Marine Application: "Flow around a ship-hull"

(Courtesy of HSVA, Germany)

Problem Overview

HSVA - Hamburgische Schiffbau Versuchsanstalt - is Germany's leading hydrodynamics research and development facility. They are a private self-supporting non-profit organisation with their main business to advice the maritime industry in all fields of naval hydrodynamics. The HSVA validation case involves the flow around the submerged hull of a single-propeller ship. The computations are based on the Napa geometry description of the model (HSVA model-2962) for the containership "Sydney Express". An experimental model was constructed according to this geometry description. From the Napa description, an IGES file was generated, to be used as the basis for grid generation using the commercial grid generation software ICEM-CFD. The ship is symmetrically shaped and the test case simulates full ahead travel, thus the flow is parallel to the ship's length. The actual ship is 210 meters long, but the computational grid was scaled to match the 7.50 meter length of the experimental model, in order to conduct a direct comparison with experiment measurements.
The present computation does not take the free surface into account, and thus the top of the computation domain is a solid surface. These are generally known as "double body" computations. The focus of the computations is the flow through the propeller plane, as indicated in the figure below

Propeller plane location	Stern Geometry

Results and Conclusions

In this test case, the relevant parameters observed are as follows:

Pressure along the hull surface
Shear stress along the hull surface
Flow velocity near the hull surface
Flow velocity components on the propeller plane
y+ values along the hull surface
k and e values for the turbulence model

The pressure along hull surface was evaluated both as a visual inspection, as well as an integrated sum on the hull wall. In general, the visualization was to ensure that pressure gradients occurred where expected: increased at areas of stagnation, and decreased where velocity increased. The screenshots below illustrate the typical APUS-CFD computations compared to reference solver results.

Comparison of Pressure distribution on Hull surface between two solvers

Experimental data was gathered from model tests in the HSVA's 300 meter towing tank. The model tests used force gauges to measure the longitudinal force required to tow the model, and 5-hole pitot tubes to measure the flow velocity in the propeller plane.
The meshes computed in this test case were fully hexahedral, unstructured meshes. These were generated in ICEM-CFD. The results from the APUS-CFD computations were evaluated against both model test measurements and in-house CFD computations.

Pressure distribution on Hull surface

The measured data were organized according to the percentage of the propeller radius, (r/rp), and then from angular position, phi, from 0 to 360 , with 0 being in the 6 o'clock position. Generally, the 70% radius is considered most important. This is a widely used benchmarking characteristic, because much of the propeller thrust is produced here. The figure below shows the axial flow in this region, comparing the APUS-CFD results to the measured values and the reference solver results.

Comparison of Velocity distribution between APUS-CFD, Reference Solver and test data

This figure shows the axial component of velocity at the 70% radius as the propeller blade travels through one period. The blade starts at the 0º, the 6 o'clock position, with an axial component of about 0.7 of the free stream flow. The blade sees the lowest value as it reaches the 180 degree position, where the velocity component dips below 0.4 of the free stream flow. In this position, the ship hull blocks much of the flow, causing this dip in flow velocity. The APUS-CFD results agree quite well with the model measurements and the reference solver results.

Parallel performance

The parallel version of the APUS-CFD solver was benchmarked and tested during the validation exercise. The effect of number of processors on performance was assessed and the numerical efficiency of the solver was observed. For all the tests performed the solver converged with almost the same number of iterations, regardless the number of processors used (from 1 CPU to 128 CPUs).
Execution times, for a mesh of 1.3 Million cells, and the corresponding speed-up achieved are shown in the figures below. The system used for this benchmark was the SGI Altix 3700 Bx2 (1600 MHz).
The performance and efficiency of the parallel solver are illustrated in the table below, for up to 128 processors.