The research focuses on the design space optimisation of National Advisory Committee for Aeronautics (NACA) submerged inlets through the formulation of a hybrid data fusion methodology. Submerged inlets have drawn considerable attention owing to their potential for good on-design performance, for example during cruise flight conditions. However, complexities due to the geometrical topology and interactions among various design variables remain a challenge. This research enhances the current design knowledge of submerged inlets through the utilisation of data mining and Computational Fluid Dynamics (CFD) methodologies, focusing on design space optimisation. A two-pronged approach is employed where the first step encompasses a low-fidelity model through data mining and surrogate modelling to predict and optimise the design parameters, while the second step uses the Design of Experiments (DOE) approach based on the CFD results for the candidate design geometry to construct a surrogate model with high fidelity for design refinement. The feasibility of the proposed methodology is demonstrated for the optimisation of the total pressure recovery of a NACA submerged inlet for the subsonic flight regime. The proposed methodology is found to provide good agreement between the surrogate and CFD-based model and reduce the optimisation processing time by half in comparison with conventional (global-based) CFD optimisation approaches.

中文翻译：

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基于数据融合的混合模型构建无人机水下进水口设计空间优化

该研究的重点是通过制定混合数据融合方法来优化美国国家航空咨询委员会 (NACA) 水下进气道的设计空间。水下进气道因其良好的设计性能潜力而备受关注，例如在巡航飞行条件下。然而，由于几何拓扑结构和各种设计变量之间的相互作用导致的复杂性仍然是一个挑战。这项研究通过利用数据挖掘和计算流体动力学 (CFD) 方法增强了当前的水下入口设计知识，重点是设计空间优化。采用双管齐下的方法，第一步包括通过数据挖掘和代理建模来预测和优化设计参数的低保真模型，第二步使用基于候选设计几何的 CFD 结果的实验​​设计 (DOE) 方法来构建具有高保真度的替代模型以进行设计细化。为优化亚音速飞行状态下 NACA 水下入口的总压力恢复，证明了所提出方法的可行性。发现所提出的方法在替代模型和基于 CFD 的模型之间提供了良好的一致性，并且与传统的（基于全局的）CFD 优化方法相比，优化处理时间减少了一半。为优化亚音速飞行状态下 NACA 水下入口的总压力恢复，证明了所提出方法的可行性。发现所提出的方法在替代模型和基于 CFD 的模型之间提供了良好的一致性，并且与传统的（基于全局的）CFD 优化方法相比，优化处理时间减少了一半。为优化亚音速飞行状态下 NACA 水下入口的总压力恢复，证明了所提出方法的可行性。发现所提出的方法在替代模型和基于 CFD 的模型之间提供了良好的一致性，并且与传统的（基于全局的）CFD 优化方法相比，优化处理时间减少了一半。