An important development for the next generation of civil aircraft is the significant reduction of fuel consumption through the integration of high-bypass-ratio (BPR) engines. However, the large diameter of such engines poses ground clearance challenges for traditional wing-mounted configurations. The over-the-wing engine mount (OWEM) configuration avoids this issue by installing engines above the wings. Furthermore, mounting engines above the wing’s trailing edge offers inherent aerodynamic advantages, such as weakening the shock wave intensity on the wing’s upper surface. Nevertheless, compared with conventional configurations, the OWEM configuration is more sensitive to upper-surface flow, making it prone to lift degradation and strong engine-wing interference. During the design process, critical interactions among wing shape changes, wing static aeroelastic deformation, engine installation position, and engine inlet/exhaust effects must be fully considered, which pose significant challenges to traditional design methods based directly on cruise configuration. To address these challenges, this paper establishes a forward aerodynamic optimization design framework based on the open-source multidisciplinary coupled optimization platform OpenMDAO and an open-source adjoint solver. The framework simultaneously accounts for the influence of the aforementioned multidisciplinary coupling effects on the wing upper-surface flow during optimization, enabling a forward design workflow that directly optimizes from the jig shape to the cruise shape. The proposed method is then applied to the drag reduction problem of an OWEM configuration. Results show an 11.14% drag reduction during cruise without loss of lift and while satisfying structural stress constraints. Optimization results indicate that placing the engine near the shock wave location of the clean wing and adjusting its vertical position to minimize interference with the wing flow helps reduce shock wave drag and interference drag without excessively compromising lift. Moreover, coupling wing shape optimization with static aeroelastic deformation during engine positioning optimization improves the spanwise load distribution and reduces induced drag, thereby forming an overall design that effectively leverages the advantages of the OWEM configuration. This study aims to provide a reference for the engineering application of similar OWEM configurations in the future.

The current widely used turbulence models often exhibit significant discrepancies with experimental results for largely separated flows primarily due to the strong adverse pressure gradients present in the flows, which violate the fundamental assumptions of turbulence models. To enhance the computational accuracy for largely separated flow problems, it is necessary to modify the turbulence models. A combined approach of field inversion and data-driven methods is proposed to introduce a neural network for the correction of the Spalart-Allmaras (S-A) one-equation model based on Liutex. Initially, correction coefficients for the S-A one-equation model generation terms are obtained through field inversion with discrete adjoint. Feature selection indicates that Liutex exhibits the highest correlation with the correction coefficients for the S-A one-equation model generation terms and is suitable as an input for the neural network. Subsequently, Liutex and other variables are used as inputs to construct a neural network to approximate the correction coefficients for the S-A model generation terms, establishing the neural network structure for the generation term correction coefficients. This correction method is validated through separated flow results for the S809 and S814 airfoils, demonstrating the ability to significantly improve the computational accuracy of largely separated flows.