arXiv subjects

Sahaj Jain

Publications and source records attributed to Sahaj Jain.

3 recordsLinked to original sources

arXiv2022

Prediction of drag components on rough surfaces using effective models

Owing to the multiscale nature and the consequent high computational cost associated with simulations of flows over rough surfaces, effective models are being developed as a practical means of dealing with such flows. Existing effective models focus primarily on accurately predicting interface velocities using slip length. Moreover, they are concerned mainly with flat interfaces and do not directly address the drag computation. In this work, we formulate the Transpiration-Resistance model in polar coordinates and address the challenge of computing drag components on rough surfaces. Like slip length, we introduce two constitutive parameters called shear and pressure correction factors that encompass information about how the total drag is partitioned into viscous and pressure components. Computation of these non-empirical parameters does not necessitate solving additional microscale problems; they can be obtained from the same microscale problem used for the slip-length calculation. We demonstrate the effectiveness of the proposed parameters for the Couette flow over rough surfaces. Moreover, using the flow over a rough cylinder as an example, we present the accuracy of predicting interface velocity and drag components by comparing the effective model results with those obtained from geometry-resolved simulations. Numerical simulations presented in this paper prove that we can accurately capture both viscous and pressure drag over rough surfaces for flat- and circular-interface problems by using the proposed constitutive parameters.

physics.flu-dyn↗

arXiv2021

Self-similar velocity and solid fraction profiles in silos with eccentrically-located outlets

We examine the gravity-induced flow of dry and cohesionless granular media through an outlet placed eccentrically in a planar silo, employing computations based on a soft-sphere discrete element method. The vertical velocity profiles, measured at the outlet, are self-similar when the eccentricity is given in terms of the gap ($s$) between the wall and the corner of the outlet nearest to the wall. On the other hand, the self-similarity of vertical velocity does not always hold for all eccentricities ($e$) given by the distance between the centers of an outlet and the silo base, which is a typical metric of eccentricity. Similar observations are noted for the profiles of solid fraction. For the former measure of eccentricity, the flow conditions are observed to be similar for different outlet sizes. In contrast, we observe, the latter leads to differing flow patterns for the highest eccentricity wherein the largest outlet touches the sidewall and the rest are located at a distance. This study establishes the importance of $s$ compared to $e$ from the viewpoint of the self-similarity of the vertical velocity and solid fraction profiles at the outlet, and generalizes the notion of the scaling of velocity and solid fraction reported by Janda et al. [Phys. Rev. Lett. 108, 248001 (2012)] in a silo with a centric exit to the one with eccentric granular discharge.

cond-mat.soft↗

arXiv2021

Generation of COVID-19 Chest CT Scan Images using Generative Adversarial Networks

SARS-CoV-2, also known as COVID-19 or Coronavirus, is a viral contagious disease that is infected by a novel coronavirus, and has been rapidly spreading across the globe. It is very important to test and isolate people to reduce spread, and from here comes the need to do this quickly and efficiently. According to some studies, Chest-CT outperforms RT-PCR lab testing, which is the current standard, when diagnosing COVID-19 patients. Due to this, computer vision researchers have developed various deep learning systems that can predict COVID-19 using a Chest-CT scan correctly to a certain degree. The accuracy of these systems is limited since deep learning neural networks such as CNNs (Convolutional Neural Networks) need a significantly large quantity of data for training in order to produce good quality results. Since the disease is relatively recent and more focus has been on CXR (Chest XRay) images, the available chest CT Scan image dataset is much less. We propose a method, by utilizing GANs, to generate synthetic chest CT images of both positive and negative COVID-19 patients. Using a pre-built predictive model, we concluded that around 40% of the generated images are correctly predicted as COVID-19 positive. The dataset thus generated can be used to train a CNN-based classifier which can help determine COVID-19 in a patient with greater accuracy.

eess.IV↗