The design of axial compressors is dictated by the maximisation of flow
efficiency at on design conditions whereas at part speed the requirement for
operation stability prevails. Among other stability aids, compressor variable
geometry is employed to rise the surge line for the provision of an adequate
surge margin. The schedule of the variable vanes is in turn typically obtained
from expensive and time consuming rig tests that go through a vast combination
of possible settings. The present paper explores the suitability of stochastic
approaches to derive the most flow efficient schedule of an axial compressor for
a minimum variable user defined value of the surge margin. A genetic algorithm
has been purposely developed and its satisfactory performance validated against
four representative benchmark functions. The work carries on with the necessary
thorough investigation of the impact of the different genetic operators employed
on the ability of the algorithm to find the global extremities in an effective
and efficient manner. This deems fundamental to guarantee that the algorithm is
not trapped in local extremities. The algorithm is then coupled with a
compressor performance prediction tool that evaluates each individual's
performance through a user defined fitness function. The most flow efficient
schedule that conforms to a prescribed surge margin can be obtained thereby fast
and inexpensively. Results are produced for a modern eight stage high bypass
ratio compressor and compared with experimental data available to the research.
The study concludes with the analysis of the existent relationship between surge
margin and flow efficiency for the particular compressor under scrutiny. The
study concludes with the analysis of the existent relationship between surge
margin and flow efficiency for the particular compressor under scrutiny
