基于数值模拟和机器学习的风场快速重构方法

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

近年来受大风天气影响,树倒墙塌、高空坠物、广告牌散落等情况时有发生,严重影响社区居民安全,迫切需要识别出大风天气下社区内重点危险区域并加以防范。该文以北京市石景山区某社区风场为例进行场景建模,利用数值模拟与机器学习寻找社区特征点,完成特征点风速数据到风场数据的预测。以10个特征点数据预测6 681个点的风场数据为例,基于7 917个训练风场、 2 026个测试风场的模型训练测试结果表明:东西方向1 m/s以上速度的预测值平均相对误差为5.8%,南北方向1 m/s以上速度的预测值平均相对误差为6.2%。采用该方法可以快速获得满足精度要求的社区背景风场以指导社区风险防范、应急决策与救援,对于保障社区安全具有重要意义。

[Objective]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.[Methods]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.[Results]Taking the field data of 6681 points predicted by 10 feature points as an example,the model training test results of 7917 training wind fields and 2026 testing wind fields were as follows:The average relative errors of the predicted values of speeds above 1m/s in the x-and y-axes were 5.8%and 6.2%,respectively.Among them,the average relative error of model prediction between 1m/s and 2m/s is 11.9%,for model prediction between 2m/s and 5m/s was 6.0%,for model prediction between 5m/s and 10m/s was 3.2%,and for model prediction above 10m/s was 3.5%.[Conclusions]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.