A software tool that facilitates the development of image reconstruction algorithms, and the design of optimal capacitance sensors for a capacitance-based 12-electrode tomographic flow imaging system are described. The core of this software tool is the finite element (FE) model of the sensor, which is implemented in OCCAM-2 language and run on the Inmos T800 transputers. Using the system model, the in-depth study of the capacitance sensing fields and the generation of flow model data are made possible, which assists, in a systematic approach, the design of an improved image-reconstruction algorithm. This algorithm is implemented on a network of transputers to achieve a real-time performance. It is found that the selection of the geometric parameters of a 12-electrode sensor has significant effects on the sensitivity distributions of the capacitance fields and on the linearity of the capacitance data. As a consequence, the fidelity of the reconstructed images are affected. Optimal sensor designs can, therefore, be provided, by accommodating these effects.

The design of the sensor electronics for a tomographic imaging system based on electrical capacitance sensors is described. The performance of the sensor electronics is crucial to the performance of the imaging system. The problems associated with such a measurement process are discussed and solutions to these are described. Test results show that the present design has a resolution of 0.3 femtofarad (For a 12-electrode system imaging an oil/gas flow, this represents a 2% gas void fraction change at the centre of the pipe) with a low noise level of 0.08 fF (rms value), a large dynamic range of 76 dB and a data acquisition speed of 6600 measurements per second. This enables sensors with up to 12 electrodes to be used in a system with a maximum imaging rate of 100 frames per second, and thus provides an improved image resolution over the earlier 8-electrode system and an adequate electrode area to give sufficient measurement sensitivity.

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