arXiv subjects

Som Dutta

Publications and source records attributed to Som Dutta.

10 recordsLinked to original sources

arXiv2026

Effect of initial Rayleigh mode on drop deformation under impulsive acceleration

One of the fundamental ways of representing a droplet shape is through its Rayleigh-modes, where each mode corresponds to distinct surface-energy. Previous studies have focused on the effect of these modes on free oscillations of drops. In this paper, we systematically quantify how the different prescribed initial axisymmetric Rayleigh modes modulate aerodynamic energy uptake and the resulting deformation of an impulsively accelerated drop. Using experimentally validated VOF-based multiphase numerical simulations, we isolate the coupled effects of finite-amplitude surface oscillation modes and the associated initial surface-energy state by initializing the drops with well-defined $(n,0)$ modes and phases $\{0,\pi\}$, while conserving the equivalent drop volume. We find that the deformation outcome is governed by the drag due to the drop's initial geometry, and the dynamic coupling between the free modal oscillations and the forced aerodynamic deformation. We find that constructive superposition amplify deformation, whereas destructive superposition can stabilize the drop even when the aerodynamic forcing is sufficient to deform an analogous spherical drop to breakup. Initial modes and phases that channel a larger fraction of the input power into deformation, in the form of oscillatory kinetic energy and additional surface energy, attain larger deformations and are closer to the fragmentation threshold. These coupling effects are especially pronounced in high-viscosity systems, where viscous dissipation is large and facilitates the transfer of a larger fraction of the total energy to translational kinetic energy instead of oscillatory kinetic energy. For low density-ratio systems, early-time coupling and energy transfer is the dominant mechanism that governs drop deformation.

physics.flu-dyn↗

arXiv2025

Quantifying Out-of-Training Uncertainty of Neural-Network based Turbulence Closures

Neural-Network (NN) based turbulence closures have been developed for being used as pre-trained surrogates for traditional turbulence closures, with the aim to increase computational efficiency and prediction accuracy of CFD simulations. The bottleneck to the widespread adaptation of these ML-based closures is the relative lack of uncertainty quantification (UQ) for these models. Especially, quantifying uncertainties associated with out-of-training inputs, that is when the ML-based turbulence closures are queried on inputs outside their training data regime. In the current paper, a published algebraic turbulence closure1 has been utilized to compare the quality of epistemic UQ between three NN-based methods and Gaussian Process (GP). The three NN-based methods explored are Deep Ensembles (DE), Monte-Carlo Dropout (MCD), and Stochastic Variational Inference (SVI). In the in-training results, we find the exact GP performs the best in accuracy with a Root Mean Squared Error (RMSE) of $2.14 \cdot 10^{-5}$ followed by the DE with an RMSE of $4.59 \cdot 10^{-4}$. Next, the paper discusses the performance of the four methods for quantifying out-of-training uncertainties. For performance, the Exact GP yet again is the best in performance, but has similar performance to the DE in the out-of-training regions. In UQ accuracy for the out-of-training case, SVI and DE hold the best miscalibration error for one of the cases. However, the DE performs the best in Negative Log-Likelihood for both out-of-training cases. We observe that for the current problem, in terms of accuracy GP > DE > SV I > MCD. The DE results are relatively robust and provide intuitive UQ estimates, despite performing naive ensembling. In terms of computational cost, the GP is significantly higher than the NN-based methods with a $O(n^3)$ computational complexity for each training step

cs.LG↗

arXiv2025

General Field Evaluation in High-Order Meshes on GPUs

Robust and scalable function evaluation at any arbitrary point in the finite/spectral element mesh is required for querying the partial differential equation solution at points of interest, comparison of solution between different meshes, and Lagrangian particle tracking. This is a challenging problem, particularly for high-order unstructured meshes partitioned in parallel with MPI, as it requires identifying the element that overlaps a given point and computing the corresponding reference space coordinates. We present a robust and efficient technique for general field evaluation in large-scale high-order meshes with quadrilaterals and hexahedra. In the proposed method, a combination of globally partitioned and processor-local maps are used to first determine a list of candidate MPI ranks, and then locally candidate elements that could contain a given point. Next, element-wise bounding boxes further reduce the list of candidate elements. Finally, Newton's method with trust region is used to determine the overlapping element and corresponding reference space coordinates. Since GPU-based architectures have become popular for accelerating computational analyses using meshes with tensor-product elements, specialized kernels have been developed to utilize the proposed methodology on GPUs. The method is also extended to enable general field evaluation on surface meshes. The paper concludes by demonstrating the use of proposed method in various applications ranging from mesh-to-mesh transfer during r-adaptivity to Lagrangian particle tracking.

cs.MS↗

arXiv2024

Quantifying Model Uncertainty of Neural Network-based Turbulence Closures

With increasing computational demand, Neural-Network (NN) based models are being developed as pre-trained surrogates for different thermohydraulics phenomena. An area where this approach has shown promise is in developing higher-fidelity turbulence closures for computational fluid dynamics (CFD) simulations. The primary bottleneck to the widespread adaptation of these NN-based closures for nuclear-engineering applications is the uncertainties associated with them. The current paper illustrates three commonly used methods that can be used to quantify model uncertainty in NN-based turbulence closures. The NN model used for the current study is trained on data from an algebraic turbulence closure model. The uncertainty quantification (UQ) methods explored are Deep Ensembles, Monte-Carlo Dropout, and Stochastic Variational Inference (SVI). The paper ends with a discussion on the relative performance of the three methods for quantifying epistemic uncertainties of NN-based turbulence closures, and potentially how they could be further extended to quantify out-of-training uncertainties. For accuracy in turbulence modeling, paper finds Deep Ensembles have the best prediction accuracy with an RMSE of $4.31\cdot10^{-4}$ on the testing inputs followed by Monte-Carlo Dropout and Stochastic Variational Inference. For uncertainty quantification, this paper finds each method produces unique Epistemic uncertainty estimates with Deep Ensembles being overconfident in regions, MC-Dropout being under-confident, and SVI producing principled uncertainty at the cost of function diversity.

physics.flu-dyn↗

arXiv2022

On the Threshold of Drop Fragmentation under Impulsive Acceleration

Secondary fragmentation of an impulsively accelerated drop depends on fluid properties and velocity of the ambient. The critical Weber number $(\mathit{We}_{cr})$, the minimum Weber number at which a drop undergoes non-vibrational breakup, depends on density ratio $(\rho)$, the drop $(\mathit{Oh}_d)$, and the ambient $(\mathit{Oh}_o)$ Ohnesorge numbers. The current study uses VoF based interface-tracking multiphase flow simulations to quantify the effect of different non-dimensional groups on the threshold at which secondary fragmentation occur. For $\mathit{Oh}_d \leq 0.1$, a decrease in $\mathit{Oh}_d$ was found to significantly influence the breakup morphology, plume formation, and $\mathit{We}_{cr}$. The balance between the pressure difference between the poles and the periphery, and the shear stresses on the upstream surface, was found to be controlled by $\rho$ and $\mathit{Oh}_o$. These forces induce flow inside the initially spherical drop, resulting in deformation into pancakes and eventually the breakup morphology of forward/backward bag. The evolution pathways of the drop morphology based on their non-dimensional groups have been charted. With inclusion of the data from the expanded parameter-space, the traditional $\mathit{We}_{cr}-\mathit{Oh}_d$ diagram used to illustrate the dependence of critical Weber number on $\mathit{Oh}_d$, was found to be inadequate in predicting the minimum initial $\mathit{We}$ required to undergo fragmentation. A new non-dimensional parameter $C_{breakup}$ is derived based on the competition between the forces driving the drop deformation and the forces resisting the drop deformation. Tested using available experimental data and current simulations, $C_{breakup}$ is found to be a robust predictor for the threshold of drop fragmentation.

physics.flu-dyn↗

arXiv2021

Multirate Timestepping for the Incompressible Navier-Stokes Equations in Overlapping Grids

We develop a multirate timestepper for semi-implicit solutions of the unsteady incompressible Navier-Stokes equations (INSE) based on a recently-developed multidomain spectral element method (SEM). For {\em incompressible} flows, multirate timestepping (MTS) is particularly challenging because of the tight coupling implied by the incompressibility constraint, which manifests as an elliptic subproblem for the pressure at each timestep. The novelty of our approach stems from the development of a stable overlapping Schwarz method applied directly to the Navier-Stokes equations, rather than to the convective, viscous, and pressure substeps that are at the heart of most INSE solvers. Our MTS approach is based on a predictor-corrector (PC) strategy that preserves the temporal convergence of the underlying semi-implicit timestepper. We present numerical results demonstrating that this approach scales to an arbitrary number of overlapping grids, accurately models complex turbulent flow phenomenon, and improves computational efficiency in comparison to singlerate timestepping-based calculations.

physics.flu-dyn↗

arXiv2020

Stability analysis of a singlerate and multirate predictor-corrector scheme for overlapping grids

We use matrix stability analysis for a singlerate and multirate predictor-corrector scheme (PC) used to solve the incompressible Navier-Stokes equations (INSE) in overlapping grids. By simplifying the stability analysis with the unsteady heat equation in 1D, we demonstrate that, as expected, the stability of the PC scheme increases with increase in the resolution and overlap of subdomains. For singlerate timestepping, we also find that the high-order PC scheme is stable when the number of corrector iterations ($Q$) is odd. This difference in the stability of odd- and even-$Q$ is novel and has not been demonstrated in the literature for overlapping grid-based methods. We address the odd-even behavior in the stability of the PC scheme by modifying the last corrector iterate, which leads to a scheme whose stability increases monotonically with $Q$. For multirate timestepping, we observe that the stability of the PC scheme depends on the timestep ratio ($η$). For $η=2$, even-$Q$ is more stable than odd-$Q$. For $η\ge3$, even-$Q$ is more stable than odd-$Q$ for a small nondimensional timestep size and the odd-even behavior vanishes as the timestep size increases. The stability analysis presented in this work gives novel insight into a high-order temporal discretization for ODEs and PDEs, and has helped us develop an improved PC scheme for solving the incompressible Navier-Stokes equations.

math.NA↗

arXiv2020

Scalability of High-Performance PDE Solvers

Performance tests and analyses are critical to effective HPC software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this paper, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for PDEs that govern a wide range of physical applications. We consider a sequence of PDE- motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.

cs.PF↗

arXiv2020

Direct Numerical Simulation of Rotating Ellipsoidal Particles using Moving Nonconforming Schwarz-Spectral Element Method

We present application of a highly-scalable overlapping grid-based nonconforming Schwarz-spectral element method (Schwarz-SEM) to study the dynamics of rotating ellipsoidal particles. The current study is one of the first to explore the effect of rotation on ellipsoidal particles using fully resolved simulations (direct numerical simulation). The rotating ellipsoidal particles show substantial difference in the dynamics of the flow, when compared against non-rotating particles. The difference is primarily due to periodic attachment and separation of the flow to the surface of the particle for the rotating cases, which results in a higher drag on the particles when compared to the corresponding non-rotating cases. The dynamics is also different from a rotating spherical particle, where a steady shear layer develops near the surface of the sphere. For the rotating ellipsoidal particles, this mechanism results in a phase-difference between the position of observed maximum and minimum drag, and the position of expected maximum and minimum drag (i.e., maximum and minimum projected area). A similar phase-difference is also observed for the lift acting on the rotating ellipsoidal particles. The results presented here demonstrate the importance of explicitly modeling the shape and rotation of particles when we study the dynamics of non-spherical particles. Finally, the study also validates the use of non-conforming Schwarz-SEM for tackling problems in fully resolved particulate flow dynamics.

physics.flu-dyn↗

arXiv2018

Nonconforming Schwarz-Spectral Element Methods For Incompressible Flow

We present scalable implementations of spectral-element-based Schwarz overlapping (overset) methods for the incompressible Navier-Stokes (NS) equations. Our SEM-based overset grid method is implemented at the level of the NS equations, which are advanced independently within separate subdomains using interdomain velocity and pressure boundary-data exchanges at each timestep or sub-timestep. Central to this implementation is a general, robust, and scalable interpolation routine, {\em gslib-findpts}, that rapidly determines the computational coordinates (processor $p$, element number $e$, and local coordinates $(r,s,t) \in \hatΩ := [-1,1]^3$) for any arbitrary point $\mathbf{x}^* =(x^*,y^*,z^*) \in Ω\subset {\rm I\!R}^3$. The communication kernels in $gslib$ execute with at most $\log P$ complexity for $P$ MPI ranks, have scaled to $P > 10^6$, and obviate the need for development of any additional MPI-based code for the Schwarz implementation. The original interpolation routine has been extended to account for multiple overlapping domains. The new implementation discriminates the possessing subdomain by distance to the domain boundary, such that the interface boundary data is taken from the inner-most interior points. We present application of this approach to several heat transfer and fluid dynamic problems, discuss the computation/communication complexity and accuracy of the approach, and present performance measurements for $P > 12,000$.

physics.flu-dyn↗