Robust design on the arrangement of a sail and control planes for improvement of underwater Vehicle’s maneuverability
0
조회
0
다운로드
3
인용
0
참고문헌
키워드
초록
The purpose of this study is to discuss how to improve the maneuverability of lifting and diving for underwater vehicle’s vertical motion. Therefore, to solve these problems, applied the 3-D numerical simulation, Taguchi’s Design of Experiment (DOE), and intelligent parameter design methods, etc. We planned four steps as follows: firstly, we applied the 2-D flow simulation with NACA series, and then through the Taguchi’s dynamic method to analyze the sensitivity (b). Secondly, take the data of pitching torque and total resistance from the Taguchi orthogonal array (L9), the ignal-to-noise ratio (SNR), and analysis each factorial contribution by ANOVA. Thirdly, used Radial Basis Function Network (RBFN) method to train the non-linear meta-modeling and found out the best factorial combination by Particle Swarm Optimization (PSO) and Weighted Percentage Reduction of Quality Loss (WPRQL). Finally, the application of the above methods gives the global optimum for multi-quality characteristics and the robust design configuration, including L/D is 9.4:1, the foreplane on the hull (Bow-2), and position of the sail is 0.25 Ls from the bow. The result shows that the total quality is improved by 86.03% in comparison with the original design.
Abstract
The purpose of this study is to discuss how to improve the maneuverability of lifting and diving for underwater vehicle’s vertical motion. Therefore, to solve these problems, applied the 3-D numerical simulation, Taguchi’s Design of Experiment (DOE), and intelligent parameter design methods, etc. We planned four steps as follows: firstly, we applied the 2-D flow simulation with NACA series, and then through the Taguchi’s dynamic method to analyze the sensitivity (b). Secondly, take the data of pitching torque and total resistance from the Taguchi orthogonal array (L9), the ignal-to-noise ratio (SNR), and analysis each factorial contribution by ANOVA. Thirdly, used Radial Basis Function Network (RBFN) method to train the non-linear meta-modeling and found out the best factorial combination by Particle Swarm Optimization (PSO) and Weighted Percentage Reduction of Quality Loss (WPRQL). Finally, the application of the above methods gives the global optimum for multi-quality characteristics and the robust design configuration, including L/D is 9.4:1, the foreplane on the hull (Bow-2), and position of the sail is 0.25 Ls from the bow. The result shows that the total quality is improved by 86.03% in comparison with the original design.
참고문헌 (21)
- "Computational Fluid Dynamics (CFD) Analysis of NACA 0012 Airfoil" (2015)[URL]
- Ariful Hoque, Md, "Cfd analysis OF pressure coefficient for NACA 4412 at various DEPTHS", Proceedings of 50th IASTEM International Conference (2017)
- Bahatmaka, A., "Optimization of thrust propeller design for an ROV (remotely operated vehicle) consideration by genetic algorithms", MATEC Web of Conf, 138 (2017)
- Eberhart, R.C, "A new optimizer using particle swarm theory", Proc. Sixth International Symposium on Micro Machine and Human Science, pp.39~43 (1995)
- Gao, T, "Optimum design of an AUV by using computational fluid dynamic analysis", Proceedings of 2012 International Conference on Mechanical Engineering and Material Science (MEMS 2012) (2012)
- George, Derringer, "Simultaneous optimization of several response variables", J. Qual. Technol, 12, pp.214~219 (1980)[DOI]
- James, Kennedy, "Particle swarm optimization", Proceedings of ICNN’95 - International Conference on Neural Networks, 4, pp.1942~1948 (1995)
- "Flow over NACA-0015 Airfoil" (2012)[URL]
- "Taguchi Methods: Principles and Practices of Quality Design" (2008)[URL]
- "Preliminary design of an autonomous underwater vehicle using a multiple-objective genetic optimizer" (2008)[URL]
- "Computation of total resistance of a ship and a submarine by a RANSE based CFD" (2014)[URL]
- Pan, Y.C, "Numerical prediction of submarine hydrodynamic coefficients using CFD simulation", J. Hydrodyn, 24(6), pp.840~847 (2012)
- Qasim, I, "Study on optimization design of pressure hull for underwater vehicle", World Acad. Sci. Eng. Technol. Int. J. Trans. Veh. Eng, 12(3) (2018)
- Rand N, Conger, "Pressure measurements on a pitching airfoil in a water channel", AIAA J, 32(1), pp.108~115 (1994)
- Ridwan Utina, M, "Numerical and experiment investigation of lift performance over hydroplane of submarine", J. Subsea and Offshore Sci. Eng, 5 (2016)
- Saadia, Adjali, "A numerical study of steady 2D ow around NACA 0015 and NACA 0012 hydrofoil with free surface using VOF method", EPJ Web Conf, 92 (2015)
- Santhakumar, M., "Analysis of parameter sensitivity using robust design techniques for a flatfish type Autonomous underwater vehicle", Int. J. Qual. Statistics, and Reliab (2009)[DOI]
- Taguchi, G., "Taguchi’s Quality Engineering Handbook" (2005), John wiley & Sons, Inc
- Wu, F. C., "Optimization of robust design for multiple quality characteristics", Int. J. Prod. Res, 42, pp.1080~ (2004)[DOI]
- Zhan, Z. H., "Adaptive particle swarm optimization", IEEE Trans. Syst. Man Cybern. B Cybern, 39(6), pp.1362~1381 (2009)[DOI]
- Zhang, N, "Validation of numerical simulation on resistance and flow field of submarine and numerical optimization of submarine hull form", J. Ship Mech, 9(1), pp.1~13 (2005)