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LES and DNS of Multiphase Flows in Industrial Devices: Application of High-Performance Computing

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Two-Phase Flow for Automotive and Power Generation Sectors

Part of the book series: Energy, Environment, and Sustainability ((ENENSU))

Abstract

High fidelity solutions of turbulent flow equations are obtained by large eddy simulation (LES) and direct numerical simulation (DNS). These techniques are devoted for resolving most of the energy-carrying scales in a turbulent flow. Grid resolution in LES or DNS is determined by the lengths of the finest scale of motion which is to be directly simulated. In multiphase flows, further refinement in the grid topology is required to capture the bubble or droplet front and also to resolve the small structures that are created in the wake zone of the bubble/droplet. Owing to the grid size and finer timescales, the computational complexities in LES or DNS are extremely high, and parallel computing resources are often deployed. The present chapter reviews computational efforts involved in turbulent multiphase flow simulation in industrial devices. Several high-performance computing (HPC) strategies like distributed computing using message passing interface (MPI), general purpose graphics processing unit (GPGPU) accelerated computing using CUDA and their hybridizations are also reviewed. Estimations of the computational requirement for simulation of large industrial devices are presented, and potential use of modern computational science and hardware are critically assessed.

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Abbreviations

\( C_{s}^{2} \) :

Smagorinsky constant

f i :

ith component of body force

k :

Turbulent kinetic energy

p :

Pressure

Re :

Reynolds number

S ij :

Large-scale strain rate tensor

t :

Time

u i :

ith component of velocity

x i :

ith coordinate

y + :

Wall coordinate

σ ij :

Viscous stress term

\( \tau_{ij}^{t} \) :

Sub-grid stress tensor

ε :

Turbulent dissipation rate

η :

Kolmogorov length scale

\( \nu_{t} \) :

Sub-grid eddy viscosity

DNS:

Direct numerical simulation

GPGPU:

General purpose graphics processing unit

IBM:

Immersed boundary method

LBM:

Lattice Boltzman methods

LES:

Large eddy simulation

MPI:

Message passing interface

MRF:

Multiple reference frame

OpenMP:

Open multiprocessing

PBM:

Population balance model

PCI:

Peripheral component interconnect

RANS:

Reynolds-averaged Navier–Stokes

VOF:

Volume of fluids

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Acknowledgements

Author acknowledges support from Mr. Apurva Raj, Research Scholar, Department of Aerospace Engineering, Indian Institute of Technology Kharagpur, in preparation of the figures.

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  1. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Somnath Roy

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Editors and Affiliations

  1. Centre for Energy Studies, Indian Institute of Technology Delhi, New Delhi, India

    Kaushik Saha

  2. Department of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India

    Avinash Kumar Agarwal

  3. Department of Mechanical Engineering, Jadavpur University, Kolkata, India

    Koushik Ghosh

  4. Argonne National Laboratory, Lemont, IL, USA

    Sibendu Som

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Roy, S. (2019). LES and DNS of Multiphase Flows in Industrial Devices: Application of High-Performance Computing. In: Saha, K., Kumar Agarwal, A., Ghosh, K., Som, S. (eds) Two-Phase Flow for Automotive and Power Generation Sectors. Energy, Environment, and Sustainability. Springer, Singapore. https://doi.org/10.1007/978-981-13-3256-2_9

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  • DOI: https://doi.org/10.1007/978-981-13-3256-2_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3255-5

  • Online ISBN: 978-981-13-3256-2

  • eBook Packages: EngineeringEngineering (R0)

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