For acquiring the flow regime information of two-phase flow,a flow regime identification method using the Hilbert-Huang Transform (HHT) combined with Radial Basis Function neural networks was put forward.In this study...For acquiring the flow regime information of two-phase flow,a flow regime identification method using the Hilbert-Huang Transform (HHT) combined with Radial Basis Function neural networks was put forward.In this study,oil-gas two-phase flow in horizontal pipe was taken as the experimental object, differential pressure signals coming from Venturi tube were handled by Hilbert-Huang Transform,and characteristic vector closely associated with the flow regime were obtained.Flow regime was identified by using Radial Basis Function neural networks.While oil flux was in the range of 4.2 to 7.0 m3·h -1 and gas flux was 0 to 30 m3·h -1, this method showed high identification precision for bubble flow, slug flow, churn flow and annular flow et al.The experimental study showed that this method could precisely identify the flow regime and could be used easily.展开更多
Oil-air two-phase flow measurement was investigated with a Venturi and void fraction meters in this work. This paper proposes a new flow rate measurement correlation in which the effect of the velocity ratio between g...Oil-air two-phase flow measurement was investigated with a Venturi and void fraction meters in this work. This paper proposes a new flow rate measurement correlation in which the effect of the velocity ratio between gas and liquid was considered. With the pressure drop across the Venturi and the void fraction that was measured by electrical capacitance tomography apparatus, both mixture flow rate and oil flow rate could be obtained by the correlation. Experiments included bubble-, slug-, wave and annular flow with the void fraction ranging from 15% to 83%, the oil flow rate ranging from 0.97 kg/s to 1.78 kg/s, the gas flow rate ranging up to 0.018 kg/s and quality ranging nearly up to 2.0%. The root-mean-square errors of mixture mass flow rate and that of oil mass flow rate were less than 5%. Furthermore, coefficients of the correlation were modified based on flow regimes, with the results showing reduced root-mean-square errors.展开更多
文摘For acquiring the flow regime information of two-phase flow,a flow regime identification method using the Hilbert-Huang Transform (HHT) combined with Radial Basis Function neural networks was put forward.In this study,oil-gas two-phase flow in horizontal pipe was taken as the experimental object, differential pressure signals coming from Venturi tube were handled by Hilbert-Huang Transform,and characteristic vector closely associated with the flow regime were obtained.Flow regime was identified by using Radial Basis Function neural networks.While oil flux was in the range of 4.2 to 7.0 m3·h -1 and gas flux was 0 to 30 m3·h -1, this method showed high identification precision for bubble flow, slug flow, churn flow and annular flow et al.The experimental study showed that this method could precisely identify the flow regime and could be used easily.
基金Project (No. 2001AA413210) supported by the Hi-Tech Researchand Development Program (863) of China
文摘Oil-air two-phase flow measurement was investigated with a Venturi and void fraction meters in this work. This paper proposes a new flow rate measurement correlation in which the effect of the velocity ratio between gas and liquid was considered. With the pressure drop across the Venturi and the void fraction that was measured by electrical capacitance tomography apparatus, both mixture flow rate and oil flow rate could be obtained by the correlation. Experiments included bubble-, slug-, wave and annular flow with the void fraction ranging from 15% to 83%, the oil flow rate ranging from 0.97 kg/s to 1.78 kg/s, the gas flow rate ranging up to 0.018 kg/s and quality ranging nearly up to 2.0%. The root-mean-square errors of mixture mass flow rate and that of oil mass flow rate were less than 5%. Furthermore, coefficients of the correlation were modified based on flow regimes, with the results showing reduced root-mean-square errors.
