A new contactless bubble/slug velocity measurement method of the gas-liquid two-phase flow in small channels is proposed by extending the contactless conductivity detection technique to the contactless impedance detection (CID) technique. Two kinds of CID sensors with different constructions (tubular construction and radial construction) are developed to obtain the total impedance information (the real part, the imaginary part, and the amplitude) of the gas-liquid two-phase flow. Two bubble/slug velocity measurement systems with different constructions, the tubular three-electrode velocity measurement system and the radial four-electrode velocity measurement system, are developed. With the obtained total impedance information, three initial estimation values of the bubble/slug velocity are determined by the principle of the cross correlation velocity measurement. Then, with the real-time flow pattern identification result implemented by the fuzzy C-means, a corresponding velocity measurement model [which is the linear combination of the three initial estimation values of the bubble/slug velocity and is developed by the penalty function method and the Davidon-Fletcher-Powell algorithm] is applied to obtain the final bubble/slug velocity. Two groups of the bubble/slug velocity measurement system prototypes with three different inner diameters are developed. The bubble/slug velocity measurement experiments are carried out. The experimental results show that the proposed method is effective. For the two bubble/slug velocity measurement systems with different constructions, the maximum relative errors of the bubble/slug velocity measurement are all less than 4.5%. The research results also indicate that making full use of the total impedance information is an effective approach to improve the accuracy of the velocity measurement.

Combining C ⁴ D technique and cross-correlation velocity measurement technique, a new measurement method, which is suitable for the bubble/slug velocity measurement in millimeter-scale pipelines, is proposed. Based on the series resonance principle and the simulated inductor technique, a new C ⁴ D sensor is developed. With two conductance signals obtained by two new C ⁴ D sensors (the upstream sensor and the downstream sensor), the bubble/slug velocity measurement is implemented by the cross-correlation velocity measurement technique. Experiments are carried out in three pipelines with different inner diameters of 4.50, 5.46, and 6.44 mm, respectively. The experimental results show that the proposed bubble/slug velocity measurement method is effective, the development of the new C ⁴ D sensor is successful, and the velocity measurement accuracy is satisfactory. The relative error of bubble velocity measurement is less than 5.41% and the relative error of slug flow velocity measurement is less than 4.90%.

An image reconstruction algorithm, which is developed for a 12-electrode capacitively coupled electrical resistance tomography (CCERT) system under 2-electrode excitation strategy, is proposed. Based on L-curve and Reginska’s method, truncated singular value decomposition (TSVD) is used to reconstruct the initial image. The algebraic reconstruction technique (ART) algorithm is used to obtain the final reconstructed image. Image reconstruction experiments are conducted by a 12-electrode CCERT system. The proposed algorithm (TSVD + ART) is compared with conventional linear back projection (LBP), Tikhonov, Landweber, ART, simultaneous iterative reconstruction technique (SIRT), total variation (TV), conjugate gradient (CG), and TSVD to evaluate its image reconstruction performance. Image reconstruction results show the proposed algorithm (TSVD + ART) can effectively exploit the advantages of 2-electrode excitation strategy and hence realize higher quality image reconstruction. Under 2-electrode excitation strategy, the proposed algorithm has an obvious advantage over conventional image reconstruction algorithms. Under 1-electrode excitation strategy, the image reconstruction performance is comparable or slightly improved compared with that of conventional image reconstruction algorithms. Image reconstruction results also indicate the TSVD is effective to obtain the initial reconstructed image. The quality of the initial reconstructed image can be significantly improved compared with that of classic LBP, either under 2-electrode excitation strategy or 1-electrode excitation strategy.

In this work, a new dual-frequency imaging framework of capacitively coupled electrical impedance tomography (CCEIT) is presented. Unlike conventional single-frequency imaging and recently emerged multifrequency imaging, the dual-frequency imaging adopts two different working frequencies to obtain the real part and the imaginary part of the impedance, respectively. With the real part image and the imaginary part image reconstructed at the two frequencies, the framework further introduces image fusion to obtain the fused image. To achieve the optimal selection of the two frequencies, data collection in a wide frequency range is carried out with a 12-electrode CCEIT sensor, an impedance analyzer, and a computer to obtain the real part and the imaginary part measurements. The multifrequency data are then analyzed in depth, and other two aspects, including the sensitivity distribution and the imaging quality at different frequencies, are also investigated. Research results show that a low working frequency is recommended for the real part, while a relatively high working frequency is recommended for the imaginary part. Within the investigated frequency range of 200 kHz–20 MHz, the working frequencies for the real part and the imaginary part are optimized under the investigated distribution setups. Results of verification experiment show that the proposed framework is effective. Compared with single-frequency CCEIT, dual-frequency CCEIT with the two optimized frequencies has much better imaging performance.

Based on density peaks clustering (DPC) and K-means, this work aims to propose a new image reconstruction algorithm for capacitively coupled electrical resistance tomography (CCERT). To better apply DPC and K-means to CCERT, DPC is improved by automatically selecting the cluster centers and K-means is improved by introducing a post-processing in consider of the non-uniform sensitivity characteristic in the sensing area. With the proposed algorithm, linear back projection (LBP) is adopted to obtain the initial image. With the initial image, the improved DPC is adopted to identify the number of targets and get the region of each target. The improved K-means is adopted to determine the gray level threshold in the region of each target according to the distance between the centroid of the target and the center of the pipe. The final image is obtained by gray level threshold filtering. Image reconstruction experiments are carried out by a 12-electrode CCERT system. The experimental results verify the effectiveness of the proposed image reconstruction algorithm. And, the improvements of DPC and K-means are successful. Compared with conventional image reconstruction algorithms, the proposed image reconstruction algorithm could get better image reconstruction results with less manual intervention.

An analytical method for 3D sound source localization based on a five-element microphone array is proposed in this work. With four time-difference-of-arrival (TDOA) values, the proposed method realizes the sound source localization by analytical solution. A new five-element microphone array configuration (which is characterized in that four microphones are in a same plane and at four vertices of a concave quadrilateral respectively, while the fifth microphone is out of the plane) is analyzed and studied. It is proved that with the proposed configuration, the five-element microphone array can obtain a unique and analytical solution. To investigate the effectiveness and the localization performance of the proposed method, a practical five-element microphone array is developed and 3D sound source localization experiments are carried out. The TDOA values are obtained by the generalized cross correlation (GCC) method based on the phase transform (PHAT). The experimental results show that the proposed method is an effective near-field localization method. The localization performance of the practical five-element microphone array is satisfactory. Compared with the conventional methods, the proposed method realizes the 3D localization by analytical solution and has the advantages of simple construction and lower computational complexity.