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Comparative assessment of URANS, SAS and DES turbulence modeling in the predictions of massively separated ship airwake characteristics
Shukla S., Singh S.N., Sinha S.S.,
Published in Elsevier Ltd
2021
Volume: 229
   
Abstract
An early assessment of the ship airwake characteristics is one of the most challenging tasks associated with the designing of vessels. The design of warship superstructures has traditionally followed the basic polyhedron shape (box type structures) to achieve the desired stealth capability. However, presence of such a box shape bluff superstructure generates massively separated airwake over the ship helodeck region. This airwake results into complex flow phenomena which carry strong velocity gradients in space and time, along with widely varying turbulence length scales. Under such conditions, the launch and recovery of a shipboard helicopter operations are very hazardous. Thus, an accurate assessment of the resultant ship airwake flow phenomena at early design stages is desirable. We present a comparative time-accurate assessment study in order to gain a better understanding of the capability of the Unsteady Reynolds-Averaged Navier-Stokes (URANS), the Scale-Adaptive Simulation (SAS) and the Detached Eddy Simulation (DES) turbulence models in predicting turbulent ship airwake characteristics. Detailed comparisons are conducted with respect to the in-house experimental data. Results show that the DES and SAS produce nearly similar trends of the mean flow properties when compared to the experimental results. However, comparisons of velocity spectra indicate that SAS can resolve the dominant large-scale turbulent flow structures with less computational burden. Further, this study also attempts to compare the variation of mean flow quantities with steady RANS approach in order to quantify the percentage variation between the predictions of the steady and unsteady turbulence modelling approach. © 2021 Elsevier Ltd
About the journal
JournalData powered by TypesetOcean Engineering
PublisherData powered by TypesetElsevier Ltd
Open AccessNo