# Prediction Method of Unsteady Flow Load of Compressor
###### Abstract
[PERSON] [16] carried out a detailed experimental study on the stall of a four-stage low-speed compressor, focusing on blade aerodynamic excitation. Under various working conditions, the rotor-stator interaction has a great influence on the blade excitation force. At present, the compressor operating conditions are complex and changeable, and the vibration safety problem is prominent. For the study of compressor blades, the accurate and rapid prediction of the unsteady forces is particularly critical. With the rapid development of neural network technology, the combination of CFD technology and intelligent algorithm has been born. [PERSON] [17] reviewed the advantages and development trends of the combination of CFD technology and machine learning methods as well as the interdisciplinary approaches. It has been concluded that the method of machine learning has a great positive effect on the application and popularization of computational fluid dynamics. [PERSON] [18] combined a deep neural network (DNN) to study the flow characteristics. [PERSON] [19] built a simplified DNN model based on DNS computing data. The research shows that the use of a network is helpful to reveal the characteristics of Reynolds stress. [PERSON] [20] found through research that using a DCNN network to establish a composite model can be used to break through the problem of multivariable model association. Using an HDNN network, [PERSON] [21] conducted a flow field analysis under various transient conditions, and the error between the DNN prediction results and the numerical calculation values was very small. [PERSON] [22] used a CNN and an LSTM to construct a composite model to effectively predict the unsteady flow, while [PERSON] [23] used a multi-core neural network to improve the calculation accuracy of the model. [PERSON] [24] used an ASA and an RRBF to build a flow field prediction model, which was more accurate than the single RRBF model was. [PERSON] [25] built a blade excitation analysis model based on machine learning, which could achieve the fast and accurate prediction of unsteady aerodynamic forces. [PERSON] [26] adopted the DCNN network which can precisely give rich flow field information and performance characteristics within 3 ms of training to enhance the design optimization problem of the end wall profile of a turbine stator blade. [PERSON] [27] also applied the DCNN structure in the field of airfoil design optimization to generate fundamental airfoil profiles with only three basic parameters, with high prediction accuracy when it was compared to the traditional machine learning methods. [PERSON] [28] established a data-driven deep learning off-design aerodynamic prediction model to improve the inefficiency of the traditional off-design analyses for a S-CO\({}_{2}\) centrifugal turbine based on a deep CNN structure. [PERSON] [29] proposed a real-time data-driven high-fidelity wing shape optimization method to meet the interactive requirements of aircraft design, rather than using expensive computational fluid dynamics simulations, which were validated in several single point, multi-point, and multi-objective wing design optimization problems. [PERSON] [30] compared different deep learning methods for energy conversion and heat transfer problems, concluding that the CNN was a promising surrogate model due to its acceptable prediction error and ability to reconstruct the physical fields. It should be noted that at present, the application of artificial intelligence methods in fluid mechanics mainly focuses on the flow field characteristics, but there is little research on the application of network modeling to unsteady aerodynamic loads. In this paper, a machine learning algorithm and a unsteady flow analysis method were combined to establish a compressor blade flow excitation prediction model. The unsteady flow characteristics can be captured by small batch of numerical simulation results data training.