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Communication Dans Un Congrès Année : 2023

Computational validation of a robust design methodology using probabilistic learning (PLoM) for the detuning optimization of nonlinear bladed-disks

Résumé

Technologically, an interesting way for reducing the dynamical amplifications induced by the blade mistuning of turbine engines is to use a detuning strategy. This means that a few different blade designs (in general two) are used in order to define a blades pattern that constitutes a detuned configuration of the full bladed-disk. The objective of this research is to validate a robust design methodology allowing for the detuning optimization in presence of random mistuning and in finite displacements. This latter consideration is justified by a green aviation context that involves lighter blades with thinner profiles. The main difficulty is related to the huge number of possible detuning configurations that exponentially increases with the cyclic order of the structure. In [1,2], many research efforts have been done to construct a high-fidelity nonlinear stochastic computational model (HFCM). In the mistuning context, the outputs characterize frequency peaks of the most unfavorable blade and the corresponding realizations usually present a large scatter. As a consequence, a careful attention has to be paid to the construction of the cost function in order to be representative of the vibrational behavior. Such cost function is parameterized with respect to a discrete parameter representing a given detuned configuration. It is then computed at a limited number of points during the training set, yielding an available small data training set. The main idea is then to construct a continuous approximation of this cost function, based on the use of the probabilistic learning PLoM tool [3], and that will be used in the learning step. Several difficulties inherent to the definition of the cost function (weak contrast and numerous local minima) require to reformulate the definition of the optimum. A numerical validation is proposed, using an available full data basis of a bladed-disk structure with 12 blades and for which a very few improving detuning configurations exist. It is shown that the proposed method is able to capture most of these optima using a small training set that only contains non optimal detuning configurations. References [1] A. Picou, E. Capiez-Lernout, C. Soize, M. Mbaye, Robust dynamic analysis of detuned-mistuned rotating bladed disks with geometric nonlinearities, Computational Mechanics 65 (3) (2020) 711–730. [2] E. Capiez-Lernout, C. Soize, Nonlinear stochastic dynamics of detuned bladed-disks with uncertain mistuning and detuning optimization using a probabilistic machine learning tool, International Journal of Non-Linear Mechanics, Elsevier, 2022, 143, pp.104023. [3] C. Soize, R. Ghanem, Data-driven probability concentration and sampling on manifold, Journal of Computational Physics, 321 (2016) 242-258.
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Dates et versions

hal-04146437 , version 1 (30-06-2023)

Identifiants

  • HAL Id : hal-04146437 , version 1

Citer

Evangéline Capiez-Lernout, Christian Soize. Computational validation of a robust design methodology using probabilistic learning (PLoM) for the detuning optimization of nonlinear bladed-disks. XII International Conference on Structural Dynamics, EURODYN 2023, Jul 2023, Delft, Netherlands. ⟨hal-04146437⟩
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