This project pertains to the verification and validation (V&V) techniques applied to the simulation of problems of high industrial relevance (additive manufacturing) to be solved with state-of-the-art numerical methodologies (reduced order models).
V&V is an essential approach to modern computational mechanics for industrial problems, aiming at certifying that 1) the model corresponds to the physical reality (Validation) and 2) the numerical solution is accurate enough to meet the requirement of the end user (Verification).
Fast, reliable and robust simulations are a major goal for industrial competitiveness. Once feasible computations for the desired problem are available, verification and validation (V&V) techniques assess reliability of both model & numerical approximation. However, both input data and measurements used for validation are most times subject to uncertainties that have to be also accounted for in the model. The reduced order solutions in multiparametric setups allow resolving a large number of stochastic dimensions describing uncertainty. The aim of this project is to develop a methodology able to joint the expertise of the academic partners on V&V and parameterized reduced order models. The longstanding and solid experience of the industrial partner guarantees relevant contributions where uncertainty is at the heart of the simulation. The parameterized generalized solutions (computational vademecums) obtained with the corresponding accuracy for both models and methods allow efficiently applying uncertainty quantification techniques.
PhD candidate: Simona Vermiglio (Universitat Politècnica de Catalunya, ESI, École Centrale de Nantes)
Academic supervisors: Prof. P. Díez, Dr. A. García-González (UPC), Prof. F. Chinesta, Dr. D. Borzacchiello (ECN)
Industrial supervisor: Dr. J.-L. Duval, Dr. J.-C. Kedzia (ESI)
