Dynamic space reduction optimization framework and its application in hull form optimization

2021 ◽  
Vol 114 ◽  
pp. 102812
Author(s):  
Qiang Zheng ◽  
Bai-Wei Feng ◽  
Hai-Chao Chang ◽  
Zu-Yuan Liu
Keyword(s):  
Hull Form ◽  
Optimization Framework ◽  
Space Reduction

scholarly journals Application of Improved Particle Swarm Optimisation Algorithm in Hull Form Optimisation

2021 ◽  
Vol 9 (9) ◽  
pp. 955
Author(s):  
Qiang Zheng ◽  
Bai-Wei Feng ◽  
Zu-Yuan Liu ◽  
Hai-Chao Chang
Keyword(s):  
Data Processing ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Resistance Coefficient ◽  
Particle Swarm Optimisation ◽  
Hull Form ◽  
Space Reduction ◽  
Design Variables ◽  
Particle Swarm Optimisation Algorithm ◽  
Improved Pso

The particle swarm optimisation (PSO) algorithm has been widely used in hull form optimisation owing to its feasibility and fast convergence. However, similar to other intelligent algorithms, PSO also has the disadvantages of local premature convergence and low convergence performance. Moreover, optimization data are not used to analyse and reduce the range of values for relevant design variables. Our study aimed to solve these existing problems in the PSO algorithm and improve PSO from four aspects, namely data processing of particle swarm population initialisation, data processing of iterative optimisation, particle velocity adjustment, and particle cross-boundary configuration, in combination with space reduction technology. The improved PSO algorithm was used to optimise the hull form of an engineering vessel at Fn = 0.24 to reduce the wave-making resistance coefficient under static constraints. The results showed that the improved PSO algorithm could effectively improve the optimisation efficiency and reliability of PSO and effectively overcome the drawbacks of the PSO algorithm.

Advanced Hull Form Inshore Demonstrator. Model Strut and Propulsor Performance in Uniform Flow

2002 ◽  
Author(s):  
Kirk J. Anderson ◽  
Carmen G. Borda ◽  
Martin J. Donnelly
Keyword(s):  
Uniform Flow ◽  
Hull Form

scholarly journals Multi-fidelity black-box optimization for time-optimal quadrotor maneuvers

2021 ◽  
pp. 027836492110333
Author(s):  
Gilhyun Ryou ◽  
Ezra Tal ◽  
Sertac Karaman
Keyword(s):  
Real World ◽  
High Speed ◽  
Analytical Approximation ◽  
Black Box ◽  
Bayesian Optimization ◽  
Optimization Framework ◽  
Quadrotor Aircraft ◽  
Time Optimal ◽  
Feasibility Constraints ◽  
Generation Problem

We consider the problem of generating a time-optimal quadrotor trajectory for highly maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the optimal trajectory is located on the boundary of the set of dynamically feasible trajectories. This boundary is hard to model as it involves limitations of the entire system, including complex aerodynamic and electromechanical phenomena, in agile high-speed flight. In this work, we propose a multi-fidelity Bayesian optimization framework that models the feasibility constraints based on analytical approximation, numerical simulation, and real-world flight experiments. By combining evaluations at different fidelities, trajectory time is optimized while the number of costly flight experiments is kept to a minimum. The algorithm is thoroughly evaluated for the trajectory generation problem in two different scenarios: (1) connecting predetermined waypoints; (2) planning in obstacle-rich environments. For each scenario, we conduct both simulation and real-world flight experiments at speeds up to 11 m/s. Resulting trajectories were found to be significantly faster than those obtained through minimum-snap trajectory planning.

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