Our research group is focused on the design of large-scale adaptive structures and systems able to change their properties and/or functionality over time to improve performance, reliability, and efficiency. 

As machines are becoming more agile, more autonomous and more connected, they need to adapt to their environment and operational conditions. Structures, as the core of every machine and mechanical system, can be designed in novel ways utilizing responsiveness and shape adaptation. Adaptability is achieved by introducing smart (meta)materials and /or system integration. We work on advanced design frameworks that integrate analytical and numerical modeling, data-based simulations, design optimization methods, and experimental validation.

To enhance the adaptability of transport machines for goods handling we explore metastructures with tailored properties for shape morphing, wear reduction or energy absorption. Parametric and generative design as well as machine-learning techniques are applied for design exploration and optimization. Topology optimization and shape optimization approaches are integrated with nonlinear behaviour of structures. Uncovering the potential of smart materials into the design of adaptive machines requires understanding the scaling up effects from property side as well as from functionality perspective. The developments in the additive manufacturing domain can support the applicability of tailorable properties in large-scale structures. 

For the structural analysis of transportation equipment we use Finite Element Method (FEA) and Multi Body Dynamics (MBD). When coupled, they are used to study the influences of the different loads by checking a number of design criteria such as: stress levels, deformations, buckling, eigen frequencies and fatigue.

Research & projects