Majumdar, D

Sarkar, S

Shah, CL

Sundar, R

English

Spiral - Imperial College Digital Repository

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/361367081Recurrent Neural Network based Surrogate Modeling of Unsteady ForcesActing on a Plunging AirfoilConference Paper · September 2021CITATIONS0READS364 authors:Some of the authors of this publication are also working on these related projects:Stochastic modeling of Fin buffeting View projectData driven methods for model order reduction of unsteady flows View projectRahul SundarIndian Institute of Technology Madras8 PUBLICATIONS   0 CITATIONS   SEE PROFILEDipanjan MajumdarImperial College London31 PUBLICATIONS   19 CITATIONS   SEE PROFILEChhote Lal ShahIndian Institute of Technology Madras21 PUBLICATIONS   3 CITATIONS   SEE PROFILESunetra SarkarIndian Institute of Technology Madras162 PUBLICATIONS   1,092 CITATIONS   SEE PROFILEAll content following this page was uploaded by Dipanjan Majumdar on 17 June 2022.The user has requested enhancement of the downloaded file.Recurrent Neural Network based surrogate modelingof unsteady forces acting on a plunging airfoilRahul Sundar∗, Dipanjan Majumdar∗, Chhote Lal Shah∗ and Sunetra Sarkar∗∗ Department of Aerospace Engineering, IIT Madras, Chennai - 600036, IndiaMay 14, 2021AbstractOwing to the inherent nonlinearity in flow, both periodic and aperiodic dynamical statesare observed in the flow-field of flapping airfoils[1] at various parametric regimes, even when thekinematical model is periodic. Due to significant computational costs of high fidelity simulationsof such complex flow problems, developing accurate yet computationally cheap surrogate modelsfor prediction of the aerodynamic forces are of specific interest. Such surrogate models can beused to carry out control or optimisation studies in a robust manner. Traditional time seriesforecasting methods such as Auto Regressive Integrated Moving Average(ARIMA) are found tomodel data with underlying linear or periodic relationships better, and therefore might not bebest suited for capturing the aerodynamic load behaviour in the aperiodic regimes. Hence, asurrogate model that captures both periodicity and aperiodicity in the force coefficients is inves-tigated. Recurrent neural networks(RNNs)[2] are a class of deep neural networks prominentlyused for inherently nonlinear sequence modeling problems such as natural language processing[3],video captioning[4] and time series modeling[5]. Hence, in the present study, a RNN based para-metric surrogate model is proposed to predict the aerodynamic load coefficients’ time historiesfor unsteady incompressible flow past a plunging airfoil at Reynolds number, Re = 300. Thereduced frequency(k) and non-dimensional plunging amplitude(h) are the parameters consideredfor the surrogate model.Two variants of the model are developed where, (i) given k, h and time histories of theaerodynamic load coefficients for first few cycles, time histories for the next cycles are predicted,and (ii) given only the k and h, the entire time histories of aerodynamic load coefficients arepredicted. The effectiveness of the surrogate model variants is demonstrated by comparing thespectral properties of the true and predicted time series and comparing the predictions with thatof an ARIMA model for both periodic and aperiodic dynamical states.References[1] Sandeep Badrinath, Chandan Bose, and Sunetra Sarkar. Identifying the route to chaos inthe flow past a flapping airfoil. European Journal of Mechanics-B/Fluids, 66:38–59, 2017.[2] Zachary C Lipton, John Berkowitz, and Charles Elkan. A critical review of recurrent neuralnetworks for sequence learning. arXiv preprint arXiv:1506.00019, 2015.[3] Kanchan M Tarwani and Swathi Edem. Survey on recurrent neural network in naturallanguage processing. Int. J. Eng. Trends Technol, 48:301–304, 2017.[4] Sheng Li, Zhiqiang Tao, Kang Li, and Yun Fu. Visual to text: Survey of image and videocaptioning. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(4):297–312, 2019.1[5] Benjamin Lindemann, Timo Müller, Hannes Vietz, Nasser Jazdi, and Michael Weyrich. Asurvey on long short-term memory networks for time series prediction. Procedia CIRP,99:650–655, 2021.2View publication stats

Recurrent Neural Network based Surrogate Modeling of Unsteady Forces Acting on a Plunging Airfoil

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Recurrent Neural Network based Surrogate Modeling of Unsteady Forces Acting on a Plunging Airfoil

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