Tomographic Flow Imaging Research Articles

Gas microstrip detectors for X-ray tomographic flow imaging

Abstract A investigation into the suitability of gas microstrip detector technology for a high-speed industrial X-ray tomography system is reported. X-ray energies in the region 20– 30 keV are well suited to the application, which involves imaging two-dimensional slices through gas/liquid multiphase pipeline flows for quantitative component fraction measurement. Stable operation over a period representing several hundred individual tomographic scans at gas gains of 500 is demonstrated using a Penning gas mixture of krypton/propylene.

Three-component tomographic flow imaging using artificial neural network reconstruction

An introduction to electrical capacitance tomography and a brief background of multi-modal tomography is given. The motivation for a neurocomputing solution to the inverse problem of image reconstruction is discussed together with a brief overview of previous work in this field. The techniques developed form the basis for training a single artificial neural network to perform three-component flow imaging using simulated data. A dedicated thresholding function is demonstrated to accommodate three distinct and stable regions for gas, oil and water component estimation. Preliminary results indicate the feasibility of this neural solution for three-component imaging. Noise performance of the three-component reconstructor is also analysed, and the image reconstruction for test patterns and noise performance of this network are illustrated.

Experimental evaluation of capacitance tomographic flow imaging systems using physical models

An experimental method for evaluating the performance of capacitance tomographic flow imaging systems is described. Criteria are defined to describe the performance of these systems, namely spatial and permittivity resolution, accuracy (system errors) and signal to noise ratio. Static physical models simulating typical flow distribution patterns are used as standard test objects and The criteria are quantified by comparing the reconstructed images with the standards. Systems with 8 and 12 electrodes have been tested and the results compared to show the improvement obtained with the increased number of electrodes.

Electrical capacitance tomography for flow imaging: system model for development of image reconstruction algorithms and design of primary sensors

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 trans- puters to achieve a real-time performance. It is found that the selection of the geometric param- eters 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.

8-electrode capacitance system for two-component flow identification. Part 2: Flow regime identification

Following the first paper on tomographic flow imaging, the authors describe a method for identifying the patterns of two-component flows using the same 8-electrode capacitance system, but using a simpler signal-processing system than that for tomographic reconstruction. The measured capacitance vector of an unknown flow is compared with the stored ‘fingerprint’ vectors of various known flow patterns, and, hence, the actual flow regime can be identified.

8-electrode capacitance system for two-component flow identification. Part 1: Tomographic flow imaging

An 8-electrode capacitance system for tomographic imaging of two-component flows has been designed based upon a 2-dimensional finite element model. Emphasis is made on the development of a linear back-projection image reconstruction algorithm. Some reconstructed images obtained using this algorithm are given. Limitations and possible future improvements of the system are discussed.

Tomographic imaging of two-component flow using capacitance sensors

Describes a tomographic flow imaging system based on capacitance sensors. It uses eight capacitance electrodes to perform a 'body scan' of the fluid-conveying pipe, and a linear back projection algorithm is developed to reconstruct the cross-sectional image of the two-component flow from the measured capacitance values. A prototype system has been successfully assembled and reconstructed images of static distribution models are presented. Possible industrial applications and practical limitations of the system are discussed.

Capacitance-based tomographic flow imaging system

An instrumentation system for obtaining cross-sectional images of two-component flows is described. It uses measurements made with capacitance electrodes mounted on the outer surface of the fluid-conveying pipe, and an image reconstruction algorithm based on backprojection. Images of static physical models simulating a stratified solids/air flow are presented.