## 4 Discussion  
Accurate prediction of the high spatiotemporal resolution 3D ship airwake field within the glide path corridor is a complex multidisciplinary problem. The main challenges in this research include computational resource limitations, data scarcity, and the nonlinear variability of dynamic environments. Existing studies have several limitations:  
* Computational efficiency constraints: Sole reliance on CFD for ship airwake prediction results in high computational complexity, making it difficult to meet real-time requirements. * High-resolution bottleneck: While DWL is used for ship airwake measurements, it faces trade-offs between distance resolution and measurement accuracy, as well as between temporal resolution and scanning coverage. * Insufficient model integration: The deep integration of CFD simulation data, DWL measurement data, and data-driven artificial intelligence models is still in its early stages, and no efficient prediction framework has been established for the complex ship airwake. To address these issues, this study proposes a prediction model for high spatiotemporal resolution 3D ship airwake along the glide path, integrating CFD simulation, BP neural networks, and DWL technologies. The main steps include the following:  
Figure 14: Prediction results of the vertical wind BP model along the glide path at various distances behind the stern (**a**–**e**). Step 1: Using CFD methods to generate high spatial resolution (3 m) sample data for the 3D ship airway along the glide path. Step 2: Constructing high-precision BP neural network prediction models by using CFD sample data to guide the learning of the ship airway characteristics. Step 3: Utilizing high temporal resolution (3 Hz) inflow measurement data from DWL as input to the BP prediction models, thereby enabling precise prediction of the high spatiotemporal resolution 3D ship airway in the glide path. The prediction framework proposed in this study offers an effective solution for predicting high spatiotemporal resolution (3 m, 3 Hz) 3D ship airflow fields within the glide path corridor. The integration of CFD, DWL, and BP neural networks provides a novel approach to addressing the trade-off between high resolution and high temporal requirements. This fusion of new technologies not only presents new opportunities for research on complex airflow field prediction but also lays a technical foundation for the future design and operational capability enhancement of naval vessels. However, this integrated method still faces several challenges. For instance, in the CFD simulation, the simulation results of the carrier's airflow field have not been experimentally verified. Although the use of unverified CFD data can demonstrate the theoretical and operational feasibility for training/optimizing the neural network, discrepancies between CFD simulations and actual physical conditions may lead to significant errors; thus, the neural network trained with unverified CFD data cannot be used for practical engineering applications. Future studies could incorporate CFD simulation data validated or corrected through wind tunnel experiments, full-scale ship measurements, or mesoscale simulations to improve the accuracy and reliability of this prediction model in practical applications. In terms of using neural networks to predict airflow fields, the neural network is trained and optimized using steady CFD samples instead of transient CFD results. The unsteady characteristics of the ship airway are imply that the wind speed value at a certain position on the glide path is not a stable constant when the same inflow conditions are present, but will change as the computation time progresses. When the transient CFD results are used as training samples, where there is no stable relationship between the input (inflow speed and direction) and the output (3D wind speed), the training and optimization of the BP prediction model will be a disaster, because the input of the BP neural network is deterministic, but its output is not.