A combined PPAC-RCCE-ISAT methodology for efficient implementation of combustion chemistry

Newale, Ashish S.; Liang, Youwen; Pope, Stephen B.; Pepiot, Perrine

doi:10.1080/13647830.2019.1606453

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…One effective strategy to alleviate the costs implied with the inclusion of detailed chemical mechanisms in CFD simulations is to consider an adaptive-chemistry approach, as it allows to reduce the CPU time by means of the adaptation of the kinetic mechanism according to the local flame conditions. Several adaptive approaches have been proposed in the literature, such as the Dynamic Adaptive Chemistry and the Pre-Partitioning Adaptive Chemistry (Schwer et al, 2003;Liang et al, 2009;Shi et al, 2010;Contino et al, 2011;Ren et al, 2014aRen et al, , 2014bKomninos, 2015;Liang et al, 2015;Zhou and Wei, 2016;Newale et al, 2019Newale et al, , 2020, as well as the use of lookup tables with B-spline interpolants (Bode et al, 2019), with benefits in terms of both CPU time (if compared to the detailed simulations) and accuracy (if compared with simulations using globally reduced mechanisms).…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

D’Alessio

Cuoci

Parente

2021

DCE

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The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the context of the Sample- Partitioning Adaptive Reduced Chemistry approach was investigated in this work, to increase the on-the-fly classification accuracy for very large thermochemical states. The proposed methodology was firstly compared with an on-the-fly classifier based on the Principal Component Analysis reconstruction error, as well as with a standard ANN (s-ANN) classifier, operating on the full thermochemical space, for the adaptive simulation of a steady laminar flame fed with a nitrogen-diluted stream of n-heptane in air. The numerical simulations were carried out with a kinetic mechanism accounting for 172 species and 6,067 reactions, which includes the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to C $ {}_{20} $ . Among all the aforementioned classifiers, the one exploiting the combination of an FE step with ANN proved to be more efficient for the classification of high-dimensional spaces, leading to a higher speed-up factor and a higher accuracy of the adaptive simulation in the description of the PAH and soot-precursor chemistry. Finally, the investigation of the classifier’s performances was also extended to flames with different boundary conditions with respect to the training one, obtained imposing a higher Reynolds number or time-dependent sinusoidal perturbations. Satisfying results were observed on all the test flames.

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Section: Introductionmentioning

confidence: 99%

Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

D’Alessio

Cuoci

Parente

2021

DCE

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“…Moreover, different unsupervised algorithms for the thermochemical space partitioning were compared a posteriori to achieve the optimal performance in terms of kinetic reduction [32] and, finally, a new classification algorithm based on Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs) was proposed and validated a posteriori to increase the simulation accuracy when large kinetic mechanisms and soot precursors are taken into account [33]. Alternative PPAC approaches were also proposed by Newale and coworkers [29,34,35] for a LES/PDF framework, with a reduction of the average simulation wall clock time per timestep of 39% with respect to the full detailed mechanism simulation.…”

Section: Introductionmentioning

confidence: 99%

Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows

Amaduzzi,

D’Alessio,

Pagani

et al. 2024

Combustion and Flame

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“…Several adaptive methodologies are already available in the literature [5][6][7][8], although the achievable speed-up is somehow limited by the overhead associated to the on-the-fly mechanism reduction step. Recently, also additional solutions were proposed to efficiently handle the combustion chemistry with an adaptive approach, coupling a pre-partitioning phase with Rate-Constrained Chemical Equilibrium (RCCE) and In-Situ Adaptive Tabulation (ISAT), and remarkable results were obtained [9]. A Sample-Partitioning Adaptive Chemistry (SPARC) approach, based on the coupling between machine learning and Directed Graph with Error Propagation (DRGEP) [10], was proposed by the authors to overcome the on-the-fly mechanism reduction overhead, building in a preprocessing phase a library of reduced mechanisms to be used in different regions of the domain during the multidimensional CFD simulation.…”

Section: Introductionmentioning

confidence: 99%

Impact of the Partitioning Method on Multidimensional Adaptive-Chemistry Simulations

et al. 2020

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The large number of species included in the detailed kinetic mechanisms represents a serious challenge for numerical simulations of reactive flows, as it can lead to large CPU times, even for relatively simple systems. One possible solution to mitigate the computational cost of detailed numerical simulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive Reduced Chemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemical space partitioning for the generation of locally reduced mechanisms, but this task is often challenging because of the high-dimensionality, as well as the high non-linearity associated to reacting systems. Moreover, the importance of this step in the overall approach is not negligible, as it has effects on the mechanisms’ level of chemical reduction and, consequently, on the accuracy and the computational speed-up of the adaptive simulation. In this work, two different clustering algorithms for the partitioning of the thermochemical space were evaluated by means of an adaptive CFD simulation of a 2D unsteady laminar flame of a nitrogen-diluted methane stream in air. The first one is a hybrid approach based on the coupling between the Self-Organizing Maps with K-Means (SKM), and the second one is the Local Principal Component Analysis (LPCA). Comparable results in terms of mechanism reduction (i.e., the mean number of species in the reduced mechanisms) and simulation accuracy were obtained for both the tested methods, but LPCA showed superior performances in terms of reduced mechanisms uniformity and speed-up of the adaptive simulation. Moreover, the local algorithm showed a lower sensitivity to the training dataset size in terms of the required CPU-time for convergence, thus also being optimal, with respect to SKM, for massive dataset clustering tasks.

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A combined PPAC-RCCE-ISAT methodology for efficient implementation of combustion chemistry

Cited by 7 publications

References 35 publications

Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

Automated adaptive chemistry for Large Eddy Simulations of turbulent reacting flows

Impact of the Partitioning Method on Multidimensional Adaptive-Chemistry Simulations

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