A coupling numerical methodology has been developed using the Navier–Stokes solver for ship/rotor flowfield simulation during the helicopter shipboard launch. The steady rotor model (SRM) based on the momentum source approach and unsteady rotor model (URM) based on the moving overset mesh method are respectively employed to account for rotor operations. Studies of coupling ship/rotor flowfield on two typical ships highlight complex interactions over the flight deck. It is shown that the rotor-shipboard vortex interaction and recirculation zone are more serious for small-sized-deck destroyers, while on the straight-through-deck ship the helicopter operations are sensitive to asymmetric distribution of lateral velocity caused by the island. Qualitative and quantitative comparisons in terms of vorticity and velocity between SRM and URM solvers have been conducted. The results demonstrate that both methods can capture complex interactions well. The velocity difference is within 1 m/s in most flowfield areas, except the rotor-wake dominant region. With the consideration of computational efficiency, the momentum source approach could be used to effectively conduct the study of helicopter shipboard operations.

A coupled Eulerian–Lagrangian methodology was developed in this paper in order to provide an efficient and accurate tool for rotor wake and flow prediction. A Eulerian-based Reynolds-averaged Navier–Stokes (RANS) solver was employed to simulate the grid-covered near-body zone, and a grid-free Lagrangian-based viscous wake method (VWM) was implemented to model the complicated rotor-wake dynamics in the off-body wake zone. A carefully designed coupling strategy was developed to pass the flow variables between two solvers. A sample case of a forward flying rotor was performed first in order to show the capabilities of the VWM for wake simulations. Next, the coupled method was applied to rotors in several representative flight conditions. Excellent agreement regarding wake geometry, chordwise pressure distribution and sectional normal force with available experimental data demonstrated the validity of the method. In addition, a comparison with the full computational fluid dynamics (CFD) method is presented to illustrate the efficiency and accuracy of the proposed coupled method.

As a high resolution airload with accurate rotor wake is pivotal for rotor BVI (Blade-vortex interaction) analysis, a hybrid method with combined Navier-Stokes equation, viscous wake model, and FW-H (Ffowcs Williams-Hawkings) equation is developed for BVI airload and noise in this paper. A comparison with the CFD (Computational Fluid Dynamics)/FW-H method for the AH-1/OLS (Operational Load Survey) rotor demonstrates its capability for favorable accuracy and high computation efficiency. This paper further discusses the mechanisms for the impacts of four flight parameters (i.e., tip-path-plane angle, thrust coefficient, tip Mach number, advance ratio) on BVI noise. Under the BVI condition, several BVI events concurrently occur on the rotor disk. Each interaction has a distinct radiation direction which depends on the interaction azimuth, and its noise intensity is highly associated with the characteristic parameters (e.g., miss-distance, interaction angle, vortex strength). The BVI noise is dominated by the interactions at 30–90° in azimuth on the advancing side, of which the wake angle range is from 180° to 540°. Furthermore, the tip-path-plane angle, thrust coefficient, and tip Mach number change the noise intensity mainly via miss-distance, interaction angle, and vortex strength, but for different advance ratios, the noise intensity and propagation direction are more dependent on the interaction angle and interaction azimuth.

Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction of velocity fields and aerodynamic coefficients of TEF-equipped airfoils. The proposed model combines a Swin Transformer (Swin-T) for flow field prediction with a multi-layer perceptron (MLP) dedicated to lift coefficient prediction. Both networks undergo gradient updates through the shared encoder component of the Swin Transformer. Such a trained network model for computational fluid dynamics simulations is both effective and robust, significantly improving the efficiency of complex aerodynamic shape design optimization and flow control. The study further investigates the impact of integrating multi-task learning loss functions, skip connections, and the network’s structural design on prediction accuracy. Additionally, the effectiveness of deep learning in improving the aerodynamic simulation efficiency of airfoils with TEF is examined. Results demonstrate that the multi-task deep learning approach provides accurate predictions for TEF airfoil flow fields and lift coefficients. The strategic combination of these tasks during network training, along with the optimal selection of loss functions, significantly enhances prediction accuracy compared with the single-task network. In a specific case study, the MT-Swin-T model demonstrated a prediction time that was 1/7214 of the time necessitated by CFD simulation.

The low-frequency in-plane thickness noise generating from the displacement of air by rotating blades has an important influence on helicopter detection. An on-blade control technique to reduce thickness noise is developed in this paper based on the principle of sound field cancellation. Following the theoretical study on the mechanism of thickness noise reduction using in-plane unsteady force, a 2-m diameter rotor with an active trailing-edge winglet are designed and tested in a fully anechoic chamber. The winglet installed on the outboard blade is used to generate the unsteady force and anti-noise to counteract the thickness noise. The results demonstrate that effective reduction of thickness noise up to 3 dB is achieved in the front of the rotor when the winglet is under the one-harmonic control with $3^{°}$ of deflection angle. Moreover, the experiments of frequency, amplitude, and phase scanning are carried out to study the parametric effects of winglet motions on noise reduction. The ability of noise reduction is proportional to the deflection amplitude of the winglet in each frequency. The control phase determines where noise can be reduced. There is an optimal phase angle at each frequency to minimize the noise at the observations, and it varies with different frequencies. The relationship among observation position, control phase, and frequency is derived, and the approximate expression of the optimal phase is presented.