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Journal Articles Mechanics & Industry Year : 2022

Uncertainty quantification for industrial design using dictionaries of reduced order models

Thomas Daniel
  • Function : Author
Fabien Casenave
Nissrine Akkari
  • Function : Author
  • PersonId : 772337
  • IdRef : 179237098
David Ryckelynck
Christian Rey

Abstract

We consider the dictionary-based ROM-net (Reduced Order Model) framework [T. Daniel, F. Casenave, N. Akkari, D. Ryckelynck, Model order reduction assisted by deep neural networks (ROMnet), Advanced modeling and Simulation in Engineering Sciences 7 (16), 2020] and summarize the underlying methodologies and their recent improvements. The main contribution of this work is the application of the complete workflow to a real-life industrial model of an elastoviscoplastic highpressure turbine blade subjected to thermal, centrifugal and pressure loadings, for the quantification of the uncertainty on dual quantities (such as the accumulated plastic strain and the stress tensor), generated by the uncertainty on the temperature loading field. The dictionary-based ROM-net computes predictions of dual quantities of interest for 1008 Monte Carlo draws of the temperature loading field in 2 hours and 48 minutes, which corresponds to a speedup greater than 600 with respect to a reference parallel solver using domain decomposition, with a relative error in the order of 2%. Another contribution of this work consists in the derivation of a meta-model to reconstruct the dual quantities of interest over the complete mesh from their values on the reduced integration points.
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Dates and versions

hal-03507112 , version 1 (03-01-2022)

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Thomas Daniel, Fabien Casenave, Nissrine Akkari, David Ryckelynck, Christian Rey. Uncertainty quantification for industrial design using dictionaries of reduced order models. Mechanics & Industry, 2022, 23 (3), ⟨10.1051/meca/2022001⟩. ⟨hal-03507112⟩
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