Showing papers on "Eulerian path published in 2022"
TL;DR: In this paper , the authors reviewed probabilistic and deterministic research methods, such as the Wells-Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method and the experimental approach, that have been used for studying the airborne transmission mechanism.
Abstract: Since the outbreak of COVID-19 in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has spread worldwide. This study summarized the transmission mechanisms of COVID-19 and their main influencing factors, such as airflow patterns, air temperature, relative humidity, and social distancing. The transmission characteristics in existing cases are providing more and more evidence that SARS CoV-2 can be transmitted through the air. This investigation reviewed probabilistic and deterministic research methods, such as the Wells–Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method, and the experimental approach, that have been used for studying the airborne transmission mechanism. The Wells–Riley equation and dose-response model are typically used for the assessment of the average infection risk. Only in combination with the Eulerian method or the Lagrangian method can these two methods obtain the spatial distribution of airborne particles' concentration and infection risk. In contrast with the Eulerian and Lagrangian methods, the Monte-Carlo model is suitable for studying the infection risk when the behavior of individuals is highly random. Although researchers tend to use numerical methods to study the airborne transmission mechanism of COVID-19, an experimental approach could often provide stronger evidence to prove the possibility of airborne transmission than a simple numerical model. All in all, the reviewed methods are helpful in the study of the airborne transmission mechanism of COVID-19 and epidemic prevention and control.
27 citations
TL;DR: In this paper , the second-gradient internal work functionals in Lagrangian and Eulerian descriptions were derived and the corresponding expressions for the Piola transformations of stress and double-stress tensors and of external forces and doubleforces.
Abstract: In this paper, we represent second-gradient internal work functionals in Lagrangian (referential) and Eulerian (spatial) descriptions, and we deduce the corresponding expressions for the Piola transformations of stress and double-stress tensors and of external forces and double-forces. We also derive, in both the Eulerian and Lagrangian description, the expression of surface and edge contact interactions (which include forces and double-forces) for second-gradient continua in terms of the normal and the curvature of contact boundary surfaces and edge shapes.
14 citations
TL;DR: In this article , the Piola transformations of the contact surface and line forces as well as double-forces are derived for external virtual work functionals compatible with second-gradient internal work functions.
14 citations
TL;DR: In this paper , the absolute nodal coordinate formulation (ANCF) is examined in the framework of the Arbitrary Lagrangian-Eulerian (ALE) formulation for cable simulation.
11 citations
TL;DR: In this paper , the authors focus on physical properties and circumstances of 3D particle-based measurements and what knowledge can be used for advancing reconstruction accuracy and spatial and temporal resolution, as well as completeness.
Abstract: In the past few decades various particle image–based volumetric flow measurement techniques have been developed that have demonstrated their potential in accessing unsteady flow properties quantitatively in various experimental applications in fluid mechanics. In this review, we focus on physical properties and circumstances of 3D particle–based measurements and what knowledge can be used for advancing reconstruction accuracy and spatial and temporal resolution, as well as completeness. The natural candidate for our focus is 3D Lagrangian particle tracking (LPT), which allows for position, velocity, and acceleration to be determined alongside a large number of individual particle tracks in the investigated volume. The advent of the dense 3D LPT technique Shake-The-Box in the past decade has opened further possibilities for characterizing unsteady flows by delivering input data for powerful data assimilation techniques that use Navier–Stokes constraints. As a result, high-resolution Lagrangian and Eulerian data can be obtained, including long particle trajectories embedded in time-resolved 3D velocity and pressure fields. Expected final online publication date for the Annual Review of Fluid Mechanics, Volume 55 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
11 citations
TL;DR: In this paper, a hybrid partitioned deep learning framework for the reduced-order modeling of moving interfaces and predicting fluid-structure interaction is presented, which combines two separate data-driven models for fluid and solid subdomains via DL-ROMs.
10 citations
TL;DR: In this paper , a comprehensive evaluation of the combination of the regional scale chemistry-transport model DEHM (Danish Eulerian Hemispheric Model) and the Gaussian plume-in-grid model UBMv10 (Urban Background Model) is presented.
10 citations
TL;DR: In this article , a Lagrangian-based PINN architecture is proposed to solve non-linear convection-diffusion problems with partial differential equation (PDE)-constraint optimization problems.
10 citations
TL;DR: In this paper , a hybrid partitioned deep learning framework for the reduced-order modeling of moving interfaces and predicting fluid-structure interaction is presented, which combines two separate data-driven models for fluid and solid subdomains via DL-ROMs.
10 citations
TL;DR: In this paper, the two-phase flow is described by a Eulerian-Eulerian approach and the discrepancy between the predictions and measurements can most clearly be quantified in terms of the peak values of the axial velocity in the forward flow region enveloping the inner recirculation zone.
10 citations
TL;DR: In this article , a variational derivation of the equilibrium equations for second gradient materials and their transformation from the Eulerian to the Lagrangian form was presented, where volume, face and edge contributions to the inner virtual work were provided through integration by parts and by repeated applications of the divergence theorem extended to curved surfaces with border.
Abstract: Abstract After the wide premise of Part I, where the equations for Cauchy’s continuum were retrieved through the energy minimization and some differential geometric perspectives were specified, the present paper as Part II outlines the variational derivation of the equilibrium equations for second gradient materials and their transformation from the Eulerian to the Lagrangian form. Volume, face and edge contributions to the inner virtual work were provided through integration by parts and by repeated applications of the divergence theorem extended to curved surfaces with border. To sustain double forces over the faces and line forces along the edges, the role of the third rank hyperstress tensor was highlighted. Special attention was devoted to the edge work, and to the evaluation of the variables discontinuous across the edge belonging to the contiguous boundary faces. The detailed expression of the contact pressures was provided, including multiple products of normal vector components, their gradient and a combination of them: in particular, the dependence on the local mean curvature was shown. The transport of the governing equations from the Eulerian to the Lagrangian configuration was developed according to two diverse strategies, exploiting novel differential geometric formulae and revealing a coupling of terms transversely to the involved domains.
TL;DR: In this article , the two-phase flow is described by a Eulerian-Eulerian approach and the discrepancy between the predictions and measurements can most clearly be quantified in terms of the peak values of the axial velocity in the forward flow region enveloping the inner recirculation zone.
TL;DR: In this paper , a damage evolution law is proposed to consider the frictional behavior within an Eulerian material after full damage as a Lagrangian material, where the mesh explicitly describes the newly born interface interactions.
TL;DR: In this article, a damage evolution law is proposed to consider the frictional behavior within an Eulerian material after full damage as a Lagrangian material, where the mesh explicitly describes the newly born interface interactions.
TL;DR: In this article , the authors compared eight methods for mapping discrete particle information to continuous fluid field via the simulation of a packed bed, their pros and cons were analyzed and discussed in detail; a kernel function method was then selected to simulate the hydrodynamics and heat transfer of gas-solid bubbling fluidized beds.
TL;DR: In this article , a double-moment bin scheme and a Lagrangian super-droplet scheme are compared via large-eddy simulations of non-precipitating and precipitating cumulus-congestus clouds.
Abstract:
Advanced microphysics schemes (such as Eulerian bin and Lagrangian super-droplet) are becoming standard tools for cloud physics research and parameterization development. This study compares a double-moment bin scheme and a Lagrangian super-droplet scheme via large-eddy simulations of non-precipitating and precipitating cumulus-congestus clouds. Cloud water mixing ratio in the bin simulations is reduced compared to the Lagrangian simulations in the upper part of the cloud, likely from numerical diffusion which is absent in the Lagrangian approach. Greater diffusion in the bin simulations is compensated by more secondary droplet activation (activation above cloud base), leading to similar or somewhat higher droplet number concentrations and smaller mean droplet radius than the Lagrangian simulations for the non-precipitating case. The bin scheme also produces a significantly larger standard deviation of droplet radius than the super-droplet method, likely due to diffusion associated with the vertical advection of bin variables. However, the spectral width in the bin simulations is insensitive to the grid spacing between 50 and 100 m, suggesting other mechanisms may be compensating for diffusion as the grid spacing is modified. For the precipitating case, larger spectral width in the bin simulations initiates rain earlier and enhances rain development in a positive feedback loop. However, with time, rain formation in the super-droplet simulations catches up to the bin simulations. Off-line calculations using the same drop size distributions in both schemes show that the different numerical methods for treating collision-coalescence also contribute to differences in rain formation. The stochastic collision-coalescence in the super-droplet method introduces more variability in drop growth for a given rain mixing ratio.
TL;DR: A review on numerical simulations of proppant transport based on the Eulerian-Lagrangian methods is presented in this paper , where some important mechanisms (e.g., hydrodynamic drag, particle-particle and particle-wall interactions, and gravitational settling) dominating the proplant transport process are introduced and discussed.
TL;DR: In this article , the effect of the flow field on pig launching by the 3D fluid-structure interaction (FSI) model based on the Coupled Eulerian-Lagrangian (CEL) method was investigated.
TL;DR: In this paper , a computational coupled large deformation periporomechanics paradigm was proposed for modeling dynamic failure and fracturing in variably saturated porous media, where the horizon of a mixed material point remains spherical and its neighbor points are determined in the current configuration.
Abstract: The large‐deformation mechanics and multiphysics of continuous or fracturing partially saturated porous media under static and dynamic loads are significant in engineering and science. This article is devoted to a computational coupled large‐deformation periporomechanics paradigm assuming passive air pressure for modeling dynamic failure and fracturing in variably saturated porous media. The coupled governing equations for bulk and fracture material points are formulated in the current/deformed configuration through the updated Lagrangian–Eulerian framework. It is assumed that the horizon of a mixed material point remains spherical and its neighbor points are determined in the current configuration. As a significant contribution, the mixed interface/phreatic material points near the phreatic line are explicitly considered for modeling the transition from partial to full saturation (vice versa) through the mixed peridynamic state concept. We have formulated the coupled constitutive correspondence principle and stabilization scheme in the updated Lagrangian–Eulerian framework for bulk and interface points. We numerically implement the coupled large deformation periporomechanics through a fully implicit fractional‐step algorithm in time and a hybrid updated Lagrangian–Eulerian meshfree method in space. Numerical examples are presented to validate the implemented stabilized computational coupled large‐deformation periporomechanics and demonstrate its efficacy and robustness in modeling dynamic failure and fracturing in variably saturated porous media.
TL;DR: In this paper , a graph neural network is used to predict the temporal evolution of dissipative dynamic systems. But the accuracy of the resulting integration scheme is low, achieving relative mean errors of less than 3% in all the tested examples.
Abstract: In this paper we present a deep learning method to predict the temporal evolution of dissipative dynamic systems. We propose using both geometric and thermodynamic inductive biases to improve accuracy and generalization of the resulting integration scheme. The first is achieved with Graph Neural Networks, which induces a non-Euclidean geometrical prior with permutation invariant node and edge update functions. The second bias is forced by learning the GENERIC structure of the problem, an extension of the Hamiltonian formalism, to model more general non-conservative dynamics. Several examples are provided in both Eulerian and Lagrangian description in the context of fluid and solid mechanics respectively, achieving relative mean errors of less than 3% in all the tested examples. Two ablation studies are provided based on recent works in both physics-informed and geometric deep learning.
20 Jun 2022
TL;DR: GlennICE as discussed by the authors is a computational ice accretion solver that uses a Lagrangian framework to predict the water impingement on a geometry of interest and uses a new refinement methodology implemented in GlennICE and compares it to the previous implementation.
Abstract: A requirement of computational ice accretion solvers is the ability to predict the water impingement on a geometry of interest. This portion of the tool is typically implemented in an Eulerian or Lagrangian methodology with both approaches having various pros and cons. GlennICE utilizes a Lagrangian framework. For this approach to be tractable in an engineering sense, the trajectories simulated must be chosen in a more intelligent fashion than naïve uniform refinement. This paper investigates a new refinement methodology implemented in GlennICE and compares it to the previous implementation.
TL;DR: In this article , an Eulerian formulation based on evolving microstructural vectors was used to model anisotropic inelastic deformation rate in sheet metals, which is insensitive to arbitrariness of reference and intermediate configurations.
TL;DR: In this article, a particle tracking method for Eulerian-lagrangian simulations is presented, which leverages both hardware ray tracing (RT) cores and GPU parallel computing technology.
TL;DR: In this paper, the e-positivity of the trivariate second-order Eulerian polynomials was studied in the context of ternary trees and Stirling permutations.
TL;DR: In this paper , the authors proposed an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model.
TL;DR: In this paper , a Lanczos-based proper orthogonal decomposition (LPOD) is employed to efficiently generate a set of POD bases, and a series of multidimensional functions of the POD coefficients are constructed using a surrogate interpolation method.
TL;DR: In this paper, the authors proposed an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model.
TL;DR: In this article , the authors investigated the effect of the choice of kernel function on the accuracy of the immersed boundary (IB) method, particularly at intermediate-to-high Reynolds numbers, or under different loading conditions.
TL;DR: The novel Cauchy-Lagrange algorithm is applied to a flow, which might develop finite time singularities, resulting in a loss of smoothness, and a pseudo-spectral type approach in space by approximating the flow fields by Chebyshev-Fourier polynomials is presented.