CAJ | 학술논문

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Fr_g、密度比R_d、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0.25～0.35MPa,X为0.02～0.6,Fr_g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.
이공기수위개질대조식공판진행료습기계량특성시험연구,제출료일충신적기우신경망락적조식공판습기계량수정모형.모형이Lockhart-Martinelli삼수X、기체불로덕준수Fr_g、밀도비R_d、공경비β사개무량강삼수작위모형적수입,"허고"OR작위수출.결과표명,재표압위0.25～0.35MPa,X위0.02～0.6,Fr_g위0.5～2.7,β위0.5～0.75적측시범위내,모형능구흔호지예측실제"허고",용신수정모형대유우액상존재이인입적기상류량오차진행수정후,기상류량상대오차재95%적치신도하소우±4%,명현우우기타조식공판습기계량수정모형,가이만족생산계량적정도요구.
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, Lockhart-Martinelli parameter X, gas Froude number, the gas to liquid density ratio R_d, 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.