Design of a commercial aircraft environment control system using Bayesian optimization techniques

TL;DR

This paper applies Bayesian multi-objective optimization to design an aircraft environment control system, balancing weight and entropy generation, while handling complex constraints with limited simulations.

Contribution

It introduces a Bayesian optimization approach for multi-objective design of aircraft ECS with non-linear constraints and hidden failures, optimizing multiple geometries simultaneously.

Findings

Identified trade-off solutions between weight and entropy generation.

Demonstrated effectiveness of Bayesian optimization with limited simulations.

Handled complex constraints and hidden failures in the design process.

Abstract

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.