The Simulation And Optimized Design Of The Sensor For Electrical Capacitance Tomography System

Flow imaging is a new technique developed rapidly in recent years, which has great developmental potential and wide industrial application prospect. Having many distinct advantages such as low cost, wide application field, simple structure, non-invade and better safety, Electrical Capacitance Tomography(ECT) has being the most popular research direction and the main development in flow imaging technique. But the optimum design of the sensor of electric capacity has been to hinder Electrical Capacitance Tomography (ECT) to develop; it is one of the key problem and the main difficult point. This research can raise systematic development speed, lower costs and help to raise the whole performance of Electrical Capacitance Tomography (ECT). Thus promote the further development of this technology and practicality. The object of this dissertation is based on the research of 8-electrode Electrical Capacitance Tomography system. Through proposed the mathematics model of principle of the sensor and analyzed the sensor performance index that influence on ECT performance, Set up the mathematics models of optimum design. Using modern finite element model, calculate accurately and analyses, Thus can design out it to each kind of concrete size of sensor of using the request fast , And have very good performance indexes .The main problem researched in the dissertation and achievements are as follows:1. The progress and the problems of the flow imaging technique are briefly explained. The advantages of electrical capacitance tomography are discussed.2. The principle of the capacitance tomography is analyzed and mathematical model of sensitivity field is proposed.3. Finite element model is proposed, and computation of sensitivity field is accomplished on the basis of this model. This model is correct and has better accuracy, which is proved by experiment. 4. Analyze emphatically the influence of structure parameters, axial guard electrodes, dielectric constants and the number of electrodes on the performance of sensor. The optimum design of sensor is done, and performance of the sensor is improved.5. Choose an image reconstruction algorithm that is based on the gene algorithms, according to the datum after optimizing, high quality image is obtained. It is verified that Optimization is Correct and feasible.