Application of reduced-order models based on PCA &amp; Kriging for the development of digital twins of reacting flow applications

Aversano, Gianmarco; Bellemans, André; Li, Zhiyi; Coussement, Axel; Gicquel, Olivier; Parente, Alessandro

doi:10.1016/j.compchemeng.2018.09.022

Cited by 73 publications

(24 citation statements)

References 34 publications

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“…Although the e↵ectiveness of PCA-based techniques in combustion applications has been already proved both a priori and a posteriori [13,14,11,15], it has to be mentioned that for strongly non-linear systems, such as reacting flows, a large number of PCs may be required in order to properly describe the system. The motivation is mainly due to the intrinsically multi-linear nature of the technique [13,16].…”

Section: Clustering Via Local Principal Component Analysismentioning

confidence: 99%

Adaptive chemistry via pre-partitioning of composition space and mechanism reduction

D’Alessio

Parente

Stagni

et al. 2020

Combustion and Flame

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Numerical simulations of multi-dimensional laminar flames with complex kinetic mechanisms are computationally very demanding, because of the large number of species and the strong non-linearity and sti↵ness of governing equations. In this work, we present and apply a novel adaptive chemistry methodology for mitigating the computational cost of such simulations, based on machine-learning algorithms which automatically classify the composition space via a priori defined classifiers. The methodology, called SPARC (Sample-Partitioning Adaptive Reduced Chemistry), is based on four steps: generation of data sets covering the temperature and composition space which is expected to be visited by the multi-dimensional flame; partitioning of the composition space in a prescribed number of clusters with similar composition via Local Principal Component Analysis (LPCA); generation of reduced kinetic mechanisms for each cluster via Directed Relation Graph with Error Propagation (DRGEP); CFD simulation of a multi-dimensional flame based on locally reduced mechanisms. The approach has been firstly demonstrated for the CFD simulations of steady and transient laminar coflow di↵usion flames fed with a mixture of CH 4 and N 2 burning in air. The transient behaviour was artificially induced by di↵erent sinusoidal perturbations in the velocity profiles. Several numerical tests were carried out to explore the impact of partitioning parameters and the degree of mechanism reduction on the flame simulation, and very satisfactory results were observed, both in terms of accuracy and computational ⇤

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Section: Clustering Via Local Principal Component Analysismentioning

confidence: 99%

Adaptive chemistry via pre-partitioning of composition space and mechanism reduction

D’Alessio

Parente

Stagni

et al. 2020

Combustion and Flame

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“…For this purpose, time constraints must be satisfied. Reduced order models supply digital twins with tools to face such disadvantage [16] [17] [18] [19]. Kapteyn et al [20], for instance, work on physically-constrained digital twins developed in a reduced order space for aircraft replanning.…”

Section: Related Workmentioning

confidence: 99%

Physically sound, self-learning digital twins for sloshing fluids

et al. 2020

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In this paper, a novel self-learning digital twin strategy is developed for fluid sloshing phenomena. This class of problems is of utmost importance for robotic manipulation of fluids, for instance, or, in general, in simulation-assisted decision making. The proposed method infers the (linear or non-linear) constitutive behavior of the fluid from video sequences of the sloshing phenomena. Real-time prediction of the fluid response is obtained from a reduced order model (ROM) constructed by means of thermodynamics-informed data-driven learning. From these data, we aim to predict the future response of a twin fluid reacting to the movement of the real container. The constructed system is able to perform accurate forecasts of its future reactions to the movements of the containers. The system is completed with augmented reality techniques, so as to enable comparisons among the predicted result with the actual response of the same liquid and to provide the user with insightful information about the physics taking place.

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“…Indeed, ANNs have received extensive attention in tabulation of combustion chemistry, since their first usage back in the 90's [24,25], up to recent studies demonstrating that modern ANN architectures offer good predictions with great savings in CPU time and memory requirements [26][27][28]. Data driven approaches have also been coupled with principal component analysis for developing closure models in turbulent combustion using experimental multi-scalar measurements [29] and for building digital-twins to progress towards furnace control [30]. Along similar lines, convolutional neural networks (CNN), based on image-like treatment of the thermochemical fields, were shown useful to tackle the modeling of turbulent flames [31][32][33] and their control [34].…”

Section: Introductionmentioning

confidence: 99%

Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects

Wan

Barnaud

Vervisch

et al. 2020

Combustion and Flame

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A chemistry reduction approach based on machine learning is proposed and applied to direct numerical simulation (DNS) of a turbulent non-premixed syngas oxy-flame interacting with a cooled wall. The training and the subsequent application of artificial neural networks (ANNs) rely on the processing of 'thermochemical vectors' composed of species mass fractions and temperature (ANN input), to predict the corresponding chemical sources (ANN output). The training of the ANN is performed aside from any flow simulation, using a turbulent non-adiabatic non-premixed micro-mixing based canonical problem with a reference detailed chemistry. Heat-loss effects are thus included in the ANN training. The performance of the ANN chemistry is then tested aposteriori in a two-dimensional DNS against the detailed mechanism and a reduced mechanism specifically developed for the operating conditions considered. Then, three-dimensional DNS are performed either with the ANN or the reduced chemistry for additional a-posteriori tests. The ANN reduced chemistry achieves good agreement with the Arrhenius-based detailed and reduced mechanisms, while being in terms of CPU cost 25 times faster than the detailed mechanism and 3 times faster than the reduced mechanism when coupled with DNS. The major potential of the method lies both in its data driven character and in the handling of the stiff chemical sources. The former allows for easy implementation in the context of automated generation of case-specific reduced chemistry. The latter avoids the Arrhenius rates calculation and also the direct integration of stiff chemistry, both leading to a significant CPU time reduction.

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Application of reduced-order models based on PCA & Kriging for the development of digital twins of reacting flow applications

Cited by 73 publications

References 34 publications

Adaptive chemistry via pre-partitioning of composition space and mechanism reduction

Adaptive chemistry via pre-partitioning of composition space and mechanism reduction

Physically sound, self-learning digital twins for sloshing fluids

Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects

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