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https://hdl.handle.net/20.500.14279/31029
Τίτλος: | Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A | Συγγραφείς: | Koukouvinis, Foivos (Phoevos) Rodriguez, Carlos Hwang, Joonsik Karathanassis, Ioannis K. Gavaises, Manolis Pickett, Lyle M. |
Major Field of Science: | Natural Sciences | Field Category: | Computer and Information Sciences | Λέξεις-κλειδιά: | Artificial Neural Network;Fuel injection;Machine Learning;multiphase flows;real-fluid thermodynamics;regression;transcritical mixing | Ημερομηνία Έκδοσης: | 1-Σεπ-2022 | Πηγή: | International Journal of Engine Research, 2022, vol. 23, iss. 9, pp. 1556 - 1572 | Volume: | 23 | Issue: | 9 | Start page: | 1556 | End page: | 1572 | Περιοδικό: | International Journal of Engine Research | Περίληψη: | 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.). | URI: | https://hdl.handle.net/20.500.14279/31029 | ISSN: | 14680874 | DOI: | 10.1177/14680874211020292 | Rights: | © IMechE Attribution-NonCommercial-NoDerivatives 4.0 International |
Type: | Article | Affiliation: | University of London Mississippi State University Sandia National Laboratories |
Publication Type: | Peer Reviewed |
Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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