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中文摘要：
以空气-水为介质对槽式孔板进行了湿气计量特性试验研究,提出了一种新的基于神经网络的槽式孔板湿气计量修正模型。模型以Lockhart-Martinelli参数X、气体弗劳德准数Frg、密度比Rd、孔径比β四个无量纲参数作为模型的输入,“虚高”OR作为输出。结果表明,在表压为0.25～0.35 MPa,X为0.02～0.6,Frg为0.5～2.7,β为0.5～0.75的测试范围内,模型能够很好地预测实际“虚高”,用新修正模型对由于液相存在而引入的气相流量误差进行修正后,气相流量相对误差在95%的置信度下小于±4%,明显优于其他槽式孔板湿气计量修正模型,可以满足生产计量的精度要求。
英文摘要：
The wet gas metering characteristics of slotted orifice meter were discussed by using air-water as media. A novel wet gas metering correction model based on BP neural network was proposed. In the model, Lockbart-Martinelli parameter X, gas Froude number, the gas to liquid density ratio Rd, and bore diameter ratio β are the inputs, and over-reading is the output. The results show that the new correction model can predict the over-reading accurately, and correct the liquid-induced gas flow rate prediction error of the wet gas flow to ＋4% at 95% confidence level under the conditions of pressure from 0. 25 MPa to 0.35 MPa, X from 0. 02 to 0. 6, gas Froude number from 0. 5 to 2. 7, and β from 0. 5 to 0. 75. The model is superior to other correction model and can satisfy the accuracy requirement of production metering.
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