| Autor: |
Alessandro La Ferlita, Yan Qi, Emanuel Di Nardo, Karoline Moenster, Thomas E. Schellin, Ould EL Moctar, Christoph Rasewsky, Angelo Ciaramella |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Journal of Marine Science and Engineering, Vol 11, Iss 10, p 1854 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2077-1312 |
| DOI: |
10.3390/jmse11101854 |
| Popis: |
The authors proposed a direct comparison between white- and black-box models to predict the engine brake power of a 15,000 TEU (twenty-foot equivalent unit) containership. A Simplified Naval Architecture Method (SNAM), based on limited operational data, was highly enhanced by including specific operational parameters. An OAT (one-at-a-time) sensitivity analysis was performed to recognize the influences of the most relevant parameters in the white-box model. The black-box method relied on a DNN (deep neural network) composed of two fully connected layers with 4092 and 8192 units. The network consisted of a feed-forward network, and it was fed by more than 12,000 samples of data, encompassing twenty-three input features. The test data were validated against realistic operational data obtained during specific operational windows. Our results agreed favorably with the results obtained for the DNN, which relied on sufficiently observed data for the physical model. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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