Welcome to MOTIVATE
MOTIVATE is an Innovation Action within the European Commission's Horizon 2020 Clean Sky 2 program under Grant Agreement No. 754660, supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 17.00064.
Our team involves Airbus Operations SAS as Topic Manager as well as University of Liverpool (the Coordinator), Empa, Dantec Dynamics GmbH and Athena Research and Innovation Center as Beneficiaries.
The goal of the project is to deploy a CEN validation method to numerical results from FE simulations of a subcomponent test based on measurements using Digital Image Correlation. Results of this test at an Airbus site as well as preliminary tests at Empa will be documented on this website. An introductory video is found on Empa TV.
The work is based on a Support Action that was conducted in the FP7 project VANESSA. We have made significant progress on methods for DIC calibration and model validation, notably on measuring the quality of data comparison. A special session at the BSSM international conference in Belfast on September 11th 2019 as well as a Knowledge Exchange workshop in Zurich on November 5th 2019 were held to present and discuss the latest relevant findings. The project results have been summarized on CORDIS.
Now if you are new to the field of validation or want to consult related documents, you will find relevant publications and presentations on this website.
Clean Sky 2 Joint Undertaking
Clean Sky is the largest European research programme developing innovative, cutting-edge technology aimed at reducing CO2, gas emissions and noise levels produced by aircraft. Funded by the EU’s Horizon 2020 programme, Clean Sky contributes to strengthening European aero-industry collaboration, global leadership and competitiveness.
The MOTIVATE project team held its final project meeting on April 30th, 2020. Due to the Corona pandemic they gathered on-line to review the project outcomes which include a novel validation flowchart; digital tools to implement it; a validation metric to quantify the extent to which a model represents an experiment; and methodologies to estimate DIC measurement uncertainty in an industrial environment.