Solving large deformation problems in geotechnical and geo-environmental engineering with the smoothed particle hydrodynamics: a state-of-the-art review of constitutive solutions

Onyelowe, Kennedy C.; Mojtahedi, S. Farid F.; Darzi, Ali Golaghaei; Kontoni, Denise‐Penelope N.

doi:10.1007/s12665-023-11079-8

Cited by 5 publications

(2 citation statements)

References 345 publications

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“…The SPH method is a fully Lagrangian meshless technique that discretizes the continuum domain into a finite number of particles and calculates the field variations through interactions among neighboring particles. Originally introduced for astrophysics ( Gingold and Monaghan, 1977 ; Lucy, 1977 ), it has since been extended to a wide range of fields such as hydrodynamics ( Huang et al, 2022 ), geophysics ( Onyelowe et al, 2023 ), biophysics ( Zhang et al, 2021 ), electromagnetics ( Ala et al, 2006 ), elastic and plastic dynamics ( Greto and Kulasegaram, 2020 ), and explosion mechanics ( Ming et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of fluid force and flow fields during gliding in swimming using smoothed particle hydrodynamics method

Liu,

Yu,

Meng

et al. 2024

Front. Bioeng. Biotechnol.

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Gliding is a crucial phase in swimming, yet the understanding of fluid force and flow fields during gliding remains incomplete. This study analyzes gliding through Computational Fluid Dynamics simulations. Specifically, a numerical model based on the Smoothed Particle Hydrodynamics (SPH) method for flow-object interactions is established. Fluid motion is governed by continuity, Navier-Stokes, state, and displacement equations. Modified dynamic boundary particles are used to implement solid boundaries, and steady and uniform flows are generated with inflow and outflow conditions. The reliability of the SPH model is validated by replicating a documented laboratory experiment on a circular cylinder advancing steadily beneath a free surface. Reasonable agreement is observed between the numerical and experimental drag force and lift force. After the validation, the SPH model is employed to analyze the passive drag, vertical force, and pitching moment acting on a streamlined gliding 2D swimmer model as well as the surrounding velocity and vorticity fields, spanning gliding velocities from 1 m/s to 2.5 m/s, submergence depths from 0.2 m to 1 m, and attack angles from −10° to 10°. The results indicate that with the increasing gliding velocity, passive drag and pitching moment increase whereas vertical force decreases. The wake flow and free surface demonstrate signs of instability. Conversely, as the submergence depth increases, there is a decrease in passive drag and pitching moment, accompanied by an increase in vertical force. The undulation of the free surface and its interference in flow fields diminish. With the increase in the attack angle, passive drag and vertical force decrease whereas pitching moment increases, along with the alteration in wake direction and the increasing complexity of the free surface. These outcomes offer valuable insights into gliding dynamics, furnishing swimmers with a scientific basis for selecting appropriate submergence depth and attack angle.

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Section: Introductionmentioning

confidence: 99%

Analysis of fluid force and flow fields during gliding in swimming using smoothed particle hydrodynamics method

Liu,

Yu,

Meng

et al. 2024

Front. Bioeng. Biotechnol.

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“…The interfaces between fluid and deformable structures, free surfaces, and shock waves are considered examples of free boundaries [9][10][11]. Many problems arise while performing numerical simulations of free boundaries [12][13][14] which includes the evolution in time, discrete representation, and techniques and methods that have been used for resolving issues associated with free boundaries in the form of numerical simulations such as finite-difference or finite-element grids [15][16][17][18].…”

mentioning

confidence: 99%

Numerical Investigation of Multiphase Flow in 2D Microchannel Using Volume of Fluid Method: A Study on Water Droplet Dynamics

Sattar,

Bofeng,

Manzoor

et al. 2024

Preprint

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The study encompasses the visualization and analysis of dynamic water behavior, pressure drop, and saturation interaction under various boundary condition cases. Additionally, the characteristics of the flow in and out of the two-dimensional (2D) channel and the microinjection of water droplets are investigated. The simulations via the Volume of Fluid (VOF) Method include modeling liquid water dynamics with different wall angles in the 2D channel, considering varying air and water inlet velocities. Key parameters such as water volume fraction and pressure drop are thoroughly analyzed. The simulation results demonstrate substantial agreement with the integral characteristics of the process, including dynamics trend, induction time of droplet breakup (saturation phase), and pressure drop. This comprehensive analysis enhances understanding of water droplet dynamics in microchannels and contributes to the optimization of microfluidic devices for a spectrum of applications, from drug delivery to chemical reactions.

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A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

Yaghoubi,

Khamees

et al. 2024

Neural Comput & Applic

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Artificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

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Solving large deformation problems in geotechnical and geo-environmental engineering with the smoothed particle hydrodynamics: a state-of-the-art review of constitutive solutions

Cited by 5 publications

References 345 publications

Analysis of fluid force and flow fields during gliding in swimming using smoothed particle hydrodynamics method

Analysis of fluid force and flow fields during gliding in swimming using smoothed particle hydrodynamics method

Numerical Investigation of Multiphase Flow in 2D Microchannel Using Volume of Fluid Method: A Study on Water Droplet Dynamics

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

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