A Computationally Efficient Implementation of Tabulated Combustion Chemistry based on Polynomials and Automatic Source Code Generation

Weise, Steffen; Popp, Sebastian; Messig, Danny; Hasse, Christian

doi:10.1007/s10494-017-9826-x

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2019

2024

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Cited by 7 publications

(1 citation statement)

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“…Weise et al [26] proposed a memory abstraction layer (MAL) method to reduce the memory requirement. Subsequently, they provided a unique method for generating polynomial descriptions of the interpolation-focused database utilized in the original study [27]. Machine learning (ML) techniques like Artificial Neural Networks (ANNs) are another option.…”

Section: Introductionmentioning

confidence: 99%

Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Rahnama

Novella

et al. 2023

Energy and AI

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Section: Introductionmentioning

confidence: 99%

Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Rahnama

Novella

et al. 2023

Energy and AI

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Mind the gap: Turbulent combustion model validation and future needs

Hochgreb

2019

Proceedings of the Combustion Institute

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This overview collects a range of well characterized experiments used in the step-wise validation of turbulent combustion models, from gas phase non-premixed jet flames to spray flames, and from simple symmetric jets to real device geometries, focusing primarily on statistically steady state experiments. We discuss how the experiments and models are constructed, approaches to modelling, and the tradeoffs between the level of detail and computational demands. The review highlights a number of experiments used for benchmarking models, selecting a few examples where models have clearly succeeded, as well as some areas where there are clear needs in the experimental database. In particular, the areas of turbulent spray combustion and soot prediction, as well as combustion under high pressures appear as the least developed and present the clearest gaps for both models and experiments. Based on the successful application of advanced methods of uncertainty quantification to a number of problems in reacting flows, we suggest that these methods might be used to advantage in the design of experiments. This would enable an upfront examination of the extent to which comparisons between measurable scalars and velocities allow clear distinction between model features.

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Numerical and experimental analysis of the formation of nitrogen oxides in a non-premixed industrial gas burner

Ortolani,

Yeadon,

Ruane

et al. 2024

Results in Engineering

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A Computationally Efficient Implementation of Tabulated Combustion Chemistry based on Polynomials and Automatic Source Code Generation

Cited by 7 publications

References 37 publications

Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Combining flamelet-generated manifold and machine learning models in simulation of a non-premixed diffusion flame

Mind the gap: Turbulent combustion model validation and future needs

Numerical and experimental analysis of the formation of nitrogen oxides in a non-premixed industrial gas burner

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