Nonlinear response of passively flapping foils

Farooq, Hamayun; Khalid, Muhammad Saif Ullah; Akhtar, Imran; Hemmati, Arman

doi:10.1016/j.oceaneng.2022.112071

Cited by 3 publications

(3 citation statements)

References 48 publications

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“…The fractional step method is an effective and strong candidate technique for dealing with incompressible Navier-Stokes equation. It transforms the momentum equation into the convection-diffusion equation and pressure-Poisson equation [32,40,51,66]. We utilize a non-staggered grid in which the velocity and pressure fields are computed at the center of each cell.…”

Section: Discretization Strategymentioning

confidence: 99%

“…For more details on grid convergence and time-step independence, interested readers are referred to the Refs. [ 40 , 51 ].…”

Section: Validationmentioning

confidence: 99%

“…Alternatively, the flapping foils/wings can also be employed to harness energy from the fluid flows, passing over them. Flapping foils-based power extraction systems are usually classified into three categories according to their activation mode [ 38 , 39 ]: (i) a fully forced system in which both plunge and pitch motions are prescribed [ 38 , 40 , 41 , 42 , 43 , 44 ]; (ii) a semi-passive system in which the pitch motion is prescribed while the plunge motion is induced from the interactions between the foil, the incoming flow, and the elastic supports [ 45 , 46 , 47 , 48 , 49 , 50 ]; and (iii) a fully-passive system in which both plunge and pitch motions are entirely driven by interactions of the foil with the surrounding fluid and the supporting dynamical mechanisms [ 42 , 44 , 51 , 52 , 53 , 54 ]. In this work, we consider a fully forced system for developing a deep-learning-based ROM for power generation by flapping foils.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Learning-Based Reduced-Order Model for Power Generation Capacity of Flapping Foils

et al. 2023

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Inspired by nature, oscillating foils offer viable options as alternate energy resources to harness energy from wind and water. Here, we propose a proper orthogonal decomposition (POD)-based reduced-order model (ROM) of power generation by flapping airfoils in conjunction with deep neural networks. Numerical simulations are performed for incompressible flow past a flapping NACA-0012 airfoil at a Reynolds number of 1100 using the Arbitrary Lagrangian–Eulerian approach. The snapshots of the pressure field around the flapping foil are then utilized to construct the pressure POD modes of each case, which serve as the reduced basis to span the solution space. The novelty of the current research relates to the identification, development, and employment of long-short-term neural network (LSTM) models to predict temporal coefficients of the pressure modes. These coefficients, in turn, are used to reconstruct hydrodynamic forces and moment, leading to computations of power. The proposed model takes the known temporal coefficients as inputs and predicts the future temporal coefficients followed by previously estimated temporal coefficients, very similar to traditional ROM. Through the new trained model, we can predict the temporal coefficients for a long time duration that can be far beyond the training time intervals more accurately. It may not be attained by traditional ROMs that lead to erroneous results. Consequently, the flow physics including the forces and moment exerted by fluids can be reconstructed accurately using POD modes as the basis set.

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Section: Discretization Strategymentioning

confidence: 99%

“…For more details on grid convergence and time-step independence, interested readers are referred to the Refs. [ 40 , 51 ].…”

Section: Validationmentioning

confidence: 99%