Machine learning classification of boiling regimes with low speed, direct and indirect visualization
• Low speed/resolution image acquisition used for boiling regime classification. • Machine learning algorithms can identify pool boiling regimes with over 93% accuracy. • Non-intrusive, fast and accurate boiling regime classification methodology suggested.
Gespeichert in:
| Autor*in: |
Hobold, Gustavo M. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Analytical and computational investigation on host-guest interaction of cyclohexyl based thiosemicarbazones: Construction of molecular logic gates using multi-ion detection - Basheer, Sabeel M. ELSEVIER, 2019, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:125 ; year:2018 ; pages:1296-1309 ; extent:14 |
| Links: |
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| DOI / URN: |
10.1016/j.ijheatmasstransfer.2018.04.156 |
|---|
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ELV043672523 |
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