Global optimization for cross-domain aircraft based on kriging model and particle swarm optimization algorithm

Document Type : Article

Authors

1 School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China

2 College of Mechanical and Electrical Engineering, Jiaxing University, Jiaxing 314001, China

Abstract

To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathe-matical model for calculations. The architecture of the water-air amphibious aerial vehicle is especially crucial to the whole product, which impacts its performances in many different sides. a new architecture of low-submerged ducted water-air amphibious aerial vehi-cle with double rotor wings is designed on the basis of the studies home and abroad. Both of the system architecture and the dynamic model are established and both of the water-flow and airflow are analyzed with Fluent based on the 3D structure models built by Solidworks software, which mainly aims at the impact factors of body thrust force and lift force. And the CFD simulations of the layout are also accomplished based on the former analysis results as well. Compared with the results from PSO algorithm, kriging model and orthogonal test, the most suitable shape architecture is optimized. Finally, the optimized results were simulated by Fluent. The results show that the Global optimization thought based on the Kriging model and the PSO algorithm significantly improve the lift and drive performance of cross-domain aircraft and computer operational efficiency.

Keywords

References

References:
1. Drews, P.L.J., Neto, A.A., and Campos, M.F.M. "A survey on aerial submersible vehicles", Proc. IEEE/OES Oceans Int. Conf., pp. 1-7 (2009). https://www.researchgate.net/pro le/Paulo Drews- Jr/publicat ion/263314909 A Surv ey on Aerial Submersible Vehicles/links/0a85e53a8ace0d663900 0000.pdf.
2. Yang, X., Wang, T., Liang, J., et al. "Survey on the novel hybrid aquaticaerial amphibious air- craft: Aquatic unmanned aerial vehicle (AquaUAV)", Progress in Aerospace Sciences, 74, pp. 131-151 (2015).
3. Gao, A. and Techet, A.H. "Design considerations for a robotic ying sh", Oceans'11 MTS/IEEE KONA, pp. 1-8 (2011).DOI: 10.23919/OCEANS.2011.6107039. https://ieeexplore.ieee.org/document/6107039.
4. Fabian, A., Feng, Y., and Swartz, E., Hybrid Aerial Underwater Vehicle, Lexington, USA: MIT Lincoln Lab (2012).
5. Liang, J.H., Yao, G.C., Wang, T.M., et al. "Wing load investigation of the plunge-diving locomotion of a gannet Morus inspired submersible aircraft", Sci-ence China Technological Sciences, 57(2), pp. 390-402 (2014).
6. Siddall, R. and Kovac, M. "Launching the Aqua- MAV: bioinspired design for aerial-aquatic robotic platforms", Bioinspiration & Biomimetics, 9(3), pp. 1-18 (2014).
7. Siddall, R., Ancel, A.O., and Kovac, M. "Wind and water tunnel testing of a morphing aquatic micro air vehicle", Interface Focus, 7(1), pp. 1-15 (2017).
8. Chen, Y., Helbling, E.F., Gravish, N., et al. "Hybrid aerial and aquatic locomotion in an at-scale robotic insect", International Conference on Intelligent Robots and Systems, IEEE, pp. 331-338 (2015).
9. Chen, Y., Wang, H., Helbling, E.F., et al. "A bio-logically inspired, apping-wing, hybrid aerial-aquatic microrobot", Science Robotics, 2(11), pp. 1-11 (2017).
10. Alzu0Bi, H., Akinsanya, O., Kaja, N., et al. "Evaluation of an aerial quadcopter power-plant for under- water operation", 10th International Symposium on Mechatronics and Its Applications (ISMA), IEEE, pp. 1-4 (2015).
11. Alzu0Bi, H., Mansour, I., and Rawashdeh, O. "Loon copter: Implementation of a hybrid unmanned aquaticaerial quadcopter with active buoyancy control", Journal of Field Robotics, 3, pp. 1-15 (2018).
12. Maia, M.M., Soni, P., and Diez, F.J. "Demonstration of an aerial and submersible vehicle capable of ight and underwater navigation with seamless air-water transition", Compute Science, pp. 1-9 (2015).
13. Villegas, A., Mishkevich, V., Gulak, Y., et al. "Analysis of key elements to evaluate the performance of a multirotor unmanned aerial aquatic vehicle", Aerospace Science and Technology, 70, pp. 412-418 (2017).
14. Mercado, D., Maia, M., and Diez, F.J. "Aerial- underwater systems, a new paradigm in unmanned vehicles", Journal of Intelligent & Robotic Systems, 95(10), pp. 229-238 (2019). DOI: 10.1007/s10846-018-0820-x.
15. Drews, P.L.J, Neto, A.A., and Campos, M.F.M. "Hybrid unmanned aerial underwater vehicle: Modeling and simulation", International Conference on Intelligent Robots & Systems, IEEE, pp. 4637-4642 (2014).
16. Rosa, R.T.S., Evald, P.J.D.O., and Drews, P.L.J. "A comparative study on sigma-point Kalman lters for trajectory estimation of hybrid aerial-aquatic vehicles", International Conference on Intelligent Robots and Systems (IROS), IEEE, pp. 7460-7465 (2018).
17. Qi, D., Feng, J., and Li, Y. "Dynamic model and ADRC of a novel water-air unmanned vehicle for water entry with in-ground e ect", Journal of Vibroengineering, 18(6), pp. 3743-3756 (2016).
18. Ma, Z.C., Feng, J.F., and Yang, J. "Research on vertical air water trans-media control of hybrid un-manned aerial underwater vehicles based on adaptive sliding mode dynamical surface control", International Journal of Advanced Robotic Systems, 15(2), pp. 1-10 (2018).
19. Lu, D., Xiong, C., Lyu, B., et al. "Multi-mode hybrid aerial underwater vehicle with extended endurance", OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO), IEEE, pp. 1-7 (2018).
20. Ferziger, J.H. and Peric, M., Computational Methods for Fluid Dynamics, 3rd Edn., Springer-Verlag Berlin Heidelberg, pp. 71-90, New York, USA (2002).
21. Antony, J., Design of Experiments for Engineers and Scientists, 2nd Edn, pp. 29-42, Elsevier Science & Technology Books, Amsterdam, Holland (2003).
22. Xiao, Q., Wang, L., and Xu, H. "Application of kriging models for a drug combination experiment on lung cancer", Statistics in Medicine, 38(2), pp. 236-246 (2019).
23. Kim, K. and June, J. "Multiobjective optimization for a plasmonic nanoslit array sensor using Kriging models", Applied Optics, 56(21), pp. 5838-5843 (2017).
24. Park, T., Yum, B., Hung, Y., et al. "Robust Kriging models in computer experiments", Journal of the Operational Research Society, 67(4), pp. 644-653 (2016).
25. Kaveh, A. and Nasrollahi, A. "Charged system search and particle swarm optimization hybridized for optimal design of engineering structures", Scientia Iranica, 21(2), pp. 295-305 (2014).
26. Li, C., Zhai, R., Liu, H., et al. "Optimization of a heliostat field layout using hybrid PSO-GA algorithm", Applied Thermal Engineering, 128, pp. 33-41 (2018).
27. Nobile, M.S., Cazzaniga, P., Besozzi, D., et al. "Fuzzy self-tuning PSO: A settings-free algorithm for global optimization", Swarm and Evolutionary Computation, 39, pp. 70-85 (2018).
Scientia Iranica
Volume 28, Issue 1
Transactions on Mechanical Engineering (B)
January and February 2021
Pages 209-222

Files

Share

How to cite

Statistics