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This article presents a joint numerical study on the Multi Regime Burner configuration. The burner design consists of three concentric inlet streams, which can be operated independently with different equivalence ratios, allowing the operation of stratified flames characterized by different combustion regimes, including premixed, non-premixed, and multi-regime flame zones. Simulations were performed on three LES solvers based on different numerical methods. Combustion kinetics were simplified by using tabulated or reduced chemistry methods. Finally, different turbulent combustion modeling strategies were employed, covering geometrical, statistical, and reactor based approaches. Due to this significant scattering of simulation parameters, a conclusion on specific combustion model performance is impossible. However, with ten numerical groups involved in the numerical simulations, a rough statistical analysis is conducted: the average and the standard deviation of the numerical simulation are computed and compared against experiments. This joint numerical study is therefore a partial illustration of the community's ability to model turbulent combustion. This exercise gives the average performance of current simulations and identifies physical phenomena not well captured today by most modeling strategies. Detailed comparisons between experimental and numerical data along radial profiles taken at different axial positions showed that the temperature field is fairly well captured up to 60 mm from the burner exit. The comparison reveals, however, significant discrepancies regarding CO mass fraction prediction. Three causes may explain this phenomenon. The first reason is the higher sensitivity of carbon monoxide to the simplification of detailed chemistry, especially when multiple combustion regimes are encountered. The second is the bias introduced by artificial thickening, which overestimates the species’ mass production rate. This behavior has been illustrated by manufa, SCOPUS: ar.j, info:eu-repo/semantics/published

This article presents a joint numerical study on the Multi Regime Burner configuration. The burner design consists of three concentric inlet streams, which can be operated independently with different equivalence ratios, allowing the operation of stratified flames characterized by different combustion regimes, including premixed, non-premixed, and multi-regime flame zones. Simulations were performed on three LES solvers based on different numerical methods. Combustion kinetics were simplified by using tabulated or reduced chemistry methods. Finally, different turbulent combustion modeling strategies were employed, covering geometrical, statistical, and reactor based approaches. Due to this significant scattering of simulation parameters, a conclusion on specific combustion model performance is impossible. However, with ten numerical groups involved in the numerical simulations, a rough statistical analysis is conducted: the average and the standard deviation of the numerical simulation are computed and compared against experiments. This joint numerical study is therefore a partial illustration of the community's ability to model turbulent combustion. This exercise gives the average performance of current simulations and identifies physical phenomena not well captured today by most modeling strategies. Detailed comparisons between experimental and numerical data along radial profiles taken at different axial positions showed that the temperature field is fairly well captured up to 60 mm from the burner exit. The comparison reveals, however, significant discrepancies regarding CO mass fraction prediction. Three causes may explain this phenomenon. The first reason is the higher sensitivity of carbon monoxide to the simplification of detailed chemistry, especially when multiple combustion regimes are encountered. The second is the bias introduced by artificial thickening, which overestimates the species’ mass production rate. This behavior has been illustrated by man

An artificial neural network (ANN) is trained on moderate or intense low-oxygen dilution (MILD) combustion to predict the sub-grid filtered density function (FDF) in large eddy simulation (LES). For wide usability, a new quantity \psi is calculated by scaling the filtered mixture fraction using the stoichiometric value and a logarithmic function, which is used as an input feature of the ANN. Self-testing on MILD combustion is first conducted for validation and determining the best layout of ANN. An ANN with 4 hidden layers and the activation function of rectified linear unit (ReLU) has the highest accuracy. This ANN is subsequently tested on five different premixed combustion cases. Overall predictions of the progress variable FDF are satisfactory, and the filtered chemical source term modelled by the FDFs is in good agreement with the DNS data for each case. However, an underprediction of the filtered reaction rate occurs for the hydrogen–air flames, and the ANN accuracy is lower than that of a presumed \beta-FDF approach. DNS data of one premixed flame are then added to the training datasets, and newly trained ANNs are tested on remaining premixed flame cases. When the testing cases are similar to the added training dataset, improved predictions of marginal FDFs are observed, and the modelled progress variable source terms show good comparisons with the corresponding DNS data. Compared to the presumed \beta-FDF method, the new ANN shows an improved accuracy and serves as a viable alternative for FDF-based combustion modelling in the LES of turbulent flames., info:eu-repo/semantics/published

info:eu-repo/semantics/published

Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynamics (CFD) framework that integrates the strengths of machine learning (ML) and graphics processing unit (GPU) to demonstrate their combined capability. Within this framework, all computational operations are solely executed on GPU, including ML-accelerated chemistry integration, fully-implicit solving of PDEs, and computation of thermal and transport properties, thereby eliminating the CPU-GPU memory copy overhead. Optimisations both within the kernel functions and during the kernel launch process are conducted to enhance computational performance. Strategies such as static data reorganisation and dynamic data allocation are adopted to reduce the GPU memory footprint. The computational performance is evaluated in two turbulent flame benchmarks using quasi-DNS and LES modelling, respectively. Remarkably, while maintaining a similar level of accuracy to the conventional CPU/CVODE-based solver, the GPU/ML-accelerated approach shows an overall speedup of over two orders of magnitude for both cases. This result highlights that high-fidelity turbulent combustion simulation with finite-rate chemistry that requires normally hundreds of CPUs can now be performed on portable devices such as laptops with a medium-end GPU.

Machine learning has long been considered as a black box for predicting combustion chemical kinetics due to the extremely large number of parameters and the lack of evaluation standards and reproducibility. The current work aims to understand two basic questions regarding the deep neural network (DNN) method: what data the DNN needs and how general the DNN method can be. Sampling and preprocessing determine the DNN training dataset, further affect DNN prediction ability. The current work proposes using Box-Cox transformation (BCT) to preprocess the combustion data. In addition, this work compares different sampling methods with or without preprocessing, including the Monte Carlo method, manifold sampling, generative neural network method (cycle-GAN), and newly-proposed multi-scale sampling. Our results reveal that the DNN trained by the manifold data can capture the chemical kinetics in limited configurations but cannot remain robust toward perturbation, which is inevitable for the DNN coupled with the flow field. The Monte Carlo and cycle-GAN samplings can cover a wider phase space but fail to capture small-scale intermediate species, producing poor prediction results. A three-hidden-layer DNN, based on the multi-scale method without specific flame simulation data, allows predicting chemical kinetics in various scenarios and being stable during the temporal evolutions. This single DNN is readily implemented with several CFD codes and validated in various combustors, including (1). zero-dimensional autoignition, (2). one-dimensional freely propagating flame, (3). two-dimensional jet flame with triple-flame structure, and (4). three-dimensional turbulent lifted flames. The results demonstrate the satisfying accuracy and generalization ability of the pre-trained DNN. The Fortran and Python versions of DNN and example code are attached in the supplementary for reproducibility.

The physical mechanism leading to flame local extinction remains a key issue to be further understood. An analysis of large eddy simulation (LES) data with presumed probability density function (PDF) based closure (Chen et al., 2020, Combust. Flame, vol. 212, pp. 415) indicated the presence of localised breaks of the flame front along the stoichiometric line. These observations and their relation to local quenching of burning fluid particles, together with the possible physical mechanisms and conditions allowing their appearance in LES with a simple flamelet model, are investigated in this work using a combined Lagrangian-Eulerian analysis. The Sidney/Sandia piloted jet flames with compositionally inhomogeneous inlet and increasing bulk speeds, amounting to respectively 70 and 90% of the experimental blow-off velocity, are used for this analysis. Passive flow tracers are first seeded in the inlet streams and tracked for their lifetime. The critical scenario observed in the Lagrangian analysis, i.e., burning particles crossing extinction holes on the stoichiometric iso-surface, is then investigated using the Eulerian control-volume approach. For the 70% blow-off case the observed flame front breaks/extinction holes are due to cold and inhomogeneous reactants that are cast onto the stoichiometric iso-surface by large vortices initiated in the jet/pilot shear layer. In this case an extinction hole forms only when the strain effect is accompanied by strong subgrid mixing. This mechanism is captured by the unstrained flamelets model due to the ability of the LES to resolve large-scale strain and considers the SGS mixture fraction variance weakening effect on the reaction rate through the flamelet manifold. Only at 90% blow-off speed the expected limitation of the underlying combustion model assumption become apparent, where the amount of local extinctions predicted by the LES is underestimated compared to the experiment. In this case flame front breaks are still obser, Flight Performance and Propulsion

info:eu-repo/semantics/nonPublished

The physical mechanism leading to flame local extinction remains a key issue to be further understood. An analysis of large eddy simulation (LES) data with presumed probability density function (PDF) based closure (Chen et al., 2020, Combust. Flame, vol. 212, pp. 415) indicated the presence of localised breaks of the flame front along the stoichiometric line. These observations and their relation to local quenching of burning fluid particles, together with the possible physical mechanisms and conditions allowing their appearance in LES with a simple flamelet model, are investigated in this work using a combined Lagrangian-Eulerian analysis. The Sidney/Sandia piloted jet flames with compositionally inhomogeneous inlet and increasing bulk speeds, amounting to respectively 70 and 90% of the experimental blow-off velocity, are used for this analysis. Passive flow tracers are first seeded in the inlet streams and tracked for their lifetime. The critical scenario observed in the Lagrangian analysis, i.e., burning particles crossing extinction holes on the stoichiometric iso-surface, is then investigated using the Eulerian control-volume approach. For the 70% blow-off case the observed flame front breaks/extinction holes are due to cold and inhomogeneous reactants that are cast onto the stoichiometric iso-surface by large vortices initiated in the jet/pilot shear layer. In this case an extinction hole forms only when the strain effect is accompanied by strong subgrid mixing. This mechanism is captured by the unstrained flamelets model due to the ability of the LES to resolve large-scale strain and considers the SGS mixture fraction variance weakening effect on the reaction rate through the flamelet manifold. Only at 90% blow-off speed the expected limitation of the underlying combustion model assumption become apparent, where the amount of local extinctions predicted by the LES is underestimated compared to the experiment. In this case flame front breaks are still obser, Flight Performance and Propulsion

info:eu-repo/semantics/published

0, info:eu-repo/semantics/published