The Nonlinear Dynamics & Control research team is led by Dr Ludovic Renson.
Nonlinearity in mechanical structures can arise from many different sources such as large amplitude vibrations, buckling, material behaviour, fluid-structure interactions, or simply friction and free-play between components. The presence of nonlinearity poses important challenges to engineers because nonlinear systems can exhibit a wide range of complicated dynamic behaviours that are very difficult to predict and potentially disastrous. As such, the presence of nonlinearity often leads to untimely delays and additional development costs.
The group’s main activity is to develop new tools and methods to advance our understanding of nonlinearity and our ability to predict its effects on the dynamics of structures. This involves the development and exploitation of advanced computational, experimental and control techniques. The group looks at a wide range of mechanical applications, including aircraft and spacecraft structures, rotating machines, and aero-elastic systems.
Methods and tools developed by the group are not limited to mechanical systems, and we strive to apply them to other engineering disciplines and in the applied sciences. For instance, we have recently been applying computational and experimental bifurcation analysis techniques to (synthetic) biological systems (stem cells and neurons). We also exploit techniques for nonlinear modal analysis to study the behaviour of spider webs.
Ongoing projects
- Experimental bifurcation analysis in biological experiments using Control-based Continuation (CBC)
- Experimental bifurcation analysis of a rotor rig exhibiting whirl
- Experimental bifurcation analysis using Control-based Continuation (CBC)
- Hardware-in-the-loop testing of complex nonlinear mechanical structures
- Nonlinear modal analysis and control using the Koopman operator
- Parameter-dependent model reduction of highly flexible aeronautic structures
- Physics-guided, data-augmented model of a flapping beam
- Predicting self-excited oscillations using physics-guided, machine-learnt models
- Sensors inspired by vibrations in spider webs