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Academic Journal of Computing & Information Science, 2021, 4(1); doi: 10.25236/AJCIS.2021.040106.

Numerical simulation of biological network


Alexander Liang

Corresponding Author:
Alexander Liang

The Lawrenceville School, Lawrenceville, NJ 08648, USA

Email: 1052231061@qq.com


Nowadays, more and more unmanned aerial vehicles are used in the logistics industry. Yet these delivery drones are heavy, needing to be charged very often and can’t transport many packages. We hope to solve these issues by reducing the weight of wings and slightly increasing the size of the drone by using elastic transportation network based on energy optimization. This would help extend the work time of these drones and allowing for maximum transportation amount. We first figured out the total elastic potential energy of the wings and the gravity potential energy. Then we minimized the total energy to find the best geometric value (radius of each pipelines within the wings). Next simulated on MATLAB, wings were built according to the pipeline wing radius and the total energy result was updated continuously to constantly minimize the energy with a more ideal pipeline radiuses. We expect a final pipeline elastic structure similar to that of a dragonfly’s wing with minimum total energy that will significantly help reduce the weight of large drone wings. This project will help design significantly lighter wings of drones based on elastic transportation network mainly through MATLAB simulations. Drones can then be expanded in size to transport more products and fly for longer periods.


network, simulation, transportation

Cite This Paper

Alexander Liang. Numerical simulation of biological network. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 1: 35-43. https://doi.org/10.25236/AJCIS.2021.040106.


[1] Tero A, Takagi S, Saigusa T, et al. Rules for Biologically Inspired Adaptive Network Design[J]. Science, 2010, 327(5964): 439-442.

[2] Katifori E, Szollosi G J, Magnasco M O. Damage and Fluctuations Induce Loops in Optimal Transport Networks[J]. Physical Review Letters, 2010, 104(4): 048704.1-048704.4.

[3] Bohn S, Magnasco M O. Structure, Scaling, and Phase Transition in the Optimal Transport Network[J]. Physical Review Letters, 2007, 98(8): 088702.

[4] Tero A, Kobayashi R, Nakagaki T. A Mathematical Model For Adaptive Transport Network in Path Finding by True Slime Mold[J]. Journal of Theoretical Biology, 2007, 244(4): 553-564.

[5] Kurz H, Burri P H, Djonov V G. Angiogenesis and Vascular Remodeling by Intussusception: From Form to Function[J]. News Physiol Sci, 2003, 18(2): 65-70.