基于Fluent和LSTM神经网络的超声波测风仪阴影效应补偿研究

超声波测风仪因其结构坚固,维修成本低等优点,在气象、生活及农业等领域有着广泛应用。但由于其结构特点造成的阴影效应,会导致其风速测量精度下降,是当前测风领域中不可忽视的问题。针对该问题,提出一种基于Fluent软件以及LSTM长短期记忆神经网络的超声波阴影效应的补偿算法,对不同风速风向以及不同温度下的阴影效应进行补偿。利用Fluent仿真得到样本数据完成LSTM预测模型训练;基于Fluent仿真数据对SVR和MLR等模型与LSTM模型对超声波测风仪阴影效应进行对比实验,验证LSTM算法模型的有效性及优越性;通过风洞数据对LSTM神经网络修正算法的可行性进一步验证。实验结果表明:该算法可对阴影效应所造成的误差进行有效补偿,其精确度得到显著提高,为减小超声波测风仪的阴影效应提供了一定的参考价值。

SHADOW EFFECT COMPENSATION OF ULTRASONIC WIND MEASURER BASED ON FLUENT AND LSTM NEURAL NETWORK

Because of its strong structure and low maintenance cost,ultrasonic anemometer has been widely used in meteorology,life and agriculture.However,due to the shadow effect caused by its structural characteristics,the accuracy of wind speed measurement will be reduced,which is a problem that cannot be ignored in the field of wind measurement.In order to solve this problem,this paper proposed a compensation algorithm based on Fluent software and LSTM long-term and short-term memory neural network to compensate the shadow effect under different wind speed,wind direction and temperature. The LSTM prediction model was trained by using the sample data obtained by fluent simulation. The shadow effects of the SVR and MLR models were compared with the LSTM model proposed in this paper. The validity and superiority of the LSTM algorithm model were verified. The feasibility of the LSTM neural network correction algorithm was further validated by wind tunnel data.The experimental results show that the proposed prediction model based on Fluent and LSTM neural network can effectively compensate the error caused by the shadow effect,and its accuracy is significantly improved,which provides a certain reference value for reducing the shadow effect of the ultrasonic anemometer.