Vision and ambition of the project

Building trusted virtual models for structures subject to high dynamic loads is a process we call “dynamic virtualisation”.


The aim of this innovative training network is to enable a new generation of Early Stage Researchers (ESRs) to face the urgent challenge of how to model the performance of engineering structures that operate in dynamic environments.

Overview of the DyVirt Project (PDF, 1.79MB)

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It is intended that all of the ESRs receiving training through this network will

  • obtain a PhD from an internationally-recognised university
  • gain crucial skills in developing accurate models of dynamic structures much needed in industry
  • gain experience of applying their research skills in non-academic organisations
  • receive training in transferable skills such as commercialisation and communication
Members of the Dyvirt Project standing in a line

To achieve this research aim DyVirt will:

  1. Create new methods of dynamic verification and validation for applications with significant nonlinear effects, including friction in joints, large deformations, and hysteretic behaviour.
  2. Develop hierarchies of models where test-validated sub-models can be assembled into full-structure models such that the full model can be considered validated (fit-for-purpose) with quantifiable confidence.
  3. Develop new uncertainty quantification and propagation (UQP) tools powerful enough to complement the new state-of-the-art techniques for V&V and model assembly.
  4. Develop algorithms for characterisation and prediction of the expected envelope of environmental and operational variations for a structure under real-life conditions
  5. Create enhanced and accelerated test protocols and forms of modelling that can capture real effects such as fatigue loading, but require less time and effort.
DyVirt logo with EU flag

Dynamic Virtualisation: Modelling performance of engineering structures

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 764547.