Handbook of Turbomachinery Second Edition Revised - Ventech!

single composite function is developed by combining the objective functions
using weight factors. This approach tends to be highly empirical in nature
and relies heavily on the experience of the designer to devise the proper
arrangement of the design objectives in the optimization process.
There have been a number of gas turbine design applications coupling
formal optimization techniques in the recent past. Haendler et al. [27]
developed an aerothermal design procedure for highly thermally loaded
film-cooled first-stage gas turbine blade. Goel and Lamson [28] have used a
combination of heuristic-search and numerical optimization techniques with
a quasi-3D aerodynamic analysis for the design of turbine blades.
Chattopadhyay et al. [12] have developed an optimization procedure for
efficient aerodynamic design of turbine blades that eliminated the sharp
variations in the velocity field near the blade leading edge without
compromising overall blade performance. A sensitivity analysis procedure
for turbine blade components was developed by Kolonay and Nagendra [29]
using a Jacobian derivative-based methodology to determine semianalytic
sensitivities of isotropic eight-noded (hexahedron) isoparametric finite
elements with respect to geometric shape design variables. Kodiyalam et
al. [30] have coupled heat-transfer and structural analysis to optimize a
composite engine structure using a modified method of feasible directions
algorithm. The optimization procedure used was a variation of the weighted
summation method and did not include the effect of heat transfer on the
blade shape. A coupled aerodynamic-structural shape optimization procedure
was developed and demonstrated on a low-pressure turbine blade by
Kao et al. [31]. Fatigue strength at high operating temperatures was used as
the design criterion. The coupling between the two disciplines (aerodynamics
and structures) was achieved though sensitivities of the optimum
solutions from the subsystem designs. Tappeta et al. [32] have developed a
multidisciplinary optimization approach using a concurrent subspace
optimization procedure for designing an engine blade with internal cavities.
The blade was modeled as a stepped beam with rectangular cavities, and the
weight of the structure was used as the objective function. Optimal design of
turbine blade for minimum weight has been carried out by Queau and
Trompette [33].
The application of artificial neural networks and genetic algorithm
(GA) for turbine blade optimization is gaining popularity. A neural
network-based parametric coupling in turbine design was developed by Goel
and Hajela [34]. The effect of heat transfer on the blade external shape was
not considered in the development. The technique was applied to a threestage
power turbine and an aircraft engine turbine design. Shelton et al. [35]
optimized a 2D transonic turbine airfoil using an artificial intelligence
engineering design shell coupled with an inviscid, adaptive CFD solver. The
Copyright © 2003 Marcel Dekker, Inc.