Data-assisted combustion simulations with dynamic submodel assignment using random forests

Chung, Wai Tong; Mishra, Aashwin; Perakis, Nikolaos; Ihme, Matthias

doi:10.1016/j.combustflame.2020.12.041

Cited by 32 publications

(11 citation statements)

References 55 publications

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“…For methane/air combustion we neglect NOx species from this analysis. The species mass fractions exhibiting highest correlations are consistent with variables typically used in the literature to construct progress variables [4,38,37,39,40]. This is especially the case when Pareto scaling is used on a dataset = .…”

Section: =1supporting

confidence: 77%

Local manifold learning and its link to domain-based physics knowledge

Zdybał¹,

D’Alessio²,

Attili³

et al. 2022

Preprint

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In many reacting flow systems, the thermo-chemical state-space is known or assumed to evolve close to a low-dimensional manifold (LDM). Various approaches are available to obtain those manifolds and subsequently express the original high-dimensional space with fewer parameterizing variables. Principal component analysis (PCA) is one of the dimensionality reduction methods that can be used to obtain LDMs. PCA does not make prior assumptions about the parameterizing variables and retrieves them empirically from the training data. In this paper, we show that PCA applied in local clusters of data (local PCA) is capable of detecting the intrinsic parameterization of the thermo-chemical state-space. We first demonstrate that utilizing three common combustion models of varying complexity: the Burke-Schumann model, the chemical equilibrium model and the homogeneous reactor. Parameterization of these models is known a priori which allows for benchmarking with the local PCA approach. We further extend the application of local PCA to a more challenging case of a turbulent non-premixed -heptane/air jet flame for which the parameterization is no longer obvious. Our results suggest that meaningful parameterization can be obtained also for more complex datasets. We show that local PCA finds variables that can be linked to local stoichiometry, reaction progress and soot formation processes.

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Section: =1supporting

confidence: 77%

Local manifold learning and its link to domain-based physics knowledge

Zdybał¹,

D’Alessio²,

Attili³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For methane/air combustion we neglect NOx species from this analysis. The species mass fractions exhibiting highest correlations are consistent with variables typically used in the literature to construct progress variables [4,39,37,40,41]. This is especially the case when Pareto scaling is used on a dataset X = Y i .…”

Section: The Homogeneous Reactor Modelsupporting

confidence: 73%

Local manifold learning and its link to domain-based physics knowledge

Zdybał¹,

D’Alessio²,

Attili³

et al. 2023

Applications in Energy and Combustion Science

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“…Random Forests are an ensemble learning algorithm where a meta-model is developed using a group (or ensemble) of decision trees (J. Panda & Warrior, 2022;Chung, Mishra, Perakis, & Ihme, 2021). Decision Trees (also referred to as Classification And Regression Trees) are a simpler modeling algorithm.…”

Section: Random Forests (Rf)mentioning

confidence: 99%

Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool

Das¹,

Das²,

Panda³

et al. 2022

Preprint

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In recent times Mechanical and Production industries are facing increasing challenges related to the shift toward sustainable manufacturing. In this article machining was performed in dry cutting condition with a newly developed coated insert called AlTiSiN coated carbides coated through scalable pulsed power plasma technique in dry cutting condition and a dataset was generated for different machining parameters and output responses. The machining parameters are speed, feed, depth of cut and the output responses are surface roughness, cutting force, crater wear length, crater wear width and flank wear. The data collected from the machining operation is used for the development machine learning (ML) based surrogate models to test, evaluate and optimize various input machining parameters. Different ML approaches such as polynomial regression (PR), random forest (RF) regression, gradient boosted (GB) trees and adaptive boosting (AB) based regression are used to model different output responses in the hard machining of AISI D6 steel. The surrogate models for different output responses are used to prepare a complex objective function for the germinal center algorithm based optimization of the machining parameters of the hard turning operation.

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Data-assisted combustion simulations with dynamic submodel assignment using random forests

Cited by 32 publications

References 55 publications

Local manifold learning and its link to domain-based physics knowledge

Local manifold learning and its link to domain-based physics knowledge

Local manifold learning and its link to domain-based physics knowledge

Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool

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