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The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning

Chatterjee, T and Essien, A and Ganguli, R and Friswell, MI (2021) The stochastic aeroelastic response analysis of helicopter rotors using deep and shallow machine learning. In: Neural Computing and Applications .

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Official URL: https://doi.org/10.1007/s00521-021-06288-w


This paper addresses the influence of manufacturing variability of a helicopter rotor blade on its aeroelastic responses. An aeroelastic analysis using finite elements in spatial and temporal domains is used to compute the helicopter rotor frequencies, vibratory hub loads, power required and stability in forward flight. The novelty of the work lies in the application of advanced data-driven machine learning (ML) techniques, such as convolution neural networks (CNN), multi-layer perceptron (MLP), random forests, support vector machines and adaptive Gaussian process (GP) for capturing the nonlinear responses of these complex spatio-temporal models to develop an efficient physics-informed ML framework for stochastic rotor analysis. Thus, the work is of practical significance as (i) it accounts for manufacturing uncertainties, (ii) accurately quantifies their effects on nonlinear response of rotor blade and (iii) makes the computationally expensive simulations viable by the use of ML. A rigorous performance assessment of the aforementioned approaches is presented by demonstrating validation on the training dataset and prediction on the test dataset. The contribution of the study lies in the following findings: (i) The uncertainty in composite material and geometric properties can lead to significant variations in the rotor aeroelastic responses and thereby highlighting that the consideration of manufacturing variability in analyzing helicopter rotors is crucial for assessing their behaviour in real-life scenarios. (ii) Precisely, the substantial effect of uncertainty has been observed on the six vibratory hub loads and the damping with the highest impact on the yawing hub moment. Therefore, sufficient factor of safety should be considered in the design to alleviate the effects of perturbation in the simulation results. (iii) Although advanced ML techniques are harder to train, the optimal model configuration is capable of approximating the nonlinear response trends accurately. GP and CNN followed by MLP achieved satisfactory performance. Excellent accuracy achieved by the above ML techniques demonstrates their potential for application in the optimization of rotors under uncertainty. © 2021, The Author(s).

Item Type: Journal Article
Publication: Neural Computing and Applications
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to Authors
Keywords: Aeroelasticity; Decision trees; Deep learning; Helicopters; Learning systems; Loads (forces); Manufacture; Multilayer neural networks; Nonlinear analysis; Safety factor; Statistical tests; Stochastic models; Stochastic systems; Support vector machines; Turbomachine blades; Uncertainty analysis, Convolution neural network; Helicopter rotor blades; Manufacturing uncertainty; Manufacturing variability; Material and geometric properties; Multi layer perceptron; Performance assessment; Spatio-temporal models, Helicopter rotors
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 01 Dec 2021 14:40
Last Modified: 01 Dec 2021 14:40
URI: http://eprints.iisc.ac.in/id/eprint/70019

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