Trans RINA, Vol 154, Part C1, Intl J Marine Design, Jan - Jun 2012
reduce the degrees of freedom of the parametric model and the dimension of the design-space.
For the chosen example, the hull type (displacement, monohull) and choice of propulsion (twin-screw) had been fixed. The key parameters of interest were those that define the shape and geometry of the main hull form since these are the principal features that affect the performance measures of interest (resistance, sea- keeping and stability). These are listed in Table 2 and a typical instance of the parametric model is shown in Figure 1. The full details of the parametric model are described by Harries [4].
Table 2: Parameters chosen to define the hull form. min.
Prismatic coef. of fore part of hull, CPF DWL half angle of entrance, iE [deg] DWL fullness coefficient, Cfull(DWL)
Bulb area to midship area ratio, fBT = ABT / AM
Bulb fullness coefficient, Cfull(Bulb)
Longitudinal position of section with max. cross-sectional area [% LPP], LAX / LPP %
max.
Length between perpendiculars, LPP [m] 68.00 72.00 Beam on DWL, BDWL [m] Midship area coefficient, CM
0.89 0.63 18.0 0.62
14.00 14.25 0.82 0.60 14.0 0.58
0.092 0.098 0.75
44.0
0.85 48.0
variants. The range of performance measures required to produce meaningful to be
considered depends on the scope of the study as well as the tools available and the level of detail of the ship model
results; care
should be taken to ensure that there is sufficient computational resource and time available to perform the design-space exploration with the tools that have been selected. The parametric model should also be examined to determine if it provides sufficient detail for the selected analysis tools to be able to accurately differentiate and rank design variants; similarly if the analysis tools are not sufficiently sensitive to detect changes in certain parameters, these parameters should be removed from the model (or kept fixed at some average value) to reduce the number of dimensions of the design-space.
The numerical tools chosen to calculate the vessel performance for the example problem are described below. The tools presented cover three of the main areas of interest during initial design: resistance, sea-keeping and static stability. However it would be entirely feasible to include other tools to compute additional performance measures, for example production cost, manoeuvring, etc.
2.3 (a) Flow Simulation and Resistance Prediction
When predicting calm-water resistance, there is generally a trade-off between accuracy and computational effort. Since only the bare hull was modelled, it was considered appropriate to employ potential flow theory to solve the non-linear wave resistance problem with free sinkage and trim combined with a thin boundary layer theory calculation for the frictional resistance, further details are given by Harries [4]. SHIPFLOW was used to perform these calculations. When fine-tuning appendages, such as brackets, later in the design, a RANSE CFD calculation should be undertaken to accurately capture
viscous
phenomena, for example Brenner [5]. A typical panel arrangement
for the potential flow solver and corresponding results are shown in Figure 2.
Figure. 1: Example of typical bare hull with bulbous bow generated from the fully parametric model.
2.3 PERFORMANCE PREDICTION
The final stage is to select the computational tools that will be used to estimate the performance of the design
©2012: The Royal Institution of Naval Architects
Figure 2: Typical panel arrangement of free surface and hull (upper) with wave-wake height contours and hull streamlines at FN = 0.393 (lower).
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