Recent progress of lattice Boltzmann method and its applications in fluid-structure interaction

Wang, Li; Liu, Zhengliang; Rajamuni, Methma M.

doi:10.1177/09544062221077583

Cited by 15 publications

(7 citation statements)

References 221 publications

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“…Hence, compared with the SRT model by Nath & Ray (2021), we use the MRT scheme to improve the model stability and extend the parameter range (Li et al. 2016 b ; Wang, Liu & Rajamuni 2022). Moreover, we incorporate the fluid–particle interaction force term explicitly at the right-hand side of the LB equation, instead of regarding it as a velocity increment (Qin et al.…”

Section: Numerical Modellingmentioning

confidence: 99%

Lattice Boltzmann modelling of colloidal suspensions drying in porous media accounting for local nanoparticle effects

et al. 2023

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A two-dimensional (2-D) double-distribution lattice Boltzmann method (LBM) is implemented to study isothermal drying of a colloidal suspension considering local nanoparticle effects. The two LBMs solve isothermal two-phase flow and nanoparticle transport, respectively. The three local nanoparticle effects on the fluid dynamics considered in this paper are viscosity increase, surface tension drop and local drying rate reduction. The proposed model is first validated by the study of the drying of a 2-D suspended colloidal droplet for two different Péclet numbers, where the evolution of the diameter squared agrees well with experimental results. The model is further validated looking at drying of a colloid in a 2-D capillary tube with two open ends. Compared with experimental results, the best agreement in terms of deposition profile and drying time is obtained when considering all three nanoparticle effects. Afterwards, we apply the model to investigate the complicated drying of a colloidal suspension in a 2-D porous asphalt, considering all three local nanoparticle effects. The drying dynamics, resultant nanoparticle transport, accumulation and deposition are first analysed for a base case. Then a parametric study is conducted varying the initial nanoparticle concentration, porous medium contact angle, nanoparticle contact angle and nanoparticle diffusion coefficient. The influence of these parameters on drying dynamics, drying rate, deposition process and final deposition configurations is analysed in detail, together with the mutual influence of local nanoparticle behaviour. Finally, a unified relation between the average drying rate and the studied parameters is proposed and verified, covering the full parameter ranges of simulations.

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Section: Numerical Modellingmentioning

confidence: 99%

Lattice Boltzmann modelling of colloidal suspensions drying in porous media accounting for local nanoparticle effects

et al. 2023

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“…However, over the last three decades, scholars have progressively developed mesoscopic computational methods founded on statistical mechanics, notably the lattice Boltzmann method (LBM). Owing to innate advantages for parallelization and resolving complex boundaries, LBM has attained widespread adoption for simulating phenomena such as multiphase flow [6], deformable fluid-filled bodies [7], fluid-structure interaction [8], Phase Change Material Energy Storage [9], fuel cells [10], acoustics [11], combustion applications [12], boiling, and evaporation [13].…”

Section: Introductionmentioning

confidence: 99%

Lattice Boltzmann Simulation of Cavitating Flow in a Two-Dimensional Nozzle with Moving Needle Valve

Yang,

Dai,

Jin

2024

Processes

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A cascaded pseudo-potential lattice Boltzmann model and refilling algorithms for moving boundary treatment were used to simulate the large density ratio cavitating flow in a two-dimensional nozzle with the periodic motion of the needle valve. The relationships between density variation at the cavitation zone, the evolution of force acting on the lower boundary of the sack wall region, and the surface of the needle valve with time under different needle valve motion frequencies were obtained. The results indicate that the inception and evolution of cavitation mainly exist in the vicinity of the lower boundary of the sack wall region. The density at cavitation decreases by approximately three orders of magnitude, while the force on the lower boundary of the sack wall region decreases by about one order of magnitude. Since cavitation does not exist in the vicinity of the needle valve, the forces are mainly influenced by the periodic motion of the needle valve and do not change significantly. Changes in the frequency of needle valve motion affect the time taken for cavitation evolution to reach a relatively steady state but do not significantly affect the forces acting on the different components.

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“…To construct the predicting tool based on the ML, an idealised function, which could be arbitrary options with stochastic disturbance, is adopted to generate a large number of stenosis profiles. The learning datasets, the mean flow fields of the constructed stenosed arteries are obtained by using an immersed boundary-lattice Boltzmann method (IB-LBM) (Tian et al, 2011;Wang and Tian, 2018;Xu et al, 2018;Ma et al, 2020) which incorporates the immersed boundary method for its excellent capability in handling complex boundaries and the LBM for its efficient modelling for unsteady flows (Wang et al, 2022). Finally, a DNN is trained and tested for the fast prediction of the mean blood flow in stenosed arteries.…”

Section: Introductionmentioning

confidence: 99%

Fast prediction of blood flow in stenosed arteries using machine learning and immersed boundary-lattice Boltzmann method

2022

Self Cite

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A fast prediction of blood flow in stenosed arteries with a hybrid framework of machine learning and immersed boundary-lattice Boltzmann method (IB–LBM) is presented. The integrated framework incorporates the immersed boundary method for its excellent capability in handling complex boundaries, the multi-relaxation-time LBM for its efficient modelling for unsteady flows and the deep neural network (DNN) for its high efficiency in artificial learning. Specifically, the stenosed artery is modelled by a channel for two-dimensional (2D) cases or a tube for three-dimensional (3D) cases with a stenosis approximated by a fifth-order polynomial. An IB–LBM is adopted to obtain the training data for the DNN which is constructed to generate an approximate model for the fast flow prediction. In the DNN, the inputs are the characteristic parameters of the stenosis and fluid node coordinates, and the outputs are the mean velocity and pressure at each node. To characterise complex stenosis, a convolutional neural network (CNN) is built to extract the stenosis properties by using the data generated by the aforementioned polynomial. Both 2D and 3D cases (including 3D asymmetrical case) are constructed and examined to demonstrate the effectiveness of the proposed method. Once the DNN model is trained, the prediction efficiency of blood flow in stenosed arteries is much higher compared with the direct computational fluid dynamics simulations. The proposed method has a potential for applications in clinical diagnosis and treatment where the real-time modelling results are desired.

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Recent progress of lattice Boltzmann method and its applications in fluid-structure interaction

Cited by 15 publications

References 221 publications

Lattice Boltzmann modelling of colloidal suspensions drying in porous media accounting for local nanoparticle effects

Lattice Boltzmann modelling of colloidal suspensions drying in porous media accounting for local nanoparticle effects

Lattice Boltzmann Simulation of Cavitating Flow in a Two-Dimensional Nozzle with Moving Needle Valve

Fast prediction of blood flow in stenosed arteries using machine learning and immersed boundary-lattice Boltzmann method

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