Physics-informed machine learning approach for reduced-order modeling of integrally bladed rotors: Theory and application

• Published in: Journal of sound and vibration Vol. 596; p. 118773
• Main Authors: Kelly, Sean T., Epureanu, Bogdan I.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.02.2025
• Subjects: Blisks; Physics-informed neural networks; Reduced-order modeling; Structural dynamics; Turbomachinery; Structural dynamics; Turbomachinery; Reduced-order modeling; Physics-informed neural networks; Blisks
• ISSN: 0022-460X
• DOI: 10.1016/j.jsv.2024.118773

Integrally bladed rotors are commonly used in aircraft and rocket turbomachinery and known to exhibit complex dynamics when subject to operational loading conditions. Though nominally cyclic-symmetric structures, in practice, cyclic symmetry is destroyed due to mistuning caused by random sector-to-sector imperfections in material properties and geometry. Simulating mistuned blisk dynamics using high-fidelity models can be computationally expensive, thus, a variety of physics-based reduced-order models have been previously developed. However, these models cannot easily incorporate experimental data nor leverage potential benefits of data-driven and machine-learning-based approaches. Here, we present a novel first-of-its-kind physics-informed machine learning modeling approach that incorporates physical laws directly into a novel network architecture while maintaining a sector-level viewpoint. The approach is combined with an assembly procedure resulting in a significantly smaller linear system based on blade-alone response data, and can directly incorporate physical response data like that measured with blade tip timing and/or traveling-wave excitation. Validation is shown using a large-scale finite-element model, with multiple traveling-wave forced-response predictions and response selection cases considered. Using only as little as a single degree of freedom per sector from the blade tip, this approach shows high accuracy relative to high-fidelity simulations. •Novel reduced-order model for large-scale integrally bladed rotors.•Based on sector-level physics-informed neural networks using blade tip data only.•Maintains derived analytical relationships and known mistuned system dynamics.•Integrated with numerical techniques to improve accuracy and robustness.•Extensively validated and generalized for as little as 1 degree of freedom per blade.