University of Tasmania
Browse

File(s) under permanent embargo

Comparison of unsteady Reynolds-averaged Navier-Stokes prediction of self-propelled container ship squat against empirical methods and benchmark data

journal contribution
posted on 2023-05-20, 16:07 authored by Zhen Kok, Jonathan DuffyJonathan Duffy, Shuhong ChaiShuhong Chai, Jin, Y
The demand to increase port throughput has driven container ships to travel relatively fast in shallow water whilst avoiding grounding and hence, there is need for more accurate high speed squat predictions. A study has been undertaken to determine the most suitable method to predict container ship squat when travelling at relatively high speeds (Frh ≥ 0.5) in finite water depth (1.1 ≤ h/T ≤ 1.3). The accuracy of two novel self-propelled URANS CFD squat model are compared with that of readily available empirical squat prediction formulae. Comparison of the CFD and empirical predictions with benchmark data demonstrates that for very low water depth (h/T < 1.14) and when Frh < 0.46; Barass II (1979), ICORELS (1980), and Millward’s (1992) formulae have the best correlation with benchmark data for all cases investigated. However, at relatively high speeds (Frh ≥ 0.5) which is achievable in deeper waters (h/T ≥ 1.14), most of the empirical formulae severely underestimated squat (7-49%) whereas the quasi-static CFD model presented has the best correlation. The changes in wave patterns and effective wake fraction with respect to h/T are also presented.

History

Publication title

Transactions of the Royal Institution of Naval Architects, Part A: International Journal of Maritime Engineering

Volume

162

Issue

Part A2

Pagination

193-206

ISSN

1479-8751

Department/School

Australian Maritime College

Publisher

Royal Institution of Naval Architects

Place of publication

United Kingdom

Rights statement

© RINA 2020

Repository Status

  • Restricted

Socio-economic Objectives

Water safety

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC