Identification of governing physical processes of irregular combustion through machine learning

Grogan, Kevin; Ihme, Matthias

doi:10.1007/s00193-018-0852-y

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…The appliances of ML to combustion studies were reviewed in the paper. Among the combustion theory studies, most of which are relevant to fuels, [30][31][32][33][34][35] part of which are targeted to study combustion phenomenon, [26][27][28][29] and only a few of studies concentrate on CFD simulation. [23][24][25] In industrial studies, ML has been used to study power plant boiler, [41][42][43][44][45] engine, [46][47][48][49][50][51][52][53][54] fuel [55][56][57][58] and other fields.…”

Section: Discussionmentioning

confidence: 99%

“…[22] ML seems to be able to solve problems where the dependencies are nonlinear relationships between inputs and outputs or insufficiently known to be programmed in a traditional and rigid manner. [22] The advantages make ML play an important role in studies related to combustion theory, such as Computational Fluid Dynamics (CFD) simulation of combustion, [23][24][25] prediction of combustion phenomenon [26][27][28][29] and fuels. [30][31][32][33][34][35] ML can help to reduce mechanism scales, [23] optimize probability density function (PDF) [24] and Large Eddy Simulation (LES) [25] method in combustion simulation.…”

Section: Theoretical Researchmentioning

confidence: 99%

“…[30][31][32][33][34][35] ML can help to reduce mechanism scales, [23] optimize probability density function (PDF) [24] and Large Eddy Simulation (LES) [25] method in combustion simulation. When investigating combustion phenomenon, ML algorithms were used to detect thermoacoustic combustion oscillations, [26,27] distinguish donation and ignition [28] and reconstruct the detonation surface. [29] In various fuel studies, ML also played an important role in the investigations about fuel properties, [30][31][32] the bio- [33,34] and next generation [35] fuels.…”

Section: Theoretical Researchmentioning

confidence: 99%

“…Grogan et al [28] used ML algorithm to estimate the predictive capability of different combustion parameters like ignition delay time, heat release, chemical sensitivity and etc. under different facilities, fuels and configurations.…”

Section: Prediction Of Combustion Phenomenonmentioning

confidence: 99%

See 3 more Smart Citations

Progress in the Application of Machine Learning in Combustion Studies

Zheng¹,

Lin²,

Yang³

et al. 2020

ES Energy Environ.

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Combustion is the main source of energy and environmental pollution. The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions. In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field. In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed. Combustion studies combined with ML can be divided into theoretical and industrial aspects. Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel. ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation. ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation. ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields. ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants. In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines. Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters. The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Researchmentioning

confidence: 99%

Section: Theoretical Researchmentioning

confidence: 99%

Section: Prediction Of Combustion Phenomenonmentioning

confidence: 99%

See 2 more Smart Citations

Progress in the Application of Machine Learning in Combustion Studies

Zheng¹,

Lin²,

Yang³

et al. 2020

ES Energy Environ.

View full text Add to dashboard Cite

show abstract

“…Classifiers in numerical combustion have been employed by Grogan and Ihme [30] to discriminate between regular combustion and irregular ignition regimes in detonations, analyzing experimental data from disparate sources. They concluded the analysis stating that the correct classification of the combustion process under study is low dimensional, after assessing the relative importance of the features, i.e., parameters employed to label the data.…”

Section: Introductionmentioning

confidence: 99%

Local combustion regime identification using machine learning

Galassi

Ciottoli

Valorani

et al. 2021

Combustion Theory and Modelling

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A new combustion regime identification methodology using the neural networks as supervised classifiers is proposed and validated. As a first proof of concept, a binary classifier is trained with labeled thermochemical states obtained as solutions of prototypical one-dimensional models representing premixed and nonpremixed regimes. The trained classifier is then used to associate the regime to any given thermochemical state originating from a multi-dimensional reacting flow simulation that shares similar operating conditions with the training problems. The classification requires local information only, i.e., no gradients are required, and operates on reduceddimension thermochemical states, in order to cope with experimental data as well. The validity of the approach is assessed by employing a two-dimensional laminar edge flame data as a canonical configuration exhibiting multi-regime combustion behavior. The method is readily extendable to additional classes to identify criticality phenomena, such as local extinction and re-ignition. It is anticipated that the proposed classifier tool will be useful in the development of turbulent multi-regime combustion closure models in large scale simulations.

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