Structural parameters optimization of submerged inlet using least squares support vector machines and improved genetic algorithm-particle swarm optimization approach

• Main Authors: Houju Pei, Yonglong Cui, Benben Kong, Yanlong Jiang, Hong Shi
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Engineering Applications of Computational Fluid Mechanics
• Subjects: aircraft submerged inlet; structural parameters optimization; ls-svm prediction model; ga-pso; cfd; artificial intelligence methods
• Online Access: http://dx.doi.org/10.1080/19942060.2021.1893226

It is important to optimize the structure of inlet due to the increasing demand of ram air. In this paper, structural optimization of the submerged inlet is pursued using a hybrid model by integrating least squares support vector machines (LS-SVM) prediction model and improved genetic algorithm-particle swarm optimization (GA-PSO). Inlet shape is controlled by changing three-dimensional geometric parameters. Ramp angle, width to depth ratio and ramp length play significant parts in this optimization process. Ram efficiency and mass flow are the main objectives of the performance evaluation. Results show that the prediction error of the mass flow and ram efficiency is 2.31% and 0.54%, respectively. Comparison with the original geometry is used to prove the optimization capabilities of the proposed optimization method. The mass flow and ram efficiency are increased by 29.2% and 10.0%, respectively. In addition, the characteristics of the optimized submerged inlet geometry are numerically investigated. The numerical results are compared to the optimization results and indicate that this optimization method has high validity. The error of ram efficiency and mass flow is 0.30% and 0.66%, respectively. Consequently, this optimization method can be valuable to aircraft engineers-by providing a novel approach for the design of the submerged inlet.