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Optimized Ship Design Using
HEEDS & STAR-CCM+
Damian Tatum – Downey Engineering
Darren Preston - Downey Engineering
Timothy Yen – CD-adapco
Nate Chase – Red Cedar Technology
Abstract
Cargo ship design requires a challenging balancing act between construction costs
and operational efficiency. Naval architects strive to minimize hull drag while
maximizing propulsive efficiency with limits on cavitation, erosion, and up-front material
costs. In this current study, we leveraged recent advances in simulation-based design,
multidisciplinary design exploration, and scalable computation to automate the
identification of new and efficient cargo ship designs.
The commercial HEEDS multidisciplinary design exploration software was used to
automatically drive hull form geometry changes and hydrostatic performance
evaluation in the MultiSurf software followed by a drag and propulsive power
assessment in the STAR-CCM+ computational fluid dynamics (CFD) software. Design
variables included basic hull parameters such as length and width, bulb geometry,
skeg geometry, and propeller design features. Hybrid, adaptive design search
techniques were utilized to identify designs that yielded considerable reductions in
drag along with increases in propulsive power, directly translating into reduced fuel and
operational costs.
This study demonstrates considerable advantages over traditional ship design
methodologies and opens up new avenues to leverage inexpensive high-performance
computing resources to wring out higher performance and lower cost designs.
Background
This project utilized the same methodology and framework as a
proprietary project performed for a similar vessel with a more diverse
operational profile.
The vessel design considered was an alternative single-skeg, twin screw
design for aforementioned project.
The process deployed throughout this project is applicable to any design.
The vessel chosen here is for demonstration purposes only.
Motivation
Ship efficiency and emissions regulations (EEDI) are gradually increasing
the need for more holistic design approaches
– Fuel efficiency devices provide marginal improvements to existing designs
– De-rating of engines and simply reducing speed can only take
owners/operators so far
Vessels with varied operational profiles require trade-off analyses that
make a test regime exponentially more difficult.
It’s very hard to know how subtle changes in hull form can effect overall
performance, operational expenses and acquisition costs
– Any wisdom is very general
Overall Objective
Bulbous bow, and twin screw, skeg stern configurations are used to
improve efficiencies
Advantages:
– Reduces wave-making resistance (bulbous bow)
– Provides propeller and maneuverability redundancy
– Improves flow into the propellers and directional stability
Disadvantages:
– Traditional classical rules and standard practices do not lend themselves
alone to determine tradeoffs and balancing of configurations for these types of
designs
– Advanced methodologies will be required to effectively improve efficiencies
Specific Objective
To explore the fuel-consumption performance of a parametrically variable
hull design running as a self-propelled vessel in calm water with realistic
operational constraints
– The design geometry is constrained in maximum global dimensions to legacy
PANAMAX
– Mass displacement (Δ) is a function of dimensions and cargo deadweight
– Minimum intact stability (GM) requirements and hydrostatic trim allowances
Implicitly Explored Design Features:
– Wave-making resistance attenuation with bulbous bows, entry angle, stern
shaping, shoulder shaping.
– Propeller design exploration (B-Series)
– Propeller, hull, shafting, and rudder interactions
Problem Particulars
The goal is to find a family of optimal hull designs and propeller
parameters that minimize fuel consumption rates for the following
mission:
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–
–
–
–
–
28,000 DWT Medium Range bulk carrier
Twin screws
Single skeg
Constrained to legacy PANAMAX dimensions
Installed two medium speed diesels
In the follow operating conditions:
• Full Load: 28,000 DWT
• Ballast Load: 70% Heavy Condition (19,600 MT)
Performance considerations:
– Trim requirements
– Intact initial stability requirements (GMT)
– Available installed power and reduction gearing efficiencies
Solution: Drive Product Innovation with CAE
Design Exploration Studies leveraging modern simulation tools:
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–
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Hull Modeling Software – MultiSurf by AeroHydro
Hydrostatic Calculations – MultiSurf by AeroHydro
Computational Fluid Dynamics – STAR-CCM+ by CD-adapco
Pareto Optimization – HEEDS by CD-adapco
Scalable Computation – High Performance Computing Cluster
Solution: Drive Product Innovation with CAE
MultiSurf
STAR-CCM+
n nodes
Hull Modeling
& Hydrostatics
CFD Analysis
2. Process Automation
m cores per node
1. Validated CAE Models
5. Sensitivity & Robustness
3. Scalable Computation
4. Efficient Exploration
1 - Validated CAE Models – MultiSurf
A parameterized hull model was developed in MultiSurf/Hydro 8.7,
developed by AeroHydro
– MultiSurf is a software suite used for the parametric design of 3D complex
objects involving freeform curves and surfaces, specializing in ship hull design
– Entire model is updated whenever underlying points, lines, or surfaces are
updated, and fairing tools allow surfaces to be faired and refined with
geometry updates
– Surfaces composing the hull body are durably joined, allowing subsequent
analysis packages to treat the generated hull as a “meshable” solid
1 - Validated CAE Models – MultiSurf
MultiSurf Hydro outputs of interest:
– Trim Overall (must be between -0.15 m and 1 m at Heavy load for a valid
design)
– GMT (must be greater than 0.3 m under both load conditions for a valid
design)
– Maximum Draft (must be less than 12 m under Heavy load to meet PANAMAX
requirements)
MultiSurf Hull Parametric Model outputs of interest:
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–
–
–
–
–
LCG (from both load conditions)
VCG (from both load conditions) (constant)
Center of flotation (ZCF)
Hull Length
Propeller outside diameter
Propeller location (coordinates)
Displacement weight
1 - Validated CAE Models – STAR-CCM+
For this study, full scale design simulations were conducted on a
longitudinally-symmetric half-domain using CFD analysis performed using
STAR-CCM+ v9.06.11
Roll and yaw variations were not considered, consistent with an
assumption that the hull should be optimized for even-keel, straightahead navigation
Water conditions were assumed to be seawater (1025 kg/m3)
1 - Validated CAE Models – STAR-CCM+
Wrapper was used to seal co-planar hull surfaces and dynamically positioned and sized appendages
(skeg, rudder, shafting)
Surface meshing was accomplished using a local surface resolution size of 0.75 m globally with
refinement around curvature and appendages
Volumetric meshing involved an orthogonal 3D mesh that subdivides elements in the bow wave and wake
regions as well as the free surface, using the Trimmer mesher
Boundary layer was captured using the STAR-CCM+ prism layer mesher with an initial skin cell thickness
of 0.6 mm to properly capture near-wall viscous effects.
Model mesh sizes ranged between 2.5-3 million cells
1 - Validated CAE Models – STAR-CCM+
Two load cases considered:
– Heavy load (28,000 ton deadweight): 16.5 knots
– Ballast condition (19,600 ton deadweight): 12.5 knots
Heavy Load:
16.5 knots at 28,000 ton deadweight
Ballast Load:
12.5 knots at 19,600 ton deadweight
1 - Validated CAE Models
Each design was evaluated by a self-propulsion simulation at the specified displacement and speed. The
propeller was free to turn at any RPM to produce the thrust to counteract the overall drag.
2nd order segregated flow implicit unsteady solver was utilized with a fixed time step size tailored to the
length of the ship and it’s speed to provide a suitable Courant number
Turbulence was treated with the Reynolds-averaged Navier-Stokes (RANS) realizable K-epsilon turbulence
model with two-layer all-y+ wall treatment
Free surface of the water was treated using the volume of fluid (VoF) model
The vessel was treated as a dynamic fluid body interaction entity (DFBI) and placed in a global reference
frame, initialized at the desired run speed. The ship was free to trim and heave.
The propeller effects, pressure gradient and swirl, were accomplished using the Virtual Propeller model
based on open-water curves computed by published polynomials for B-Series (Bernitsas, 1981, U. of
Michigan)
1 - Validated CAE Models – STAR-CCM+
Self-propelled tests were conducted using a propulsion element using the
Star CCM+ virtual disk model
– Marks all cells in a volume of space occupied by the propeller.
– Imparts torque and axial force to the fluid in those cells as if the propeller were
acting on them, accounting for the upstream and downstream flow conditions as a
propeller would.
– Requires the input of a propeller open water performance curves
– The propeller properties, such as diameter, blade count, and pitch ratio were input
into the STAR-CCM+ model and appropriate propeller curves generated for each
load condition
– STAR-CCM+ was able to choose the operating point based on the necessary
thrust and the measured velocity of advance of the propeller. The thrust of the
propeller was feathered to maintain the speed being investigated, and the program
automatically adjusted the propeller RPM on the fly.
1 - Validated CAE Models
3,200 timesteps performed
Resistance computed as the sum of the viscous and pressure forces acting
on all elements in the –x direction. Forces were averaged over the time history
once it stabilized
Heave:
Trim:
2 – Process Automation
MultiSurf MultiSurfSTAR-CCM+
Hull Design Parameters
Hull Modeling
& Hydrostatics
CFD Analysis
Hull Modeling
& Hydrostatics
2 – Process Automation
An MS Excel interface is used by HEEDS to drive the parameterized MultiSurf model using
the VBA API available within MultiSurf
The Excel file specifies variable values for a given design and calculates the applied weight
based upon the load case and beam and length characteristics
The VBA script is contained within a Macro in Excel which does the following steps:
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Initiates MultiSurf in the background
Opens the baseline MultiSurf model
Modifies the applied weight
Reads in from the Excel worksheet the variable values and updates the hull shape accordingly
Saves a new MultiSurf database for reference
Exports an IGES file for STAR-CCM+ usage
Executes Hydro
Writes Hydro calculation results to a text file
Writes outputs to text file for STAR-CCM+ usage
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•
•
•
•
•
Propeller coordinates
Propeller outside diameter
LCG
VCG
Length
Displacement Weight
2 – Process Automation
MultiSurf MultiSurfSTAR-CCM+
Hull Design
Parameters
Hull Modeling
& Hydrostatics
CFD Analysis
Geometry IGES file
Hydrostatics
Results File
Hull Modeling
& Hydrostatics
2 – Process Automation
MultiSurf MultiSurf
Geometry IGES
file
Hull Design Parameters
Hull Design
Parameters
Hull Modeling
& Hydrostatics
Hydrostatics
Results File
STAR-CCM+
Geometry
CFD Analysis
IGES file
Hydrostatics
Results File
Hull Modeling
& Hydrostatics
2 – Process Automation
MultiSurf
STAR-CCM+
Geometry IGES
file
Hull Design Parameters
Hull Modeling
& Hydrostatics
Hydrostatics
Results File
CFD Analysis
STAR-CCM+
CFD Analysis
2 – Process Automation
STAR-CCM+ is driven through a java script that reads in design
conditions from a text file along with the given hull geometry
– The hull geometry is passed by HEDS from MultiSurf in the form of an IGES
file
– The values for propeller coordinates, propeller outside diameter, LCG, VCG,
ZCF, Displacement Weight, and Length are passed from MultiSurf to STARCCM+ by HEEDS for both load conditions through the design condition file
– Values for other variables dictating propulsion characteristics are supplied by
HEEDS to STAR-CCM+ through the design condition file
•
•
•
•
Propeller number of blades
Propeller pitch ratio
Propeller blade area ratio
Propeller handedness (left or right)
2 – Process Automation
After the conditions and geometry are read-into STAR-CCM+ the propeller
performance curves are extrapolated and the simulations conducted :
– Heavy load (28,000 MT deadweight) @ 16.5 knots
– Ballast condition (19,600 MT deadweight) @ 12.5 knots
At the conclusion of the simulations, relevant outputs are written to a results text file
and the data stored by HEEDS for the design for both load cases
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Draft
Displacement
Speed
Drag
Effective Power
Delivered Power
Trim
Heave
RPM
Fuel rate is calculated for each load case by HEEDS utilizing the delivered power in its
calculations
2 – Process Automation
Propulsive Design Parameters
STAR-CCM+
Geometry IGES file
Hydrodynamic Results
Hydrostatics Results
File
CFD Analysis
2 – Process Automation
Propulsive Design Parameters
Propulsive Design Parameters
Hull Design Parameters
Geometry IGES file
MultiSurf
STAR-CCM+
Geometry IGES
file
Hull Modeling
& Hydrostatics
Hydrostatics
Results File
Hydrostatics Results
File
CFD Analysis
STAR-CCM+
Hydrodynamic Results
Hydrodynamic
CFD Analysis
Results
2 – Process Automation
Propulsive Design Parameters
MultiSurf
Hull Design Parameters
Hull Modeling
& Hydrostatics
Geometry IGES
file
STAR-CCM+
Hydrostatics
Results File
CFD Analysis
Hydrodynamic Results
3 – Scalable Computation
HEEDS and Multi-Surf were performed using a single core of a 64-bit
Windows HP PC with an Intel Xeon 2.8GHz processor with 12 GB of RAM
– Average MultiSurf Simulation Time for Heavy Load case: 3 mins
– Average MultiSurf Simulation Time for Ballast Load case: 2 mins
STAR-CCM+ was run on 84-cores of a High Performance Compute
Cluster (HPC)
– Average STAR-CCM+ Simulation Time: 5.5 hours
7 nodes
12 cores per node
4 – Efficient Exploration
HEEDS MDO has a robust, efficient, easy to use optimization search
technology called SHERPA
– One input parameter
• Number of Evaluations (budget)
• No tuning required
– Hybrid
• Blend of search strategies used simultaneously
• Global and local search performed together
• Leverages the best of all methods
– Adaptive
• Adapts itself to the design space
• Efficiently searches simple and very complicated spaces
• Very cost effective for complex problems
4 – Efficient Exploration
A dual stage approach was taken to optimize the bulk carrier (and
proprietary vessel for which this project was based)
Design Exploration Study 1: Optimization for Hydrostatics Only
MultiSurf
Geometry IGES
file
Hull Modeling
& Hydrostatics
Hydrostatics
Results File
Hull Design Parameters
4 – Efficient Exploration
A dual stage approach was taken to optimize the bulk carrier (and
proprietary vessel for which this project was based)
Design Exploration Study 2: Optimization for Hydrostatics and
Hydrodynamics
– Start with good hydrostatic design concepts from Design Exploration Study 1
to speed up the search process
High Performing Design
Concepts from Hydrostatics
Design Exploration Study 1:
Optimization for Hydrostatics Only
Design Exploration Study 2:
Optimization for Hydrostatics and Hydrodynamics
Design Exploration Study 1: Optimization for
Hydrostatics Only
Variables: BEAM
14.75 m
16.155 m
Design Exploration Study 1: Optimization for
Hydrostatics Only
Variables: STATION_10_X
210.85 m
294.13 m
Design Exploration Study 1: Optimization for
Hydrostatics Only
Variables: STATION_5_X
20 m
172m
NOTE: Here STATION_4_X is set to 20 m
Design Exploration Study 1: Optimization for
Hydrostatics Only
Variables: STATION_4_X
20 m
131.58m
NOTE: Here STATION_5_X is set to 172 m
NOTE: STATION_4_X is designed semi-independently such
that it is always < STATION_5_X
Design Exploration Study 1: Optimization for
Hydrostatics Only
Variables: BULB_WIDTH
2.5 m
5m
Design Exploration Study 1: Optimization for
Hydrostatics Only
Variables: BULB_HEIGHT
BULB_0_Z (m)
8.5 m
Design Exploration Study 1: Optimization for
Hydrostatics Only
23 High Performing Hydrostatic Design Concepts Identified
Design Exploration Study 2: Optimization for
Hydrostatics & Hydrodynamics
Objectives (Pareto Optimization/Trade-off Study):
– Minimize: Fuel Rate (kg/hr) for Heavy Condition
– Minimize: Fuel Rate (kg/hr) for Ballast Condition
Constraints:
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–
–
–
–
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Maximum Draft (Full load case) < 12 m
-0.15 m < Trim Overall (Full load case) < 1 m
GMT (Full load case) > 0.3
GMT (Ballast condition) > 0.3
Propeller RPM (Heavy load case) <110
Propeller RPM (Ballast condition) <110
By Varying:
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Same 19 geometric variables as with previous optimization
PROPELLER_#_OF_BLADES= 4 or 5
0.5 < PROPELLER_PITCH_RATIO < 1.4
0.3 <PROPELLER_BLADE_AREA_RATIO < 0.77
PROPELLER_HANDEDNESS = RIGHT OR LEFT (+1 or -1)
Design Exploration Study 2: Optimization for
Hydrostatics & Hydrodynamics
Baseline Design:
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Fuel Rate (1) Heavy Condition = 1377.8
Fuel Rate (2) Ballast Condition = 830.42
Maximum Draft (Heavy load case) = 11.44 m (meets design criteria)
Trim Overall (Heavy load case) = -1.37 m (DOES NOT MEET DESIGN CRITERIA)
GMT (Heavy load case) = 3.397 m (meets design criteria)
GMT (Ballast condition) = 4.063 m (meets design criteria)
RPM (1) Heavy Condition = 110.88 (DOES NOT MEET DESIGN CRITERIA)
RPM (2) Ballast Condition = 90.64 (meets design criteria)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
23 good hydrostatic concepts identified from Design Exploration Study 1
are used to “kick start” Design Exploration Study 2
120 Pareto optimization evaluations performed using SHERPA
High Performing Design
Concepts from Hydrostatics
Design Exploration Study 1:
Optimization for Hydrostatics Only
Design Exploration Study 2:
Optimization for Hydrostatics and Hydrodynamics
Design Exploration Study 2
Propulsive Design Parameters
STAR-CCM+
MultiSurf
Hull Design
Parameters
Geometry
IGES file
Hydrostatics
Results File
Hydrodynamic
Results
SHERPA
Feasible
Infeasible
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Baseline
Injected
Feasible
Infeasible
Baseline
Injected
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Feasible
Infeasible
Baseline
Injected
Pareto Optimal Solutions (Family of Ship Designs)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Pareto Optimal Solutions (Family of Ship Designs)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
21 % reduction in Heavy load case fuel consumption
7% reduction in Ballast load case fuel consumption
Pareto Optimal Solutions (Family of Ship Designs)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
17 % reduction in Heavy load case fuel consumption
9% reduction in Ballast load case fuel consumption
Pareto Optimal Solutions (Family of Ship Designs)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
10 % reduction in Heavy load case fuel consumption
11% reduction in Ballast load case fuel consumption
Pareto Optimal Solutions (Family of Ship Designs)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
1 % increase in Heavy load case fuel consumption
13% reduction in Ballast load case fuel consumption
Pareto Optimal Solutions (Family of Ship Designs)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
2 % increase in Heavy load case fuel consumption
18% reduction in Ballast load case fuel consumption
Pareto Optimal Solutions (Family of Ship Designs)
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Feasible
Responses:
Variables:
Variables:
Infeasible
Pareto Set
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Feasible
Responses:
Variables:
Variables:
Infeasible
Pareto Set
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Feasible
Responses:
Variables:
Variables:
Infeasible
Pareto Set
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
Design Exploration Study 2: Optimization for
Hydrostatics and Hydrodynamics
The Effect of Drag
Band in fuel rates for a given drag observed
Family of designs are on lower fuel rates of ballast condition band but not
necessarily on the heavy condition band due to the tradeoffs between the
two
Feasible
Pareto
The Effect of Propulsive Efficiency
Propulsion also affects drag
Feasible
Pareto
The Effect of Propulsive Efficiency
Propulsion also affects drag
Feasible
Pareto
The Effect of Propulsive Efficiency
Propulsion also affects drag
Feasible
Pareto
The Effect of Propulsive Efficiency
Propulsion also affects drag
Feasible
Pareto
Case Study
The Pareto front identifies high performing design candidates applicable for
a trade-off analysis. Specific operating conditions determine the actual best
design.
The case study will make the following operating conditions and fuel
situations.
– 60% of the time at sea (219 days)
– 60% at Full Displacement & 40% at Ballast Displacement when at sea
• 30% of operating time in Sulphur Emissions Control Areas (ECA)
– 0.1% Suphur, Marine Gas Oil (LSMGO) @ $550USD/MT
• Operations in ECA
– 3.8% Sulphur, 180 cSt, Intermediate Fuel Oil (IFO180) @ $350USD/MT
Case Study
Ratio
Total Annual
Consumption
[MT]
Annual
Bunkering
Cost
Savings
Fuel Consumption [kg/hr]
Design
Full Load
Ballast
Baseline
1377.8
830.4
1.7
6090.9
$2,497,265
---
101
1087.9
773.4
1.4
5056.8
$2,073,290
$423,975
46
1147.8
756.7
1.5
5210.7
$2,136,373
$360,892
118
1237.6
739.1
1.7
5456.8
$2,237,273
$259,992
117
1391.3
721.2
1.9
5903.9
$2,420,583
$76,682
86
1409.3
681.5
2.1
5877.0
$2,409,573
$87,692
Minimizing Full Load consumption rate appears to have the greatest
effect on the annual bunkering cost
– Best design reduces consumption by 1e6 kg of fuel oil
Summary
Fully automated design optimization of a medium range bulk carrier was
done to find Pareto front to allow for trade-off studies for various operating
conditions
– Optimization implicitly balanced hull particulars, bow & stern shaping and
propeller matching.
– HEEDS efficiently explored design concepts that may have been overlooked in
a manually specified parametric sweep
Acknowledgements