Design Of Aircraft Cabin Environment Based On Intelligent Optimization Algorithms

The civil aviation industry of our country is developing rapidly, but the thermal comfort and air quality in current aircraft cabin are not satisfactory. The air distribution system of aircraft plays important role in maintaining the thermal comfort and supplying fresh air to the cabin. Currently, the researchers at home and abroad just study typical cases under specific ventilation patterns, but few of them optimize the cabin environment from the point view of optimization design. Faced with the above situation, this study uses intelligent optimization algorithms to optimize the thermal comfort and air quality in aircraft cabin.The first-class cabin environment of an MD-82 commercial airliner was optimized by using a multi-objective particle swarm optimization, with PMV, DR and air age as the design objectives. In the end, 19 groups of optimal parameters(air supply velocity, angle and temperature) were obtained. According to the 19 groups of optimal parameters, different combination of air supply velocity, angle and temperature, could build up a comfortable cabin environment with optimal air quality. It meant that multiple solutions existed in the design of aircraft cabin environment. On the foundation of the above research, a method by twice using cluster analyses was developed to obtain ranges of optimal air supply parameters, which overcame the limitation of failing to obtain parameters' range for general optimization algorithms. Because the proposed method can calculate the continuous distribution range of parameters, it avoids the possible failure of optimal solution from fluctuations in real air supply operation. Using this method, four kinds of ranges of optimal air supply parameters were obtained. The parameter ranges recommended by this paper were: air supply velocity: 0.83 ~ 0.88m/s, air supply angle: 35 ~ 38°, air supply temperature: 19.1 ~ 19.8 ?, and these ranges included adequate optimal solutions.Last but not least, considering CFD calculation requiring large amounts of time and calculating resources, a novel optimization algorithm, called HGPA, was proposed to save the total calculation. In this algorithm, genetic algorithm(GA) and particle swarm optimization(PSO) are the main framework, and artificial bee colony(ABC) is served as the hub connecting GA and PSO. Computational experiments on 13 benchmark functions showed that HGPA could save amount of calculation by 17% ~ 69% compared with GA, and by 20% ~ 73% compared with PSO. This novel algorithm can not only be used to optimize the aircraft cabin environment, but can also play role in other field.