Aircraft survivability is a classical consideration of combat aircraft design and tactical development, but the fundamental model of aircraft survivability must be updated to be able to consider modern tactical scenarios that are applicable to unmanned aircraft. This paper seeks therefore to define the set of design tradeoffs and an evaluation of the tactical effectiveness for unmanned aircraft survivability. Traditional and modern survivability evaluation methods are presented and integrated into a computational simulation to create a probabilistic evaluation of unmanned aircraft survivability. The results demonstrate the development of design tradeoffs for a hypothetical unmanned C-130J Hercules against a single man-portable air defense system. The discussion focuses on the demonstration of the utility of this survivability evaluation framework for consideration of survivability in unmanned aerial vehicle (UAV) design, the utility of considering survivability in the design of multi-UAV configurations (including the loyal wingman and swarms), and the value of the probabilistic survivability model for multi-aircraft simulations.
GarciaR, et al. Unmanned aircraft systems as wingmen. J Defens Model Simulat2011; 9: 1–11.
2.
GirardA, et al. Border patrol and surveillance missions using multiple unmanned air vehicles. In: 43rd IEEE conference on decision and control (eds GirardAnouckHowellAdamHendrickJohn Karl), Nassau, Bahamas, 14–17 December 2004, paper no. 0191-2216, vol.1, pp.620–625. New York City: IEEE.
3.
BallRE. The fundamentals of aircraft combat survivability analysis and design. Reston, VA: American Institute of Aeronautics and Astronautics, 2003.
4.
JackmanJ,et al. The effect of pre-emptive flare deployment on first generation man-portable air-defence (MANPAD) systems. J Defens Model Simulat2010; 7: 181–189.
5.
WangX,et al. Analytic model for aircraft survivability assessment of a one-on-one engagement. J Aircraft2009; 46: 223–229.
6.
YangP,et al. A generic calculation model for aircraft single-hit vulnerability assessment based on equivalent target. Chin J Aeronaut2006; 19: 183–189.
7.
YangP,et al. A direct simulation method for calculating multiple-hit vulnerability of aircraft with overlapping components. Chin J Aeronaut2009; 22: 612–619.
8.
SullivanJF. Aircraft armor. Perforation resistance characteristics of magnesium alloys dowmetal (Type FS, J-1). Watertown Arsenal Labs MA, 1944.
9.
YangP, et al. Shot line geometric description method for aircraft vulnerability calculation. Chin J Aeronaut2009; 22: 498–504.
10.
HallDKetchamR. Survivability models and simulations: past, present, and future. In: 50th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics, and materials conference (eds HallDavidKetchamRonald), Palm Springs, CA, 5 April–7 May 2009, paper no. 978-1-60086-975-4, 2397–2399. Reston, VA: AIAA.
11.
HelldinTErlandssonT. Automation guidelines for introducing survivability analysis in future fighter aircraft. In: 28th congress of the International Council of the Aeronautical Sciences (eds HelldinToveErlandssonTina), Brisbane, Australia, 23–28 October 2012. Edinburgh, UK: Optimage.
12.
HelldinTErlandssonT. Decision support system in the fighter aircraft domain: the first steps. Halmstad, Sweden: Orebro University, 2011.
13.
HumphreysCJ. Optimal control of an uninhabited loyal wingman. Wright-Patterson Air Force Base, Ohio: Air Force Institute of Technology, 2016.
14.
HumphreysCJ. Optimal mission path for the uninhabited loyal wingman. In: AIAA/ISSMO multidisciplinary analysis and optimization conference (eds HumphreysClayCobbRichardJacquesDavid, et al.), Dallas, TX, 22–26 June 2015, paper no. 61970279. Reston, VA: AIAA.
15.
HumphreysCJ, et al. Dynamic re-plan of the loyal wingman optimal control problem. In: AIAA guidance, navigation, and control conference (eds HumphreysClayCobbRichardJacquesDaivd, et al.), Grapevine, TX, 9–13 January 2017. Reston, VA: AIAA.
16.
SauterJA, et al. Demonstration of digital pheromone swarming control of multiple unmanned air vehicles. Collection of Technical Papers - InfoTech at Aerospace: Advancing Contemporary Aerospace Technologies and Their Integration, vol. 2. 2005, pp.1256–1263. Reston, VA: AIAA.
17.
SauterJ, et al. Performance of digital pheromones for swarming vehicle control. In: proceedings of the international conference on autonomous agents (eds SauterJohnMatthewsRobert), 25–29 July 2005, pp.1037–1044. London UK: AAMAS.
18.
FryeAJMehielEA. Modeling and simulation of vehicle performance in a UAV swarm using horizon simulation framework. In: AIAA SciTech 2019 forum (eds FryeAdamMehielEricDiegoSan), 7–11 January 2019, pp.1–20. Reston, VA: AIAA.
19.
WangX. Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory. Chin J Aeronaut2019; 33: 353–364.
20.
BiedigerD, et al. Investigating the survivability of drone swarms with flocking and swarming flight patterns using virtual reality. In: 2019 IEEE 15th international conference on automation science and engineering (CASE) (eds BiedigerDanielMahadevArunBeckerAaron), Vancouver, BC, Canada, 22-26 August 2019, paper no. 2161–8089, pp. 718–1723. Piscataway, USA: CASE.
RaymerDP. Aircraft design: a conceptual approach. Reston, VA: American Institute of Aeronautics and Astronautics, 1989.
23.
Lowry.PolhamusE. A method for predicting lift increments due to flap deflection at low angles of attack in incompressible flow. NASA TN-3911, 1957.
24.
TaylorBN. The international system of units. National Institute of Standards and Technology. NIST special publication 330. 2008 edition. March2008, p.52.
25.
OberkampfWLRoyCJ. Verification and validation in scientific computing. 1sted. Cambridge, UK: Cambridge University Press, 2010.
26.
KlineSMcClintockF. Describing uncertainties in single-sample experiments. Mech Eng1953; 75: 3–8.
