  In this paper, we present the application of a recently developed algorithm
for Bayesian multi-objective optimization to the design of a commercial
aircraft environment control system (ECS). In our model, the ECS is composed of
two cross-flow heat exchangers, a centrifugal compressor and a radial turbine,
the geometries of which are simultaneously optimized to achieve minimal weight
and entropy generation of the system. While both objectives impact the overall
performance of the aircraft, they are shown to be antagonistic and a set of
trade-off design solutions is identified. The algorithm used for optimizing the
system implements a Bayesian approach to the multi-objective optimization
problem in the presence of non-linear constraints and the emphasis is on
conducting the optimization using a limited number of system simulations.
Noteworthy features of this particular application include a non-hypercubic
design domain and the presence of hidden constraints due to simulation
failures.
