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Multi-Objective Robust Optimization of Air Engine Using IOSO Technology

[+] Author Affiliations
Egorov N. Igor, Kretinin V. Gennady, Leshchenko A. Igor, Kuptzov V. Sergey

IOSO Technology Center, Moscow, Russia

Paper No. GT2004-53504, pp. 157-163; 7 pages
doi:10.1115/GT2004-53504
From:
  • ASME Turbo Expo 2004: Power for Land, Sea, and Air
  • Volume 2: Turbo Expo 2004
  • Vienna, Austria, June 14–17, 2004
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-4167-7 | eISBN: 0-7918-3739-4
  • Copyright © 2004 by ASME

abstract

This paper demonstrates the multi-objective optimization of air engine in aircraft system using either Deterministic or Robust Design Optimization statements. The goal is to obtain the Pareto-optimum frontier for the air engine and aircraft parameters. Performance characteristics of engine include the following: specific fuel consumption; thrust, with external resistance included, for any flight operating modes of aircraft; weight; the engine size parameters; engine’s life period; level of engine noise; and maintenance costs of the engine. Performance characteristics of an aircraft include passenger-per-kilometer fuel consumption, direct maintenance expenditures, maintenance cost, terrain noise level, take-off runway length, maximum flight altitude, maximum flight Mach number for different parameters of the operation process of the engine, and the various aircraft geometry parameters. While solving a problem of optimizing an engine in an aircraft system, conditions may exist where values of objective function and constraints can not be calculated. This can be caused by both the unfeasibility of a whole system for certain combinations of design variables, and the instability of numerical schemes used as mathematical models. Such conditions can even lead to a crash of the mathematical model. The existence of such areas usually substantially complicates the solution of optimization tasks and in some cases makes it impossible to find optimal solution. The paper illustrates that IOSO algorithms can deal with such cases very efficiently. This paper presents the result of the probabilistic statement of the multi-objective optimization problem, which decreases technical risks when developing modern objects and systems with the highest level of efficiency.

Copyright © 2004 by ASME

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