A review of current progress in multiscale simulations for fluid flow and heat transfer problems: The frameworks, coupling techniques and future perspectives

Tong, Zi-Xiang; He, Ya‐Ling; Tao, Wen‐Quan

doi:10.1016/j.ijheatmasstransfer.2019.04.004

Cited by 54 publications

(31 citation statements)

References 214 publications

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“…Multiscale simulations cover quantum mechanics, molecular dynamics (MD), coarse-grained particle dynamics, and continuum mechanics [4,5]. Multiscale modeling in science and engineering couples several methods that produce the best predictions at each scale [6], which can then be applied to materials engineering [7], computational chemistry [8], systems biology [9,10], molecular biology [11], drug delivery [12], semiconductor manufacturing [13], reaction engineering [14], fluid flow [5,15], and process engineering [2,16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Multiscale modeling has been used in the form of sequential and concurrent couplings [4,15]. In a sequential coupling framework, often referred to as parameter passing between scales, the macroscale model uses a small number of parameters calculated in the microscale model.…”

Section: Introductionmentioning

confidence: 99%

“…Challenge remains in nanofluid flow simulations using a CFD model that includes multiphysics and a multiscale CFD model. Tong et al (2019) reviewed multiscale methods divided into a domain decomposition scheme and a hierarchical scheme in fluid flows with heat transfer for coupling MD with particle-based mesoscale methods such as the lattice Boltzmann method (LBM) and CFD [15]. LBM using the particle distribution function is suitable for simulating fluid flows involving interfacial dynamics and complex boundaries [15,19].…”

Section: Introductionmentioning

confidence: 99%

“…Tong et al (2019) reviewed multiscale methods divided into a domain decomposition scheme and a hierarchical scheme in fluid flows with heat transfer for coupling MD with particle-based mesoscale methods such as the lattice Boltzmann method (LBM) and CFD [15]. LBM using the particle distribution function is suitable for simulating fluid flows involving interfacial dynamics and complex boundaries [15,19]. The fundamental idea of LBM is that the macroscopic dynamics of a fluid are the result of the collective behavior of many microscopic particles in the system [19].…”

Section: Introductionmentioning

confidence: 99%

“…LBM is an alternative approach in CFD, which has been applied to fluid flows in porous media, microfluidics, and particulate flows [19,20]. However, because CFD based on LBM focuses on the microscopic domain with complex boundary conditions, macroscopic Eulerian CFD is preferred for investigating hydrodynamics on the process level [2,15].…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Eulerian CFD of Chemical Processes: A Review

Ngo

Lim

2020

ChemEngineering

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This review covers the scope of multiscale computational fluid dynamics (CFD), laying the framework for studying hydrodynamics with and without chemical reactions in single and multiple phases regarded as continuum fluids. The molecular, coarse-grained particle, and meso-scale dynamics at the individual scale are excluded in this review. Scoping single-scale Eulerian CFD approaches, the necessity of multiscale CFD is highlighted. First, the Eulerian CFD theory, including the governing and turbulence equations, is described for single and multiple phases. The Reynolds-averaged Navier–Stokes (RANS)-based turbulence model such as the standard k-ε equation is briefly presented, which is commonly used for industrial flow conditions. Following the general CFD theories based on the first-principle laws, a multiscale CFD strategy interacting between micro- and macroscale domains is introduced. Next, the applications of single-scale CFD are presented for chemical and biological processes such as gas distributors, combustors, gas storage tanks, bioreactors, fuel cells, random- and structured-packing columns, gas-liquid bubble columns, and gas-solid and gas-liquid-solid fluidized beds. Several multiscale simulations coupled with Eulerian CFD are reported, focusing on the coupling strategy between two scales. Finally, challenges to multiscale CFD simulations are discussed. The need for experimental validation of CFD results is also presented to lay the groundwork for digital twins supported by CFD. This review culminates in conclusions and perspectives of multiscale CFD.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiscale Eulerian CFD of Chemical Processes: A Review

Ngo

Lim

2020

ChemEngineering

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Iterative symbolic regression for learning transport equations

et al. 2022

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Computational fluid dynamics (CFD) analysis is widely used in chemical engineering. Although CFD calculations are accurate, the computational cost associated with complex systems makes it difficult to obtain empirical equations between system variables. Here, we combine active learning (AL) and symbolic regression (SR) to get a symbolic equation for system variables from CFD simulations. Gaussian process regression‐based AL allows for automated selection of variables by selecting the most instructive points from the available range of possible parameters. The results from these experiments are then passed to SR to find empirical symbolic equations for CFD models. This approach is scalable and applicable for any desired number of CFD design parameters. To demonstrate the effectiveness, we use this method with two model systems. We recover an empirical equation for the pressure drop in a bent pipe and a new equation for predicting backflow in a heart valve under aortic insufficiency.

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