Conflicting Objectives in Ship Design:

Environmental and Safety Regulations

Conspire to Complicate Optimization

Richard Korpus

American Bureau of Shipping

CD-adapco™ STAR Global Conference

San Diego, March 16-18, 2015

222

Motivation and Objectives

Owners and operators want to reduce operating costs. Designers

and builders want their products competitive.

The International Maritime Organization (IMO) provides additional

motivation by regularly decreasing greenhouse gas emission limits.

Since the objective is to minimize both fuel consumption and

emissions; hull resistance, propulsive efficiency, and engine

performance all have to be addressed simultaneously.

Only viscous flow CFD captures all the physics necessary to

accurately model the resistance and propulsor parts.

Reducing main engine size can improve overall efficiency, but

conflicts with safety-oriented requirements for reserve power.

Without adequate reserve power a vessel could have maneuvering

problems in extreme weather.

Optimization requires a balance between economy and safety.

333

Engines Often Run De-Rated

3

Engine manufactures suggest an efficient operating range.

Owners specify single design point, but range of desired operating conditions.

Owners typically demand 15% extra power for adverse conditions.

These requirements conspire to make the engine spend most of its life

operating outside the manufacturer’s best efficiency envelope.

Lowering required power (including the margin) can reduce associated losses.

Two objectives: lower power requires optimization; and less wasted engine

capacity requires more precise understanding of minimum power margins.

444

Biggest Challenge: Propeller Design

Propellers operate in the non-

uniform viscous wake of a hull.

Efficient propellers need to be

designed in their true operating

environment.

Today’s state-of-the-art still

assumes steady inflow with

corrections for spatial and

temporal averages of inflow.

Hull wake is only available at

model scale.

CFD provides full-scale pre-

dictions operating in actual ship

wake inflow.

Nominal Wake Effective Wake

555

Design Improvement: Automation Required

Design inputs:

• parameterized pitch,

chord, rake, skew vs.

radius,

• blade section shape

versus radius,

• Hull shape NURBS

• Rudder shape NURBS

1

2 3 4

Performance Outputs:

• SHP

• Minimum blade

pressure vs shaft angle.

• Minimum field pressure

vs shaft angle.

5

HEEDS / SHERPA

Propeller optimization example

666

One More Problem: Cavitation

• If pressure falls below thermodynamic

boiling point, water evaporates to vapor.

• When pressure increases again the

process reverses – violently.

• Tip vortex cavitation is inevitable.

777

The Well-Posed Optimization

Single Objective

• Minimize shaft

horsepower;

• at an RPM varied for

each design to provide a

given level of thrust;

• subject to an inequality

constraint on minimum

blade surface pressure;

• the pressure constraint

being enforced only

inboard of the tip vortex.

Multi-Objective or complex

constraints?

• Minimum power for safe

maneuvering seems like an

inequality constraint.

• But speed of propeller wake

affects rudder performance.

• This is multi-objective problem.

• Long run times for maneuv-

ering make this impractical.

• Inequality constraints are

needed that are functions of

propeller / rudder interaction.

888

Design Parameterization

Chord

Skew

999

Design Parameterization, Continued

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

0 0.2 0.4 0.6 0.8 1

Mu

ltip

lier

r / R

Chord PerturbationPitch Perturbation

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 0.2 0.4 0.6 0.8 1

Pit

ch

or

Ch

ord

(m

m)

r / R

Pitch

Chord

Design Variables

Perturbed by Multiplicative

Function of Radius

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 0.2 0.4 0.6 0.8 1

Ch

ord

(m

m)

r / R

Basline Design

Modified Design

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 0.2 0.4 0.6 0.8 1

Pit

ch

(m

m)

r / R

Basline Design

Modified Design

Perturbed Chord

Perturbed Pitch

-2000

-1500

-1000

-500

0

500

1000

1500

2000

2500

0 1000 2000 3000 4000

Ch

ord

(m

m)

Radius (mm)

Basline Design

Modified Design

101010

HEEDS Interface Files

1.195 pitch linear perturbation at hub (PPH)

0.855 pitch linear perturbation at tip (PPT)

-0.035 pitch quadratic perturbation (PPQ)

1.190 chord linear perturbation at hub (CPH)

1.110 chord linear perturbation at tip (CPT)

1025.0 density in kg / m^3

6.70 advance speed in m/s

922000.0 required thrust in Nts (equality constraint satisfied by CFD)

-1.33 rotational speed in RPS

constant term is independent of radius

linear term is linear with radius

baseline pitch and chord are modified according to:

pitch = pitch_base * [PPH + (PPT-PPH)*(r-rhub)/(radius-rhub)]

chord = chord_base * [CPH + (CPT-CPH)*(r-rhub)/(radius-rhub)]

reasonable ranges appear to be:

0.75 < PPH < 1.25

0.75 < PPT < 1.25

-0.20 < PPQ < 0.05

0.75 < CPH < 1.25

0.75 < CPT < 1.25

3 number of current design

values at thrust balance

1104279. torque (Nt-m)

8798052. power (Watts)

0.20817 thrust KT

0.03463 torque KQ

0.73386 advance ratio

0.70213 efficiency

values at RPM-5%

0.20129 thrust KT

0.03380 torque KQ

0.74677 advance ratio

values at RPM+5%

0.23920 thrust KT

0.03835 torque KQ

0.67565 advance ratio

Min. pressure for cavitation constraint 64085.

Variables File “opt_ind.txt” Responses File “opt_obj.txt”

111111

Example 1: Linear Pitch Variation & SHERPA

1.1%

• Two free design parameters

for pitch

• Two free design parameters

for chord

• Thrust same as base design

• Cavitation no worse than

base design

• 150 design evaluations

• Power reduced by 1.1%

121212

Example 1: Design Trends on Power

Pitch Change at Root Pitch Change at Tip

Chord Change at Root Chord Change at Tip

131313

Example 1: Design Trends on Cavitation

Pitch Change at Root Pitch Change at Tip

Chord Change at Root Chord Change at Tip

141414

Example 2: Quadratic Pitch Variation & SHERPA

• Three free design

parameters for pitch

• Two free design parameters

for chord

• Thrust same as base design

• Cavitation no worse than

base design

• 150 design evaluations

• Power reduced by 2.0%

2.0%

2.0% reduction in fuel

usage corresponds to

$500,000 per year for

the operator of a large

containership.

151515

Develop an Inequality Constraint for Power

• We’ve used CFD to predict

power needed for straight-ahead

navigation in extreme conditions.

• We’ve used CFD to predict

power needed for maneuvering

in calm conditions.

• Until recently we had not

combined the two.

161616

Approach

A generic VLCC is simulated with 5.5 meter beam seas and

37 knots of side wind:

• The simulation starts with the ship at low speed and straight

rudder to build fully-developed Kelvin and viscous wakes;

• The vessel is free to move in 6-DOF to include the effects of

added resistance and lost propulsive efficiency in waves;

• Once the wakes are developed (and propeller forces stabilized)

the rudder is put over 20 degrees and power increased to full;

• The simulation is repeated over a range of full power settings;

• The vessel accelerates under the influence of a prescribed

propeller RPM and should turn into the weather;

• At power levels below some point the ship just blows sideways.

171717

STAR-CCM+® Model

# of Cells = 6,923,139

# of Faces = 23,116,649

(note that this is too coarse)

Overset grid for 6-DoF body;

Large background Earth-fixed domain.

• Initial simulations performed at

model scale to enable validation

• Model size = 3.2 meters

• Background Doman Size is

X × Y × Z = 20m × 14m × 15m

• Simulations made at 3 power

levels and 2 displacements:

181818

Viscous Wake Propeller Detail

Results: “Normal” MCR, Flat Seas

191919

Results: “Normal” MCR with Wind and Seas

202020

Trajectories: Reduced Power with Wind & Seas

100% MCR Power 80% MCR Power

212121

Summary

CFD is now a sufficiently mature tool (if properly handled) to both

optimize propulsive performance and assess maneuverability.

HEEDS with SHERPA provides an effective and simple option to

drive the optimization.

CFD-based optimization can identify substantial savings, but long

run times should be expected. The propeller examples provided

took 2-3 weeks for a single operating speed and draft.

The example demonstrated 2% savings in power, or as much as

$500,000 / year savings for a large containership.

Incorporating a maneuvering objective within optimization is not

practical since a single design evaluation takes weeks of run time.

CFD can predict maneuvering performance for the purpose of

adding inequality constraints, but more research is required.

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