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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. [ABSTRACT FROM AUTHOR]

In this article, we introduce a machine learning–based reduced-order modeling (ML-ROM) framework through the integration of proper orthogonal decomposition (POD) and deep neural networks (DNNs), in addition to long short-term memory (LSTM) networks. The DNN is utilized to upscale POD temporal coefficients and their respective spatial modes to account for the dynamics represented by the truncated modes. In the second part of the algorithm, temporal evolution of the POD coefficients is obtained by recursively predicting their future states using an LSTM network. The proposed model (ML-ROM) is tested for flow past a circular cylinder characterized by the Navier–Stokes equations. We perform pressure mode decomposition analysis on the flow data using both POD and ML-ROM to predict hydrodynamic forces and demonstrate the accuracy of the proposed strategy for modeling lift and drag coefficients. [ABSTRACT FROM AUTHOR]

This study numerically investigates two-dimensional incompressible flows over elastically mounted foils, undergoing semi-passive and fully passive motion. In our strongly coupled numerical models, we employ linear and cubic stiffness and damping terms in order to examine their highly nonlinear response. The undamped model of the fully passive system exhibits various responses from periodic to chaotic and then to flip-over for the reduced velocity, ranging from 1 to 10. However, introducing the cubic damping terms causes a significant decrease in the magnitude of plunging and pitching amplitudes without affecting the onset point of bifurcation. Also, plunging and pitching amplitudes are altered significantly after the point of onset. Furthermore, the performance metrics of each passive system are computed for power generation applications to demonstrate that semi-passive system attain efficiency up to 20% for a pitching amplitude of 50° with the excitation frequency in the narrow range of 0.15 to 0.20. On the other hand for a fully passive system, the efficiency of around 34% is obtained near the onset point of a bifurcation with a low mass ratio and linear damping terms. However, introducing cubic damping terms causes degradation in efficiency to bring it down to 14 − 20 % for a wide range of reduced velocity. • Nonlinear dynamical models are strongly coupled with an in-house CFD-based solver. • Nonlinear damping and stiffness are introduced in the computational models. • For the semi-passive dynamical system, an efficiency of 20% is attained. • The fully passive system leads to an efficiency of up to 17% for low mass ratios. • Introducing cubic damping degrades the efficiency to 8%–9% for fully-passive foils. [ABSTRACT FROM AUTHOR]

Micro-power generators are increasingly becoming popular to meet the power requirements of micro-electromechanical systems, such as small sensors. One such resource of harnessing energy is through exploiting flow instabilities found in vortex-induced vibrations, flutter, etc. In this work, we numerically investigate the hydrodynamic performance of fully forced flapping foils with the goal to exploit their underlying physical mechanisms for the development of micro-power generators. We consider prescribed combination of plunging and pitching motions imposed to a NACA-0012 airfoil. We conduct a parametric study by varying the Strouhal number and the amplitude of the pitching angle to identify two operational flow regimes: power generation and thrust-producing propulsion using the feathering criterion. In the latter regime, the foil performs positive work on the surrounding fluid and therefore, the positive propulsive efficiency can be attained as long as the horizontal hydrodynamic force remains negative. For the power generation regime, the product of the lift force and plunging velocity is found mostly positive over the oscillating cycle, which indicates that the flowing fluid carries out work on the foil. The parametric study reveals that the foil can reach up to 42% power generation efficiency when setting the pitching amplitude in the range of 60° to 70°. For foils operating in the power generation regime, we present a piezoelectric energy harvester that can efficiently harness usable electric power from high fluid pressure regions. We identify two core locations based on the pressure field at which the attachment of piezoelectric patches can lead to significant energy harvesting. As such, the present study provides guidance for the design enhancement of micro-power generators relying on the interactions of flapping foils with the surrounding fluid. • Develop a computational model to simulate incompressible flows over moving bodies. • Analyze the hydrodynamic characteristics and propulsive efficiency of flapping foils. • Identify the flow regimes of flapping foils: power generation and thrust-producing propulsion. • Propose a design of a piezoelectric energy harvester. [ABSTRACT FROM AUTHOR]