Ship-scale CFD benchmark study of a pre-swirl duct on KVLCC2
Ship-scale CFD benchmark study of a pre-swirl duct on KVLCC2
Installing an energy saving device such as a pre-swirl duct (PSD) is a major investment for a ship owner and prior to an order a reliable prediction of the energy savings is required. Currently there is no standard for how such a prediction is to be carried out, possible alternatives are both model-scale tests in towing tanks with associated scaling procedures, as well as methods based on computational fluid dynamics (CFD). This paper summarizes a CFD benchmark study comparing industrial state-of-the-art ship-scale CFD predictions of the power reduction through installation of a PSD, where the objective was to both obtain an indication on the reliability in this kind of prediction and to gain insight into how the computational procedure affects the results. It is a blind study, the KVLCC2, which the PSD is mounted on, has never been built and hence there is no ship-scale data available. The 10 participants conducted in total 22 different predictions of the power reduction with respect to a baseline case without PSD. The predicted power reductions are both positive and negative, on average 0.4%, with a standard deviation of 1.6%-units, when not considering two predictions based on model-scale CFD and two outliers associated with large uncertainties in the results. Among the variations present in computational procedure, two were found to significantly influence the predictions. First, a geometrically resolved propeller model applying sliding mesh interfaces is in average predicting a higher power reduction with the PSD compared to simplified propeller models. The second factor with notable influence on the power reduction prediction is the wake field prediction, which, besides numerical configuration, is affected by how hull roughness is considered.
Benchmark study, KVLCC2, Pre-swirl duct, Ship-scale CFD
Andersson, Jennie
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Shiri, Alex Abolfazl
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Bensow, Rickard E
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Yixing, Jin
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Chengsheng, Wu
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Gengyao, Qiu
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Deng, Ganbo
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Queutey, Patrick
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Xing-Kaeding, Yan
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Horn, Peter
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Lucke, Thomas
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Kobayashi, Hiroshi
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Ohashi, Kinihide
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Sakamoto, Nobuaki
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Gao, Yuling
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Yang, Fan
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Winden, Bjorn
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Meyerson, Max G
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Maki, Kevin J
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Turnock, Stephen
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Hudson, Dominic
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Banks, Joseph
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Terziev, Momchil
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Tezdogan, Tahsin
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Vesting, Florian
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Hino, Takanori
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Werner, Sofia
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1 June 2022
Andersson, Jennie
2f7063f9-7f45-4ace-8817-532142f2775f
Shiri, Alex Abolfazl
f629e01c-bd00-4de3-9d78-e26bdd6581f3
Bensow, Rickard E
3d717f1e-beea-4fe7-b342-e95740dfb24e
Yixing, Jin
9edfcfa1-97e3-4d21-ae23-c1059cb5091a
Chengsheng, Wu
2011dd80-3779-4af5-a1a3-2750cc07be3b
Gengyao, Qiu
e07b30b9-1d43-470b-b37f-aa6b82639191
Deng, Ganbo
04adf582-1f64-4e2e-8a73-e385461d1aa9
Queutey, Patrick
638e816b-132f-4071-bd3a-95d613f8ce8e
Xing-Kaeding, Yan
487443f5-8a29-4658-8a24-92d2eec5eb1d
Horn, Peter
7f70e828-9531-4940-b67a-d26dac04d6fe
Lucke, Thomas
54f92436-6887-42a3-81b3-cf8d566418a2
Kobayashi, Hiroshi
c9de7e4a-b038-457f-9507-88cf21767882
Ohashi, Kinihide
70c98b44-0c38-4eae-8a6f-b20b078f99dc
Sakamoto, Nobuaki
195875d7-e023-4f8d-aba0-5cedd2d74ab9
Gao, Yuling
143dd5ed-87e5-4970-840c-3611b39f6a0a
Yang, Fan
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Winden, Bjorn
b1093655-db40-4f63-b92f-aa70ed3a70a5
Meyerson, Max G
e437e30d-d640-4d0c-9dbf-b2e6ce280b06
Maki, Kevin J
49d43430-c81c-40c5-a797-39a8d3e4bc3f
Turnock, Stephen
d6442f5c-d9af-4fdb-8406-7c79a92b26ce
Hudson, Dominic
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Banks, Joseph
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Terziev, Momchil
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Tezdogan, Tahsin
7e7328e2-4185-4052-8e9a-53fd81c98909
Vesting, Florian
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Hino, Takanori
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Werner, Sofia
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