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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Mon, 26 Feb 2024 21:59:26 GMT2024-02-26T21:59:26ZStochastic Metamodel for Probability of Detection Estimation of Eddy-Current Testing Problem in Random Geometric
http://hdl.handle.net/10985/16564
Stochastic Metamodel for Probability of Detection Estimation of Eddy-Current Testing Problem in Random Geometric
ABDELLI, Djamel Eddin; NGUYEN, Thanh Hung; CLENET, Stéphane; CHERIET, Ahmed
The calculation of the Probability Of Detection (POD) in Non Destructive Eddy Current Testing requires the solution of a stochastic model requiring numerous calls of a numerical model leading to a huge computation time. To reduce this computation time, we propose in this paper to combine either the use of a stochastic metamodel and a mapping which avoids the remeshing step. The stochastic metamodel is constructed using the Least Angle Regression Method. This approach is tested on a axisymmetric problem with 6 random input paramters which shows its efficiency and its accuracy.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/165642019-01-01T00:00:00ZABDELLI, Djamel EddinNGUYEN, Thanh HungCLENET, StéphaneCHERIET, AhmedThe calculation of the Probability Of Detection (POD) in Non Destructive Eddy Current Testing requires the solution of a stochastic model requiring numerous calls of a numerical model leading to a huge computation time. To reduce this computation time, we propose in this paper to combine either the use of a stochastic metamodel and a mapping which avoids the remeshing step. The stochastic metamodel is constructed using the Least Angle Regression Method. This approach is tested on a axisymmetric problem with 6 random input paramters which shows its efficiency and its accuracy.