A machine learning approach for propeller design and optimization: Part II

Pranav Sumanth Doijode*, Stefan Hickel, Tom van Terwisga, Klaas Visser

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
10 Downloads (Pure)

Abstract

We propose and analyse an optimization method that uses a machine learning approach to solve multi-objective, constrained propeller optimization problems. The method uses an online learning strategy where explainable supervised classifiers learn the location of the Pareto front and advise search strategies. The classifiers are trained with orthogonal features that capture geometric variation in radial distribution of pitch, skew, camber and chordlength. Based on orthogonal features, the classifiers predict whether or not a design lies on the Pareto front. If the design is predicted to lie on the Pareto front, the method verifies this with an evaluation. If the design is predicted to not lie on the Pareto front with a high confidence level, then the design is ignored. This skipped evaluation reduces the computational effort of optimization. The method is demonstrated on a cavitating, unsteady flow case of the Wageningen B-4 70 propeller with P/D = 1.0 operating in the Seiun-Maru wake. Compared to the classical Non-dominated Sorting Genetic Algorithm — III (NSGA-III) the optimization method is able to reduce 30% of evaluations per generation while reproducing a comparable Pareto front. Trade-offs between suction side, pressure side, tip-vortex cavitation and efficiency are identified from the Pareto front. The non-elitist NSGA-III search algorithm in conjunction with the explainable supervised classifiers also find very diverse solutions. Among the solutions, a design with no pressure side cavitation, low suction side cavitation and reasonable tip-vortex cavitation is found.

Original languageEnglish
Article number103174
Number of pages17
JournalApplied Ocean Research
Volume124
DOIs
Publication statusPublished - 2022

Keywords

  • Dynamic optimization
  • Machine learning
  • Orthogonal parametric model
  • Propeller design and optimization
  • Uncertainty

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