Mach Number Prediction and Analysis of Multi-Mode Wind Tunnel System

Guo, Jun; Zhang, Ran; Cui, Xiaochun; Huang, Xin; Zhao, Luping

doi:10.1109/cac51589.2020.9327683

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Taking Mode 5 as an example, 70% of the data is selected for training, and 30% of the data is tested. The prediction results of the proposed model are compared with the prediction results of the BP neural network and PLS regression prediction adopted in previous related work [25], as shown in Figure 6. As seen from Figure 6a, the changing trend of the predicted Mach number is closer to the actual Mach number via the proposed method.…”

Section: Ma Prediction Results Based On Narx-bp Modelmentioning

confidence: 99%

A Novel Model Prediction and Migration Method for Multi-Mode Nonlinear Time-Delay Processes

2023

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Most industrial processes have nonlinear and time-delay characteristics leading to difficulty in prediction modeling. In addition, the working conditions of most industrial processes are complex, which results in multiple modes. Testing and modeling for each mode is a waste of time and resources. Therefore, it is urgent to complete model migration between different modes. In this work, a new prediction model, a nonlinear autoregressive model with exogenous inputs and back propagation neural network (NARX-BP), is proposed for the nonlinear and time-delay processes, where the input data order of the model is determined by the feedforward neural network (FNN) method, and the nonlinear relation is realized by the BP neural network. For the multi-mode characteristic, a new migration optimization algorithm, input–output slope/bias correction and differential evolution (IOSBC-DE), is provided for using a small amount of data under a new mode to correct the slope and bias of the relationship between the input and output variables through DE. The modeling and migration methods are applied to a wind tunnel system, and the simulation result shows the effectiveness of the proposed method.

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Section: Ma Prediction Results Based On Narx-bp Modelmentioning

confidence: 99%

A Novel Model Prediction and Migration Method for Multi-Mode Nonlinear Time-Delay Processes

2023

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“…This stage is the most important part of the whole blowing test and requires the controller to use a well-designed control system to adjust the Mach number to the required accuracy range. However, the change pattern of the wind tunnel flow field is rapid and complex, and improving the control accuracy of the Mach number has been a difficult problem for wind tunnel control [5].…”

Section: Introductionmentioning

confidence: 99%

NARX-Elman Based Mach Number Prediction and Model Migration of Wind Tunnel Conditions

2023

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Mach number, as an important index to judge the system performance in wind tunnel tests, its stability determines the quality of the flow field of this wind tunnel and needs to be controlled precisely. Due to the complex process in the wind tunnel test, it is difficult to ensure the smooth operation of the Mach number by the traditional control strategy, therefore, a Mach number prediction model based on a nonlinear autoregressive exogenous model (NARX)-Elman is proposed in this paper. Firstly, the NARX model is adopted as the basic framework of this model, the false nearest neighbor (FNN) is used for model order solving, and the Elman network is used for dynamic nonlinear fitting of the model. Secondly, considering the wind tunnel system with multiple conditions and the high cost of conducting complete data experiments, a new model migration method, the input-output slope/bias correction-genetic algorithm (IOSBC-GA), is proposed, which uses IOSBC method as the basic framework to construct the migration model based on the historical condition model and a small amount of data of the new condition, and uses GA to find the slope of deviation in the new model and correct the input-output relationship between the old and new conditions, so as to establish the model for the new condition. By comparing the model respectively with the traditional algorithm and the model built without migration, the root mean square error (RMSE) and the maximum deviation (MD) of this model are less than 0.001, indicating that the model has high prediction accuracy.

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KPLS-Based Mach Number Prediction for Multi-Mode Wind Tunnel Flow System

Pan

Zhao

2022

Processes

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In wind tunnels, the Mach number needs to be precisely monitored and controlled. It is difficult to obtain the Mach number directly online, especially when the wind tunnel system is operating in multiple modes. To deal with this problem, a Mach number prediction algorithm based on the kernel partial least squares method is proposed for multi-mode wind tunnel systems. First, in order to reflect real-time changes, the time-slice partial least squares regression method is adopted. Then, in order to enable the model to represent information about the whole working mode divided by key process variables, the mean-value partial least squares model is established and is compared with the time-slice model. Then, considering that wind tunnel systems exhibit strong nonlinear characteristics, the kernel partial least squares method, which is suitable for nonlinear systems, is used to predict the Mach number. The results show that the mean-value model is better than the time-slice model, the models for single modes show better prediction abilities than those for multiple modes, and the kernel partial least square method is more suitable for wind tunnel systems than the partial least square method.

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Mach Number Prediction and Analysis of Multi-Mode Wind Tunnel System

Cited by 4 publications

References 7 publications

A Novel Model Prediction and Migration Method for Multi-Mode Nonlinear Time-Delay Processes

A Novel Model Prediction and Migration Method for Multi-Mode Nonlinear Time-Delay Processes

NARX-Elman Based Mach Number Prediction and Model Migration of Wind Tunnel Conditions

KPLS-Based Mach Number Prediction for Multi-Mode Wind Tunnel Flow System

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