# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 1 Introduction
In the design, analysis, and optimization of aerodynamics, flow fields are typically simulated using computational fluid dynamics (CFD). However, CFD simulations are often computationally expensive, memory-intensive, and time-consuming, which limits the exploration of the design space and possibilities for interactive design. [PERSON] et al. employed a convolutional neural network (CNN) to construct an approximate model capable of predicting non-uniform steady laminar flows in 2D or 3D domains in real time, while exploring various geometric representations and network architectures. The results demonstrate that the CNN is two orders of magnitude faster than a GPU-accelerated CFD solver and four orders of magnitude faster than a CPU-based CFD solver in predicting the velocity field, all while maintaining a low error rate. This approach offers a valuable reference for real-time design iterations in the early stages of design development [19]. [PERSON] et al. sought to enhance the full-envelope acceleration performance of a gas turbine engine (GTE) by integrating an input selection strategy (CIS) with a multilayer perceptron (MLP). The CIS strategy is employed to identify the most relevant inputs, which, combined with a weighted integral loss function, results in the design of a highly accurate and robust full-envelope acceleration schedule (FEAS). The MLP further improves the accuracy of the FEAS. Compared to other classical machine learning models, the MLP network demonstrates superior effectiveness in enhancing FEAS accuracy [20]. [PERSON] et al. developed an MLP model for accurately predicting the stress and temperature of a compressor disk using artificial neural network techniques. Engine measurable parameters were used as input for the MLP model, and the back-propagation (BP) neural network algorithm was applied for training. The study also examined the impact of various optimization algorithms on the model's prediction performance. The results indicate that the model's predictions are highly consistent with traditional finite element calculations, with relative deviations under 1%, coefficients of determination exceeding 0.95, and root-mean-square errors (JRMSE) within 5. Additionally, the computational time was reduced from hours to minutes or seconds, significantly enhancing the efficiency of the engineering applications. This approach is essential for the life analysis of engine compressor disks and provides a critical foundation for future engineering practices [21]. Existing studies have demonstrated that artificial-neural-network-based modeling methods are increasingly being adopted by scholars in various fields of research. However, there is relatively limited literature in the area of structural stress prediction, where convolutional neural networks (CNNs) are commonly used for prediction modeling. Although CNNs perform well in field prediction tasks, they impose strict requirements on the input and output data structures, which limits the model's flexibility, particularly when handling unstructured data or multidimensional feature combinations. This increases the complexity of training and the time required. While traditional machine learning methods offer good performance and speed in regression tasks, they face challenges in dealing with complex nonlinear relationships and high-dimensional data, often necessitating intricate feature analysis and processing, which not only increases the workload but can also result in information loss. In the context of aircraft structural health management, real-time, reliable data sources and rapid prediction capabilities are essential. The MLP possesses strong fitting capabilities and excels at complex nonlinear mapping [22,23]. Compared to other machine learning algorithms, MLP is more adept at capturing intricate nonlinear relationships between input and output when processing high-dimensional data.