A data-driven probabilistic learning approach for the prediction of controllable pitch propellers performance

Cita com:
hdl:2117/330025
Document typeConference report
Defense date2019
PublisherCIMNE
Rights accessOpen Access
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Abstract
The multi-fidelity machine learning framework proposed in this paper leverages
a probabilistic approach based on Gaussian Process modeling for the formulation of
stochas- tic response surfaces capable of describing propeller performance for different
mission profiles. The proposed multi-fidelity techniques will help coping with the scarcity of
high-fidelity measure- ments by using lower-fidelity numerical predictions. The existing correlation
of the multi-fidelity data sets is used to infer high-fidelity measurements from lower
fidelity numerical predictions. The probabilistic formulations embedded in Gaussian Process
regressions gives the unique op- portunity to learn the target functions describing
propeller performance at different operating conditions, while quantifying the uncertainty
associated to that specific prediction. While the multi-fidelity autoregressive scheme
allows to construct high accurate response surfaces using only few experimental data,
Uncertainty Quantification (UQ) provides an important metric to asses the quality of the learning
process. We demonstrate the capability of the proposed frame- work to predict the performance
of a controllable pitch propeller using few experimental data coming from towing tank
experiments and many medium-fidelity predictions obtained using an
in-house developed BEM, validated and verified in many previous studies.
CitationGaggero, S. [et al.]. A data-driven probabilistic learning approach for the prediction of controllable pitch propellers performance. A: MARINE VIII. "MARINE VIII : proceedings of the VIII International Conference on Computational Methods in Marine Engineering". CIMNE, 2019, p. 544-555. ISBN 978-84-949194-3-5.
ISBN978-84-949194-3-5
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