IJE TRANSACTIONS C: Aspects Vol. 31, No. 9 (September 2018) 1593-1601    Article in Press

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S. Abbasi, M. Zeinali and P. Nejadabbasi
( Received: November 11, 2017 – Accepted in Revised Form: February 23, 2018 )

Abstract    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 Non-dominated Sorting Genetic Algorithm-II (NSGA-II), as an evolutionary algorithm is employed. In addition, predefined geometrical constraints were 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 were effectively improved.


Keywords    Autonomous Underwater Vehicles, Hull Shape Design, Multi-objective Optimization



در مقاله حاضر یک روش جدید برای بهینه‌سازی هندسه بدنه یک وسیله زیرسطحی خودکنترل ارائه شده است. به کمک این روش دماغه و دم وسیله زیرسطحی به گونه‌ای طراحی می‌شود که قیود طولی ناشی از جانمایی اجزاء متفاوت در داخل بدنه مورد ملاحظه قرار گیرد. در پژوهش حاضر یک طراحی بهینه برای بدنه وسیله زیرسطحی اژدری شکل انجام شده است و روش بهینه‌سازی چند هدفه بر مبنای الگوریتم بهینه‌سازیNSGA-II به کار گرفته شده است با بهینه کردن پارامترهای هندسی پروفیل جدید به کارگرفته شده، علاوه بر ماکزیمم کردن حجم بدنه می‌توان به سطح تر شده کمتری دست یافت. ضمن آنکه در مقایسه با پروفیل متداول مایربنگ می‌توان به تنوعی از شکل‌های بدنه در یک شرایط هندسی خاص دست یافت. شبیه‌سازی عددی جریان در طرح بهینه نهایی نشان می‌دهد که به کمک پروفیل به کارگرفته شده پارامترهای هیدرودینامیکی بدنه زیرسطحی خودکنترل به طور موثری بهبود می‌یابد.


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. 

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