Dreher, Daniel ; Schmidt, Marius ; Welch, Cooper ; Ourza, Sara ; Zündorf, Samuel ; Maucher, Johannes ; Peters, Steven ; Dreizler, Andreas ; Böhm, Benjamin ; Hanuschkin, Alexander (2023)
Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine.
In: International Journal of Engine Research, 2021, 22 (11)
doi: 10.26083/tuprints-00020179
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
Machine learning (ML) models based on a large data set of in-cylinder flow fields of an IC engine obtained by high-speed particle image velocimetry allow the identification of relevant flow structures underlying cycle-to-cycle variations of engine performance. To this end, deep feature learning is employed to train ML models that predict cycles of high and low in-cylinder maximum pressure. Deep convolutional autoencoders are self-supervised-trained to encode flow field features in low dimensional latent space. Without the limitations ascribable to manual feature engineering, ML models based on these learned features are able to classify high energy cycles already from the flow field during late intake and the compression stroke as early as 290 crank angle degrees before top dead center (-290° CA) with a mean accuracy above chance level. The prediction accuracy from -290° CA to -10° CA is comparable to baseline ML approaches utilizing an extensive set of engineered features. Relevant flow structures in the compression stroke are revealed by feature analysis of ML models and are interpreted using conditional averaged flow quantities. This analysis unveils the importance of the horizontal velocity component of in-cylinder flows in predicting engine performance. Combining deep learning and conventional flow analysis techniques promises to be a powerful tool for ultimately revealing high-level flow features relevant to the prediction of cycle-to-cycle variations and further engine optimization.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Dreher, Daniel ; Schmidt, Marius ; Welch, Cooper ; Ourza, Sara ; Zündorf, Samuel ; Maucher, Johannes ; Peters, Steven ; Dreizler, Andreas ; Böhm, Benjamin ; Hanuschkin, Alexander |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine |
| Sprache: | Englisch |
| Publikationsjahr: | 28 November 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | November 2021 |
| Ort der Erstveröffentlichung: | London |
| Verlag: | SAGE Publications |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Engine Research |
| Jahrgang/Volume einer Zeitschrift: | 22 |
| (Heft-)Nummer: | 11 |
| DOI: | 10.26083/tuprints-00020179 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/20179 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (Abstract): | Machine learning (ML) models based on a large data set of in-cylinder flow fields of an IC engine obtained by high-speed particle image velocimetry allow the identification of relevant flow structures underlying cycle-to-cycle variations of engine performance. To this end, deep feature learning is employed to train ML models that predict cycles of high and low in-cylinder maximum pressure. Deep convolutional autoencoders are self-supervised-trained to encode flow field features in low dimensional latent space. Without the limitations ascribable to manual feature engineering, ML models based on these learned features are able to classify high energy cycles already from the flow field during late intake and the compression stroke as early as 290 crank angle degrees before top dead center (-290° CA) with a mean accuracy above chance level. The prediction accuracy from -290° CA to -10° CA is comparable to baseline ML approaches utilizing an extensive set of engineered features. Relevant flow structures in the compression stroke are revealed by feature analysis of ML models and are interpreted using conditional averaged flow quantities. This analysis unveils the importance of the horizontal velocity component of in-cylinder flows in predicting engine performance. Combining deep learning and conventional flow analysis techniques promises to be a powerful tool for ultimately revealing high-level flow features relevant to the prediction of cycle-to-cycle variations and further engine optimization. |
| Freie Schlagworte: | Deep learning, machine learning, feature analysis, particle image velocimetry, in-cylinder flow, cycle-to-cycle variations, IC engine |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-201798 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet Reaktive Strömungen und Messtechnik (RSM) |
| Hinterlegungsdatum: | 28 Nov 2023 10:36 |
| Letzte Änderung: | 29 Nov 2023 06:12 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine. (deposited 28 Nov 2023 10:36) [Gegenwärtig angezeigt]
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