An efficient modeling framework for wall heat flux prediction in rocket combustion chambers using non adiabatic flamelets and wall-functions

Indelicato, Giuseppe; Lapenna, Pasquale Eduardo; Remiddi, Arianna; Creta, Francesco

doi:10.1016/j.ijheatmasstransfer.2021.120913

Cited by 15 publications

(2 citation statements)

References 64 publications

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“…The models for velocity and temperature should necessarily be coupled in flows with strong temperature variations, as for example was proposed by Cabrit and Nicoud (2009). This coupled wall model has been validated and compared to uncoupled wall modeling approaches and differential wall models in various physical configurations (Maestro et al, 2017;Kraus et al, 2018;Muto et al, 2019Muto et al, , 2021Indelicato et al, 2021;Potier et al, 2022). Finally, machine-learning wall models have recently emerged following the development of machine-learning technologies in image classification, speech recognition, natural language processing as well as turbulence simulation and modeling (LeCun et al, 2015;Duraisamy et al, 2019;Brunton et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Using graph neural networks for wall modeling in compressible anisothermal flows

Dupuy,

Odier,

Lapeyre

2024

DCE

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Compressible anisothermal flows, which are commonly found in industrial settings such as combustion chambers and heat exchangers, are characterized by significant variations in density, viscosity, and heat conductivity with temperature. These variations lead to a strong interaction between the temperature and velocity fields that impacts the near-wall profiles of both quantities. Wall-modeled large-eddy simulations (LESs) rely on a wall model to provide a boundary condition, for example, the shear stress and the heat flux that accurately represents this interaction despite the use of coarse cells near the wall, and thereby achieve a good balance between computational cost and accuracy. In this article, the use of graph neural networks for wall modeling in LES is assessed for compressible anisothermal flow. Graph neural networks are a type of machine learning model that can learn from data and operate directly on complex unstructured meshes. Previous work has shown the effectiveness of graph neural network wall modeling for isothermal incompressible flows. This article develops the graph neural network architecture and training to extend their applicability to compressible anisothermal flows. The model is trained and tested a priori using a database of both incompressible isothermal and compressible anisothermal flows. The model is finally tested a posteriori for the wall-modeled LES of a channel flow and a turbine blade, both of which were not seen during training.

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Section: Introductionmentioning

confidence: 99%

Using graph neural networks for wall modeling in compressible anisothermal flows

Dupuy,

Odier,

Lapeyre

2024

DCE

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“…Indelicato et al [23] performed a numerical study of a single-injector combustor from TUM using both 2D and 3D URANS approaches. A new non-adiabatic flamelet model was applied, which showed good agreement with the experimental data both for wall heat fluxes and pressures.…”

Section: Introductionmentioning

confidence: 99%

Simulation of a GOx-GCH4 Rocket Combustor and the Effect of the GEKO Turbulence Model Coefficients

et al. 2021

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In this study, a single injector methane-oxygen rocket combustor is numerically studied. The simulations included in this study are based on the hardware and experimental data from the Technical University of Munich. The focus is on the recently developed generalized k–ω turbulence model (GEKO) and the effect of its adjustable coefficients on the pressure and on wall heat flux profiles, which are compared with the experimental data. It was found that the coefficients of ‘jet’, ‘near-wall’, and ‘mixing’ have a major impact, whereas the opposite can be deduced about the ‘separation’ parameter Csep, which highly influences the pressure and wall heat flux distributions due to the changes in the eddy-viscosity field. The simulation results are compared with the standard k–ε model, displaying a qualitatively and quantitatively similar behavior to the GEKO model at a Csep equal to unity. The default GEKO model shows a stable performance for three oxidizer-to-fuel ratios, enhancing the reliability of its use. The simulations are conducted using two chemical kinetic mechanisms: Zhukov and Kong and the more detailed RAMEC. The influence of the combustion model is of the same order as the influence of the turbulence model. In general, the numerical results present a good or satisfactory agreement with the experiment, and both GEKO at Csep = 1 or the standard k–ε model can be recommended for usage in the CFD simulations of rocket combustion chambers, as well as the Zhukov–Kong mechanism in conjunction with the flamelet approach.

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Numerical study of combustion and cooling performance of a gaseous oxygen and gaseous methane rocket combustor with the needle-bolt injector

Zhang

Li²,

Xing

et al. 2023

Applied Thermal Engineering

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An efficient modeling framework for wall heat flux prediction in rocket combustion chambers using non adiabatic flamelets and wall-functions

Cited by 15 publications

References 64 publications

Using graph neural networks for wall modeling in compressible anisothermal flows

Using graph neural networks for wall modeling in compressible anisothermal flows

Simulation of a GOx-GCH4 Rocket Combustor and the Effect of the GEKO Turbulence Model Coefficients

Numerical study of combustion and cooling performance of a gaseous oxygen and gaseous methane rocket combustor with the needle-bolt injector

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