arXiv subjects

Murali Damodaran

Publications and source records attributed to Murali Damodaran.

3 recordsLinked to original sources

arXiv2023

Deep Learning-Driven Nonlinear Reduced-Order Models for Predicting Wave-Structure Interaction

Long Short-Term Memory (LSTM) network-driven Non-Intrusive Reduced Order Model (NROM) for predicting the dynamics of a floating box on the water surface in a wavemaker basin is addressed in this study. The ground truth or actual data for these wave-structure interactions (WSI) problems, namely box displacements and hydrodynamic forces and moments acting on the box due to wave interaction corresponding to a particular wave profile, are computed using the Smoothed Particle Hydrodynamics (SPH). The dimensionality of the system is first reduced using the Discrete Empirical Interpolation Method (DEIM) and the LSTM is applied to the reduced system resulting in a DEIM-LSTM network for developing a surrogate for prediction. The network is further enhanced by incorporating the physics information into the loss function resulting in a physics-informed LSTM (LSTM-PINN) for predicting the rigid body dynamics of box motion. The performance of predictions for these networks is assessed for the two-dimensional wave basin WSI problem as a proof-of-concept demonstration.

physics.flu-dyn↗

arXiv2019

Models for Predicting Transonic Flutter of a Wing-Section with Sloshing in an Embedded Fuel Tank

The present study focuses on the development, application, and comparison of three computational frameworks of varying fidelities for assessing the effects of fuel sloshing in internal fuel tanks on the aeroelastic characteristics of a wing section. The first approach uses the coupling of compressible flow solver for external aerodynamics integrated with structural solver and incompressible multiphase flow solver for fuel sloshing in the embedded fuel tank As time-domain flutter solution of these coupled solvers is computationally expensive, two approximate surrogate models to emulate sloshing flows are considered. One surrogate model utilizes a linearised approach for sloshing load computations by creating an Equivalent Mechanical System (EMS) with its parameters derived from potential flow theory. The other surrogate model aims to efficiently describe the dominant dynamic characteristics of the underlying system by employing the Radial Basis Function Neural Networks (RBF-NN) using limited CFD-based data to calibrate this model. The flutter boundaries of a wing section with and without the effects of fuel sloshing are compared. The limitation of the EMS surrogate to represent nonlinearities are reflected in this study. The RBF-NN surrogate shows remarkable agreement with the high-fidelity solution for sloshing with significantly low computational cost, thereby motivating extension to three-dimensional problems.

physics.flu-dyn↗

arXiv2019

Machine Learning Surrogates for Predicting Response of an Aero-Structural-Sloshing System

This study demonstrates the feasibility of developing machine learning (ML) surrogates based on Recurrent Neural Networks (RNN) for predicting the unsteady aeroelastic response of transonic pitching and plunging wing-fuel tank sloshing system by considering an approximate simplified model of an airfoil in transonic flow and sloshing loads from a partially filled fuel tank rigidly embedded inside the airfoil and undergoing a free unsteady motion. The ML surrogates are then used to predict the aeroelastic response of the coupled system. The external aerodynamic loads on the airfoil and the two-phase sloshing loads data for training the RNN are generated using open-source computational fluid dynamics (CFD) codes. The aerodynamic force and moment coefficients are predicted from the surrogate model based on its motion history. Similarly, the lateral and vertical forces and moments from fuel sloshing in the fuel tank are predicted using the surrogate model resulting from the motion of the embedded fuel tank. Comparing the free motion of the airfoil without sloshing tank to the free motion of the airfoil with a partially-filled fuel tank shows that the effects of sloshing on the aeroelastic motion of the aero-structural system. The effectiveness of the predictions from RNN are then assessed by comparing with the results from the high-fidelity coupled aero-structural-fuel tank sloshing simulations. It is demonstrated that the surrogate models can accurately and economically predict the coupled aero-structural motion.

cs.CE↗