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.

由于冲压空气的需求不断增加，优化进气口的结构非常重要。本文通过混合最小二乘支持向量机（LS-SVM）预测模型和改进的遗传算法-粒子群优化（GA-PSO），使用混合模型对淹没进气口进行结构优化。入口形状是通过更改三维几何参数来控制的。坡道角度，宽深比和坡道长度在此优化过程中起着重要作用。活塞效率和质量流量是性能评估的主要目标。结果表明，质量流量和柱塞效率的预测误差分别为2.31％和0.54％。通过与原始几何图形的比较来证明所提出的优化方法的优化能力。质量流量和冲压效率分别提高了29.2％和10.0％。此外，还通过数值研究了优化的水下进水口几何形状的特性。将数值结果与优化结果进行比较，表明该优化方法具有较高的有效性。柱塞效率和质量流量的误差分别为0.30％和0.66％。因此，这种优化方法对于飞机工程师来说是有价值的，因为它为水下进气道的设计提供了一种新颖的方法。30％和0.66％。因此，这种优化方法对于飞机工程师来说是有价值的，因为它为水下进气道的设计提供了一种新颖的方法。30％和0.66％。因此，这种优化方法对于飞机工程师来说是有价值的，因为它为水下进气道的设计提供了一种新颖的方法。