numerical and experimental evaluation of waterjet

11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011

NUMERICAL AND EXPERIMENTAL EVALUATION OF WATERJET

PROPELLED DELFT CATAMARANS

Manivannan Kandasamy1, Svetlozar Georgiev

2, Evgeni Milanov

2, Frederick Stern

1

1 IIHR-Hydroscience & Engineering, the University of Iowa

2 BSHC- Bulgarian Ship Hydrodynamics Centre

ABSTRACT

The accurate prediction of waterjet propulsion using CFD is

of interest in the standpoint of performance analyses of

existing waterjet designs as well as design optimization of

new waterjet propulsion systems for high-speed marine

vehicles. Currently, the design and analysis of waterjets

follow the ITTC '05 recommended procedures and

guidelines which was validated by a rigorous experimental

campaign through standardized testing. The current study

focuses on validation of detailed duct flow simulations on

catamarans using the Delft catamaran as the model. The

validation work is conducted as a pre-requisite for

subsequent URANS based optimization. The Delft

catamaran model was build at BSHC and a customized

waterjet was designed for the model based on pre-existing

stock waterjet designs. Data from the model testing using

the ITTC '05 procedures include net jet thrust, thrust-

deduction, water-jet volume flow-rate, sinkage, trim, and jet

velocity surveys at nozzle exit. Simulations were performed

over a speed range of 0.4<Fr<0.75 using URANS and an

actuator disk body-force model. The computed net jet thrust,

thrust deduction, sinkage and trim compare well with

experiments indicating that the present approach is an

efficient tool to predict the performance of waterjet

propelled JHSS and paves way for future optimization work.

KEY WORDS

Water-jet, Self-propulsion, URANS, Validation, Catamaran

1.0 INTRODUCTION

Nowadays, there is a growing interest in waterjet propulsion

because it has a lot of benefits over conventional screw

propeller such as shallow draft design, smooth engine load,

less vibration, lower water borne noise, no appendage drag,

better efficiency at high speeds and good maneuverability.

These advantages have increased the demand of waterjet

propulsion systems for a variety of marine vehicles

including high-speed naval sealift. Since waterjet propulsion

systems are relatively new, the powering performance

analysis of waterjet appended hulls using tow tank model

testing has been a recent, ongoing area of research.

The ITTC Waterjet Performance Prediction Specialist

Committee (Van Terwisga, 2005) developed a model testing

procedure for waterjet propulsion. Rigorous experimental

testing was conducted by nine separate ITTC member

organizations using a 5.5 m scale model of the U.S. navy's

research vessel Athena at Fr=0.6. The scatter in resistance,

i.e., the difference between minimum and maximum

measured resistance was 9% of the mean. Dynamic trim

had an overall scatter of 10.6%, which was reduced to 2.9%

if the outliers were removed. Heave had an overall scatter of

117%, reduced to 55% with the elimination of outliers.

Looking at the quantities the dynamic trim was measurable

to within 0.3 degrees and heave to within 16 mm. The

scatter in inlet wake fraction was 7.6%. Of all the methods

used to determine flow rate directly, the most accurate and

repeatable was the use of a high density laser doppler survey

at the inlet opening or internal to the waterjet system. The

scatter in flow rate for equal impeller speed appeared was

0.8%. However, there was a 3.5% maximum deviation from

the mean in the estimation of model waterjet speed for the

self-propulsion point. Consequently, the scatter in the

estimated model thrust at self propulsion at Fr=0.6 was

18%. The committee concluded that the mechanics of the

experimental procedures applied for the submitted data sets

were generally sound, but the complicated nature of the

measurements and data reduction resulted in the substantial

facility bias.

Recent innovations in CFD and high performance

computing have enabled faster and cost effective approach

for predicting waterjet propulsive characteristics. This has

enabled detailed analysis of the flow through the waterjet

ducts, which would require prohibitively expensive Laser

Doppler Velocimetry (LDV) measurements if the whole

flow field has to be measured. Such detailed flow analysis is

required for a deeper understanding of the flow physics

giving insights into further improvement of the performance

characteristics of the waterjet. However, the CFD has to be

thoroughly validated before relying on it for performance

analysis, design, and optimization.

Bulten (2006) performed a detailed investigation both

experimentally and numerically on a waterjet test setup

where the waterjet inlet was mounted on top of a cavitation

tunnel. The mass flow rate in the tunnel was adjusted to get

the desired inlet velocity ratio (IVR) values. This was

modeled in the CFD using a prescribed velocity profile at

the inlet of the cavitation tunnel and a constant pressure

boundary condition at the outflow plane. The waterjet stator

and rotor geometry was also modeled. Validation

demonstrated that the standard two equation turbulence

model in combination with wall functions was able to

predict the non uniformities in the duct flow field with

© 2011 American Society of Naval Engineers 217

acceptable accuracy. The results showed that the main inlet

flow characteristics such as cavitation inception at cutwater

where the flow to the duct separates from the main flow,

velocity distribution at the impeller plane, flow separation at

the inlet, the shape of the inlet stream tube are related to the

IVR. The author recommends a dedicated inlet design for

each ship since variations in design ship speed and power

density of the installations cause the design IVR to vary.

The analysis of waterjet for the use of amphibian vehicle

was performed by Jang et al., (2004) to provide detail

understanding of complicated three-dimensional viscous

flow phenomena including interactions of intake duct, rotor,

stator, and contracted discharge nozzle. RANS flow solver

with moving, non-orthogonal multi-block grid system was

used. The CFD results were compared with experimental

fluid dynamics (EFD) and the complex viscous flow feature

of the waterjet, such as the secondary flow inside of the

intake duct, the recovery of axial flow by the action of the

stator, and tip vortex were predicted. The performance

prediction of waterjet for the use of similar vehicle by

diameter sizes and weights were investigated both

numerically and experimentally by Kim et al., (2009).

An extensive study was undertaken to analyze the effect of

integrating RANS calculations into experimental waterjet

powering prediction by Delaney et al., (2009). The

experimental data for the validation was provided by Jessup

et al., (2007), who conducted model tests for the joint high

speed sealift (JHSS) powered with four waterjet systems

and testing incorporated all of the approaches explored by

the ITTC. These included LDV surveys, bollard tests, single

total head probes, and direct measure using weight scales.

Two different JHSS models were considered; each model

houses either axial-flow or mixed-flow waterjet. The hull,

waterjet inlets, and shafts were modeled in the simulation.

Multi-element unstructured grids and boundary layer prism

elements were generated around waterjet geometry. The free

surface was treated as a symmetry plane, and the ship was

modeled at sinkage and trim prescribed by the propelled

experiment. RANS simulation used experimentally

determined volumetric flow rates through the pump as a

condition for the thrust provided by the actuator disk model.

The full scale simulations (Fn=0.35, Reynolds number

Rn=5.3×109) were also performed in order to investigate the

scaling effects by comparing boundary layers. The RANS

delivered pump power predictions showed good agreement

with EFD within one percent at model scale, and within two

percent at full scale.

Hino et al., (2009) performed RANS analysis of a free

surface flow around waterjet propelled high-speed ship

(Fn=1.0, Rn=1.0×106). Free surface location was predicted

using single-phase level set approach. An actuator disk

model in which duct geometry is modeled in a

computational grid was used to simulate the self-propelled

condition. The nozzle shape was not modeled, and dynamic

motions were not predicted. The flow fields of waterjet

propelled simulations, such as free surface elevations,

pressure distributions in the duct center planes, and limiting

streamlines on a ship were compared with the towed

simulations; however, the detailed V&V results were not

given. Takai et al., (2010) investigated the capability of

URANS with an actuator disk model for the simulation of

the JHSS appended with axial waterjets, including waterjet-

hull interactions. The computational setup differs from

Delaney et al., (2009) in that the waterjet-hull interactions

and waterjet-wake interactions are also predicted with free

surface and dynamic motions. The effects of waterjet-hull

interaction are highly non-linear as they include the effect of

the dynamic trim on boundary layer ingestion and shape of

inflow stream tube, together with the effect of the waterjet

induced vertical forces on the dynamic motion. The

waterjet-wake interactions do not significantly affect the

propulsion characteristics, but are of interest in the study of

wake signatures. Self propulsion simulations are carried out

at model scale with full scale thrust identity similarity. The

simulations are carried out over a range of ship speed at

different IVR ratios for the waterjet. On the finest grid with

13 million points, the jet volume flow rate was under

predicted by 6% and was attributed to interpolation errors

caused by extensive use of overset grids within the duct;

each duct had five overset grids. It was recommended that

the number of overset grids be restricted within the duct. An

accurate flow rate measurement is very important since the

estimation of power is dependent upon velocity cubed and

thrust by the velocity squared.

The current work extends Takai et al. (2010) to waterjet

propelled catamarans. The Delft catamaran was selected as

the candidate geometry and was fitted with a custom

designed waterjet based on existing stock waterjets by

BSHC. Data from the model testing using the ITTC '05

procedures include net jet thrust, thrust-deduction, water-jet

volume flow-rate, sinkage, trim, and jet velocity surveys at

nozzle exit. The model was shipped to INSEAN after testing

to quantify facility bias, and testing is currently being

performed in INSEAN. This CFD validation study was done

as a prerequisite for subsequent design optimization of both

the water-jet inlet and the hull form. In future, the optimized

hull and waterjet will be built and model tested at INSEAN.

Kandasamy et al., (2010) derived a simplified integral

force/moment waterjet model for ship powering predictions

that includes the effect of waterjet induced sinkage and trim

on the powering performance without requiring detailed

simulations for the waterjet system (nozzle, pump, ducting

system, and inlet). The CFD waterjet model was also

validated for the Delft catamaran since it will be used in the

preliminary bare hull optimization, before resorting to the

waterjet inlet shape optimization.

The remainder of the paper will be structured as follows:

section 2 presents an overview of the ITTC recommended

procedure for model testing and the CFD waterjet model;

section 3 presents the experimental method used at BSHC;

section 4 presents the computational method; section 5

presents the results from the experiments and the

computations, followed by the conclusions in section 6.

218 © 2011 American Society of Naval Engineers

2.0 ITTC & CFD CONTROL VOLUME METHODS

Fig. 1. ITTC control volume

The ITTC waterjet model combines model testing with

control volume/integral analysis for ship powering

predictions. The selected control volume (Fig. 1) is defined

by a streamtube consisting of the nozzle, pump, ducting

system, inlet, and upstream imaginary surface BC in the

flow through which it is assumed no mass transport occurs

by definition with one outlet A6 and inlet A1A. Vertical

reaction force, weight of the working fluid, and pressure and

shear forces acting on BC are neglected. The inlet A1A is

selected to avoid major flow distortions by the intake

geometry and as practical choice is usually one impeller

diameter in front of the ramp tangency point.

The first step is the resistance test and wake-field

measurements where a resistance test is carried out for a

bare hull model with closed intakes that is free to sink and

trim. The total bare hull resistance RTBH is obtained and is

used later to estimate the thrust deduction factor, t. During

the resistance test the boundary layer velocity profile

u1AX(y,z) is measured at Station 1A. This profile will be used

later to calculate the following items: the intake area at

station 1A, the average velocity at station 1A, , and the

momentum correction factor at Station 1A, cm1, which is

calculated at any station N using Eq. (1-2)

21

NMN

ANN

uc dA

A V

(1)

1

N

NA

N

V V n dAA

(2)

Next is the calibration and propulsion test. The purpose of

the calibration test is to establish a relation between a

measurement signal at the jet (often a differential pressure

transducer or Kiel probe is used) and the jet-thrust (TJx)

which is measured through the Bollard pull test. The flow

rate (Q) is then calibrated through the momentum flux

approach, since direct measurement of flow-rate is prone to

higher uncertainties. Assuming negligible inlet axial

velocity, TJx is equal to the momentum flux at the jet nozzle

providing Eq. (3) for estimating Q.

6

6 cos

Jx

m

T AQ

c (3)

The momentum correction factor at the jet exit cm6 is

obtained from detailed jet velocity profiles using LDV. α is

the jet angle relative to the horizontal at the nozzle (station

6). This calibration is assumed to hold good even with non-

zero forward velocity, so that the Kiel probe measurements

taken during self-propulsion tests could be used to estimate

Q.

After calibration, the propulsion test is carried out to

determine relation between speed, flow-rate and thrust at

self propulsion point. The calibrated Kiel probe

measurements provide Q at the self-propulsion point. Q is

then used to determine the size of the inlet capture area from

the inlet wake-field measurements by applying conservation

of mass. Once the capture area is determined cm1 can be

calculated. All the relevant variables to predict the waterjet

net thrust (RX) from Eq. (4) are now known.

2

6 0

6

cosX M Min

QR c Qc U

A

(4)

Note that the ITTC model ignores vertical forces and

pitching moments. These are not relevant for the purposes

of the original ITTC model, since sinkage and trim are

measured during the thrust evaluation procedure. For a CFD

simulation, however, it is desirable to estimate the resulting

waterjet-induces forces and moments affecting sinkage and

trim, since the final attitude of the ship affects the resistance

and wave-generation characteristics of the ship. Hence, a

modified control volume is selected as shown by the shaded

area in Fig. 2.

Fig. 2. CFD control volume

The choice of the proposed control volume which is for the

waterjet is motivated by the possibility of experimentally

measuring the pressure and velocities at the waterjet inlet,

which allows for the computation of the inlet flow angle ,

momentum flux correction factor CMin, pressure force

in inP A , and pressure distribution at stern which provides

the waterjet/hull interaction stern force ΔFS. The model can

then be used during early design optimization stages to

includes the effect of waterjet induced sinkage and trim on

the powering performance without requiring detailed

simulations for the waterjet system (nozzle, pump, ducting

system, and inlet).


3.0 EXPERIMENTAL METHODS

Table 1 provides the main particulars of the Delft catamaran

model.

Table 1. Main particulars for the Delft catamaran model

Main Particulars Symbol Model

Length overall, m LOA 3.8220

Length between perpendiculars,

m

LPP 3.6274

Length on waterline, m LWL 3.6274

Breadth moulded, single hull, m B 0.2904

Clearance b/n hull CPs, m - 0.8470

Draft at FP, m TF 0.1815

Draft at AP, m TA 0.1815

Displacement volume, m3 Δ 0.0770

Prismatic coefficient CP 0.6160

Block coefficient CB 0.4027

Longitudinal C.B. LCB -0.0970

Wetted surface area (bare hull),

m2

S 1.4220

A waterjet propulsion unit, consisting of ducting system,

inlet opening, axial flow pump and nozzle, was incorporated

in each of the catamaran hulls. Fig. 3 shows the stock

waterjet pump arrangement used for testing.

Fig. 3. Waterjet propulsion unit used for testing

The model testing was carried out in BSHC deep water

towing tank. The towing tank main dimensions is 200 m

length x 16 m breadth x 6.5 m depth. Two wave absorbing

beaches are arranged, one front and one side dampers. The

towing carriage has a maximum speed of 6 m/s. The

carriage is used to guide the ship model and to hold the

measuring and supplementary equipment. A propeller

dynamometer was installed on the starboard hull for

impeller generated thrust and torque (delivered power)

measurements. Three forms of pressure reading

instrumentation were used for the hull model testing: i)

static pressure taps located around the inlet opening of

starboard hull; ii) one static pitot tube was used to measure

the hull boundary layer velocity profiles and one static pitot

tube for measuring the reference dynamic pressure at the

starboard hull nozzle exit; iii) a 5-hole probe was used to

determine flow-field velocity distribution at the nozzle exit

plane of starboard hull at bollard conditions, necessary to

calculate the nozzle momentum correction factor and

bollard flowrate.

During the resistance test the boundary layer velocities were

measured along a normal to hull surface, one inlet diameter

upstream of inlet centerline, with the inlets closed. The runs

were made at 7 different model speeds, corresponding to

Froude numbers 0.4, 0.45, 0.5, 0.55, 0.6, 0.65 and 0.7. The

measurements were performed by means of a static pitot

tube. It was assumed that the velocity profiles thus obtained

do not change in traverse direction (they vary only in z –

direction) and so they fully describe the flow pattern at

Station 1A. The obtained velocity profiles were later used to

calculate the intake area and the inlet momentum correction

factor Cm1 . There is some controversy on whether the

velocity survey should be made with closed intake (nominal

wakefield) or with open intake and active waterjet (total

wakefield). Terwisga (2005) states that ideally one would

like to measure the effective wake ingested by the intake.

That is the flow field including the suction effects on the

flow about the hull, without any effect of the intake flow

itself. This so called “effective wake” is however difficult to

measure (intake flow is always present) and depends on the

working point of the intake. For that reason, it is suggested

to measure the boundary layer velocity profile with closed

intake openings, which contradicts the previous tentative

procedure described in the ITTC Quality Manual(4.9-03-03-

05.2).

Bollard pulling forces were measured with both pumps

operating within a range of impeller revolutions. The

obtained data were used later for flowrate calibration. The

reference nozzle dynamic pressure (measurement signal)

necessary for flowrate calibration was measured with a

static pitot tube located at about 70% of nozzle exit radius,

i.e. about the middle of the nozzle flow area, at 12 o‟clock

position. The main objective of these tests was to define the

model self-propulsion parameters and to gain data necessary

for self-propulsion flowrate estimation. No skin friction

correction force was applied during the tests, thus all results

refer to model scale conditions. For all the tests, the model

was free to heave and pitch, but was restrained in yaw and

roll. The tests were performed with outward turning

impellers. 3D velocity survey at nozzle exit (Station 6) was

performed using 5-hole pressure probe. The obtained local

axial velocity distribution was used to evaluate the nozzle

momentum correction factor Cm6 at that Station, used for

flowrate calculation.

4.0 COMPUTATIONAL METHODS

The Unsteady Reynolds-Averaged Navier Stokes (URANS)/

Detached Eddy Simulation (DES) flow solver, CFDSHIP-

IOWA has been developed at IIHR –Hydroscience &

Engineering– over the past 15 years for ship hydrodynamics

applications (Carrica et al., 2007). For the present work,


URANS with the blended k-ε/k-ω turbulent model is

selected as a flow solver. The free surface location is

predicted by a single phase level set method. A second order

upwind scheme is used to discretize the convective terms of

momentum equations for URANS. A pressure-implicit split-

operator (PISO) algorithm is used to enforce mass

conservation on the collocated grids. The pressure Poisson

equation is solved using the PETSc toolkit (Belay et al.,

2002). All the other systems are solved using an alternating

direction implicit (ADI) method. For a high performance

parallel computing, a MPI-based domain decomposition

approach is used, where each decomposed block is mapped

to one processor. The code SUGGAR (Noack, 2005) runs as

a separate process from the flow solver to compute

interpolation coefficients for the overset grids and

communicates with a motion controller (6DOF) within

CFDSHIP at every timestep. The software USURP (Boger

and Dreyer, 2006) is used to compute area and forces on the

surface overlapped regions. In addition, a simplified body

force model is used for waterjet propelled simulation to

prescribe axisymmetric body force with axial and tangential

components (Paterson et al., 2003). The propeller model

requires thrust, torque, and advance coefficients as input and

provides the torque and thrust forces. These forces appear as

a body force term in the momentum equations for the fluid

inside the propeller disk. The location of the propeller is

defined in the static condition of the ship and moves

according to the ship motions.

Based on recommendations from Takai et al. 2010, care was

taken to limit the number of overset grids in the duct and

reduce interpolation errors for volume flow rate. The duct is

discretized using a single structured grid which overlaps

with the hull grid at the inlet ant the nozzle exit (Fig. 4).

Fig. 4. Overset grid system for Delft catamaran with

symmetry plane.

A symmetry boundary condition was used and a body-fitted

“O” type grids are generated around the port side ship hull

geometry. A rectangular background grid is used with

clustered grid near the free surface to resolve the wave field.

A cylindrical refinement block was generated immediately

following the nozzle exit to better resolve the exiting jet. In

the present work, the shaft and the downstream rotor are not

included in order to avoid the complexity of the grid design

since the present work is prerequisite for optimization work.

For self propelled simulations, a total of 6.4 million grid

points is split into 64 blocks with an average of 100K grid

points/block by the MPI based domain decomposition. The

simulation domain is extended to [-0.5, 2.5], [-0.7, 0.7], [0,

1.3] in streamwise, vertical and spanwise directions,

respectively. The boundary conditions are detailed in Table

2.

Table 2. Boundary conditions

Description p U V W

In.

Resist.

Self-

propelled

Exit

Bottom, sides

Top

Symmetry

No slip (ship

hull)

For barehull resistance computations, the ship is initially

static on calm water. The ship is then allowed to pitch and

heave under a constant inlet fluid velocity until a steady

state is reached. Ship-fixed coordinate system is used, which

means that there is no surge motion allowed for the ship and

the background grid. For self-propulsion simulation, an

actuator disk model is used to prescribe axisymmetric body

force with axial and tangential components. The simulations

mimic the experiment; the ship accelerates until the

resistance equals the prescribed thrust and added tow force

and converges to the self propulsion point. 2-5 nonlinear

iterations are required for convergence of the flow field

equations within each time step. Convergence of the

pressure equation is reached when the residual imbalance of

the Poisson equation drops six orders of magnitude. All

other variables are assumed convergence when the residuals

drop to 10-3

.

5.0 RESULTS

For all the tests, the model was free to heave and pitch, but

was restrained in yaw and roll. The model resistance,

sinkage and trim were investigated in a Froude number

range 0.055 - 0.700, corresponding to towing speed range

of 0.298 m/s - 4.175m/s. The measured barehull resistance,

sinkage and trim are compared with the CFD calculations in

Fig. 5. The sinkage was measured with two string pots for

displacement measurements, one located on the leading

post (1708 mm forward of midship) and a second located on

the tow post (97 mm aft of midship). The measured data

were used to calculate the trim angle. The sinkage is

measured at the tow post location.


(a)

(b)

(c)

Fig. 5. EFD vs CFD comparisons: (a) barehull resistance

and net jet thrust; (b) Sinkage, and (c) trim

Table 2. Model self-propulsion points

V

m/s

Fn

-

n

rps

Q

Nm

Rx

N

PdREF,

kgf/m2

2.3856 0.400 24.578 1.265 37.97 813.7

2.6838 0.450 29.885 1.888 57.86 1208.2

2.9820 0.500 33.112 2.310 71.62 1511.3

3.2802 0.550 34.808 2.567 80.44 1706.8

3.5785 0.600 36.574 2.833 87.68 1863.8

3.8767 0.650 38.433 3.132 95.96 2069.3

4.1749 0.700 40.010 3.359 101.72 2238.7

Table 2 shows the impeller rps, thrust and torque at model

self-propulsion point. The flow rates Q during self

propulsion were calculated using the reference pressure

Pdref, at Station 6 which were taken at the same static pitot

tube location as at bollard condition and the self propulsion

flow rates were using the bollard flowrate calibration

relation. The measurements were taken at the same Static

Pitot tube location, as at bollard condition. Fig 6 compares

the computed volume flow rates with the measured values.

CFD underpredicts the volume flow rate by ~2% over the

speed range.

Fig. 6. Comparison of volume flow rates

The flow rate was used to determine the size of the inlet

capture area by applying conservation of mass. For all tested

craft speeds, the inlet capture area was assumed to be

rectangular and its size was determined from BH inlet

velocity-field measurements. Fig. 7 shows the CFD inlet

inlet capture area got by seeding the flow through the duct

with stream lines which is elliptical similar to previous CFD

simulations (Takai et al., 2010). Van Terwsaga (2005)

concluded that the inlet capture area for Athena was also

elliptical, but the shape does not have significant effect on

the ingested momentum and energy flux. However, it was

noted that there is at least one reference (Roberts and

Walker, 1998) claiming that the choice of a rectangular

capture area may lead to an under-prediction of gross thrust

by ~10% for a typical high speed ferry.

Fig. 7. Inlet capture area obtained from CFD for Fr=0.53


The net jet thrust Rx was determined using Eq. (4) and

shown in figure 5a. Overall, the net jet thrust Rx is ~10%

higher than the barehull resistance. Simulations using the

CFD waterjet model, which include the effects of just the

waterjet induced sinkage and trim on the barehull indicate

an increase of ~5% in barehull resistance. The model scale

thrust deduction fraction, t, accounting for all the interaction

effects of the propulsion system on bare hull system is

shown in Fig. 8.

Fig. 8. Thrust deduction factor

Fig. 9 compares the EFD and CFD flow fields at Fr=0.55

and 0.53, respectively. The diverging wave fronts from the

bow show qualitative agreement. EFD shows considerable

splashing at the jet exit. The splash is qualitatively resolved

by the CFD (Fig. 10), but without any air entrainment

effects. Fig. 10 also shows the pressure distribution inside

the duct with a rapid pressure rise after the actuator region,

which get converted into kinetic energy while passing

through the nozzle.

Fig. 9. Flow field comparison

Fig. 10. Duct pressure distribution and exit jet splash

6.0 CONCLUSIONS

URANS simulations for both barehull and waterjet

propelled Delft catamaran is presented and compared with

model test results. CFDSHIP-IOWA V.4 is employed as a

flow solver, which solves URANS with the blended k-ε/k-ω

turbulent model, single-phase level set method, and

simplified body force model are adopted to simulate the

waterjet propelled ship flow. The present work was

performed to investigate the capability of URANS for the

accurate simulation of waterjet propelled catamarans as a

pre-requisite for CFD based design optimization of both the

hull form and the intake duct shape.

Due to the complexity of the EFD measurement process,

significant facility bias errors were encountered during the

validation of the standardized testing (Van Terwisga, 2005)

by nine separate ITTC member organizations. Hence, the

current Delft model which was built and model tested by

BSHC was shipped to INSEAN to quantify facility bias

errors and testing is currently being done.

Overall, the CFD results match reasonably well with the

experiments. The bare hull resistance was under predicted

by ~2.5%. The flow rate was under-predicted by ~3%.

Previous work on the JHSS (Takai et al., 2010) with the

same code with multiple overset grids in the duct region

showed ~6% underprediction of flow rate due to

interpolation errors. The lower error for the current case can

be attributed to the fact the duct is discretized using a single

grid, and hence there are no interpolation errors. The CFD

under-predicts the net jet thrust by ~5% over the entire

speed range.

It should be noted that certain evaluation procedures differ

between CFD and EFD. The inlet capture area for CFD is

elliptical based on streamtubes entering the duct, whereas a

rectangular capture area was used for the EFD. Also, the

velocity profiles used to determine the capture area in EFD

were measured with the inlets closed as per ITTC

recommendations, but this is not the case with CFD, where

the elliptical capture area obtained with the ducts open was

used. Differences are expected to be negligible as per Van

Terwisga (2005). However, a thorough quantification of the

differences in wake fraction due to the inlet shape changes

and differences in velocity profile with open and closed

inlets using CFD will be beneficial.


Including the effects of the waterjet induced sinkage and

trim using the CFD waterjet model captures some of the

waterjet system- bare hull system interaction effects, and

indicates an increase in resistance by 4%. This accounts for

some of the interaction effects which contribute to the thrust

deduction, but does not account for the effect of the

boundary layer ingestion on thrust which is one of the most

dominant jet-hull interaction as addressed by Van Terwisga

(1995) and Hoyt et al. (1999).

In addition to the thrust deduction, which accounts for the

effects of waterjet hull interaction, the propulsive efficiency

is an important parameter needed for accurate assessment of

delivered powering requirements. Calculation of the overall

propulsive efficiency requires accurate estimation of the

pump efficiency which necessitates modeling of the shaft,

hub, rotor and stator (Bulten and Van Esch, 2007) which is

out of scope of the present work and needs to be addressed

in future work.

The present work demonstrates the feasibility of using

URANS for performance analysis of hull-integrated waterjet

propelled ship with free surface and dynamic motions and

work paves way for waterjet inlet optimization studies with

main objective of decreasing powering requirements by

increasing the inlet efficiency through modification of

intake duct shape (Kandasamy et al., 2011).

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ACKNOWLEDGEMENTS

This work is sponsored by the US Office of Naval Research

through research grants N00014-08-0491, under the

administration of Dr. Ki-Han Kim. The simulations were

performed on 4.7GHz IBM Power 6 machine „DaVinci‟ at

the DoD NAVO center.


numerical and experimental evaluation of waterjet

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