Fast reconstruction of a wind field based on numerical simulation and machine learning

In recent years, under the influence of strong wind, trees and walls collapse, objects fall, and other situations occur from time to time, which seriously affect the safety of community residents. In traditional emergency rescue, the background wind field at the disaster site is unknown, and the accuracy of accident development assessment is affected. In the case of fire, gas leakage, strong wind, and other disasters, decision-makers and rescue teams cannot accurately locate the dangerous areas in the community because of their inability to rapidly obtain accurate background wind field information, which affects the accuracy of the judgment of the disaster scope and development trend. Key dangerous areas in the community under strong wind need to be identified.

In this study, the wind field of a community in the Shijingshan District of Beijing was taken as an example to conduct scene modeling. To generate the database of the community wind field, the wind field was generated by OpenFOAM, and a shell script was used for a batch of simulations. The speed at the feature points obtained by k-means clustering served as the input, and the wind field served as the output to train the neural network. The selected community feature points could represent the wind field information of the community. The feature point selection and neural network modeling were continuously optimized based on the training and prediction results until the accuracy met the requirements.

Taking the field data of 6 681 points predicted by 10 feature points as an example, the model training test results of 7 917 training wind fields and 2 026 testing wind fields were as follows: The average relative errors of the predicted values of speeds above 1 m/s in the x- and y-axes were 5.8% and 6.2%, respectively. Among them, the average relative error of model prediction between 1 m/s and 2 m/s is 11.9%, for model prediction between 2 m/s and 5 m/s was 6.0%, for model prediction between 5 m/s and 10 m/s was 3.2%, and for model prediction above 10 m/s was 3.5%.

Compared with the numerical simulation technology, the neural network model can rapidly generate the background wind field of the community based on the field location data. Compared with the time of the numerical simulation, the time of the neural network model to generate a field is significantly reduced. Unlike the existing neural network model, the proposed model takes actual community points as the feature points for model training and prediction, enabling the installation of sensors and the prediction of real-time wind fields. Therefore, people can organize risk prevention and emergency rescue according to the background wind field, which is of great significance for maintaining community safety.