We present a numerical study on the self-induced flapping dynamics of an inverted flexible foil in a uniform flow. A high-order coupled fluid–structure solver based on fully coupled Navier–Stokes and nonlinear structural dynamic equations has been employed. Unlike a conventional flexible foil flapping where the leading edge is clamped, the inverted elastic foil is fixed at the trailing edge and the leading edge is allowed to oscillate freely. We investigate the evolution of flapping instability of an inverted foil as a function of the non-dimensional bending rigidity, $K_{B}$, Reynolds number, $\mathit{Re}$, and structure-to-fluid mass ratio, $m^{\ast }$, and identify three distinct stability regimes, namely (i) fixed-point stable, (ii) deformed steady and (iii) unsteady flapping state. With the aid of a simplified analytical model, we show that the fixed-point stable regime loses its stability by static-divergence instability. The transition from the deformed steady state to the unsteady flapping regime is marked by a flow separation at the leading edge. We also show that an inverted foil is more vulnerable to static divergence than a conventional foil. Three distinct unsteady flapping modes have been observed as a function of decreasing $K_{B}$: (i) inverted limit-cycle oscillations, (ii) deformed flapping and (iii) flipped flapping. We characterize the transition to the deformed-flapping regime through a quasistatic equilibrium analysis between the structural restoring and the fluid forces. We further examine the effects of $m^{\ast }$ on the post-critical flapping dynamics at a fixed $\mathit{Re}=1000$. Finally, we present the net work done by the fluid and the bending strain energy developed in a flexible foil due to the flapping motion. For small $m^{\ast }$, we demonstrate that the flapping of an inverted flexible foil can generate $O(10^{3})$ times more strain energy in comparison to a conventional flexible foil flapping, which has a profound impact on energy harvesting devices.

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We investigate the effect of curvature on the accuracy of schemes used to transfer loads along the interface in coupled fluid-solid simulations involving non-matching meshes. We analyze two types of load transfer schemes for the coupled system: (a) point-to-element projection schemes and (b) common-refinement schemes. The accuracy of these schemes over the curved interface is assessed with the aid of static and transient problems. We show that the point-to-element projection schemes may yield inaccurate load transfer from the source fluid mesh to the target solid mesh, leading to a weak instability in the form of spurious oscillations and overshoots in the interface solution. The common-refinement scheme resolves this problem by providing an accurate transfer of discrete interface conditions across non-matching meshes. We show theoretically that the accurate transfer preserves the stability of the coupled system while maintaining the energy conservation over a reference interface. Finally, we introduce simple analytical error functions which correlate well with the numerical errors of the load transfer schemes.

SUMMARYWe present a detailed comparative study of three conservative schemes used to transfer interface loads in fluid-solid interaction simulations involving non-matching meshes. The three load transfer schemes investigated are the node-projection scheme, the quadrature-projection scheme and the common-refinement based scheme. The accuracy associated with these schemes is assessed with the aid of 2-D fluid-solid interaction problems of increasing complexity. This includes a static load transfer and three transient problems, namely, elastic piston, superseismic shock and flexible inhibitor involving large deformations. We show how the load transfer schemes may affect the accuracy of the solutions along the fluid-solid interface and in the fluid and solid domains. We introduce a grid mismatching function which correlates well with the errors of the traditional load transfer schemes. We also compare the computational costs of these load transfer schemes.

In this paper, we present two deep learning-based hybrid data-driven reduced-order models for prediction of unsteady fluid flows. These hybrid models rely on recurrent neural networks (RNNs) to evolve low-dimensional states of unsteady fluid flow. The first model projects the high-fidelity time series data from a finite element Navier–Stokes solver to a low-dimensional subspace via proper orthogonal decomposition (POD). The time-dependent coefficients in the POD subspace are propagated by the recurrent net (closed-loop encoder–decoder updates) and mapped to a high-dimensional state via the mean flow field and the POD basis vectors. This model is referred to as POD-RNN. The second model, referred to as the convolution recurrent autoencoder network (CRAN), employs convolutional neural networks (instead of POD) as layers of linear kernels with nonlinear activations, to extract low-dimensional features from flow field snapshots. The flattened features are advanced using a recurrent (closed-loop manner) net and up-sampled (transpose convoluted) gradually to high-dimensional snapshots. Two benchmark problems of the flow past a cylinder and the flow past side-by-side cylinders are selected as the unsteady flow problems to assess the efficacy of these models. For the problem of the flow past a single cylinder, the performance of both the models is satisfactory and the CRAN model is found to be overkill. However, the CRAN model completely outperforms the POD-RNN model for a more complicated problem of the flow past side-by-side cylinders involving the complex effects of vortex-to-vortex and gap flow interactions. Owing to the scalability of the CRAN model, we introduce an observer-corrector method for calculation of integrated pressure force coefficients on the fluid–solid boundary on a reference grid. This reference grid, typically a structured and uniform grid, is used to interpolate scattered high-dimensional field data as snapshot images. These input images are convenient in training the CRAN model, which motivates us to further explore the application of the CRAN-based models for prediction of fluid flows.