1. Federal Aviation Administration, Aviation Safety Unmanned Aircraft Program Office (UAPO), Interim Operational Approval Guidance. Unmanned Aircraft Systems Operations in the U. S. National Airspace System. 2008.
2. U.S. Department of Defense. Office of the Secretary of Defense 2007–2032 Unmanned Systems Roadmap, 2007.
3. Chaput JA. Rapid air system concept exploration – a parametric physics based system engineering design model. AIAA aviation technology, integration, and operations conference, TX, 2010.
4. Torella G. The unsteady state simulation of gas turbine engines for training purposes. Int J Turbo Jet Eng 1991;8:133–53.Google Scholar
5. Cai R. Comparison method for complex cycle analysis. Int J Turbo Jet Eng 1988;5:257–64.Google Scholar
6. Rice IG. Evaluation of the compression-intercooled reheat-gas-turbine-combined cycle. Int J Turbo Jet Eng 1987;4:21–32.Google Scholar
7. Sullerey RK, Agarwal A. Performance improvement of gas turbine cycles. Int J Turbo Jet Eng 2008;25:209–20.Google Scholar
8. Andriani R, Ghezzi U, Ingenito A, Gamma F. Fuel consumption reduction and weight estimate of an intercooled-recuperated turboprop engine. Int J Turbo Jet Eng 2012;29:165–77.Google Scholar
9. Andriani R, Gamma F, Ghezzi U. Numerical analysis of intercooled and recuperated turbofan engine. Int J Turbo Jet Eng 2011;28:139–49.Google Scholar
10. Chiang HD, Hsu CN, Huang YM. Dynamic performance of a small turbojet engine. Int J Turbo Jet Eng 2003;20:195–208.Google Scholar
11. Li J, Fan D, Sreeram V. SFC optimization for aero engine based on hybrid GA-SQP method. Int J Turbo Jet Eng 2013;30:383–91.Google Scholar
12. Sanghi V, Sane SK. Digital simulation of propulsion system performance for cycle optimization studies. Int J Turbo Jet Eng 1999;16:27–38.Google Scholar
13. Boulkeraa T, Ghenaiet A. Optimizations of turboprop engines using the non-dominated sorting genetic algorithm. Proc Inst Mech Eng G J Aerospace Eng 2010;224:1271–83.Google Scholar
14. Homaifar A, Lai HY, McCormick E. System optimization of turbofan engines using genetic algorithms. Appl Math Modell 1994;18:72–83.Google Scholar
15. Asako T, Miyagawa H, Miyata S, Kudo K. Conceptual design of aircraft engine using multidisciplinary design optimization technique. 23rd Congress of International Council of the Aeronautical Sciences, ICAS, Toronto, Canada, 2002.
16. Atashkari K, Nariman-Zadeh N, Pilechi A, Jamali A, Yao X. Thermodynamic pareto optimization of turbojet engines using multi-objective genetic algorithms. Int J Therm Sci 2005;44:14–24.Google Scholar
17. Choi JW, Sung HG. Performance analysis of an aircraft gas turbine engine using particle swarm optimization. Int J Aeronaut Space Sci 2014;15;434–43.Google Scholar
18. Adel G, Boulekraa T. Optimum design of turboprop engines using genetic algorithm. J Propuls Power 2009;25:1345–50.Google Scholar
19. Turan O, Karakoc TH. Aerothermodynamic optimization of aero-jet engines for middle and long range aircraft. The fourth world congress aviation in the XXI-century, National Aviation University (NAU), Kyiv, Ukraine, 2010.
20. Turan O. Exergetic optimization of turbofan engine with genetic algorithm method. The 6th international green energy conference (IGEC-2011), Eskisehir, Turkey, 2011.
21. Borguet S, Kelner V, Le´onard O. Cycle optimization of a turbine engine: an approach based on genetic algorithms. Proceedings of the seventh national congress on theoretical and applied mechanics, Mons, Belgium, 2006.
22. Yousef SH, Sharaf FA. Thermodynamic optimization of the turbofan cycle. Aircraft Eng Aerospace Technol 2006;78:67–480.Google Scholar
23. Berton JJ, Guynn MD. Multi-objective optimization of a turbofan for an advanced, single-aisle 10th AIAA aviation technology, integration, and operations (ATIO) conference, Fort Worth, Texas, 2010.
24. Sanghi V. Impact of a variable cycle engine on conceptual aircraft sizing. Int J Turbo Jet Eng 2003;20:83–94.Google Scholar
25. Ghenaiet A, Boulekraa T. Minimum power requirement for a propeller-driven aircraft and optimum cycle parameters of turboprop engines. Proc Inst Mech Eng Part G J Aerospace Eng 2010;224:625–36.Google Scholar
26. Benini E, Chiereghin N. Turbofan multiobjective-multipoint optimization for UAV/UCAV applications. Aircraft Eng Aerospace Technol 2013;85:366–81.Google Scholar
27. Dinc A. Optimization with elitist genetic algorithm method in preliminary design of unmanned air vehicle & its propulsion system simultaneously. PhD thesis, Anatolian University, Turkey, 2010.Google Scholar
28. Dinc A. Sizing of a turboprop unmanned air vehicle and its propulsion system. J Therm Sci Technol 2015;35(2), in press.Google Scholar
29. Austin R. Unmanned aircraft systems. West Sussex: Wiley, 2010.Google Scholar
30. Chaput JA. Conceptual design of UAV systems. Lecture Notes. Austin, TX: University of Texas, 2004.Google Scholar
31. Raymer DP. Aircraft design: a conceptual approach, 3rd ed. Reston, Virginia: AIAA, 1999.Google Scholar
32. Federation of American Scientists. Air Combat Command Concept of Operations for Endurance Unmanned Aerial Vehicles, 2014. Available at: http://www.fas.org/irp/doddir/usaf/conops_uav/part03.htm, accessed date: 06 March 2015.
33. Mattingly J, Heiser W, Pratt D. Aircraft engine design, 2nd ed. AIAA Series. Washington, DC: AIAA, 2002:95.
34. Honeywell. Product brochures, 2014. Available at: http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents/BA_brochures-documents/TPE331-10_PredatorB_0292-000.pdf, accessed date: 06 March 2015.
35. Walsh PP, Fletcher P. Gas turbine performance, 2nd ed. Oxford: Blackwell, 2004.Google Scholar
36. Kurzke J. Design and off-design performance of gas turbines, GasTurb 11 user manual, Munich, Germany, 2007.
37. Deb K. Multi-objective optimization using evolutionary algorithms. Chichester: Wiley, 2001.Google Scholar
38. Holland JH. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. Ann Arbor, MI: The University of Michigan Press, 1975.Google Scholar
39. Deb K, Agrawal S. Understanding interactions among genetic algorithm parameters. Foundations of genetic algorithms V. San Mateo, CA: Morgan Kauffman, 1999:265–86.Google Scholar
40. Goldberg DE. Sizing populations for serial and parallel genetic algorithms. TCGA Report No. 88004. Tuscaloosa, AL: University of Alabama, The Clearinghouse for Genetic Algorithms, 1988.
About the article
Published Online: 2015-07-10
Published in Print: 2016-09-01