A quasi-direct numerical simulation solver for compressible reacting flows

Li, Tao; Pan, Jiaying; Kong, Fanfu; Xu, Baopeng; Wang, Xiaohan

doi:10.1016/j.compfluid.2020.104718

Cited by 20 publications

(8 citation statements)

References 12 publications

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“…It has been shown in [22][23][24][25] that the default OpenFOAM ODE solver suffers from several drawbacks (e.g. poor accuracy and stability, slow integration, etc.).…”

Section: Chemistry Integrationmentioning

confidence: 99%

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

Mao¹,

Lin²,

Zhang³

et al. 2022

Preprint

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Recent developments in deep learning have brought many inspirations for the scientific computing community and it is perceived as a promising method in accelerating the computationally demanding reacting flow simulations. In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip -deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware.

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“…It has been shown in [22][23][24][25] that the default OpenFOAM ODE solver suffers from several drawbacks (e.g. poor accuracy and stability, slow integration, etc.).…”

Section: Chemistry Integrationmentioning

confidence: 99%

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

Mao¹,

Lin²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Then expressions for velocity (15) and phase densities (i. e. equation of state) are substituted to the equation for mixture density (9), where fluxes I #,. are already approximated using hybrid KNP [9,41] approach.…”

Section: Pressure Equationmentioning

confidence: 99%

“…For the single phase modelling including multicomponent gases, a pressure-based numerical tool employing the hybrid approximation of convective fluxes [9,10] was developed earlier. The solver has been validated against different physical conditions [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] and for the wide range of Mach numbers. The next step for the framework development was the adaptation for simulation of high-speed multicomponent real-gas flows [27,28] in context of consideration of the phase separation phenomenon.…”

Section: Introductionmentioning

confidence: 99%

An extension of the all-Mach number pressure-based solution framework for numerical modelling of two-phase flows with interface

Kraposhin¹,

Kukharskii²,

Victòria³

et al. 2022

IPT

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In this paper, we present the extension of the pressure-based solver designed for the simulation of compressible and/or incompressible two-phase flows of viscous fluids. The core of the numerical scheme is based on the hybrid Kurganov — Noele — Petrova/PIMPLE algorithm. The governing equations are discretized in the conservative form and solved for velocity and pressure, with the density evaluated by an equation of state. The acoustic-conservative interface discretization technique helps to prevent the unphysical instabilities on the interface. The solver was validated on various cases in wide range of Mach number, both for single-phase and two-phase flows. The numerical algorithm was implemented on the basis of the well-known open-source Computational Fluid Dynamics library OpenFOAM in the solver called interTwoPhaseCentralFoam. The source code and the pack of test cases are available on GitHub: https://github.com/unicfdlab/hybridCentralSolvers The research was supported by Russian Science Foundation (proj. 17-79-20445).

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“…Then expressions for velocity (15) and phase densities (i.e. equation of state) are substituted to the equation for mixture density (9) where fluxes φ k, f are already approximated using hybrid KNP 9,41 approach.…”

Section: Pressure Equationmentioning

confidence: 99%

“…For the single phase modelling including multicomponent gases, a pressure-based numerical tool employing the hybrid approximation of convective fluxes 9,10 was developed earlier. The solver has been validated against different physical conditions [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] and for the wide range of Mach numbers. The next step for the framework development was the adaptation for simulation of high-speed multicomponent real-gas flows 27,28 in context of consideration of the phase separation phenomenon.…”

Section: Introductionmentioning

confidence: 99%

An Extension of the All-Mach Number Pressure-Based Solution Framework for Numerical Modelling of Two-Phase Flows with Interface

Kraposhin¹,

Kukharskii²,

Korchagova³

et al. 2022

Preprint

View full text Add to dashboard Cite

In this paper, we present the extension of the pressure-based solver designed for the simulation of compressible and/or incompressible two-phase flows of viscous fluids. The core of the numerical scheme is based on the hybrid Kurganov— Noele — Petrova/PIMPLE algorithm. The governing equations are discretized in the conservative form and solved for velocity and pressure, with the density evaluated by an equation of state. The acoustic-conservative interface discretization technique helps to prevent the unphysical instabilities on the interface. The solver was validated on various cases in wide range of Mach number, both for single-phase and two-phase flows. The numerical algorithm was implemented on the basis of the well-known open-source Computational Fluid Dynamics library OpenFOAM in the solver called interTwoPhaseCentralFoam. The source code and the pack of test cases are available on GitHub: https://github.com/unicfdlab/hybridCentralSolvers

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A quasi-direct numerical simulation solver for compressible reacting flows

Cited by 20 publications

References 12 publications

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

An extension of the all-Mach number pressure-based solution framework for numerical modelling of two-phase flows with interface

An Extension of the All-Mach Number Pressure-Based Solution Framework for Numerical Modelling of Two-Phase Flows with Interface

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