Simulating Engineering Flows through Complex Porous Media via the Lattice Boltzmann Method

Krastev, Vesselin Krassimirov; Falcucci, Giacomo

doi:10.3390/en11040715

Cited by 31 publications

(14 citation statements)

References 55 publications

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“…The lattice Boltzmann scheme is performed to simulate in all numerical examples. The lattice Boltzmann method (LBM) had possessed latent advantages in previous studies to multiphase, single, heat and mass transfer hydrodynamic problems [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. The LBM is particularly propitious to attack complex boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

Statistics of Heat Transfer in Two-Dimensional Turbulent Rayleigh-Bénard Convection at Various Prandtl Number

Yang

Wei

Zhu

et al. 2018

Entropy

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Statistics of heat transfer in two-dimensional (2D) turbulent Rayleigh-Bénard (RB) convection for Pr = 6, 20, 100 and 10 6 are investigated using the lattice Boltzmann method (LBM). Our results reveal that the large scale circulation is gradually broken up into small scale structures plumes with the increase of Pr, the large scale circulation disappears with increasing Pr, and a great deal of smaller thermal plumes vertically rise and fall from the bottom to top walls. It is further indicated that vertical motion of various plumes gradually plays main role with increasing Pr. In addition, our analysis also shows that the thermal dissipation is distributed mainly in the position of high temperature gradient, the thermal dissipation rate ε θ already increasingly plays a dominant position in the thermal transport, ε u can have no effect with increase of Pr. The kinematic viscosity dissipation rate and the thermal dissipation rate gradually decrease with increasing Pr. The energy spectrum significantly decreases with the increase of Pr. A scope of linear scaling arises in the second order velocity structure functions, the temperature structure function and mixed structure function(temperature-velocity). The value of linear scaling and the 2nd-order velocity decrease with increasing Pr, which is qualitatively consistent with the theoretical predictions.

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Section: Introductionmentioning

confidence: 99%

Statistics of Heat Transfer in Two-Dimensional Turbulent Rayleigh-Bénard Convection at Various Prandtl Number

Yang

Wei

Zhu

et al. 2018

Entropy

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“…Computational methods play a critical role in developing fuel cells with optimum performance in a wide range of operating conditions. Krastev et al [22] developed algorithms for the simulation of reactive flows through micro-and nanoscale porous media via the Lattice Boltzmann method are presented and for the first time used in the field of microbial fuel cells. Zhang et al used EKF algorithm to estimate battery parameters in real time [23,24].…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Start‐Up Process Modelling of Sediment Microbial Fuel Cells Based on Data Driven

Yin

2019

Mathematical Problems in Engineering

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Sediment microbial fuel cells (SMFCs) are a typical microbial fuel cell without membranes. They are a device developed on the basis of electrochemistry and use microbes as catalysts to convert chemical energy stored in organic matter into electrical energy. This study selected a single-chamber SMFC as a research object, using online monitoring technology to accurately measure the temperature, pH, and voltage of the microbial fuel cell during the start-up process. In the process of microbial fuel cell start-up, the relationship between temperature, pH, and voltage was analysed in detail, and the correlation between them was calculated using SPSS software. The experimental results show that, at the initial stage of SMFC, the purpose of rapid growth of power production can be achieved by a large increase in temperature, but once the temperature is reduced, the power production of SMFC will soon recover to the state before the temperature change. At the beginning of SMFC, when the temperature changes drastically, pH will change the same first, and then there will be a certain degree of rebound. In the middle stage of SMFC start-up, even if the temperature will return to normal after the change, a continuous temperature drop in a short time will lead to a continuous decrease in pH value. The RBF neural network and ELM neural network were used to perform nonlinear system regression in the later stage of SMFC start-up and using the regression network to forecast part of the data. The experimental results show that the ELM neural network is more excellent in forecasting SMFC system. This article will provide important guidance for shortening start-up time and increasing power output.

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“…15,16 It is a mesoscopic approach that solves the Boltzmann equation (BE) with the Bhatnagar-Gross-Krook (BGK) approximation. 17 In comparison with the conventional computational fluid dynamics methods, based on the Navier-Stokes equations, the LBM offers some interesting advantages such as easy calculation procedure, a simple implementation for parallel computation, and simulation of complex fluid problems such as nanofluids, [18][19][20][21] MHD flow, 22 porous media, [23][24][25] multiphase flow, 26 melting PCM, 27 and turbulent flow. 22,28,29 Generally, there are three categories of thermal lattice Boltzmann models: the multispeed model, 30 the passive scalar model, 31 and the double-distribution model.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of low Prandtl number effect on mixed convection in a horizontal channel by the lattice Boltzmann method

2020

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The main purpose of this study is to numerically investigate the Prandtl number effect on mixed convection in a horizontal channel heated from below using the thermal lattice Boltzmann method (TLBM). The double-population model with two different lattices is used, in particular, the D2Q9 for the velocity field and D2Q5 for the thermal field. The developed lattice Boltzmann method code to simulate the fluid flow and heat transfer in the channel was validated with available literature results based on classical numerical methods, especially the finite volume method for Pr = 6.4 and the finite difference method for Pr = 0.667. The results obtained with the TLBM have shown good agreement with the conventional methods cited. The dynamic and thermal characteristics of the fluid flow were examined in the field of low Prandtl number, such that 0.05 ≤ Pr ≤ 0.667, and also compared to Pr = 6.4; for Ra = 2420 and 7400, the Reynolds number was fixed at 1. The results showed that the influence is relatively significant for the dynamic structure of flow convection for Pr ≤ 0.3 and is little influential beyond this value.

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Simulating Engineering Flows through Complex Porous Media via the Lattice Boltzmann Method

Cited by 31 publications

References 55 publications

Statistics of Heat Transfer in Two-Dimensional Turbulent Rayleigh-Bénard Convection at Various Prandtl Number

Statistics of Heat Transfer in Two-Dimensional Turbulent Rayleigh-Bénard Convection at Various Prandtl Number

Start‐Up Process Modelling of Sediment Microbial Fuel Cells Based on Data Driven

Numerical investigation of low Prandtl number effect on mixed convection in a horizontal channel by the lattice Boltzmann method

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