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: (UFRJ, USP, IPT) 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 1. The motivation and the problem 2. The objective of the work
Motivation: opportunity to inovation Investment plan of Petrobras: 2011-2015 New units Item 2015 2020 Santos Basin 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) Design with optimization (modefrontier) RESPONSE SURFACE (Neural Network) Many interations (evaluations) Resistance (ShipFlow + CFX) 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
Rt (kn) Rt (kn) Simulation of Resistance: ShipFlow + CFX 800 Resistência total em função do número de Froude: Embarcação 3 700 600 500 400 300 200 100 0 0.15 0.2 0.25 0.3 0.35 0.4 P1 P2 P3 P4 P7 P6 Série Holtrop Resistência total em função do número de Froude Simulação Computacional 800 700 600 500 Fn 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) Maximize displacement (payload) 2) Minimize seakeeping (Acceleration in crane v = 0 knots) 3) Minimize seakeeping (Acceleration in accomodation v = 15 knots) 4) 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