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dc.contributor.authorSontheimer, Moritz
dc.contributor.authorSingh, Anshul-Kumar
dc.contributor.authorVerma, Prateek
dc.contributor.authorChou, Shuo-Yan
dc.contributor.authorKuo, Yu-Lin
dc.date.accessioned2023-10-27T18:27:07Z
dc.date.available2023-10-27T18:27:07Z
dc.date.issued2023-09-25
dc.identifier.urihttps://hdl.handle.net/1721.1/152529
dc.description.abstractModeling engines using physics-based approaches is a traditional and widely-accepted method for predicting in-cylinder pressure and the start of combustion (SOC). However, developing such intricate models typically demands significant effort, time, and knowledge about the underlying physical processes. In contrast, machine learning techniques have demonstrated their potential for building models that are not only rapidly developed but also efficient. In this study, we employ a machine learning approach to predict the cylinder pressure of a homogeneous charge compression ignition (HCCI) engine. We utilize a long short-term memory (LSTM) based machine learning model and compare its performance against a fully connected neural network model, which has been employed in previous research. The LSTM model’s results are evaluated against experimental data, yielding a mean absolute error of 0.37 and a mean squared error of 0.20. The cylinder pressure prediction is presented as a time series, expanding upon prior work that focused on predicting pressure at discrete points in time. Our findings indicate that the LSTM method can accurately predict the cylinder pressure of HCCI engines up to 256 time steps ahead.en_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.relation.isversionofhttp://dx.doi.org/10.3390/machines11100924en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceMultidisciplinary Digital Publishing Instituteen_US
dc.titleLSTM for Modeling of Cylinder Pressure in HCCI Engines at Different Intake Temperatures via Time-Series Predictionen_US
dc.typeArticleen_US
dc.identifier.citationMachines 11 (10): 924 (2023)en_US
dc.contributor.departmentProgram in Media Arts and Sciences (Massachusetts Institute of Technology)
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-10-27T10:26:46Z
dspace.date.submission2023-10-27T10:26:46Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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