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Thermodynamic Optimization of Aircraft Environmental Control System Using Modified Genetic Algorithm

  • Qihang Liu
  • , Laihe Zhuang
  • , Jie Wen*
  • , Bensi Dong
  • , Zhiwei Liu
  • *Corresponding author for this work
  • Beihang University
  • Beihang Hangzhou Innovation Institute Yuhang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents an optimization method for the civil aircraft environmental control system (ECS) mainly involving two airstreams: the ram airstream for cooling and the bleed airstream for supplying the cabin. The minimum total fuel energy consumption rate (FECR), defined as the weighted sum of the shaft power extraction and propulsive power loss, is obtained under the precondition of the constant outputs in the cooling capacity and outlet pressure. A modified genetic algorithm (GA) is proposed to acquire the optimal values of the heat transfer areas, temperature ratio of bleed air, mass flow rate of ram air, and pressure ratios of the turbine, compressor, and fan. The statistical results show that the multipoint crossover and continuity improvement implemented in the modified GA improve convergence and distribution performance. The probability of reaching a satisfactory result using modified GA is 62.4% higher than standard GA. Due to the decrease of inlet parameters of bleed air and the elimination of power input in the compressor, the FECR of the optimization case can be lowered by 11.0%. In general, the evaluation method considering energy quality together with the modified optimization technique is proved effective in energy‐saving design for such energy systems such as ECS with multiple inputs and outputs.

Original languageEnglish
Article number721
JournalProcesses
Volume10
Issue number4
DOIs
StatePublished - Apr 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • aircraft
  • energy conservation
  • environmental control system
  • fuel energy consumption rate
  • genetic algorithm
  • thermo‐economics optimization

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