A new Lagrangian–Eulerian incompressible SPH method for simulating free surface flows

Heydari, Zohreh; Shobeyri, Gholamreza; Najafabadi, Seyed Hossein Ghoreishi

doi:10.1007/s40430-022-03712-7

Cited by 3 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It appeared in the pioneering work of Gingold and Monaghan (1977) and Lucy (1977) for applications in astrophysics. Subsequently, the SPH method has been used in diverse fields of physics and engineering, including the study of convection flows (Aly and Raizah, 2020;Shakibaeinia, 2020a, 2020b;Aly et al, 2021), fluid-structure interactions (Xu et al, 2019;Salehizadeh and Shafiei, 2022;Zhang et al, 2022;Dai et al, 2023;Long and Su, 2023), free surface flow (Fang et al, 2006;Violeau and Issa, 2007;Aly et al, 2013;Heydari et al, 2022;Lan et al, 2023), multiphase flows (Hu and Adams, 2006;Zhang et al, 2019;Almasi et al, 2021) and more.…”

Section: Isph Methods and Artificial Intelligencementioning

confidence: 99%

Adjoined ISPH method and artificial intelligence for thermal radiation on double diffusion inside a porous L-shaped cavity with fins

Elshehabey,

Çolak,

Aly

2024

HFF

View full text Add to dashboard Cite

Purpose The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins. Design/methodology/approach The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm. Findings The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers. Originality/value A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.

show abstract