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Surrogate models for the analysis of friction induced vibrations under uncertainty

Abstract : The automotive squeal is a noise disturbance, which has won the interest of the research and industrialists over the year. This elusive phenomenon, perceived by the vehicle purchasers as a poor-quality indicator, causes a cost which becomes more and more important for the car manufacturers, due to client’s claims. Thus, it is all the more important to propose and develop methods allowing predicting the occurring of this noise disturbance with efficiency, thanks to numerical simulations. Hence, this thesis proposes to pursue the recent works that showed the certain contributions of an integration of uncertainties into the squeal numerical simulations. The objective is to suggest a strategy of uncertainty propagation, for squeal simulations, maintaining numerical cost acceptable (especially, for pre-design phases). Several numerical methods are evaluated and improved to allow precise computations and with computational time compatible with the constraints of the industry. After positioning this thesis work with respect to the progress of the researchers working on the squeal subject, a new numerical method is proposed to improve the computation of the eigensolutions of a large quadratic eigenvalue problem. To reduce the numerical cost of such studies, three surrogate models (gaussian process, deep gaussian process and deep neural network) are studied and compared to suggest the optimal strategy in terms of methodology or model setting. The construction of the training set is a key aspect to insure the predictions of these surrogate models. A new optimisation strategy, hinging on bayesian optimisation, is proposed to efficiently target the samples of the training set, samples which are probably expensive to compute from a numerical point of view. These optimisation methods are then used to present a new uncertainty propagation method, relying on a fuzzy set modelisation.
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Submitted on : Thursday, September 15, 2022 - 3:10:03 PM
Last modification on : Saturday, September 24, 2022 - 3:58:23 AM

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  • HAL Id : tel-03778236, version 1

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Jérémy Sadet. Surrogate models for the analysis of friction induced vibrations under uncertainty. Mechanics of materials [physics.class-ph]. Université Polytechnique Hauts-de-France; Institut national des sciences appliquées Hauts-de-France, 2022. English. ⟨NNT : 2022UPHF0014⟩. ⟨tel-03778236⟩

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