TECHNICAL MECHANICS
ISSN (Print): 1561-9184, ISSN (Online): 2616-6380

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UDC 519.853:533.697

Technical mechanics, 2015, 3, 39 - 45

UTILITY OF STOCHASTIC METHODS IN SOLUTION OF PROBLEMS OF AERODYNAMIC OPTIMIZATION OF GAS-TURBINE ENGINE COMPRESSOR CASCADE SHAPE

S. V. Melashich

      ABSTRACT

      The paper presents the response surfaces of the basic aerodynamic characteristics of compressor cascades (the turning flow angle and the total pressure loss coefficient) under selected flow conditions. The parametric description of the cascade profile shape is implemented using the original method based on Bezier curves and smooth convex Hicks-Henne functions. The calculation of the objective function is performed by simulating the flow on the basis of the numerical integration of the averaged Navier - Stokes equations closed by the one-parameter Spalart - Allmaras turbulence model. The complexity of the response surfaces of the basic compressor cascades aerodynamic characteristics is demonstrated resulting in difficult searching their extremes using deterministic methods of nonlinear programming. The aerodynamic optimization of compressor cascades is carried out using a deterministic approach, namely, the adjoint gradient method and a stochastic approach, namely, the genetic algorithm. It is shown that the gradient method under different initial conditions converges to the different objective function extremes resulting in an essential decrease in the advantages of its application to the solution of aerodynamic optimization problems. The effectiveness of the genetic algorithm in the sense of the number of the objective function calculations is higher than that of the gradient method with a multi-start option. Thus, the utility of stochastic methods to the solution of problems of aerodynamic optimization of compressor cascades is illustrated by a specific example. Pdf (English)







      KEYWORDS

compressor cascade, optimization of shape, aerodynamic characteristics, geometrical parameters of cascade, response surface, genetic algorithm.

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Copyright (©) 2015 S. V. Melashich

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