Autonomous Underwater Vehicle Hull Geometry Optimization Using a Multi-objective Algorithm Approach

Authors

1 School of Mechanical Engineering, Arak University of Technology, Arak, Iran

2 School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Abstarct In this paper, a new approach to optimize an Autonomous Underwater Vehicle (AUV) hull geometry is presented. Using this methode, the nose and tail of an underwater vehicle are designed, such that their length constraints due to the arrangement of different components in the AUV body are properly addressed. In the current study, an optimal design for the body profile of a torpedo-shaped AUV is conducted, and a multi-objective optimization scheme based on the optimization algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm-II, as an evolutionary algorithm) is employed. In addition, predefined geometrical constraints are considered so that equipment with the specific dimensions can be placed inside the AUV space without any effect on the AUV volume and the wetted surface. By optimizing the parameters of the newly presented profile, in addition to maximizing the volume and minimizing the wetted surface area, more diversed shapes can be achieved than with the ‘Myring’ profile. A CFD analysis of the final optimal design indicates that with the help of the proposed profile, the hydrodynamic parameters for the AUV hull are improved effectively.

Keywords


1. Sahu, B.K., and Subudhi, B., “The state of art of autonomous underwater vehicles in current and future decades”, in First International Conference Automation, Control, Energy and Systems (ACES), (2014).

2. Carmichael, B.H., “Underwater vehicle drag reduction through choice of shape”, AIAA Second Propulsion Joint Specialist Conference, (1996).

3. Packwood, A.R., and Huggins, A., “Afterbody shaping and transition prediction for a laminar flow underwater vehicle”, Ocean Engineering, (1994), 445–459.

4. Myring, D., “A theoretical study of body drag in subcritical axisymmetric flow”, Aeronaut. Technical Report, Royal Aircraft Establishment, Hants, UK, Q. 27, (1976), 186-194.

5. Martz, M., “Preliminary Design of an Autonomous Underwater Vehicle Using a Multiple-Objective Genetic Optimizer”, (Doctoral dissertation, Virginia Polytechnic Institute and State University), (2008).

 6. Joung, T.-H., Sammut, K., He, F., and Lee, S.-K., “Shape optimization of an autonomous underwater vehicle with a ducted propeller using computational fluid dynamics analysis”, International Journal of Naval Architecture and Ocean Engineering, Vol. 4, No. 1, (2012), 44–56.

7. Alvarez, A., Bertram, V., and Gualdesi, L., “Hull hydrodynamic optimization of autonomous underwater vehicles operating at snorkeling depth”, Ocean Engineering, Vol. 36, No. 1, (2009), 105–112.

8. Koh, S.K., Jung, S.-Y., and Lee, N.J., “Optimal design of AUV endcaps”, OCEANS’11 MTS/IEEE KONA, IEEE (2011), 1–6.

9. Vasudev, K.L., Sharma, R., and Bhattacharyya, S.K., “A CAGD+CFD integrated optimization model for design of AUVs”, Oceans Engineering, , (2014), 1–8.

10. Alam, K., Ray, T., and Anavatti, S.G., “Design and construction of an autonomous underwater vehicle”, Neurocomputing, Vol. 142, No. 142, (2014), 16–29.

11. Sadati, A., Tavakkoli-Moghaddam, R., Naderi, B., and Mohammadi, M., “Solving a New Multi-objective Unrelated Parallel Machines Scheduling Problem by Hybrid Teaching-learning Based Optimization”, International Journal of Engineering - Transactions B: Applications, Vol. 30, No. 2, (2017), 224–233.

12. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, (2002), 182–197.

13. Chinneck, J.W., “Practical Optimization: a Gentle Introduction”, (2006). www.sce.carleton.ca/faculty/chinneck/po.html

14. Khalkhali, A., and Roshanfekr, S., “Multi-objective Optimization of a Projectile Tip for Normal Penetration”, International Journal of Engineering - Transactions A: Basics, Vol. 26, No. 10, (2013), 1225–1234.

15. Ponsich, A., Azzaro-Pantel, C., Domenech, S., and Pibouleau, L., “Constraint handling strategies in Genetic Algorithms application to optimal batch plant design”, Chemical Engineering and Processing: Process Intensification, Vol. 47, No. 3, (2008), 420–434.

16. Shih, T., Liou, W., Shabbir, A., Yang, Z., Fluids, J.Z.-C.&, and 1995,  undefined, “A new k-ϵ eddy viscosity model for high reynolds number turbulent flows”, Computers & Fluids, Vol. 24, No. 3, (1995), 227–238.

17. Launder, B., and, D.S.-C.M. in A.M., and 1974,  undefined, “The numerical computation of turbulent flows”, Computer Methods in Applied Mechanics and Engineering, Vol. 3, No. 2, (1974), 269–289.