New Results from Improved Pressure and Progress Variable Modeling of Turbulent Supersonic Combustion

Ladeinde, Foluso; Lou, Zhipeng; Li, Wenhai

doi:10.2514/6.2017-4744

Cited by 2 publications

References 51 publications

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Study on the application of artificial neural network-based flamelet/progress variable model in supersonic combustion

Lian,

Tang,

Wang

et al. 2023

AIP Advances

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The flamelet model has the characteristics of high efficiency and physical intuition and has excellent application prospects in supersonic turbulent combustion simulation. Expanding the dimensions of the flamelet model is a potential direction for model development in order to improve its applicability and accuracy, but the accompanying surge in memory is a problem that must be avoided. Therefore, the idea of using the artificial neural network (ANN) model to replace the flamelet database is a feasible exploration currently and has been preliminarily applied in 2D flamelet databases based on central processing unit frameworks. Based on the 3D flamelet database of the flamelet/progress variable (FPV) model, this article studies the strategy of using ANN to replace the flamelet database of the FPV model in a graphics processing unit framework. Due to the significant influence of the progress variable source term and heat release rate on the combustion calculation and the large range of these two parameters, four data processing methods are used to train the parameters separately, and three indicators are used to evaluate the training performance. Subsequently, based on the ANN model using different data processing methods mentioned earlier, calculations are conducted on a hydrogen-fueled supersonic combustion, and the computational accuracy is evaluated. The results indicate that the strategy proposed in this study can screen out artificial neural network replacement models with the same accuracy as the traditional flamelet model.

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Study on the application of artificial neural network-based flamelet/progress variable model in supersonic combustion

Lian,

Tang,

Wang

et al. 2023

AIP Advances

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Large Eddy Simulation of Supersonic Combustion using the Flamelet/Progress-Variable Approach and the Evolution-Variable Manifold Approach

Mrema

Candler

2019

AIAA Scitech 2019 Forum

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New Results from Improved Pressure and Progress Variable Modeling of Turbulent Supersonic Combustion

Cited by 2 publications

References 51 publications

Study on the application of artificial neural network-based flamelet/progress variable model in supersonic combustion

Study on the application of artificial neural network-based flamelet/progress variable model in supersonic combustion

Large Eddy Simulation of Supersonic Combustion using the Flamelet/Progress-Variable Approach and the Evolution-Variable Manifold Approach

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