Prediction and optimisation of low-frequency discrete- and broadband-spectrum marine propeller forces

Jiang, Jingwei; Qi, Jiang-Tao; Cai, Haiwen; Chen, Ke; Huang, Wei‐Xi

doi:10.1016/j.apor.2020.102114

Cited by 16 publications

(5 citation statements)

References 21 publications

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“…The values of K T for HSP and for these two optimisation methods over one rotation cycle are calculated using the unsteady panel method, and compared in Figure 8. A detailed introduction of these values and their comparison with the test data of the unsteady panel method have been presented by Jiang et al [17]. The amplitude in Figure 8, as well as the values of DST 1 and DST 2 in Table 3, demonstrate that both optimisation methods substantially reduce the DST, by more than 25% for DST 1 and~50% for DST 2 .…”

Section: Case Studymentioning

confidence: 87%

“…where R is the blade radius, θ is the circumferential angle, A 0 is the pulsating velocity amplitude, Z is the blade number, U 0 is the mean velocity and α is the coefficient, defined as: In this simulation, we set r 1 /R = 1.2 and r 2 /R = 2.8. A detailed explanation and validation of the unsteady panel method can be found in our previous paper [17].…”

Section: Unsteady Thrust Prediction Methodsmentioning

confidence: 99%

“…The above literature review shows that all skew optimisations, except for that proposed in our previous study [17], require hydrodynamic re-computation of the unsteady force at each iteration. Moreover, mainly traditional optimisation algorithms-that is, genetic algorithms-have been used, and advanced data-driven methods have not yet been applied to this problem.…”

Section: Objective Of the Present Studymentioning

confidence: 99%

“…The optimisation mentioned above require hydrodynamic re-computation of the unsteady force at each iteration, which require large computing resources. Jiang et al [17] decoupled the comprehensive optimisation of propeller hydrodynamic and noise performance into two separate optimisations by proposing the circumferential phase-shift effect of skew. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), they optimised the skew distribution and reduced the first-order component of the DS force by about 10%.…”

Section: Low-frequency Ds Force Optimisationmentioning

confidence: 99%

“…Moreover, mainly traditional optimisation algorithms-that is, genetic algorithms-have been used, and advanced data-driven methods have not yet been applied to this problem. Therefore, the primary objective of the present study is to apply the DDEO method to our previously proposed decoupled skew optimisation model [17]. We expect that this will reduce the scale of the calculation, thereby improving the calculation efficiency and the optimisation result.…”

Section: Objective Of the Present Studymentioning

confidence: 99%

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Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model

Jiang

Yang

Ren

et al. 2020

JMSE

Self Cite

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For practical problems with non-convex, large-scale and highly constrained characteristics, evolutionary optimisation algorithms are widely used. However, advanced data-driven methods have yet to be comprehensively applied in related fields. In this study, a surrogate model combined with the Non-dominated Sorting Genetic Algorithm II-Differential Evolution (NSGA-II-DE) is applied to reduce the low-frequency Discrete-Spectrum (DS) force of propeller noise. Reduction of this force has drawn a lot of attention as it is the primary signal used in the sonar-based detection and identification of ships. In the present study, a surrogate model is proposed based on a trained Back-Propagation (BP) fully connected neural network, which improves the optimisation efficiency. The neural network is designed by analysing the depth and width of the hidden layers. The results indicate that a four-layer neural network with 64, 128, 256 and 64 nodes in each layer, respectively, exhibits the highest prediction accuracy. The prediction errors for the first order of DST, second order of DST and the thrust coefficient are only 0.21%, 5.71% and 0.01%, respectively. Data-Driven Evolutionary Optimisation (DDEO) is applied to a standard high-skew propeller to reduce DST. DDEO and a Traditional Evolutionary Optimisation Method (TEOM) obtain the same optimisation results, while the time cost of DDEO is only 0.68% that of the TEOM. Thus, the proposed DDEO is applicable to complex engineering problems in various fields.

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Section: Case Studymentioning

confidence: 87%

Section: Unsteady Thrust Prediction Methodsmentioning

confidence: 99%

Section: Objective Of the Present Studymentioning

confidence: 99%

Section: Low-frequency Ds Force Optimisationmentioning

confidence: 99%

Section: Objective Of the Present Studymentioning

confidence: 99%

See 3 more Smart Citations

Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model

Jiang

Yang

Ren

et al. 2020

JMSE

Self Cite

View full text Add to dashboard Cite

show abstract

An improved spectral method and experimental tests for the low-frequency broadband noise of marine propellers

et al. 2021

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A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps

Serani,

Scholcz,

Vanzi

2024

Arch Computat Methods Eng

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This scoping review assesses the current use of simulation-based design optimization (SBDO) in marine engineering, focusing on identifying research trends, methodologies, and application areas. Analyzing 277 studies from Scopus and Web of Science, the review finds that SBDO is predominantly applied to optimizing marine vessel hulls, including both surface and underwater types, and extends to key components like bows, sterns, propellers, and fins. It also covers marine structures and renewable energy systems. A notable trend is the preference for deterministic single-objective optimization methods, indicating potential growth areas in multi-objective and stochastic approaches. The review points out the necessity of integrating more comprehensive multidisciplinary optimization methods to address the complex challenges in marine environments. Despite the extensive application of SBDO in marine engineering, there remains a need for enhancing the methodologies’ efficiency and robustness. This review offers a critical overview of SBDO’s role in marine engineering and highlights opportunities for future research to advance the field.

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Prediction and optimisation of low-frequency discrete- and broadband-spectrum marine propeller forces

Cited by 16 publications

References 21 publications

Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model

Evolutionary Optimisation for Reduction of the Low-Frequency Discrete-Spectrum Force of Marine Propeller Based on a Data-Driven Surrogate Model

An improved spectral method and experimental tests for the low-frequency broadband noise of marine propellers

A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps

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