.
Control Methods for Engineering
The Control group is a leading UK university group in developing both the theory and application of methods for the optimal control of systems, particularly for those used in the sustainable energy engineering sector. The group’s interests spans the themes of battery power management, marine energy, robotics and hybrid dynamics. Through EPRSC funding (EP/P023002/1) we are developing the methods for optimising the control of marine vessels (e.g. floating platforms, ships) during their launch and recovery from the sea which optimally determines both the sequence of operations and their time of execution. In addition the methods provide confidence measures and advice to operators to ultimately optimise the whole control of operations in order to increase efficiency and to prolong the service life of the vessels. The group’s researches also form part of a select group working on phase one of the Wave Energy Scotland (WES) project to design (in collaboration with the University of Exeter and Mocean Energy Ltd) a hierarchical and adaptive optimal control framework that can maximise wave energy conversion whilst ensuring safe operation for a varying range of sea states. Our group has also been awarded a Royal Society-Newton Advanced Fellowship “Control of Floating Wave Energy Converters with Mooring Systems” to collaborate with the National Natural Science Foundation of China to support and develop the next generation of internally recognised researchers in marine energy engineering. We also run a Royal Society International Exchange Scheme on “Fast Adaptive Optimal Control with Application to Sustainable Energy Systems” to collaborate with
Chinese researchers to resolve fundamental problems facing marine energy engineering. One Marie-Curie Individual Fellowship is awarded to the group to work on hierarchical optimal energy management of electric vehicles (EVs) by developing a novel computationally efficient optimal control framework incorporating transportation information and drivers’ habits.
Computational Reactor Physics
The division has strong research activities in developing the UK’s next generation of models for simulating and predicting the operation of nuclear reactor cores, covering the fields of neutron transport, thermal hydraulics, fluid-solid interactions, and heat transfer within solids and fluids. Underpinned by EPSRC funding (EP/M022684/2) we are developing models to not only predict behaviour of reactor cores, but also to quantify the uncertainty in the results due to any unknowns or imprecise measurements upon which our model calculations are based. We collaborate with researchers based at Imperial College to develop the FETCH model which is currently being incorporated within the WOOD ANSWERS software which is the UK’s main reactor simulation software used extensively by the nuclear industry. Our members work with the MOD to develop fast reactor physics model that provides accurate but real-time simulations of cores, and these models can be used to train operators and explore a range operating scenarios quickly. Our members also participate to the OECD-NEA Expert Group on Multi-physics Experimental Data, Benchmarks and Validation (EGMPEBV) setup to guide the next generation of nuclear experiments for validating current and future multi-physic codes for nuclear engineering.