A procedure to integration of AQWA with FriendShip
for the application of optimization techniques in
design of PSV vessels
Miguel Altimari Peli
Tancredi Pontin Tancredi
Laboratório de Otimização e Projeto Integrado
Escola Politécnica da Universidade de São Paulo, São Paulo, Brazil
Introduction and context
FINEP support for the development of:
“National packet for design and equipment for PSV to oil production support”
“Pacote nacional de projeto e maquinário de embarcações de apoio marítimo
à produção de petróleo no mar”
(UFRJ, USP, IPT)
1. The motivation and the problem
2. The objective of the work
Motivation: opportunity to inovation
Investment plan of Petrobras: 2011 - 2015
New units
Santos Basin
Item
2015
2020
Perfuration units (drillships and semi-sub)
22
50
Production units (FPSO and Semi-sub)
17
50
PSV
192
281
• Farther from coast: 250 to 350 km;
•
•
•
•
Ocean conditions worst than Campos basin;
Currents with more intensity: 3 to 4 knots;
Bad seakeeping of small ships;
Limitation of draught at Ports of 6 m.
Traditional design approach
• Result in a feasible solution
• There is no guaranties that the solution is the best
• Depends of the experience of engineers
• It is not a efficient approach to compare different
design solutions
The objective of the work

Solve the conceptual design problem of engineering as a
multi-objective optimization problem
• Development of a synthesis model to quick evaluation of the
main characteristics in preliminary/conceptual stage of the ship
design
The objective of the work
 In theory, the optimization process with a high fidelity model that simultaneously considers
all hull’s variables (higher hierarchical level) should find an optimal solution, in terms of
resistance.
 However, in practice, this does not occur, either by the inability to properly explore a broad
set of variables, or by the time required for the analysis or by the difficulty of the
optimization algorithm to converge to a global optimum.
Methodology based in hierarchical models and
response surface with different optimization
techniques for optimized design of ships
Modern approach: simulations in design
Parametric model of hull
(FriendShip)
Resistance
(ShipFlow + CFX)
Design with optimization
(modeFrontier)
RESPONSE SURFACE
(Neural Network)
Many interations
(evaluations)
Seakeeping
(AQWA)
Seakeeping with speed
(AQWA)
Few iterations (evaluations)
Iterative approach of optimization
Optimization
Optimized solutions
Neural response surface
training
Numerical simulations of the
optimized solutions
Convergence
reached
Initial numerical
simulations
Neural response surface
verification
Optimization
Re-training of the neural response
surface
Parametric hull model of PSV: FriendShip
Hull surface described by 3
curves:
1. Keel curve
2. Sectional area curve
3. Deck curve
Sectional area curve parameterization:
1. Integration = Displacement
2. Integration/Max area = Cb
3. Center of Area = LCB
Parametric hull model of PSV: FriendShip
Simulation of Resistance: ShipFlow + CFX
Resistência total em função do número de Froude:
Embarcação 3
800
700
Rt (kN)
600
500
400
300
200
100
0
0.15
0.2
0.25
0.3
0.35
0.4
Fn
P1
P2
P3
P4
P7
P6
Resistência total em função
do número
deSérie
Froude
Simulação Computacional
Holtrop
800
700
600
Rt (kN)
500
400
300
200
100
0
0.15
0.20
0.25
0.30
0.35
0.40
Fn
B1
B2
B3
B4
B5
Simulation of Seakeeping: AQWA
V = 15 knots
V = 0 knots
Optimization model: Optimization in DESIGN
1)
2)
3)
4)
Maximize displacement (payload)
Minimize seakeeping (Acceleration in crane v = 0 knots)
Minimize seakeeping (Acceleration in accomodation v = 15 knots)
Minimize resistance (v = 15 knots)
All usual constraints associated with ship design
Optimization model: modeFrontier
Resistance (N)
Results
Displacement (t)
Acceleration in the crane (m/s²)
Results
Displacement (t)
Acceleration in accommodation (m/s²)
Results
V = 15 knots
Displacement (t)
Conclusion: Examples of optimum solutions
Displacement (t)
Displacement (t)
Displacement (t)
Displacement (t)
Conclusion: Examples of optimum solutions
Thank you! Questions?
A special thanks to our students of
graduation and PhD in our
Laboratory!
Thanks to the FINEP to support this work