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Uncertainty Management for Robust Industrial Design in Aeronautics (UMRIDA)
Start date: Oct 1, 2013, End date: Sep 30, 2016 PROJECT  FINISHED 

Virtual prototyping (VP) is a key technology for environmental friendly and cost effective design in the aircraft industry. However, the underlying analysis and simulation tools (for loads, stresses, emissions, noise), are currently applied with a unique set of input data and model variables, although realistic operating conditions are a superposition of numerous uncertainties under which the industrial products operate (uncertainties on operational conditions, on geometries resulting from manufacturing tolerances, numerical error sources and uncertain physical model parameters). Major new developments in this new scientific area of Uncertainty Management and Quantification (UM and UQ) and Robust Design methods (RDM) are needed to bridge the gap towards industrial readiness, as the treatment of uncertainties enables a rigorous management of performance engagements and associated risks. This is the main objective of the UMRIDA project, which has the following action lines:• Address major challenges in UQ and RDM to develop and apply new methods able to handle large numbers of simultaneous uncertainties, generalized geometrical uncertainties in design and analysis within a turn-around time acceptable for industrial readiness in VP systems.• To respond to the validation requirements of UQ and RDM, a new generation of database, formed by industrial challenges (provided by the industrial partners), and more basic test cases, with prescribed uncertainties, is proposed.• The methods developed will be assessed quantitatively towards the industrial objectives on this database, during the project and at two open workshops. The gained experience will be assembled in a Best Practice Guide on UQ and RDM.It is anticipated that the UMRIDA project will have a major impact on most of the EU objectives for air transport, by enabling design methods to take into account uncertainty based risk analysis.
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