arXiv subjects

Nagabhushana Rao Vadlamani

Publications and source records attributed to Nagabhushana Rao Vadlamani.

7 recordsLinked to original sources

arXiv2026

SCoReT: Super-Resolution Compression and Reconstruction of Turbulent Flows

High-fidelity simulations of the Navier--Stokes equations (NSE) generate massive amounts of data, motivating the need for efficient compression and reconstruction strategies for turbulent flows. At the same time, reconstructing flow fields from sparse measurements while retaining spectral content, turbulence statistics, and coherent structures remains a major challenge. This work investigates two complementary paradigms for turbulent flow reconstruction: supervised reconstruction and physics-informed reconstruction, in the context of transition to turbulence induced by three-dimensional distributed roughness elements. We introduce a vorticity-augmented supervised approach and a physics-informed approach, implemented through a partially assisted compressible PINN formulation based on the three-dimensional unsteady compressible Navier--Stokes equations. Reconstruction performance is evaluated at different sparsity levels using instantaneous velocity fields, mean-squared error, energy spectra, Reynolds stresses, turbulent kinetic energy, and Q-criterion isosurfaces. Rather than establishing a universal winner, the present study characterises the respective strengths, limitations, and operating regimes of these two approaches. The results indicate that at lower sparsity levels, the vorticity-augmented supervised model yields the lowest reconstruction error, recovers key statistical and spectral features, and enables substantial data compression. The PINN shows potential to reconstruct turbulent flows from sparse measurements without high-resolution labels and exhibits comparatively stable held-out extrapolation behaviour at higher sparsity. These results suggest the potential of combining data-driven and physics-informed learning for flow data compression and physics-informed reconstruction of turbulent flows from sparse data.

physics.flu-dyn↗

arXiv2026

Acoustic Loading Beneath High-Speed Flow Over a Compression Ramp at Different Angles

Large-eddy simulations are performed to characterize the pressure fluctuations beneath a hypersonic boundary layer approaching a compression corner. The simulations are carried out at Mach 6.04 and an inlet momentum-thickness Reynolds number of $Re_θ=4340$. The compression-corner angle is varied over $10^\circ$, $20^\circ$, $30^\circ$, and $34^\circ$ spanning attached to strongly separated regimes. The peak root-mean-square wall-pressure fluctuations intensity increases sharply with separation strength, rising by $312\%$ from R20 to R30 and a further $67\%$ from R30 to R34, with the peak located downstream of reattachment. Notably, acoustic loading increases from 140 dB in the approach flow to $\approx177$ dB downstream of reattachment in the $34^\circ$ case. Further analysis reveals that intense intermittent pressure events are concentrated near the shock foot, spatially distinct from the peak acoustic-loading region, where fluctuations are relatively sustained. With increasing interaction strength, spectral energy shifts from turbulence-dominated high frequencies to broadband low-frequency motion. Band-isolated acoustic loading maps reveal high-frequency fluctuations as the dominant contributor across the interaction zone, with low-frequency fluctuations becoming locally comparable in the R34 case near the separation region. Spatio-temporal maps of bandpass-filtered pressure fluctuations reveal downstream convecting Kelvin-Helmholtz structures and upstream propagating pressure waves near the shock foot.

physics.flu-dyn↗

arXiv2025

Accuracy and scalability of asynchronous compressible flow solver for transitional flows

To overcome the communication bottlenecks observed in state-of-the-art parallel time-dependent flow solvers at extreme scales, an asynchronous computing approach that relaxes communication and synchronization at a mathematical level was previously developed. This approach preserves high-order accuracy of computations near processing element boundaries using asynchrony-tolerant (AT) schemes while significantly improving the scalability. The numerical properties of the AT schemes were studied based on simple linear and nonlinear partial differential equations (PDEs) in previous works. Allowing asynchrony in numerical schemes can minimize communication overheads in a parallel setting in two ways: one that avoids communication over a few predetermined time steps, and the other that initiates communications without enforcing synchronization. In this study, the asynchronous algorithms are incorporated into the high-order compressible flow solver COMP-SQUARE, which solves practically relevant flow problems in complex geometries in a multi-block framework. The numerical efficacy and scalability of the two asynchronous algorithms are demonstrated for three test cases: isentropic advection of a vortex, the Taylor-Green vortex, and a much more sensitive case of the flow transitioning on a NACA0012 airfoil. Speed-ups of up to $4\times$ with respect to the baseline synchronous algorithm are observed in the scaling experiments performed on up to 18,432 cores. The results of this study demonstrate the applicability of AT schemes on established CFD solvers to improve scalability at extreme scales as the scientific computing environment moves to the exascale era.

physics.comp-ph↗

arXiv2023

Artificial Neural Networks and Guided Gene Expression Programming to Predict Wall Pressure Spectra Beneath Turbulent Boundary Layers

This study evaluates the efficacy of two machine learning (ML) techniques, namely artificial neural networks (ANN) and gene expression programming (GEP) that use data-driven modeling to predict wall pressure spectra (WPS) underneath turbulent boundary layers. Different datasets of WPS from experiments and high-fidelity numerical simulations covering a wide range of pressure gradients and Reynolds numbers are considered. For both ML methods, an optimal hyperparameter environment is identified that yields accurate predictions. ANN is observed to be faster and more accurate than GEP with an order of magnitude lower training time and logarithmic mean squared error ($lMSE$), despite a higher memory consumption. Novel training schemes are devised to address the shortcomings of GEP. These include (a) ANN-assisted GEP to reduce the noise in the training data, (b) exploiting the low and high-frequency trends to guide the GEP search, and (c) a stepped training strategy where the chromosomes are first trained on the canonical datasets followed by the datasets with complex features. When compared to the baseline scheme, these training strategies accelerated convergence and resulted in models with superior accuracy ($\approx 30\%$ reduction in the median $lMSE$) and higher reliability ($\approx 75\%$ reduction in the spread of $lMSE$ in the interquartile range). The final GEP models captured the complex trends of WPS across varying flow conditions and pressure gradients, surpassing the accuracy of Goody's model.

physics.flu-dyn↗

arXiv2022

Deep Neural Networks to Correct Sub-Precision Errors in CFD

Information loss in numerical physics simulations can arise from various sources when solving discretized partial differential equations. In particular, errors related to numerical precision ("sub-precision errors") can accumulate in the quantities of interest when the simulations are performed using low-precision 16-bit floating-point arithmetic compared to an equivalent 64-bit simulation. On the other hand, low-precision computation is less resource intensive than high-precision computation. Several machine learning techniques proposed recently have been successful in correcting errors due to coarse spatial discretization. In this work, we extend these techniques to improve CFD simulations performed with low numerical precision. We quantify the precision-related errors accumulated in a Kolmogorov forced turbulence test case. Subsequently, we employ a Convolutional Neural Network together with a fully differentiable numerical solver performing 16-bit arithmetic to learn a tightly-coupled ML-CFD hybrid solver. Compared to the 16-bit solver, we demonstrate the efficacy of the hybrid solver towards improving various metrics pertaining to the statistical and pointwise accuracy of the simulation.

physics.flu-dyn↗

arXiv2022

On the use of high order central difference schemes for differential equation based wall distance computations

A computationally efficient high-order solver is developed to compute the wall distances by solving the relevant partial differential equations, namely: Eikonal, Hamilton-Jacobi (HJ) and Poisson equations. In contrast to the upwind schemes widely used in the literature, we explore the suitability of high-order central difference schemes (explicit/compact) for the wall-distance computation. While solving the Hamilton-Jacobi equation, the high-order central difference schemes performed approximately $1.4-2.8$ times faster than the upwind schemes with a marginal improvement in the solution accuracy. A new pseudo HJ formulation based on the localized artificial diffusivity (LAD) approach has been proposed. It is demonstrated to predict results with an accuracy comparable to that of the Eikonal equation and the simulations are $\approx$ 1.5 times faster than the baseline HJ solver using upwind schemes. A curvature correction is also incorporated in the HJ equation to correct for the near-wall errors due to concave/convex wall curvatures. We demonstrate the efficacy of the proposed methods on both the steady and unsteady test cases and exploit the unsteady wall-distance solver to estimate the instantaneous shape and burning surface area of a dendrite propellant grain in a solid propellant rocket motor.

cs.CE↗

arXiv2021

Artificial Compressibility Approaches in Flux Reconstruction for Incompressible Viscous Flow Simulations

Several competing artificial compressibility methods for the incompressible flow equations are examined using the high-order flux reconstruction method. The established artificial compressibility method (ACM) of \citet{Chorin1967} is compared to the alternative entropically damped (EDAC) method of \citet{Clausen2013}, as well as an ACM formulation with hyperbolised diffusion. While the former requires the solution to be converged to a divergence free state at each physical time step through pseudo iterations, the latter can be applied explicitly. We examine the sensitivity of both methods to the parameterisation for a series of test cases over a range of Reynolds numbers. As the compressibility is reduced, EDAC is found to give linear improvements in divergence whereas ACM yields diminishing returns. For the Taylor--Green vortex, EDAC is found to perform well; however on the more challenging circular cylinder at $Re=3900$, EDAC gives rise to early transition of the free shear-layer and over-production of the turbulence kinetic energy. This is attributed to the spatial pressure fluctuations of the method. Similar behaviour is observed for an aerofoil at $Re=60,000$ with an attached transitional boundary layer. It is concluded that hyperbolic diffusion of ACM can be beneficial but at the cost of case setup time, and EDAC can be an efficient method for incompressible flow. However, care must be taken as pressure fluctuations can have a significant impact on physics and the remedy causes the governing equation to become overly stiff.

physics.flu-dyn↗