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dc.contributor.authorBlasco, J.-
dc.contributor.authorFueyo, Norberto-
dc.contributor.authorDopazo, C.-
dc.contributor.authorChen, J.-Y.-
dc.date.accessioned2012-06-11T09:39:37Z-
dc.date.available2012-06-11T09:39:37Z-
dc.date.issued2000-
dc.identifierdoi: 10.1088/1364-7830/4/1/304-
dc.identifierissn: 1364-7830-
dc.identifier.citationCombustion Theory and Modelling 4: 61-76 (2000)-
dc.identifier.urihttp://hdl.handle.net/10261/51191-
dc.description.abstractSeveral alternative techniques have been proposed in the literature in order to avoid the CPU-intensive numerical integration of the thermochemical equations in the simulation of combustion processes. The present paper introduces a new approach, which is based on two artificial neural-network (ANN) paradigms, namely the self-organizing map (SOM) and the multilayer perceptron (MLP). The SOM is first employed for the automatic partitioning of the thermochemical space into subdomains. Then, a specialized MLP is trained in order to fit the thermochemical points belonging to a given subdomain. The presented strategy is tested on a partially stirred reactor (PaSR) with a reduced methane-air mechanism, and encouraging results are reported. The relatively modest CPU-time and memory requirements of the method make the SOM-MLP approach a promising technique for the inclusion of large chemical mechanisms in the context of complex applications, such as the multidimensional simulation of combustion.-
dc.language.isoeng-
dc.publisherInstitute of Physics Publishing-
dc.rightsclosedAccess-
dc.titleA self-organizing-map approach to chemistry representation in combustion applications-
dc.typeArtículo-
dc.identifier.doi10.1088/1364-7830/4/1/304-
dc.date.updated2012-06-11T09:39:37Z-
dc.description.versionPeer Reviewed-
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