A PHYSICS-INFORMED NEURAL NETWORK SURROGATE MODEL FOR MISTUNED BLADED DISKS DYNAMICS

Background:The inevitable mistuning of a bladed disk breaks the cyclic symmetry of the structure and can cause vibration localization and amplitude magnification, thereby seriously reducing the structural reliability. Although numerical simulations based on high-fidelity finite element models can precisely calculate the response under some specific mistuning patterns, repeated calculations of random mistuning cases still bring a significant computational burden, even with ad-hoc reduced-order modeling technologies. Objective:In this work, we aim to capture the dynamics of randomly mistuned bladed disks using data-driven surrogate models. Methods:To do that, a physics-informed surrogate model of a designed structural neural network (DS) is proposed, where a "shift" feature from the cyclic symmetry in physics is taken into account for data augmentation. The finite element model of a dummy bladed disk with blade stiffness mistuning is established and taken as an example to test the effectiveness of the surrogate model. Under different training set sizes and excited modes, the performance of the proposed model is compared systematically with a fully connected structural neural network model (FC) and a state-of-the-art deep neural network framework (MS-DNN). Results:The DS model can accurately predict blade displacement for blade-dominated modes with only 600 training data. The R² value of the displacement response amplitude reaches 0.99956, and the error of the amplitude magnification factor is only 0.68%. The DS model converges faster and has higher prediction accuracy than the FC model. The DS model is superior to the MS-DNN model concerning the generalization ability.