Numerical Study on an Interface Compression Method for the Volume of Fluid Approach

Okagaki, Yuria; Yonomoto, Taisuke; Ishigaki, Masahiro; Hirose, Yoshiyasu

doi:10.3390/fluids6020080

Cited by 20 publications

(9 citation statements)

References 28 publications

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“…is the unit vector normal to the interface, and c α is a compression coefficient that determines the strength of the compression, e.g. c α = 0 for no compression, c α = 1 for conservative compression and c α > 1 for high compression (Larsen, Fuhrman & Roenby 2019;Okagaki et al 2021).…”

Section: Equations and Numerical Methodsmentioning

confidence: 99%

“…2019; Okagaki et al. 2021). In the present case, we found that the intermediate numerical diffusion associated with the use of relatively fine spatial and temporal resolution levels, together with the adoption of mixed first- and second-order-accurate schemes in time and space, can be balanced reasonably by using an intermediate value of the compression coefficient .…”

Section: Direct Numerical Simulationmentioning

confidence: 99%

“…To achieve this result, a relevant role has been played by the value of the compression factor c α in the volume fraction equation (2.2). Indeed, the value of the compression term is given by the need to obtain a diffusive balance between the artificial diffusion of the numerical method and the negative diffusion of the compression term itself (Larsen et al 2019;Okagaki et al 2021). In the present case, we found that the intermediate numerical diffusion associated with the use of relatively fine spatial and temporal resolution levels, together with the adoption of mixed first-and second-order-accurate schemes in time and space, can be balanced reasonably by using an intermediate value of the compression coefficient c α = 0.5.…”

Section: Fluid Properties and Compression Coefficientmentioning

confidence: 99%

“… for no compression, for conservative compression and for high compression (Larsen, Fuhrman & Roenby 2019; Okagaki et al. 2021).…”

Section: Direct Numerical Simulationmentioning

confidence: 99%

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On wind–wave interaction phenomena at low Reynolds numbers

2023

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After decades of research efforts, wind–wave interaction mechanisms have been recognized as extremely elusive. The reason is the complex nature of the problem, which combines complex coupling mechanisms between turbulent wind and water waves with the presence of multiple governing parameters, such as the friction Reynolds number of the wind, the water depth and the wind fetch. As shown unequivocally here, the use of suitable flow settings allows us to reduce the complex problem of wind–wave interaction to its essential features, mainly as a function of the sole friction Reynolds number of the wind. The resulting numerical solution allows us to study the interactions between water and air layers with their own fluid properties, and to unveil very interesting features, such as an oblique wave pattern travelling upstream and a wave-induced Stokes sublayer. The latter is responsible for a drag reduction mechanism in the turbulent wind. Despite the simulated flow conditions being far from the intense events occurring at the ocean–atmosphere interface, the basic flow phenomena unveiled here may explain some experimental evidence in wind–wave problems. Among other things, the wave-induced Stokes sublayer may shed light on the large scatter of the drag coefficient data in field measurements where swell waves of arbitrary directions are often present. Hence the present results and the developed approach pave the way for the understanding and modelling of the surface fluxes at the ocean–atmosphere interface, which are of overwhelming importance for climate science.

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Section: Equations and Numerical Methodsmentioning

confidence: 99%

Section: Direct Numerical Simulationmentioning

confidence: 99%

Section: Fluid Properties and Compression Coefficientmentioning

confidence: 99%

“… for no compression, for conservative compression and for high compression (Larsen, Fuhrman & Roenby 2019; Okagaki et al. 2021).…”

Section: Direct Numerical Simulationmentioning

confidence: 99%

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On wind–wave interaction phenomena at low Reynolds numbers

2023

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“…Then, a discretisation scheme is devised for the advection terms, with the compressive interface capturing (CICSAM) and piecewise linear interface construction (PLIC) being the most widespread (we refer to [40] for a detailed explanation and derivation).…”

Section: Reduction Of a Multiphase Model With Manifold Transformationmentioning

confidence: 99%

The Neural Network shifted-Proper Orthogonal Decomposition: a Machine Learning Approach for Non-linear Reduction of Hyperbolic Equations

Papapicco,

Demo,

Girfoglio

et al. 2021

Preprint

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Models with dominant advection always posed a difficult challenge for projection-based reduced order modelling. Many methodologies that have recently been proposed are based on the pre-processing of the full-order solutions to accelerate the Kolmogorov N −width decay thereby obtaining smaller linear subspaces with improved accuracy. These methods however must rely on the knowledge of the characteristic speeds in phase space of the solution, limiting their range of applicability to problems with explicit functional form for the advection field. In this work we approach the problem of automatically detecting the correct pre-processing transformation in a statistical learning framework by implementing a deep-learning architecture. The purely data-driven method allowed us to generalise the existing approaches of linear subspace manipulation to non-linear hyperbolic problems with unknown advection fields. The proposed algorithm has been validated against simple test cases to benchmark its performances and later successfully applied to a multiphase simulation.

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