Modern Developments in Filtered Density Function

Sammak, Shervin; Ren, Zhuyin; Givi, Peyman

doi:10.1007/978-981-15-2643-5_8

Cited by 11 publications

(1 citation statement)

References 197 publications

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“…A major challenge in utilizing PDF is associated with its modeling and computational simulations. Combustion engineers have been trying to deal with this issue [36], and there is a continuing need to make use of advanced computational methodologies for turbulent combustion research. In this article, two examples are presented to demonstrate how ML/DL and QC can be useful in this regard.…”

Section: Applications In Turbulent Combustionmentioning

confidence: 99%

Computational Turbulent Combustion in the Age of Artificial Intelligence and Quantum Information

Givi¹

2021

ijmph

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The impact of high-performance computing on the society has been enormous, but it is easy to be taken for granted. In today's world, it is virtually impossible to imagine system design or major decision making not aided via predictive modeling and simulation. Now that we are experiencing the Data Revolution and the emergence of the Second Quantum Revolution, it is wise to consider both of these elements in computational science and engineering. Data-driven modeling approaches and demonstrated speed-ups of quantum algorithms have the potential to transform scientific discovery. This will affect the fabrics of industrialized societies in diverse disciplines. A research arena which can substantially benefit from these technologies is combustion. This field has been the subject of heavy computational research for many decades now. In this review, some examples taken from the previous works of the author are presented to demonstrate how the field of computational turbulent combustion is benefiting from modern developments in machine learning (ML) and quantum computing (QC).

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Section: Applications In Turbulent Combustionmentioning

confidence: 99%

Computational Turbulent Combustion in the Age of Artificial Intelligence and Quantum Information

Givi¹

2021

ijmph

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Application of the sparse-Lagrangian multiple mapping conditioning approach to a model supersonic combustor

2020

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The Multiple Mapping Conditioning/Large Eddy Simulation (MMC-LES) model is extended for the first time to high-speed, compressible flow conditions and validated against non-reacting and reacting experimental data from a model supersonic combustor. The MMC-LES method solves the subgrid joint composition filtered density function through a Monte Carlo approach, and it permits a low-cost numerical implementation using a sparse distribution of stochastic Lagrangian particles. The sensitivity of results to the particle resolution is examined, and similar to past low-speed applications of MMC-LES, that sensitivity is found to be low. In comparison to the model equations for subsonic turbulent combustion conditions, the pressure work and viscous heating effects have been incorporated here to account for the effects of compressibility. As expected, the viscous heating effects are small for this flow case and can be ignored, while the pressure work is not negligible and makes a significant contribution at expansion fans and shock fronts where the magnitude of the pressure derivative term in non-reacting/reacting cases is as much as 23.8%/24.5% and 19.2%/18.6% of the stochastic particle standardized enthalpy, respectively. The MMC-LES predictions show good quantitative agreement with the available experimental data for the mean and root-mean-square of axial velocity, mean temperature, and wall pressure. Good qualitative comparison to the data is also observed for major flow characteristics, including location and size of shocks, expansion fans, and recirculation zone, and combustion characteristics such as flame lift-off distance. Although the effects of the pressure work on the mean flame lift-off distance are negligible, they have a significant influence on the predicted spatial fluctuations of the flame base.

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On the modeling of scalar mixing timescale in filtered density function simulation of turbulent premixed flames

et al. 2020

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A new closure of the scalar mixing timescale is formulated to enhance the predictability of large eddy simulation (LES)/filtered density function (FDF) simulations for turbulent premixed flames. Specifically, the new model integrates a dynamic closure for turbulence-induced mixing with a closure for reaction-enhanced mixing, such that the model explicitly accounts for the subgrid mixing due to turbulence and reaction. The model adaptively adjusts the relative contribution from these two aspects according to the local state of combustion and requires no tuning for the mixing rate parameter (CM). To evaluate the model performance, LES/FDF simulations are carried out for the Sydney piloted premixed jet burner flames PM1-50 and PM1-150. Compared with the constant CM model with the baseline CM = 2, the proposed model notably improved the prediction of the overall combustion progress of both flames. The relative importance of the reaction-enhanced mixing in comparison with the turbulence-induced mixing is further investigated. For flame PM1-50, the reaction-enhanced mixing has a prominent impact throughout the combustion progress, resulting in a large variation in CM in the progress variable space. This illustrates the advantage of the proposed model for the flame close to the flamelet regime. For flame PM1-150, the variation in CM during the combustion progress is relatively small owing to the relatively weak reaction-enhanced mixing compared to PM1-50. However, this desired CM is much larger than the order of unity. Therefore, the proposed model also has its advantage for the flame close to the broken-reaction zones regime.

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Modern Developments in Filtered Density Function

Cited by 11 publications

References 197 publications

Computational Turbulent Combustion in the Age of Artificial Intelligence and Quantum Information

Computational Turbulent Combustion in the Age of Artificial Intelligence and Quantum Information

Application of the sparse-Lagrangian multiple mapping conditioning approach to a model supersonic combustor

On the modeling of scalar mixing timescale in filtered density function simulation of turbulent premixed flames

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