Data driven uncertainty quantification for computational fluid dynamics based ship design
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hdl:2117/329655
Tipus de documentText en actes de congrés
Data publicació2019
EditorCIMNE
Condicions d'accésAccés obert
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Abstract
Maritime transport is responsible for an annual emission of around 1000 million tonnes of
CO2, which is around 2.5% of the global greenhouse gas emissions. Nowadays, ships are designed
using simplified operational profiles representing the expected operational profile during the
lifetime of the ship. However, there is a discrepancy between these simplified profiles used for
design and the actual full operational profile of a ship during its lifetime. This discrepancy
leads to inefficient hydrodynamic ship design resulting in a waste of fuel and an increase of
greenhouse gas emissions.
The amount of available data on actual operational conditions of ships is rapidly increasing. The
Automatic Identification System (AIS) and onboard monitoring systems produce a huge amount of his-
torical data on ship operations. These developments call for efficient data-driven methods that
account for this data. Knowledge of operational conditions can be used for Computational Fluids
Dynamics (CFD) -based probabilistic uncertainty quantification leading to robust design: A hull
shape that is op- timal with respect to uncertain operational conditions. Robust design is a
promising approach since it makes ships energy efficient for the real usage situation.
Three UQ-methods are discussed: The perturbation method, the Polynomial Chaos Expansion (PCE)
method and the multi-fidelity PCE method. The methods are applied to a simple one-dimensional test
case to compute the stochastic moments of the effective power. The multi-fidelity Polynomial Chaos
Method is found to be the most efficient UQ method. Moreover, the multi-fidelity PCE can be used as
a surrogate for efficient Monte Carlo integration. This makes the method suitable for an
Optimisation
Under Uncertainty (OUU) algorithm leading to robust design.
CitacióScholcz, T.P. Data driven uncertainty quantification for computational fluid dynamics based ship design. A: MARINE VIII. "MARINE VIII : proceedings of the VIII International Conference on Computational Methods in Marine Engineering". CIMNE, 2019, p. 309-320. ISBN 978-84-949194-3-5.
ISBN978-84-949194-3-5
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