Design of marine propellers with prescribed and optimal spanwise circulation distributions based on genetic algorithms and neural network. (October 2022)
- Record Type:
- Journal Article
- Title:
- Design of marine propellers with prescribed and optimal spanwise circulation distributions based on genetic algorithms and neural network. (October 2022)
- Main Title:
- Design of marine propellers with prescribed and optimal spanwise circulation distributions based on genetic algorithms and neural network
- Authors:
- Xue, Ying
Yang, Chen-Jun
Dong, Xiao-Qian
Li, Wei
Noblesse, Francis - Abstract:
- Abstract: Lifting-surface design methods are proposed for marine propellers based on genetic algorithms (GAs) and a two-layer back-propagation neural network (BPNN). For propellers with prescribed spanwise circulation distributions, the design problem is solved as GA-based optimization of camber surface geometry and pitch distribution which produce a circulation distribution to best fit the prescribed one. An in-house code based on the vortex lattice method (VLM) is employed to simulate circulation distribution and hydrodynamic performance of the propeller. Computer codes are developed in this research based on existing genetic algorithms, with a measure devised to accelerate convergence and improve the quality of solution. To optimize the spanwise circulation distribution, GAs are utilized again to explore the BPNN model established via the MATLAB toolbox and the in-house VLM code. Then the optimal circulation distribution is taken as the prescribed one for the GA-based design method mentioned above. Numerical tests are conducted to determine proper ranges of modeling parameters, such as the population size for GAs and the number of vortex lattices, and to assess the performance of the BPNN established. To numerically validate the proposed methods, viscous-flow simulation by solving the Reynolds-averaged Navier-Stokes equations is carried out for the propeller designed by using both methods mentioned above. The predicted pressure distributions over blade surfaces correlateAbstract: Lifting-surface design methods are proposed for marine propellers based on genetic algorithms (GAs) and a two-layer back-propagation neural network (BPNN). For propellers with prescribed spanwise circulation distributions, the design problem is solved as GA-based optimization of camber surface geometry and pitch distribution which produce a circulation distribution to best fit the prescribed one. An in-house code based on the vortex lattice method (VLM) is employed to simulate circulation distribution and hydrodynamic performance of the propeller. Computer codes are developed in this research based on existing genetic algorithms, with a measure devised to accelerate convergence and improve the quality of solution. To optimize the spanwise circulation distribution, GAs are utilized again to explore the BPNN model established via the MATLAB toolbox and the in-house VLM code. Then the optimal circulation distribution is taken as the prescribed one for the GA-based design method mentioned above. Numerical tests are conducted to determine proper ranges of modeling parameters, such as the population size for GAs and the number of vortex lattices, and to assess the performance of the BPNN established. To numerically validate the proposed methods, viscous-flow simulation by solving the Reynolds-averaged Navier-Stokes equations is carried out for the propeller designed by using both methods mentioned above. The predicted pressure distributions over blade surfaces correlate consistently with the circulation distributions used to design the propeller, thus indicating that present design methods are effective and reasonably accurate. … (more)
- Is Part Of:
- Applied ocean research. Volume 127(2022)
- Journal:
- Applied ocean research
- Issue:
- Volume 127(2022)
- Issue Display:
- Volume 127, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 127
- Issue:
- 2022
- Issue Sort Value:
- 2022-0127-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-10
- Subjects:
- Propeller -- Design -- Optimization -- Genetic algorithm -- Neural network -- Vortex lattice method
Ocean engineering -- Periodicals
620.416205 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01411187 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.apor.2022.103318 ↗
- Languages:
- English
- ISSNs:
- 0141-1187
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 1576.240000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23327.xml