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• W2950062444 abstract "Two-phase flow is a regime that commonly existed in many applications of process industry, on which the technique of ultrasonic tomography (UT) could provide reliable measurement with benefits of non-invasion, non-radiation and low-cost. In order to provide considerable visualization of low acoustic impedance contrast configurations in two-phase flow, a regular weighted simultaneous algebraic reconstruction technique (RW-SART) using continuous-wave excitation is proposed and presented in detail. With measurement strategy and formulized reconstruction model introduced, the measurement system is introduced with modification on continuous-wave excitation module and fan-beam angle transducers. A regularized weighted least square (RWLS) framework combining total variation (TV) regularization and SART reconstruction is derived and presented in detail to address the problem that under-determined and ill-conditioned situation couldn’t be resolved simultaneously. Both numerical simulation and experimental investigation are conducted to validate the feasibility of continuous-wave excitation and the improvement of proposed reconstruction strategy, from which the distribution of oil-water two-phase flow is effectively reconstructed. The implementation of measurement system provides an option of continuous high-voltage excitation and measurement in UT. By utilizing both the geometric information and penalty constraint, the proposed reconstruction approach provides a framework that introduces regularization information to the algebraic iteration reconstruction. Consequently, it has better reconstruction result than TV, Landweber, L1, filtered back projection (FBP) and SART method." @default.
• W2950062444 created "2019-06-27" @default.
• W2950062444 creator A5068911557 @default.
• W2950062444 creator A5088664989 @default.
• W2950062444 date "2019-06-17" @default.
• W2950062444 modified "2023-09-28" @default.
• W2950062444 title "Continuous-wave ultrasonic tomography for oil/water two-phase flow imaging using regularized weighted least square framework" @default.
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• W2950062444 doi "https://doi.org/10.1177/0142331219853073" @default.
• W2950062444 hasPublicationYear "2019" @default.
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