The Stochastic Aeroelastic Response Analysis of Helicopter Rotors using Deep and Shallow Machine Learning

T Chatterjee (Swansea University), A Essien (University of Sussex), R Ganguli (Indian Institute of Science, Bangalore, India) & MI Friswell (Swansea University)

Neural Computing and Applications, Vol. 33, No. 23, December 2021, pp. 16809-16828.

Abstract

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 non-linear 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 data-set and prediction on the test data-set. The contribution of the study lies in the following findings: (i) The un- certainty 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 non-linear 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.

Paper Availability

This material has been published in the Neural Computing and Applications, Vol. 33, No. 23, December 2021, pp. 16809-16828. The paper is published with Open Access.


Link to paper using doi: 10.1007/s00521-021-06288-w

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