arXiv subjects

Rajeev Kumar Jaiman

Publications and source records attributed to Rajeev Kumar Jaiman.

10 recordsLinked to original sources

arXiv2025

Propulsive transitions and scaling relations of a heaving flexible foil in a cylinder wake

We numerically investigate the propulsive dynamics of a heaving flexible foil immersed in the wake of a stationary circular cylinder, focusing on the coupled effects of unsteady wake forcing, passive structural flexibility, and prescribed heaving kinematics. The analysis employs a high-fidelity fluid-structure interaction solver based on a partitioned variational formulation with a nonlinear iterative force correction scheme. Systematic simulations are conducted over a broad parameter space of dimensionless heaving amplitude and frequency at a Reynolds number of 3000. Five distinct response modes are identified, namely full-wake, semi-wake, full-wake-flexible, semi-wake-flexible, and vortex-flexible, based on propulsive transitions and associated flow features. An empirical boundary plane is discovered, separating regimes where the wake hinders lift performance (wake-dominated) from those where it enhances performance (flapping-dominated). Scaling relations for the force and power coefficients are formulated by decomposing the contributions of quasi-steady motion, added-mass effects, structural curvature, wake momentum deficit, and transverse flow gradients. At sufficiently large amplitude and frequency, a two-way lock-in emerges: the foil not only synchronizes with the cylinder shedding but also modulates it, accelerating the wake and enhancing lift.Flexibility is found to be detrimental in fully immersed wakes but beneficial in partial wakes, where it creates extra suction without much extra drag in the semi-wake-flexible mode. These findings elucidate the energy-saving and maneuverability strategies employed by biological propulsors and provide predictive guidelines for the design of bio-inspired energy harvesters and unmanned vehicles in disturbed flows.

physics.flu-dyn↗

arXiv2023

Coupled dynamics of steady jet flow control for flexible membrane wings

We present a steady jet flow-based flow control of flexible membrane wings for an adaptive and efficient motion of bat-inspired drones in complex flight environments. A body-fitted variational computational aeroelastic framework is adopted for the modeling of fluid-structure interactions. High-momentum jet flows are injected from the leading edge and transported to the wake flows to alter the aerodynamic performance and the membrane vibration. The phase diagrams of the coupled fluid-membrane dynamics are constructed in the parameter space of the angle of attack and the jet momentum coefficient. The coupled dynamical effect of active jet flow control on the membrane performance is systematically explored. While the results indicate that the current active flow control strategy performs well at low angles of attack, the effectiveness degrades at high angles of attack with large flow separation. To understand the coupling mechanism, the variations of the vortex patterns at different jet momentum coefficients are examined by the proper orthogonal decomposition modes in the Eulerian view and the fluid transport process is studied by the coherent flow structures in the Lagrange description. Two scaling relations that quantitatively connect the membrane deformation with the aerodynamic loads presented in our previous work are verified even when active jet flow control is applied. A unifying feedback loop that reveals the fluid-membrane coupling mechanism is proposed. This feedback loop provides useful guidance for designing optimal active flow control strategies and enhancing flight capabilities. These findings can facilitate the development of next-generation bio-inspired drones that incorporate smart sensing and intelligent control.

physics.flu-dyn↗

arXiv2023

Unsteady aeroelastic characterization and scaling relations of flexible membrane wings

We present a numerical study to characterize nonlinear unsteady aeroelastic interactions of two-dimensional flexible wings at high angles of attack. The coupled fluid-flexible wing system is solved by a body-fitted variational aeroelastic solver based on the fully-coupled Navier-Stokes and nonlinear structural equations. Using the coupled fluid-structure analysis, this study is aimed to provide physical insight and correlations for the aeroelastic behavior of flexible wings in the parameter space of the angle of attack and the aeroelastic number. The phase diagrams of the aerodynamic performance are established to obtain the envelope curves of the optimal performance and determine the transition line of the drag variation. The effects of the angle of attack and the aeroelastic number on the aeroelastic behaviors are systematically examined. The time-averaged membrane deformation is positively correlated with a non-dimensional number, the so-called Weber number. A new scaling relation is proposed based on the dynamic equilibrium between the aerodynamic force fluctuation and the combined inertia-elastic fluctuation. The unsteady aerodynamic force can be adjusted by manipulating the membrane vibration, the mass ratio, the Strouhal number and the aeroelastic number. The numerical investigations provide design guidelines and have the potential to enhance the maneuverability and flight agility of micro air vehicles with flexible wing structures.

physics.flu-dyn↗

arXiv2021

A hybrid partitioned deep learning methodology for moving interface and fluid-structure interaction

We present a hybrid partitioned deep learning framework for the reduced-order modeling of fluid-structure interaction. Using the discretized Navier-Stokes in the arbitrary Lagrangian-Eulerian reference frame, we generate the full-order flow snapshots and point cloud displacements as target data for the learning and inference of fluid-structure dynamics. The hybrid operation of this methodology comes by combining two data-driven models for fluid and solid subdomains via deep learning-based reduced-order models (DL-ROMs). The proposed framework comprises the partitioned data-driven drivers for unsteady flow and the moving point cloud displacements. At the fluid-structure interface, the force information is exchanged between the two partitioned subdomain solvers. The first component of our framework relies on the proper orthogonal decomposition-based recurrent neural network (POD-RNN) as a DL-ROM procedure to infer the point cloud with a moving interface. This model utilizes the POD basis modes to reduce dimensionality and evolve them in time via RNN. The second component employs the convolution-based recurrent autoencoder network (CRAN) as a self-supervised DL-ROM procedure to infer the nonlinear flow dynamics at static Eulerian probes. We introduce these probes as spatially structured query nodes in the moving point cloud to treat the Lagrangian-to-Eulerian conflict together with convenience in training the CRAN driver. To determine these Eulerian probes, we construct a novel snapshot-field transfer and load recovery algorithm. A popular prototypical fluid-structure interaction problem of flow past a freely oscillating cylinder is considered to assess the efficacy of the proposed methodology for a different set of reduced velocities. The proposed framework tracks the interface description with acceptable accuracy and predicts the nonlinear wake dynamics over the chosen test data range.

physics.flu-dyn↗

arXiv2021

Flow-excited membrane instability at moderate Reynolds numbers

In this paper, we study the fluid-structure interaction (FSI) of a three-dimensional (3D) flexible membrane immersed in an unsteady separated flow at moderate Reynolds numbers. We employ a body-conforming variational FSI solver based on the recently developed partitioned iterative scheme for the coupling of turbulent fluid flow with nonlinear structural dynamics. Of particular interest is to understand the flow-excited instability of a 3D flexible membrane as a function of the non-dimensional mass ratio, Reynolds number and aeroelastic number. For a wide range of the parameters, we examine two distinctive stability regimes of fluid-membrane interaction: deformed-steady state (DSS) and dynamic balance state (DBS). We propose stability phase diagrams to demarcate the DSS and DBS regimes for the parameter space of mass ratio vs. Reynolds number and mass ratio vs. aeroelastic number. Based on the aeroelastic mode analysis, we observe a frequency synchronization between the vortex shedding frequency and the membrane vibration frequency which leads to self-sustained vibrations in the dynamic balance state. To characterize the origin of the frequency lock-in, we derive an approximate analytical formula for the nonlinear natural frequency by considering the added mass effect and employing a large deflection theory for a simply supported rectangular membrane. Through our systematic high-fidelity numerical investigation, we find that the onset of the membrane vibration and the mode transition has a dependence on the frequency lock-in between the natural frequency of the tensioned membrane and the vortex shedding frequency or its harmonics. These findings on the fluid-elastic instability of membranes have implications for the design and development of control strategies for membrane wing-based unmanned systems and drones.

physics.flu-dyn↗

arXiv2021

A high-fidelity numerical study on the propulsive performance of pitching flexible plates

In this paper, we numerically investigate the propulsive performance of three-dimensional pitching flexible plates with varying flexibility and trailing edge shapes. To eliminate the effect of other geometric parameters, only the trailing edge angle is varied from 45° (concave), 90° (rectangular) to 135° (convex) while maintaining the constant area of the flexible plate. We examine the impact of the frequency ratio f* defined as the ratio of the natural frequency of the flexible plate to the actuated pitching frequency. Through our numerical simulations, we find that the global maximum mean thrust occurs near f*=1 corresponding to the resonance condition. However, the optimal propulsive efficiency is achieved around f*=1.54 instead of the resonance condition. While the convex plate with low and high bending stiffness values shows the best performance, the rectangular plate with moderate bending stiffness is the most efficient propulsion configuration. Through dynamic mode decomposition, we find that the passive deformation can help in redistributing the pressure gradient thus improving the efficiency and thrust production. A momentum-based thrust evaluation approach is adopted to link the instantaneous vortical structures with the time-dependent thrust. When the vortices detach from the trailing edge, the instantaneous thrust shows the largest values due to the strong momentum change and convection process. Moderate flexibility and convex shape help transfer momentum to the fluid, thereby improving thrust generation and promoting the transition from drag to thrust. The increase of the trailing edge angle can broaden the range of flexibility that produces positive mean thrust.

physics.flu-dyn↗

arXiv2020

Assessment of unsteady flow predictions using hybrid deep learning based reduced order models

In this paper, we present two deep learning-based hybrid data-driven reduced order models for the prediction of unsteady fluid flows. 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 POD basis vectors. This model is referred as POD-RNN. The second model, referred to as convolution recurrent autoencoder network (CRAN), employs convolutional neural networks (CNN) 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 flow past a side-by-side cylinder are selected as the test problems to assess the efficacy of these models. For the problem of flow past a single cylinder, the performance of both the models is satisfactory, with CRAN being a bit overkill. However, it completely outperforms the POD-RNN model for a more complicated problem of flow past side-by-side cylinders. Owing to the scalability of CRAN, we briefly introduce an observer-corrector method for the 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 CRAN. This motivates us to further explore the application of CRAN models for the prediction of fluid flows.

physics.flu-dyn↗

arXiv2020

Deep Convolutional Recurrent Autoencoders for Flow Field Prediction

In this paper, an end-to-end nonlinear model reduction methodology is presented based on the convolutional recurrent autoencoder networks. The methodology is developed in the context of the overall data-driven reduced-order model framework proposed in the paper. The basic idea behind the methodology is to obtain the low dimensional representations via convolutional neural networks and evolve these low dimensional features via recurrent neural networks in the time domain. The high dimensional representations are constructed from the evolved low dimensional features via transpose convolutional neural networks. With an unsupervised training strategy, the model serves as an end to end tool which can evolve the flow state of the nonlinear dynamical system. The convolutional recurrent autoencoder network model is applied to the problem of flow past bluff bodies for the first time. To demonstrate the effectiveness of the methodology, two canonical problems namely the flow past a plain cylinder and the flow past side-by-side cylinders are explored in this paper. Pressure and velocity fields of the unsteady flow are predicted in future via the convolutional recurrent autoencoder model. The performance of the model is satisfactory for both the problems. Specifically, the multiscale nature and the gap flow dynamics of the side-by-side cylinders are captured by the proposed data-driven model reduction methodology. The error metrics, the normalized squared error, and the normalized reconstruction error are considered for the assessment of the data-driven framework.

physics.flu-dyn↗

arXiv2018

A projection-based numerical integration scheme for embedded interface: Application to fluid-structure interaction

We present a projection-based numerical integration technique to deal with embedded interface in finite element (FE) framework. The element cut by an embedded interface is denoted as a cut cell. We recognize elemental matrices of a cut cell can be reconstructed from the elemental matrices of its sub-divided cells, via projection at matrix level. These sub-divided cells are termed as integration cells. The proposed technique possesses following characteristics (1) no change in FE formulation and quadrature rule; (2) consistency with the derivation of FE formulation in variational principle. It can be considered as a re-projection of the residuals of equation system in the test function space or a reduced-order modeling (ROM) technique. These characteristics significantly improves its scalability, easy-to-implementation and robustness to deal with problems involving embedded discontinuities in FE framework. Numerical examples, e.g., vortex-induced vibration (VIV), rotation, free fall and rigid-body contact in which the proposed technique is implemented to integrate the variational form of Navier-Stokes equations in cut cells, are presented.

math.NA↗

arXiv2017

3D Common-Refinement Method for Non-Matching Meshes in Partitioned Variational Fluid-Structure Analysis

We present a three-dimensional (3D) common-refinement method for non-matching meshes between discrete non-overlapping subdomains of incompressible fluid and nonlinear hyperelastic structure. To begin, we first investigate the accuracy of common-refinement method (CRM) to satisfy traction equilibrium condition along the fluid-elastic interface with non-matching meshes. We systematically assess the accuracy of CRM against the matching grid solution by varying grid mismatch between the fluid and solid meshes over a cylindrical tubular elastic body. We demonstrate second-order accuracy of CRM through uniform refinements of fluid and solid meshes along the interface. We then extend the error analysis to transient data transfer across non-matching meshes between fluid and solid solvers. We show that the common-refinement discretization across non-matching fluid-structure grids yields accurate transfer of the physical quantities across the fluid-solid interface. We next solve a 3D benchmark problem of a cantilevered hyperelastic plate behind a circular bluff body and verify the accuracy of coupled solutions with respect to the available solution in the literature. By varying the solid interface resolution, we generate various non-matching grid ratios and quantify the accuracy of CRM for the nonlinear structure interacting with a laminar flow. We illustrate that the CRM with the partitioned NIFC treatment is stable for low solid-to-fluid density ratio and non-matching meshes. Finally, we demonstrate the 3D parallel implementation of common-refinement with NIFC scheme for a realistic engineering problem of drilling riser undergoing complex vortex-induced vibration with strong added mass effects.

physics.comp-ph↗