  State-of-the-art aeronautic Low Pressure gas Turbines (LPTs) are already
characterized by high quality standards, thus they offer very narrow margins of
improvement. Typical design process starts with a Concept Design (CD) phase,
defined using mean-line 1D and other low-order tools, and evolves through a
Preliminary Design (PD) phase, which allows the geometric definition in
details. In this framework, multidisciplinary optimization is the only way to
properly handle the complicated peculiarities of the design. The authors
present different strategies and algorithms that have been implemented
exploiting the PD phase as a real-like design benchmark to illustrate results.
The purpose of this work is to describe the optimization techniques, their
settings and how to implement them effectively in a multidisciplinary
environment. Starting from a basic gradient method and a semi-random second
order method, the authors have introduced an Artificial Bee Colony-like
optimizer, a multi-objective Genetic Diversity Evolutionary Algorithm [1] and a
multi-objective response surface approach based on Artificial Neural Network,
parallelizing and customizing them for the gas turbine study. Moreover, speedup
and improvement arrangements are embedded in different hybrid strategies with
the aim at finding the best solutions for different kind of problems that arise
in this field.
