arXiv subjects

Aswin Gnanaskandan

Publications and source records attributed to Aswin Gnanaskandan.

6 recordsLinked to original sources

arXiv2026

A Conservative Hybrid Eulerian-Lagrangian Method with Persistent Structure Tracking for Multiscale Cavitation

Hybrid Eulerian Lagrangian methods for multiscale cavitation represent grid resolved vapor structures in an Eulerian formulation and unresolved bubbles in a Lagrangian description, requiring transitions between the two representations as structures evolve. Existing approaches typically treat these transitions as local geometric operations, while resolved structures are identified independently at each time step, limiting the use of temporal history in representation changes. This work presents a conservative hybrid Eulerian Lagrangian framework with embedded, on the fly persistent structure tracking. Resolved vapor structures are detected using connected component labeling and reconciled across processor boundaries through a deterministic parallel procedure. Successive observations are associated using physics informed predictions of structure position and size, maintaining persistent identities and histories through birth, death, breakup, coalescence, and representation transitions. The retained history enables history aware Eulerian to Lagrangian transition criteria and initialization of Lagrangian bubbles with the radius and radius rate information required by the bubble dynamics model. Conservative bidirectional transfer operators preserve vapor mass and linear momentum while preventing simultaneous representation of the same vapor volume. Canonical tests verify persistent tracking through fragmentation, coalescence, and close crossings, repeated representation switching, conservation of mass and momentum, and deterministic parallel behavior.

physics.flu-dyn↗

arXiv2025

BubbleOKAN: A Physics-Informed Interpretable Neural Operator for High-Frequency Bubble Dynamics

In this work, we employ physics-informed neural operators to map pressure profiles from an input function space to the corresponding bubble radius responses. Our approach employs a two-step DeepONet architecture. To address the intrinsic spectral bias of deep learning models, our model incorporates the Rowdy adaptive activation function, enhancing the representation of high-frequency features. Moreover, we introduce the Kolmogorov-Arnold network (KAN) based two-step DeepOKAN model, which enhances interpretability (often lacking in conventional multilayer perceptron architectures) while efficiently capturing high-frequency bubble dynamics without explicit utilization of activation functions in any form. We particularly investigate the use of spline basis functions in combination with radial basis functions (RBF) within our architecture, as they demonstrate superior performance in constructing a universal basis for approximating high-frequency bubble dynamics compared to alternative formulations. Furthermore, we emphasize on the performance bottleneck of RBF while learning the high frequency bubble dynamics and showcase the advantage of using spline basis function for the trunk network in overcoming this inherent spectral bias. The model is systematically evaluated across three representative scenarios: (1) bubble dynamics governed by the Rayleigh-Plesset equation with a single initial radius, (2) bubble dynamics governed by the Keller-Miksis equation with a single initial radius, and (3) Keller-Miksis dynamics with multiple initial radii. We also compare our results with state-of-the-art neural operators, including Fourier Neural Operators, Wavelet Neural Operators, OFormer, and Convolutional Neural Operators. Our findings demonstrate that the two-step DeepOKAN accurately captures both low- and high-frequency behaviors, and offers a promising alternative to conventional numerical solvers.

cs.LG↗

arXiv2025

MFC 5.0: An exascale many-physics flow solver

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work, MFC 3.0, was published, documented, and made open-source by Bryngelson et al. CPC (2021) features numerous physical features, numerical methods, and scalable infrastructure. MFC 5.0 is a significant update to MFC 3.0, featuring a broad set of well-established and novel physical models and numerical methods, as well as the introduction of GPU and APU (or superchip) acceleration. We exhibit state-of-the-art performance and ideal scaling on the first two exascale supercomputers, OLCF's Frontier and LLNL's El Capitan. Combined with MFC's single-accelerator performance, MFC achieves exascale computation in practice and has achieved the largest-to-date public CFD simulation at 200 trillion grid points, earning it a 2025 ACM Gordon Bell Prize finalist. New physical features include the immersed boundary method, $N$-fluid phase change, Euler-Euler and Euler-Lagrange sub-grid bubble models, fluid-structure interaction, hypo- and hyper-elastic materials, chemically reacting flow, two-material surface tension, magnetohydrodynamics (MHD), and more. Numerical techniques now represent the current state-of-the-art, including general relaxation characteristic boundary conditions, WENO variants, Strang splitting for stiff sub-grid flow features, and low Mach number treatments. Weak scaling to tens of thousands of GPUs on OLCF's Summit and Frontier, and LLNL's El Capitan, achieves efficiencies within 5% of ideal to over 90% of their respective system sizes. Strong scaling results for a 16-fold increase in device count show parallel efficiencies exceeding 90% on OLCF Frontier.

physics.flu-dyn↗

arXiv2025

Hardware-Accelerated Phase-Averaging for Cavitating Bubbly Flows

We present a comprehensive validation, performance characterization, and scalability analysis of a hardware-accelerated phase-averaged multiscale solver designed to simulate acoustically driven dilute bubbly suspensions. The carrier fluid is modeled using the compressible Navier-Stokes equations. The dispersed phase is represented through two distinct subgrid formulations: a volume-averaged model that explicitly treats discrete bubbles within a Lagrangian framework, and an ensemble-averaged model that statistically represents the bubble population through a discretized distribution of bubble sizes. For both models, the bubble dynamics are modeled via the Keller--Miksis equation. For the GPU cases, we use OpenACC directives to offload computation to the GPUs. The volume-averaged model is validated against the analytical Keller-Miksis solution and experimental measurements, showing excellent agreement with root-mean-squared errors of less than 8% for both single-bubble oscillation and collapse scenarios. The ensemble-averaged model is validated by comparing it to volume-averaged simulations. On an NCSA Delta node with 4 NVIDIA A100 GPUs, we observe a speedup 16-fold compared to a 64-core AMD Milan CPU. The ensemble-averaged model offers additional reductions in computational cost by solving a single set of averaged equations, rather than multiple stochastic realizations. However, the volume-averaged model enables the interrogation of individual bubble dynamics, rather than the averaged statistics of the bubble dynamics. Weak and strong scaling tests demonstrate good scalability across both CPU and GPU platforms. These results show the proposed method is robust, accurate, and efficient for the multiscale simulation of acoustically driven dilute bubbly flows.

physics.flu-dyn↗

arXiv2024

Modeling and Simulation of the Evaporation and Drying of a Two Component Slurry Droplet

In this paper, we present a mathematical model and numerical simulation of the evaporation and drying process of a liquid droplet containing suspended solids. This type of drying is commonly encountered in manufacturing processes such as spray drying and spray pyrolysis, which have applications in industries such as food and pharmaceuticals. The proposed model consists of three stages. In the first stage, we consider the evaporation of the liquid in the presence of solid particles. The second stage involves the formation of a porous crust around a wet core region, with liquid evaporation occurring through the crust layer. Finally, the third stage involves sensible heating of the dry particle to reach ambient temperature. To solve the physical models governing these processes, we use a finite difference method with a moving grid methodology. This allows us to account for the moving interface between the crust and the wet core region of the droplet. In this study, we use a non-uniform temperature model that takes into account the spatial variation of temperature inside the droplet. We also assess the validity of a uniform temperature model. To validate our model, we compare it with experimental data on the drying of a single droplet containing colloidal silica particles. We find that our model agrees well with the experimental results. We rigorously examine assumptions made in the model, such as the shape of the solid particles and the continuum flow of vapor through the porous crust. In addition, we analyze the effects of drying conditions, such as the velocity, temperature, relative humidity, and concentration of solid particles, on the drying rate and the final morphology of the particle. Finally, we develop a regime map that can be used to determine whether the final particle will be solid or hollow, based on the operating conditions.

physics.flu-dyn↗

arXiv2018

Modeling of Microbubble Enhanced High Intensity Focused Ultrasound

Heat enhancement at the target in a High Intensity Focused Ultrasound (HIFU) field is investigated by considering the effects of the injection of microbubbles in the vicinity of the tumor to be ablated. The interaction between the bubble cloud and the HIFU field is investigated using a three-dimensional numerical model. The propagation of non-linear ultrasonic waves in the tissue or in a phantom medium is modeled using the compressible Navier-Stokes equations on a fixed grid, while the microbubbles dynamics and motion are modeled as discrete singularities, which are tracked in a Lagrangian framework. These two models are coupled to each other such that both the acoustic field and the bubbles influence each other. The temperature rise in the field is calculated by solving a heat transfer equation applied over a much longer time scale. The compressible continuum part of the model is validated by conducting HIFU simulation without microbubbles and comparing the pressure and temperature fields against available experiments. The coupled Eulerian-Lagrangian approach is then validated against existing experiments with a phantom tissue. The various mechanisms through which microbubbles enhance heat deposition are then examined in detail. The effects of the initial void fraction in the cloud are then sought by considering the changes in the attenuation of the primary ultrasonic wave and the modifications of the enhanced heat deposition in the focal region. Finally, the effects of the microbubble cloud size and its localization in the focal region are shown and the effects of these parameters in altering the temperature rise and the location of the temperature peak are discussed.

physics.flu-dyn↗