Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/31029
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Koukouvinis, Foivos (Phoevos) | - |
dc.contributor.author | Rodriguez, Carlos | - |
dc.contributor.author | Hwang, Joonsik | - |
dc.contributor.author | Karathanassis, Ioannis K. | - |
dc.contributor.author | Gavaises, Manolis | - |
dc.contributor.author | Pickett, Lyle M. | - |
dc.date.accessioned | 2024-01-29T09:58:36Z | - |
dc.date.available | 2024-01-29T09:58:36Z | - |
dc.date.issued | 2022-09-01 | - |
dc.identifier.citation | International Journal of Engine Research, 2022, vol. 23, iss. 9, pp. 1556 - 1572 | en_US |
dc.identifier.issn | 14680874 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/31029 | - |
dc.description.abstract | The present work investigates the application of Machine Learning and Artificial Neural Networks for tackling the complex issue of transcritical sprays, which are relevant to modern compression-ignition engines. Such conditions imply the departure of the classical thermodynamic perspective of ideal gas or incompressible liquid, necessitating the use of costly and elaborate thermodynamic closures to describe property variation and simulation methods. Machine Learning can assist in several ways in speeding up such calculations, either as a compact, trained thermodynamic model that can be coupled to the flow solver, or as a surrogate predictive tool of spray characteristics. In this work, such applications are demonstrated and their performance is assessed against more traditional approaches. Such applications involve the prediction of macroscopic spray characteristics, for example, the spray penetration over time, or the spray distribution in space and time, and predictions of fluid properties for the thermodynamic states encountered in such applications. Macroscopic characteristics can be adequately predicted by relatively simple network structures, involving just a hidden layer of 3–4 neurons, whereas prediction of thermodynamic states requires several layers of 5–20 neurons each. The results of integrating Artificial Neural Networks in transcritical sprays are rather promising; prediction of thermodynamic properties at pressures greater than 1bar has effectively zero error, yielding simulations indistinguishable from standard tabulated approaches with minimal overhead. When used as a regression method for time-histories either of spray characteristics or spray distributions, the results are within experimental uncertainty of similar experiments, not included in the training dataset. (Figure presented.). | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | International Journal of Engine Research | en_US |
dc.rights | © IMechE | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial Neural Network | en_US |
dc.subject | Fuel injection | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | multiphase flows | en_US |
dc.subject | real-fluid thermodynamics | en_US |
dc.subject | regression | en_US |
dc.subject | transcritical mixing | en_US |
dc.title | Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A | en_US |
dc.type | Article | en_US |
dc.collaboration | University of London | en_US |
dc.collaboration | Mississippi State University | en_US |
dc.collaboration | Sandia National Laboratories | en_US |
dc.subject.category | Computer and Information Sciences | en_US |
dc.journals | Subscription | en_US |
dc.country | United Kingdom | en_US |
dc.country | United States | en_US |
dc.subject.field | Natural Sciences | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.1177/14680874211020292 | en_US |
dc.identifier.scopus | 2-s2.0-85106662456 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85106662456 | - |
dc.relation.issue | 9 | en_US |
dc.relation.volume | 23 | en_US |
cut.common.academicyear | 2022-2023 | en_US |
dc.identifier.spage | 1556 | en_US |
dc.identifier.epage | 1572 | en_US |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.grantfulltext | none | - |
crisitem.author.dept | Department of Mechanical Engineering and Materials Science and Engineering | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-3945-3707 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
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