Experimental imaging and algorithm optimization based on deep neural network for electrical capacitance tomography for LN2-VN2 flow

Abstract

The deep neural network (DNN) method is applied to better map the phase distribution compared with the conventical reconstruction algorithm for nonlinear problems of electrical capacitance tomography (ECT). Three DNNs are trained and evaluated in the numerical experiment for cryogenic two-phase flow. A capacitance acquisition unit is designed. And a cryogenic experiment with liquid nitrogen and nitrogen vapor (LN2-VN2) as the working fluids is conducted to verify the imaging solution for practical application. The DNN with the input of the measured capacitance data (DNN-C) has shown its generalization ability for the practical imaging application. Further, the DNN optimal method is coupled to the conventional LBP and Landweber iteration algorithm by evaluating the nonlinearity between the error capacitance vector and the real phase distribution vector (DNN-EC). The comparison shows that the DNN modification method can significantly improve the phase distribution closed to the pattern in the training input samples with an elimination of the artifacts and sharpening interface, but it fails to recognize the flow pattern which is not included in the samples. The result shows that compared with the DNN-EC, DNN-C is a better choice for the image reconstruction for the cryogenic application.