arXiv subjects

Vishnu R. Unni

Publications and source records attributed to Vishnu R. Unni.

9 recordsLinked to original sources

arXiv2026

An Aeroelastic Solver Integrating Reformulated-Vortex-Particle and Finite-Element Methods across Non-Conforming Interfaces

We present $\texttt{VarFlExI}$ (Variable Fidelity Unsteady Flow-FEniCS Exchange Interface), a modular aeroelastic solver for modeling two-way fluid-structure interaction (FSI) of flexible lifting surfaces. The framework employs a reformulated Vortex Particle Method (rVPM) to solve the incompressible Navier-Stokes equations without the need for computationally expensive volumetric meshing, while supporting variable-fidelity aerodynamic modeling using the $\texttt{FLOWUnsteady}$ framework. On the structural side, a Reissner-Mindlin plate formulation is discretized using the finite element method and integrated in time through the generalized-$α$ method, with the nonlinear equilibrium equations solved using a Gauss-Newton procedure within the $\texttt{FEniCS}$ framework. Fluid and structural solvers are coupled through an explicit staggered partitioned scheme, ensuring conservation of virtual work for load and displacement transfer across the non-matching interface. To accommodate the multi-representative nature of the aerodynamic loads and geometry, the interface coupling employs separate work-conservative force and reverse-geometry transfer operators via a common intermediate interface. The framework is validated against water-tunnel experiments, demonstrating accurate prediction of the coupled aeroelastic response rather than independent validation of the constituent solvers. The computational efficiency of the meshless aerodynamic solver enables simulations at significantly lower computational cost while maintaining accuracy. The solver is further evaluated through sensitivity analyses and parameter studies spanning different flow conditions, structural properties, and coupling parameters.

physics.flu-dyn↗

arXiv2026

Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking

Autonomous mid-air docking of multi-rotor vehicles under disturbance-driven target motion poses a constrained non-linear trajectory optimization challenge. This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states. The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories. State estimation with noisy measurements is incorporated to enable robust relative motion prediction, while trajectory execution is validated in a high-fidelity rigid-body MuJoCo simulation environment. The proposed framework is evaluated for stationary and constant-velocity target motions, demonstrating reliable convergence to the docking interface while satisfying geometric capture constraints. Quantitatively, the method maintains negligible docking-cone violations and terminal state errors within prescribed tolerances, and achieves consistent, safe docking performance for cone half-angles as low as 10 degrees. Robust operation is observed for wind disturbance levels up to a standard deviation of 0.5, while preserving bounded approach velocities and stable control effort. These results demonstrate the effectiveness of the SCP-based trajectory optimization framework for disturbance-robust aerial docking under estimation uncertainty.

cs.RO↗

arXiv2022

Synchronization based model for turbulent thermoacoustic systems

We present a phenomenological reduced-order model to capture the transition to thermoacoustic instability in turbulent combustors. The model is based on the framework of synchronization and considers the acoustic field and the unsteady heat release rate from turbulent reactive flow as two nonlinearly coupled sub-systems. Previous experimental studies have reported a route from low amplitude chaotic oscillation (i. e. combustion noise) to periodic oscillations through intermittency in turbulent combustors. By varying the coupling strength, our proposed model can replicate the route that is observed in experiments. Instead of assessing combustion noise as background noise to the system, the model considered a coupled oscillator system that produces multifractal chaotic oscillations to represent the combustion noise. This set of coupled oscillators is then nonlinearly coupled to a linear oscillator representing the acoustic field. As the coupling strength increases, the system loses its multifractality and exhibits large-amplitude periodic oscillations in a manner consistent with the dynamics observed in experimental studies.

nlin.AO↗

arXiv2021

Neural ODE to model and prognose thermoacoustic instability

In reacting flow systems, thermoacoustic instability characterized by high amplitude pressure fluctuations, is driven by a positive coupling between the unsteady heat release rate and the acoustic field of the combustor. When the underlying flow is turbulent, as a control parameter of the system is varied and the system approach thermoacoustic instability, the acoustic pressure oscillations synchronize with heat release rate oscillations. Consequently, during the onset of thermoacoustic instability in turbulent combustors, the system dynamics transition from chaotic oscillations to periodic oscillations via a state of intermittency. Thermoacoustic systems are traditionally modeled by coupling the model for the unsteady heat source and the acoustic subsystem, each estimated independently. The response of the unsteady heat source, the flame, to acoustic fluctuations are characterized by introducing external unsteady forcing. This necessitates a powerful excitation module to obtain the nonlinear response of the flame to acoustic perturbations. Instead of characterizing individual subsystems, we introduce a neural ordinary differential equation (neural ODE) framework to model the thermoacoustic system as a whole. The neural ODE model for the thermoacoustic system uses time series of the heat release rate and the pressure fluctuations, measured simultaneously without introducing any external perturbations, to model their coupled interaction. Further, we use the parameters of neural ODE to define an anomaly measure that represents the proximity of system dynamics to limit cycle oscillations and thus provide an early warning signal for the onset of thermoacoustic instability.

physics.flu-dyn↗

arXiv2021

Detection of Dynamical Regime Transitions with Lacunarity as a Multiscale Recurrence Quantification Measure

We propose lacunarity as a novel recurrence quantification measure and illustrate its efficacy to detect dynamical regime transitions which are exhibited by many complex real-world systems. We carry out a recurrence plot based analysis for different paradigmatic systems and nonlinear empirical data in order to demonstrate the ability of our method to detect dynamical transitions ranging across different temporal scales. It succeeds to distinguish states of varying dynamical complexity in the presence of noise and non-stationarity, even when the time series is of short length. In contrast to traditional recurrence quantifiers, no specification of minimal line lengths is required and rather geometric features beyond linear structures in the recurrence plot can be accounted for. This makes lacunarity more broadly applicable as a recurrence quantification measure. Lacunarity is usually interpreted as a measure of heterogeneity or translational invariance of an arbitrary spatial pattern. In application to recurrence plots, it quantifies the degree of heterogenity in the temporal recurrence patterns at all relevant time scales. We demonstrate the potential of the proposed method when applied to empirical data, namely time series of acoustic pressure fluctuations from a turbulent combustor. Recurrence lacunarity captures both the rich variability in dynamical complexity of acoustic pressure fluctuations and shifting time scales encoded in the recurrence plots. Furthermore, it contributes to a better distinction between stable operation and near blowout states of combustors.

physics.data-an↗

arXiv2020

Synchronization framework for modeling transition to thermoacoustic instability in laminar combustors

We, herein, present a new model based on the framework of synchronization to describe a thermoacoustic system and capture the multiple bifurcations that such a system undergoes. Instead of applying flame describing function to depict the unsteady heat release rate as the flame's response to acoustic perturbation, the new model considers the acoustic field and the unsteady heat release rate as a pair of nonlinearly coupled damped oscillators. By varying the coupling strength, multiple dynamical behaviors, including limit cycle oscillation, quasi-periodic oscillation, strange nonchaos, and chaos can be captured. Furthermore, the model was able to qualitatively replicate the different behaviors of a laminar thermoacoustic system observed in experiments by Kabiraj et al.~[Chaos 22, 023129 (2012)]. By analyzing the temporal variation of the phase difference between heat release rate oscillations and pressure oscillations under different dynamical states, we show that the characteristics of the dynamical states depend on the nature of synchronization between the two signals, which is consistent with previous experimental findings.

nlin.AO↗

arXiv2020

Modeling ambient temperature and relative humidity sensitivity of respiratory droplets and their role in Covid-19 outbreaks

One of the many unresolved questions that revolves around the Covid-19 pandemic is whether local outbreaks can depend on ambient conditions like temperature and relative humidity. In this paper, we develop a model that tries to explain and describe the temperature and relative humidity sensitivity of respiratory droplets and their possible connection in determining viral outbreaks. The model has two parts. First, we model the growth rate of the infected population based on a reaction mechanism - the final equations of which are similar to the well-known SIR model. The advantage of modeling the pandemic using the reaction mechanism is that the rate constants have sound physical interpretation. The infection rate constant is derived using collision rate theory and shown to be a function of the respiratory droplet lifetime. In the second part, we have emulated the respiratory droplets responsible for disease transmission as salt solution droplets and computed their evaporation time accounting for droplet cooling, heat and mass transfer and finally crystallization of the salt. The model output favourably compares with the experimentally obtained evaporation characteristics of levitated droplets of pure water and salt solution, respectively, ensuring fidelity of the model. Droplet evaporation/desiccation time is indeed dependent on ambient temperature and relative humidity, considered at both outdoor and indoor conditions. Since the droplet evaporation time determines the infection rate constant, ambient temperature and relative humidity are shown to impact the outbreak growth rates.

physics.flu-dyn↗

arXiv2020

Universality in spectral condensation

Self-organization is the spontaneous formation of spatial, temporal, or spatiotemporal patterns in complex systems far from equilibrium. During such self-organization, energy distributed in a broadband of frequencies gets condensed into a dominant mode, analogous to a condensation phenomena. We call this phenomenon spectral condensation and study its occurrence in fluid mechanical, optical and electronic systems. We define a set of spectral measures to quantify this condensation spanning several dynamical systems. Further, we uncover an inverse power law behaviour of spectral measures with the power corresponding to the dominant peak in the power spectrum in all the aforementioned systems.

nlin.AO↗

arXiv2019

Universality in the emergence of oscillatory instabilities in turbulent flows

Spontaneous emergence of periodic oscillations due to self-organization is ubiquitous in turbulent flows. The emergence of such oscillatory instabilities in turbulent fluid mechanical systems is often studied in different system-specific frameworks. We uncover the existence of a universal scaling behaviour during self-organization in turbulent flows leading to oscillatory instability. Our experiments show that the spectral amplitude of the dominant mode of oscillations scales inversely with the Hurst exponent of a fluctuating state variable following an inverse power law relation. Interestingly, we observe the same power law behaviour with a constant exponent near -2 across various turbulent systems such as aeroacoustic, thermoacoustic and aeroelastic systems.

nlin.AO↗