Two-phase low conductivity flow imaging using magnetic induction tomography

Magnetic Induction Tomography (MIT) is a new and emerging type of tomography technique that is able to map the distribution of all three passive electromagnetic properties, however most of the current interests are focusing on the conductivity and permeability imaging. In an MIT system, coils are used as separate transmitters or sensors, which can generate the background magnetic fleld and detect the perturbed magnetic fleld respectively. Through switching technique the same coil can work as transceiver which can generate fleld at a time and detect the fleld at another time. Because magnetic fleld can easily penetrate through the non-conductive barrier, the sensors do not need direct contact with the imaging object. These non-invasive and contactless features make it an attractive technique for many applications compared to the traditional contact electrode based electrical impedance tomography. Recently, MIT has become a promising monitoring technique in industrial process tomography, for example MIT has been used to determine the distribution of liquidised metal and gas (high conductivity two phase ∞ow monitoring) for metal casting applications. In this paper, a low conductivity two phase ∞ow MIT imaging is proposed so the reconstruction of the low contrast samples (< 6S/m) can be realised, e.g., gas/ionised liquid. An MIT system is developed to test the feasibility. The system utilises 16 coils (8 transmitters and 8 receivers) and an operating frequency of 13MHz. Three difierent experiments were conducted to evaluate all possible situations of two phase ∞ow imaging: 1) conducting objects inside a non-conducting background, 2) conducting objects inside a conducting background (low contrast) and 3) non-conducting objects inside a conducting background. Images are reconstructed using the linearised inverse method with regularisation. An experiment was designed to image the non-conductive samples inside a conducting

[1]  Manuchehr Soleimani,et al.  Pipeline inspection using magnetic induction tomographybased on a narrowband pass filtering method , 2012 .

[2]  Francesco Soldovieri,et al.  ON THE FEASIBILITY OF THE LINEAR SAMPLING METHOD FOR 3D GPR SURVEYS , 2011 .

[3]  Roland Martin,et al.  Electrical capacitance tomography two-phase oil-gas pipe flow imaging by the linear back-projection algorithm , 2005 .

[4]  K. Low,et al.  Homogeneous and Heterogeneous Breast Phantoms for Ultra-Wideband Microwave Imaging Applications , 2010 .

[5]  M. Soleimani,et al.  FOUR DIMENSIONAL RECONSTRUCTION USING MA- GNETIC INDUCTION TOMOGRAPHY: EXPERIMEN- TAL STUDY , 2012 .

[6]  Manuchehr Soleimani,et al.  A trust region subproblem for 3D electrical impedance tomography inverse problem using experimental data , 2009 .

[7]  Ze Liu,et al.  Simulation study of the sensing field in electromagnetic tomography for two-phase flow measurement , 2005 .

[8]  Daniel Flores-Tapia,et al.  A bimodal reconstruction method for breast cancer imaging , 2011 .

[9]  Manuchehr Soleimani,et al.  THREE-DIMENSIONAL NONLINEAR INVERSION OF ELECTRICAL CAPACITANCE TOMOGRAPHY DATA USING A COMPLETE SENSOR MODEL , 2010 .

[10]  USE OF SEMI-INVERSION METHOD FOR THE DIRICHLET PROBLEM IN ROUGH SURFACE SCATTERING , 2005 .

[11]  N Terzija,et al.  Electromagnetic inspection of a two-phase flow of GaInSn and argon , 2011 .

[12]  Manuchehr Soleimani,et al.  A 3D inverse finite element technique applied to experimental magnetic induction tomography data. , 2005 .

[13]  O. Bíró Edge element formulations of eddy current problems , 1999 .

[14]  Q. Liu,et al.  A Fast Inverse Polynomial Reconstruction Method Based on Conformal Fourier Transformation , 2012 .

[15]  M. El-Shenawee,et al.  Inverse Scattering of Three-Dimensional PEC Objects Using the Level-Set Method , 2011 .

[16]  Xue Wei Ping,et al.  The Factorized Sparse Approximate Inverse Preconditioned Conjugate Gradient Algorithm for Finite Element Analysis of Scattering Problems , 2009 .

[17]  Doga Gursoy,et al.  Imaging artifacts in magnetic induction tomography caused by the structural incorrectness of the sensor model , 2011 .

[18]  Stefania Bonafoni,et al.  Microwave Radiometry Imaging for Forest Fire Detection: a Simulation Study , 2011 .

[19]  Manuchehr Soleimani,et al.  Three-dimensional magnetic induction tomography imaging using a matrix free krylov subspace inversion algorithm , 2012 .

[20]  K. Preis,et al.  On the use of the magnetic vector potential in the finite-element analysis of three-dimensional eddy currents , 1989 .

[21]  William R B Lionheart,et al.  GREIT: a unified approach to 2D linear EIT reconstruction of lung images , 2009, Physiological measurement.

[22]  R. Banasiak,et al.  Improving Three-Dimensional Electrical Capacitance Tomography Imaging Using Approximation Error Model Theory , 2012 .

[23]  H. Griffiths Magnetic induction tomography , 2001 .

[24]  Manuchehr Soleimani,et al.  Hardware and software design for a National Instrument-based magnetic induction tomography system for prospective biomedical applications. , 2012, Physiological measurement.

[25]  M. Pino,et al.  STABLE SOLUTION OF THE GMT-MoM METHOD BY TIKHONOV REGULARIZATION , 1998 .

[26]  K. Preis,et al.  An edge finite element eddy current formulation using a reduced magnetic and a current vector potential , 2000 .

[27]  N Terzija,et al.  Combined Electromagnetic Tomography for Determining Two-phase Flow Characteristics in the Submerged Entry Nozzle and in the Mold of a Continuous Casting Model , 2011 .

[28]  David Isaacson,et al.  NOSER: An algorithm for solving the inverse conductivity problem , 1990, Int. J. Imaging Syst. Technol..

[29]  Robert Banasiak,et al.  Four-dimensional electrical capacitance tomography imaging using experimental data , 2009 .

[30]  Min Zhang,et al.  SAR IMAGING SIMULATION FOR COMPOSITE MODEL OF SHIP ON DYNAMIC OCEAN SCENE , 2011 .

[31]  Samim Anghaie,et al.  An experimental study of electrical impedance tomography for the two-phase flow visualization , 2002 .

[32]  A. Kameari Regularization on ill-posed source terms in FEM computation using two magnetic vector potentials , 2004, IEEE Transactions on Magnetics.

[33]  Manuchehr Soleimani,et al.  Sensitivity maps in three-dimensional magnetic induction tomography , 2006 .