Matches in SemOpenAlex for { <https://semopenalex.org/work/W2890936120> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W2890936120 endingPage "4944" @default.
- W2890936120 startingPage "4937" @default.
- W2890936120 abstract "Cycle-to-cycle variation (CCV) limits how lean a spark-ignited (SI) internal combustion engine (ICE) can stably operate at, restricts efficiency, and increases emissions through incomplete combustion. Therefore, a way to cleaner, more efficient SI ICEs is to minimize the CCV. Current methods to study CCV include experimental investigations and CFD-based numerical simulations. This study, in contrast, investigates the ability of neural networks to accurately model the indicated mean effective pressure (IMEP) and its coefficient of variation (COV of IMEP). Experimental data from a previous study of spark-ignited propane/air combustion in the TCC-III engine was used to train and evaluate a neural network. An optimized network was generated that utilizes 109 experimental inputs and is operated with 15 neurons in one hidden layer to determine IMEP for 18 engine operating conditions, with 625 individual consecutive engine cycles for each condition. The impact of training set size and the number of input parameters was also investigated. The average deviation for IMEP from the experimental measurements is 0.7–2.2% for the training data set and less than 12% for the entire predicted range of operating conditions. Data sets consisted of tests under rich, lean, and stoichiometric conditions without and with 9% nitrogen dilution. Predicted COV of IMEP strongly correlates with experimental data (R2 = 0.8453). However, a systematic over prediction of COV of IMEP for low COVs was observed while higher COVs were under-predicted by the neural network. The cause for this systematic behavior has not yet been identified but histograms of the predicted IMEP data indicate that this could be related to missing physical parameters that have a significant impact on combustion variability." @default.
- W2890936120 created "2018-09-27" @default.
- W2890936120 creator A5010983890 @default.
- W2890936120 creator A5023946054 @default.
- W2890936120 creator A5080845981 @default.
- W2890936120 date "2019-01-01" @default.
- W2890936120 modified "2023-09-26" @default.
- W2890936120 title "Neural network prediction of cycle-to-cycle power variability in a spark-ignited internal combustion engine" @default.
- W2890936120 cites W1974707715 @default.
- W2890936120 cites W1984236642 @default.
- W2890936120 cites W2006041140 @default.
- W2890936120 cites W2015422465 @default.
- W2890936120 cites W2029767409 @default.
- W2890936120 cites W2050726079 @default.
- W2890936120 cites W2051264098 @default.
- W2890936120 cites W2058544530 @default.
- W2890936120 cites W2086406410 @default.
- W2890936120 cites W2122348200 @default.
- W2890936120 cites W2335630412 @default.
- W2890936120 cites W2526884187 @default.
- W2890936120 doi "https://doi.org/10.1016/j.proci.2018.08.058" @default.
- W2890936120 hasPublicationYear "2019" @default.
- W2890936120 type Work @default.
- W2890936120 sameAs 2890936120 @default.
- W2890936120 citedByCount "25" @default.
- W2890936120 countsByYear W28909361202019 @default.
- W2890936120 countsByYear W28909361202020 @default.
- W2890936120 countsByYear W28909361202021 @default.
- W2890936120 countsByYear W28909361202022 @default.
- W2890936120 countsByYear W28909361202023 @default.
- W2890936120 crossrefType "journal-article" @default.
- W2890936120 hasAuthorship W2890936120A5010983890 @default.
- W2890936120 hasAuthorship W2890936120A5023946054 @default.
- W2890936120 hasAuthorship W2890936120A5080845981 @default.
- W2890936120 hasConcept C105923489 @default.
- W2890936120 hasConcept C119857082 @default.
- W2890936120 hasConcept C127413603 @default.
- W2890936120 hasConcept C128143373 @default.
- W2890936120 hasConcept C159985019 @default.
- W2890936120 hasConcept C171146098 @default.
- W2890936120 hasConcept C178790620 @default.
- W2890936120 hasConcept C185592680 @default.
- W2890936120 hasConcept C192562407 @default.
- W2890936120 hasConcept C199360897 @default.
- W2890936120 hasConcept C204323151 @default.
- W2890936120 hasConcept C25797200 @default.
- W2890936120 hasConcept C2781215313 @default.
- W2890936120 hasConcept C39432304 @default.
- W2890936120 hasConcept C41008148 @default.
- W2890936120 hasConcept C50644808 @default.
- W2890936120 hasConcept C511840579 @default.
- W2890936120 hasConceptScore W2890936120C105923489 @default.
- W2890936120 hasConceptScore W2890936120C119857082 @default.
- W2890936120 hasConceptScore W2890936120C127413603 @default.
- W2890936120 hasConceptScore W2890936120C128143373 @default.
- W2890936120 hasConceptScore W2890936120C159985019 @default.
- W2890936120 hasConceptScore W2890936120C171146098 @default.
- W2890936120 hasConceptScore W2890936120C178790620 @default.
- W2890936120 hasConceptScore W2890936120C185592680 @default.
- W2890936120 hasConceptScore W2890936120C192562407 @default.
- W2890936120 hasConceptScore W2890936120C199360897 @default.
- W2890936120 hasConceptScore W2890936120C204323151 @default.
- W2890936120 hasConceptScore W2890936120C25797200 @default.
- W2890936120 hasConceptScore W2890936120C2781215313 @default.
- W2890936120 hasConceptScore W2890936120C39432304 @default.
- W2890936120 hasConceptScore W2890936120C41008148 @default.
- W2890936120 hasConceptScore W2890936120C50644808 @default.
- W2890936120 hasConceptScore W2890936120C511840579 @default.
- W2890936120 hasIssue "4" @default.
- W2890936120 hasLocation W28909361201 @default.
- W2890936120 hasOpenAccess W2890936120 @default.
- W2890936120 hasPrimaryLocation W28909361201 @default.
- W2890936120 hasRelatedWork W1561388362 @default.
- W2890936120 hasRelatedWork W1983538817 @default.
- W2890936120 hasRelatedWork W2000322297 @default.
- W2890936120 hasRelatedWork W2016306304 @default.
- W2890936120 hasRelatedWork W2050451346 @default.
- W2890936120 hasRelatedWork W2211033626 @default.
- W2890936120 hasRelatedWork W2246405815 @default.
- W2890936120 hasRelatedWork W2302826228 @default.
- W2890936120 hasRelatedWork W2899084033 @default.
- W2890936120 hasRelatedWork W293349358 @default.
- W2890936120 hasVolume "37" @default.
- W2890936120 isParatext "false" @default.
- W2890936120 isRetracted "false" @default.
- W2890936120 magId "2890936120" @default.
- W2890936120 workType "article" @default.