Propeller optimization by interactive genetic algorithms and machine learning

Gypa, Ioli; Jansson, Marcus; Wolff, Krister; Bensow, Rickard

doi:10.1080/09377255.2021.1973264

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…Flow rotation around moving parts is simulated, adding Coriolis source terms and centripetal accel-erations to the momentum equation. The flow in fixed subdomains is governed by continuity and momentum equations (Equations ( 15) and ( 16)); meanwhile, the rotational subdomains are governed by continuity and momentum equations for the MRF, as seen in Equations ( 17) and (18).…”

Section: Governing Equationsmentioning

confidence: 99%

Numerical Analysis and Validation of an Optimized B-Series Marine Propeller Based on NSGA-II Constrained by Cavitation

Vázquez-Santos,

Camacho-Zamora,

Hernández-Hernández

et al. 2024

JMSE

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Constantly growing environmental concerns focused on reducing pollution, in addition to rising fuel costs in recent years, have led the maritime industry to develop and implement fuel-saving solutions. Among them is the optimization of marine propeller efficiency, as marine propellers are a crucial part of ship’s propulsion system. During the initial design stage, selecting the optimal propeller is considered a multi-objective optimization process. This research focused on maximizing propeller open water efficiency, while minimizing engine brake power constrained by thrust and cavitation. Optimization was applied to Wageningen B-series propellers and conducted using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The algorithm selected optimum parameters to create the propeller model, which was then evaluated numerically through computational fluid dynamics (CFD) with a multiple reference frame (MRF) and under the SST k-ω turbulence model, to obtain the open water hydrodynamic characteristics. In addition, the cavitation effect was evaluated using the Zwart–Gerber–Belamri cavitation model. The numerical model results were validated through comparison with open water experimental data from the Netherlands Ship Model Basin for the Wageningen B-series propellers. The results showed errors of 3.29% and 2.01% in efficiency under noncavitating and cavitating conditions, respectively. Correct performance of the functions was shown, based on neural networks trained to estimate thrust and torque coefficients instead of polynomials. The proposed optimization process and numerical model are suitable for solving multi-objective optimization problems in the preliminary design of fixed-pitch marine propellers.

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Section: Governing Equationsmentioning

confidence: 99%

Numerical Analysis and Validation of an Optimized B-Series Marine Propeller Based on NSGA-II Constrained by Cavitation

Vázquez-Santos,

Camacho-Zamora,

Hernández-Hernández

et al. 2024

JMSE

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“…If fitness is better than the global best Then (10) Update the global best hyperparameters (11) end ( 12) end ( 13) end (14) For particle in population do (15) Update particle velocity and position using the PSO formula ( 16) end (17) end (18)…”

Section: Proxy Model Flowchart and Pseudocodementioning

confidence: 99%

“…It is concluded that the prediction effect of the PSO-XGBoost model is better than that of KDT model and RF model. In addition to algorithms in the field of machine learning, this paper also compared the BPNN algorithm [18] in the field of deep learning. From the performance indicators, it can be seen that the prediction performance of PSO-XGBoost is slightly better.…”

Section: Comparison Of Proxy Modelsmentioning

confidence: 99%

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3D Vase Design Based on Interactive Genetic Algorithm and Enhanced XGBoost Model

Wang,

2024

Mathematics

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The human–computer interaction attribute of the interactive genetic algorithm (IGA) allows users to participate in the product design process for which the product needs to be evaluated, and requiring a large number of evaluations would lead to user fatigue. To address this issue, this paper utilizes an XGBoost proxy model modified by particle swarm optimization and the graphical interaction mechanism (GIM) to construct an improved interactive genetic algorithm (PXG-IGA), and then the PXG-IGA is applied to 3D vase design. Firstly, the 3D vase shape has been designed by using a bicubic Bézier surface, and the individual genetic code is binary and includes three parts: the vase control points, the vase height, and the texture picture. Secondly, the XGBoost evaluation of the proxy model has been constructed by collecting user online evaluation data, and the particle swarm optimization algorithm has been used to optimize the hyperparameters of XGBoost. Finally, the GIM has been introduced after several generations, allowing users to change product styles independently to better meet users’ expectations. Based on the PXG-IGA, an online 3D vase design platform has been developed and compared to the traditional IGA, KD tree, random forest, and standard XGBoost proxy models. Compared with the traditional IGA, the number of evaluations has been reduced by 58.3% and the evaluation time has been reduced by 46.4%. Compared with other proxy models, the accuracy of predictions has been improved up from 1.3% to 20.2%. To a certain extent, the PXG-IGA reduces users’ operation fatigue and provides new ideas for improving user experience and product design efficiency.

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“…Therefore, IGA application needs to be improved and optimized according to specific goals and requirements. Recently, IGA has been applied to the design of industrial products such as electric bicycles [18], ship compartments [19], landscape lamps [20], marine propellers [21].…”

Section: Motivation and Significancementioning

confidence: 99%