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.

The cooled cooling air technology (CCA technology) shows expected performance in solving the growing thermal challenge for advanced aero engines by reducing the temperature of cooling air. The effect of CCA technology on the overall propelling performance with or without adjusting cycle parameters is controversial. Based on this, both the energy and exergy methods have been adopted to elaborate the specific mechanisms of the above energy utilization discrepancy. As a result, the scheme of CCA technology without optimizing cycle parameters has lower propelling work and efficiency with the total exergy destruction increasing 0.5~2%. Oppositely, as for the scheme of CCA with meliorated cycle parameters, the propelling efficiency improved by around 2~4% with total exergy destruction reduced by 1~3.5%. By analyzing the distribution of exergy destruction, the avoidable and unavoidable exergy destruction caused by the combustion chamber, compressors, and turbines accounts for the largest proportion, which indicates that more attention needs to be paid in the future. During the whole flight mission, the percentage of exergy destruction is much higher in supersonic, subsonic cruise, combat, and escape conditions. In conclusion, the improvement of cycle parameters to reduce the exergy destruction should be considered when introducing CCA technology.

Real-time optimization of aircraft thermal management systems (TMS) is constrained by the computational cost of conventional approaches. Solving the existing simulating model requires several seconds, and optimization through evolutionary algorithms based on above model further extends this by at least several minutes. None are viable for onboard real-time applications. Consequently, developing the rapid and accurate determination of TMS strategy has become a pivotal challenge. This study firstly proposes a real-time regulation strategy to reduce optimization time to the millisecond scale. Specifically, an onboard dynamic simulation model is developed that resolves only the heat transfer processes among the heat loads and heat sinks, and eliminates the pressure-balance iteration. The computation time is shortened from the second scale to real-time levels. Embedding above model, a multi-objective decision optimization algorithm based on heat transfer path decoupling (HPD-MODO) is proposed. The algorithm divides TMS into three heat transfer modes: oil-dominated, fuel-dominated and air-dominated, and adopts a case-partitioned strategy to acquire the optimal lubricating oil allocation among air-oil and fuel-oil heat exchangers. Across the three flight missions tested, HPD-MODO achieves solution accuracy equivalent to NSGA-II while reducing computation time from 2092~6173 ms to 1.36~1.51 ms. Based on proposed algorithm, heat sinks are reasonably allocated and the hot fuel return flow is minimized. In summary, the established strategy provides a flexible route to implement multivariable online optimization for intricate aircraft TMSs under next-generation flight platforms.