arXiv subjects

Edward Smith

Publications and source records attributed to Edward Smith.

9 recordsLinked to original sources

arXiv2026

Molecular dynamics simulation of high slip flow of water confined between graphene nanochannels at experimentally accessible strain rates

The transient time correlation function method (TTCF) has emerged as a powerful methodology for accurately probing systems at low shear rates. In the present study, TTCF was used to evaluate the shear rate dependence of the slip length in a high-slip system consisting of water confined between graphene walls at experimentally accessible shear rates, for which classical nonequilibrium molecular dynamics (NEMD) is unfeasible. The corresponding Navier friction coefficient was computed for all shear rates spanning six orders of magnitude and compared with the equilibrium limit. We report for the first time NEMD results obtained at experimentally accessible shear rates using the TTCF approach for a system that has attracted significant interest over the past decades. The slip length calculated with TTCF is in good agreement with previous equilibrium molecular dynamics simulations and experiments. Our aim here is to highlight the extraordinary power of TTCF, particularly for high-slip (low strain-rate) systems, and to verify that equilibrium methods directly match NEMD measurements at experimentally accessible strain rates.

physics.chem-ph↗

arXiv2023

N-body simulation of binary star mass transfer using NVDIA GPUs

Binary star systems are of particular interest to astronomers because they can be used as astrophysical laboratories to study the properties and processes of stars. Between 70% to 90% of the stars in our galaxy are part of a binary star system. Among the many types of binary systems observed, the dynamics of semi-detached and contact systems are the most interesting because they exhibit mass transfer, which changes the composition and life cycle of both stars. The time scales of the mass transfer process are extremely large which makes the process impossible to capture through physical observation. Computer simulations have proved invaluable in refining our understanding of the mass transfer processes. Here we introduce an intuitive, computationally efficient, gravity centered model that simulates the filling of the Roche lobe of an expanding star and its transfer of mass through the first Lagrangian point.

astro-ph.SR↗

arXiv2022

A perspective on the microscopic pressure (stress) tensor: history, current understanding, and future challenges

The pressure tensor (equivalent to the negative stress tensor) at both microscopic and macroscopic levels is fundamental to many aspects of engineering and science, including fluid dynamics, solid mechanics, biophysics, and thermodynamics. In this perspective paper, we review methods to calculate the microscopic pressure tensor. Connections between different pressure forms for equilibrium and non-equilibrium systems are established. We also point out several challenges in the field, including the historical controversies over the definition of the microscopic pressure tensor; the difficulties with many-body and long-range potentials; the insufficiency of software and computational tools; and the lack of experimental routes to probe the pressure tensor at the nanoscale. Possible future directions are suggested.

physics.chem-ph↗

arXiv2022

A Novel CFD-DEM Coarse-Graining Method Based on the Voronoi Tessellation

In unresolved flow CFD-DEM simulations, the porosity values for each CFD cell are determined using a coarse-graining algorithm. While this approach enables coupled simulations of representative numbers of particles, the influence of the porosity local to the particles on the fluid-particle interaction force is not captured. This contribution considers a two-grid coarse-graining method that determines a local porosity for each particle using a radical Voronoi tessellation of the system. A relatively fine, regular point cloud is used to map these local porosity data to the CFD cells. The method is evaluated using two different cases considering both disperse and tightly packed particles. The data show that the method conserves porosity data, is reasonably grid-independent and can generate a relatively smooth porosity field. The new method can more accurately predict the fluid-particle interactive force for polydisperse particle system than alternative methods that have been implemented in unresolved CFD-DEM codes.

physics.flu-dyn↗

arXiv2021

Observations on cycles in a variant of the Collatz Graph

It is well known that the Collatz Conjecture can be reinterpreted as the Collatz Graph with root vertex 1, asking whether all positive integers are within the tree generated. It is further known that any cycle in the Collatz Graph can be represented as a tuple, given that inputting them into a function outputs an odd positive integer; yet, it is an open question as to whether there exist any tuples not of the form $(2,2,...,2)$, thus disproving the Collatz Conjecture. In this paper, we explore a variant of the Collatz Graph, which allows the 3x+1 operation to be applied to both even and odd integers. We prove an analogous function for this variant, called the Loosened Collatz Function (LCF), and observe various properties of the LCF in relation to tuples and outputs. We prove a certain underlying unique factorisation monoid structure for tuples to the LCF and provide a geometric interpretation of satisfying tuples in higher dimensions. Research into this variant of the Collatz Graph may provide reason as to why there exist no cycles in the Collatz Graph.

math.GM↗

arXiv2019

Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research

We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research. Kaolin provides efficient implementations of differentiable 3D modules for use in deep learning systems. With functionality to load and preprocess several popular 3D datasets, and native functions to manipulate meshes, pointclouds, signed distance functions, and voxel grids, Kaolin mitigates the need to write wasteful boilerplate code. Kaolin packages together several differentiable graphics modules including rendering, lighting, shading, and view warping. Kaolin also supports an array of loss functions and evaluation metrics for seamless evaluation and provides visualization functionality to render the 3D results. Importantly, we curate a comprehensive model zoo comprising many state-of-the-art 3D deep learning architectures, to serve as a starting point for future research endeavours. Kaolin is available as open-source software at https://github.com/NVIDIAGameWorks/kaolin/.

cs.CV↗

arXiv2018

Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation

We consider the problem of scaling deep generative shape models to high-resolution. Drawing motivation from the canonical view representation of objects, we introduce a novel method for the fast up-sampling of 3D objects in voxel space through networks that perform super-resolution on the six orthographic depth projections. This allows us to generate high-resolution objects with more efficient scaling than methods which work directly in 3D. We decompose the problem of 2D depth super-resolution into silhouette and depth prediction to capture both structure and fine detail. This allows our method to generate sharp edges more easily than an individual network. We evaluate our work on multiple experiments concerning high-resolution 3D objects, and show our system is capable of accurately predicting novel objects at resolutions as large as 512$\mathbf{\times}$512$\mathbf{\times}$512 -- the highest resolution reported for this task. We achieve state-of-the-art performance on 3D object reconstruction from RGB images on the ShapeNet dataset, and further demonstrate the first effective 3D super-resolution method.

cs.CV↗

arXiv2017

The pressure tensor across a liquid-vapour interface

Inhomogeneous fluids exhibit physical properties that are neither uniform nor isotropic. The pressure tensor is a case in point, key to the mechanical description of the interfacial region. Kirkwood and Buff, and later Irving and Kirkwood, obtained a formal treatment based on the analysis of the pressure across a planar surface [J.G. Kirkwood and F.P. Buff, J. Chem. Phys., 17(3), (1949), J.H. Irving and J.G. Kirkwood, J. Chem. Phys. 18, 817 (1950)]. We propose a generalisation of Irving and Kirkwood's argument to fluctuating, non-planar surfaces and obtain an expression for the pressure tensor that is not smeared by thermal fluctuations at the molecular scale and corresponding capillary waves [F.P. Buff, R.A. Lovett, and F.H. Stillinger, Jr., Phys. Rev. Lett. 15, 621 (1965)]. We observe the emergence of surface tension, defined as an excess tangential stress, acting exactly across the dividing surface at the sharpest molecular resolution. The new statistical mechanical expressions extend current treatments to fluctuating inhomogeneous systems far from equilibrium.

cond-mat.stat-mech↗

arXiv2017

Improved Adversarial Systems for 3D Object Generation and Reconstruction

This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain previously, they are notoriously hard to train, especially for the complex joint data distribution over 3D objects of many categories and orientations. Our method extends previous work by employing the Wasserstein distance normalized with gradient penalization as a training objective. This enables improved generation from the joint object shape distribution. Our system can also reconstruct 3D shape from 2D images and perform shape completion from occluded 2.5D range scans. We achieve notable quantitative improvements in comparison to existing baselines

cs.CV↗