A method for developing an optimal plan of fuel consumption by the maneuverable aircraft

Mathematica modeling, numerical technique and program complexes


Аuthors

Kurianskii M. K.1*, Lolaev S. G.2**, Paschenko O. B.1***, Romanova T. N.2****

1. Russian Aircraft Corporation «MiG», 7, 1st Botkinsky passage, Moscow, 125284, Russia
2. Bauman Moscow State Technical University, MSTU, 5, bldg. 1, 2-nd Baumanskaya str., Moscow, 105005, Russia

*e-mail: kuriyanskiy_mk@bk.ru
**e-mail: simon.lolaev@gmail.com
***e-mail: alexandoleg@post.ru
****e-mail: rtn@bmstu.ru

Abstract

This article considers the problem of fuel consumption control of maneuverable aircraft which fuel configuration consists of several fuel tanks distributed throughout the fuselage. Therefore, while fuel depletion in a separate tank the aircraft center of gravity changes, which leads to a change of the pitch and roll angles. Thus, the quality of fuel consumption control is determined by such criteria as the aircraft center of gravity changing relative to longitudinal and lateral axes of rotation occur within the given range; maximum fuel mass is used; the number of switches between various fuel tanks is minimal. The goal of the article consists in developing a new method of creating an optimal order of fuel tanks utilization satisfying the specified criteria.

To solve this problem, the setting of task of multicriteria combinatorial optimization was performed and the heuristic method of its solving was suggested. Since the number of switches between the fuel tanks is a discrete integer value, the problem was formulated in a discrete space. To solve the problem, the simulation model of the fuel consumption process that allows to simulate changes of aircraft’s center of gravity for a given fuel configuration and for the order of the fuel tanks utilization was created. While this method developing, the elements of greedy algorithm, method of full enumeration and branch and bound method were used.

Based on the proposed heuristic method the software implementation was developed, which solves the problem of automation of selecting optimal plan of fuel tanks utilization for a specified fuel configuration. The program can be used as a decision making support in the process of designing the fuel configuration of maneuverable aircraft.

Keywords:

combinatorial optimization, multicriteria optimization, fuel consumption, fuel configuration

References

  1. Dolgov O.S., Kuprikov N.M., Lyakishev M.A. Trudy MAI, 2010, no. 41, available at: http://trudymai.ru/eng/published.php?ID=23771

  2. Zhuravlev V.F. Osnovy teoreticheskoi mekhaniki (Fundamentals of Theoretical Mechanics), Moscow, Fizmalit, 2001, 320 p.

  3. Kini R.L., Raifa Kh. Prinyatie reshenii pri mnogikh kriteriyakh: predpochteniya i zameshcheniya (Decision analysis with multiple conflicting objectives preferences and value tradeoffs), Moscow, Radio i Sv’az, 1981, 560 p.

  4. Kormen T., Leizerson Ch., Rivest R., Shtain K. Algoritmy: postroenie i analiz (Introduction to Algorithms), Moscow, Vil’yams, 2005, 1296 p.

  5. Matthias Ehrgott. Multicriteria Optimization, Springer, 2005, 328 p.

  6. Abraham P. Punnen, Y. P. Aneja. Minmax combinatorial optimization, European Journal of Operational Research, 1995, vol., no. 3, pp. 634 – 643.

  7. Heiner Ackermann, Alantha Newman, Heiko Roglin, Berthold Vocking. Decision-making based on approximate and smoothed Pareto curves, Theoretical Computer Science, 2007, vol. 378, no. 3, pp. 253 – 270.

  8. Pavel Kopecek. Selected Heuristic Methods Used in Industrial Engineering, Procedia Engineering, 2014, vol. 69, pp. 622 – 629.

  9. Curtis S.A. The classification of greedy algorithms, Science of Computer Programming, 2003, vol. 49, no. 1–3, pp. 125 – 157.

  10. Tibor Jordan, Tamas Kis, Silvano Martello. Computational advances in combinatorial optimization, Discrete Applied Mathematics, 2018, vol. 242, pp. 1 – 3.

  11. Karpenko A.P. Sovremennye algoritmy poiskovoi optimizatsii. Algoritmy, vdokhnovlennye prirodoi (Modern algorithms of search optimization. Algorithms inspired by nature), MGTU im. N.E. Baumana, Moscow, 2014, 446 p.

  12. Michael Muller. Java Lambdas and Parallel Streams, Springer, 2016, 87 p.

  13. Richard Warburton. Java 8 Lambdas: Functional Programming For The Masses, O’Reilly Media, 2014, 182 p.

  14. Blair Archibald, Patrick Maier, Ciaran McCreesh, Robert Stewart, Phil Trinder. Replicable parallel branch and bound search, Journal of Parallel and Distributed Computing, 2018, vol. 113, pp. 92 – 114.

  15. Robert Kruz. Struktury dannykh i proektirovanie program (Data structures and program design), Moscow, Binom, Laboratoriya znanii, 2017, 768 p.

  16. Robert Lafore. Struktury dannykh i algoritmy v Java. Klassika Computers Science (Data Structures and Algorithms in Java, Classic of Computer Science), Saint Petersburg, Piter, 2013, 704 p.

  17. Rafael Marti, Manuel Laguna, Fred Glover. Principles of scatter search, European Journal of Operational Research, 2006, vol. 169, no. 2, pp. 359 – 372.

  18. Matthias Ehrgott. Approximation algorithms for combinatorial multicriteria optimization problems, International Transactions in Operational Research, 2000, vol. 7, no. 1, pp. 5 – 31.

  19. Schandl B., Klamroth K., Wiecek M.M. Norm-based approximation in multicriteria programming, Computers & Mathematics with Applications, 2002, vol. 44, no. 7, pp. 925 – 942.

  20. Carraway R.L., Morin T.L. Theory and applications of generalized dynamic programming: An overview, Computers & Mathematics with Applications, 1988, vol. 16, no. 10–11, pp. 779 – 788.


Download

mai.ru — informational site MAI

Copyright © 2000-2024 by MAI

Вход