A discrete droplet method for modelling thin film flows

S, Anand Bharadwaj; Kuhnert, Jörg; Bordas, Stéphane Pierre Alain; Suchde, Pratik

doi:10.1016/j.apm.2022.08.001

Cited by 4 publications

(4 citation statements)

References 46 publications

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“…Here, each node is a mass particle. These types of methods include the popular Smoothed Particle Hydrodynamics (SPH) [19] and derived methods, such as Moving Particle Semi-explicit (MPS) [28], Moving Particle Explicit (MPE [63] or MPS [68]) and Discrete Droplet Method (DDM) [7], and also some hybrid mesh-meshfree methods like Material Point Methods (MPM) [40]. 2.…”

Section: Types Of Meshfree Methodsmentioning

confidence: 99%

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Volume and Mass Conservation in Lagrangian Meshfree Methods

Suchde¹,

Leithäuser²,

Kuhnert³

et al. 2023

Preprint

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Meshfree Lagrangian frameworks for free surface flow simulations do not conserve fluid volume. Meshfree particle methods like SPH are not mimetic, in the sense that discrete mass conservation does not imply discrete volume conservation. On the other hand, meshfree collocation methods typically do not use any notion of mass. As a result, they are neither mass conservative nor volume conservative at the discrete level. In this paper, we give an overview of various sources of conservation errors across different meshfree methods. The present work focuses on one specific issue: unreliable volume and mass definitions. We introduce the concept of representative masses and densities, which are essential for accurate post-processing especially in meshfree collocation methods. Using these, we introduce an artificial compression or expansion in the fluid to rectify errors in volume conservation. Numerical experiments show that the introduced frameworks significantly improve volume conservation behaviour, even for complex industrial test cases such as automotive water crossing.

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Section: Types Of Meshfree Methodsmentioning

confidence: 99%

“…( 6) and the integral form Eq. (7). No matter how accurately the differential form is being solved at each node, there is no discrete equivalent of the global (integral) conservation law.…”

Section: Conservation Of Fluxesmentioning

confidence: 99%

Volume and Mass Conservation in Lagrangian Meshfree Methods

Suchde¹,

Leithäuser²,

Kuhnert³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These surrogate models are very economical and are included even in open source codes (Rohlfs, Rietz & Scheid 2018), showing remarkable accuracy even in flow regimes exhibiting highly nonlinear waves (Chakraborty et al 2014). Alternatives to models that solve partial differential equations (PDEs) are also available that compute the film height by numerical integration based on the aggregation of droplets (Bharadwaj et al 2022).…”

Section: Introductionmentioning

confidence: 99%

“…Alternatives to models that solve partial differential equations (PDEs) are also available that compute the film height by numerical integration based on the aggregation of droplets (Bharadwaj et al. 2022).…”

Section: Introductionmentioning

confidence: 99%

Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data

Martin-Linares,

Psarellis,

Karapetsas

et al. 2023

J. Fluid Mech.

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Numerical simulations of multiphase flows are crucial in numerous engineering applications, but are often limited by the computationally demanding solution of the Navier–Stokes (NS) equations. The development of surrogate models relies on involved algebra and several assumptions. Here, we present a data-driven workflow where a handful of detailed NS simulation data are leveraged into a reduced-order model for a prototypical vertically falling liquid film. We develop a physics-agnostic model for the film thickness, achieving a far better agreement with the NS solutions than the asymptotic Kuramoto–Sivashinsky (KS) equation. We also develop two variants of physics-infused models providing a form of calibration of a low-fidelity model (i.e. the KS) against a few high-fidelity NS data. Finally, predictive models for missing data are developed, for either the amplitude, or the full-field velocity and even the flow parameter from partial information. This is achieved with the so-called ‘gappy diffusion maps’, which we compare favourably to its linear counterpart, gappy POD.

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Particle-based adaptive coupling of 3D and 2D fluid flow models

Suchde

2024

Computer Methods in Applied Mechanics and Engineering

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A discrete droplet method for modelling thin film flows

Cited by 4 publications

References 46 publications

Volume and Mass Conservation in Lagrangian Meshfree Methods

Volume and Mass Conservation in Lagrangian Meshfree Methods

Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data

Particle-based adaptive coupling of 3D and 2D fluid flow models

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