Aeronautical and Space-Rocket Engineering
Thermal engines, electric propulsion and power plants for flying vehicles
DOI: 10.34759/vst-2019-4-156-165
Аuthors
1*, 2**, 3***1. ,
2. Lyulka Experimental Design Bureau, branch of the United Engine Corporation – Ufa Engine Industrial Association, 13, Kasatkina str., Moscow, 129301, Russia
3. Lyulka Desing Bureau, 13, Kasatkina str., Moscow, 129301, Russia
*e-mail: kaf205@mail.ru
**e-mail: mihail.vovk@okb.umpo.ru
***e-mail: kulalayev.viktor@gmail.com
Abstract
Aerospace industry development is impossible without implementation of up-to-date samples of high-efficiency new generation energy systems (ES). The term “technical appearance” implies the aggregate of parametric, structural and technological solutions, reflecting most substantial specifics of the system appearance [5]. It is well-known that designing and production of new technology, inclusive of ES in aerospace industry, leads to the necessity of taking compromise optimal or rational engineering and technological decisions. Besides, designer always faces the requirement for conformity of technical appearance forecast of the ES being developed to its real-life prototype. An engineering approach based on statistical analog technique for decision-making while developing new technology may be of help for the appointed tasks solution and meeting the above said requirements [10, 15]. This technique foundation consists in the fact, that deep analysis and synthesis of static structural and energy data of the ES, selected analogs and prototypes according to the parameters of technical requirements to the design according to [15-17] are performed while prospective equipment development. The article regards the energy system (ES) in general form as a mechanical machine for input energy conversion into useful work. Methodological basics of the new generation ES optimal appearance forecasting by mathematical statistics techniques [15-24]. The article demonstrates that development and introduction of the special statistical criterion, integrating all operational parameters in the form of multi-parametrical function, is urgent for solving scientific and engineering problems of new ESs development with specified properties of enhanced effectiveness. This criterion may be named forecast criterion. The introduced special forecast criterion is based on ES statistical analog data fields processing (already created and successfully operated) by mathematical statistics techniques [15-17]. The criterion of the analytical form analysis by independent parameters-arguments leads to formulation and solution of the extreme problem of a multi-parameter function optimizing by known mathematical methods [18, 20, 24], where obtained optimal parameters determine the forecast of the newly created ES optimal technical appearance. Algorithm for compiling and special forecast criterion computing in general is presented. To demonstrate the legitimacy of the criterion introduction, an example of computing the forecast of the ES technical appearance in general is given. The scientific results of the article may be used to develop a comprehensive software product for modeling technical optimal concept of a new generation ES with increased output energy operational parameters and optimal mass-dimensional (volumetric) characteristics.
Keywords:
technical appearance, special statistical criterion, statistical forecast, mathematical model, multiparametric function, extreme optimization problemReferences
-
Morfologicheskii analiz, https://mylektsii.ru/7-124821.html
-
Kirov A.V. Fundamental’nye issledovaniya, 2016, no. 9-1, pp. 31-34.
-
Fokin D.B., Selivanov O.D., Ezrokhi Yu.A. The studies on optimal shape forming of a turbo-ramjet engine as a part of a high-speed aircraft power plant. Aerospace MAI Journal, 2018, vol. 25, no. 3, pp. 82-96.
-
Emel’yantsev G.I., Moiseev E.S., Solntsev A.N. Navigatsiya i gidrografiya, 1995, no. 1, pp. 37-42.
-
Vostrikov O.V. Trudy MAI, 2011, no. 48. URL: http://trudymai.ru/eng/published.php?ID=26757
-
Anisimov K.S., Kazhan E.V., Kursakov I .A., Lysenkov A.V., Podaruev V.Y., Savel’ev A. A. Aircraft layout design employing high-precision methods of computational aerodynamics and optimization. Aerospace MAI Journal, 2019, vol. 26, no. 2, pp. 7-19.
-
Vyazgin V.A., Fedorov V.V. Matematicheskie metody avtomatizirovannogo proektirovaniya (Mathematical methods of computer-aided design), Moscow, Vysshaya shkola, 1989, 184 p.
-
Koryachko V.P., Kureichik V.M., Norenkov I.P. Teoreticheskie osnovy SAPR (Theoretical basics of CAD), Moscow, Energoatomizdat, 1987, 400 p.
-
Kozlov A.E. Export potential of an aircraft building enterprise: development trends and predictive modeling(on the example of Progress Arsenyev Aviation Company). Aerospace MAI Journal, 2018, vol. 25, no. 1, pp. 243-255.
-
Emel’yanov S.V., Larichev O.I. Mnogokriterial’nye metody prinyatiya reshenii (Multicriteria decisionmaking methods), Moscow, Znanie. Ser. “Matematika, kibernetika”, 1985, 32 p.
-
Podinovskii V.V., Nogin V.D. Pareto-optimal’nye resheniya mnogokriterial’nykh zadach (Pareto-optimal solutions for multicriteria problems), Moscow, Nauka, 1982, 256 p.
-
Mikhalevich B.C., Volkovich V.L. Vychislitel’nye metody issledovaniya i proektirovaniya slozhnykh sistem (Computational methods of complex systems study and design), Moscow, Nauka, 1982, 286 p.
-
Skibin V.A., Solonin V.I. (eds.) Inostrannye aviatsionnye dvigateli: spravochnik TsIAM (Foreign Aircraft Engines), Moscow, Aviamir, 2005, 592 p.
-
Bakulev V.I., Golubev V.A., Krylov B.A. et al. Teoriya, raschet i proektirovanie aviatsionnykh dvigatelei i energeticheskikh ustanovok (Theory, calculation and design of aircraft engines and power plants), Moscow, MAI - SATURN, 2003, 688 p.
-
Kulalaev A.V., Kulalaev V.V. Uchenye zapiski Tavricheskogo natsional’nogo universiteta im. V.I. Vernads’kogo. Seriya “Tekhnicheskie nauki”, 2018, vol. 29(68), no. 3, part 1, pp. 28-34.
-
Kobzar’ A.I. Prikladnaya matematicheskaya statistika. Dlya inzhenerov i nauchnykh rabotnikov (Applied Mathematical Statistics. For engineers and scientists), Moscow, Fizmatlit, 2006, 816 p.
-
Waerden B.L. Mathematische Statistik. Springer-Verlag Berlin Heidelberg, 1957, 360 p. DOI: 10.1007/978-3-642-64974-5
-
Wilde D.J. Optimum seeking methods. Prentice-Hall, Inc. Englewood Cliffs, N.Y., 1964, 202 p.
-
Metod gradientnogo spuska, http://www. machinelearning.ru/wiki/index.php?title=MeTOfl_ градиентного_спуска
-
Mal’tsev A.I. Osnovy lineinoi algebry (Fundamentals of linear algebra), Moscow, Gostekhizdat, 1956, 340 p.
-
Buehler R.J., Shah B.V., Kempthorne O. Some properties of steepest ascent and related procedures for finding optimum conditions. Jowa State University Statistical Laboratory. Technical rept. no. 1 on Contract Nonr-530(05). 1961, pp. 8 - 10, 18.
-
Zellnick H.E., Sondak N.E., Davis R.S. Gradient search optimization. Chemical Engineering Progress, 1962, no. 58(8), pp. 35-41.
-
Blum J.R. Approximation methods which converge with probability one. The Annals of Mathematical Statistics, 1954, vol. 2, no. 2, pp. 382-386.
-
Grinyaev S.V. Komp’yuterra, 2001. URL: http:// www.computerra.ru/offline/2001/415/13052/
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