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

Tian, Zengshan; Gao, Xin-Xin; Qiu, Limin; Zhang, Xiaobin

doi:10.1016/j.cryogenics.2022.103568

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Also, each technique is discussed for its complexity and accuracy. It should be noted that machine-learningbased techniques have found numerous applications in flow parameter estimation [13], flow regime identification [14], and imaging [15][16][17] in electrical capacitance tomography, which is attributed to the ability to handle complex nonlinearities with machine learning algorithms given that enough training data are available.…”

Section: Introductionmentioning

confidence: 99%

Propellant Mass Gauging in a Spherical Tank under Micro-Gravity Conditions Using Capacitance Plate Arrays and Machine Learning

Chowdhury,

Charleston,

Marashdeh

et al. 2023

Sensors

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Propellant mass gauging under micro-gravity conditions is a challenging task due to the unpredictable position and shape of the fuel body inside the tank. Micro-gravity conditions are common for orbiting satellites and rockets that operate on limited fuel supplies. Capacitance sensors have been investigated for this task in recent years; however, the effect of various positions and shapes of the fuel body is not analyzed in detail. In this paper, we investigate this with various fill types, such as annular, core-annular, and stratified fills at different positions. We compare the performance among several curve-fitting-based approaches and a machine-learning-based approach, the latter of which offers superior performance in estimating the fuel content.

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Section: Introductionmentioning

confidence: 99%

Propellant Mass Gauging in a Spherical Tank under Micro-Gravity Conditions Using Capacitance Plate Arrays and Machine Learning

Chowdhury,

Charleston,

Marashdeh

et al. 2023

Sensors

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“…With the development of advanced computing technology, the powerful ability of deep learning to solve strong nonlinear problems has gradually been developed. For example, Tian et al improved the LBP and Landweber iterative algorithms by a dynamic neural network (DNN) to reconstruct electrical capacitance tomography (ECT) images [16]. The results showed that a DNN has the ability to partially solve nonlinear problems.…”

Section: Introductionmentioning

confidence: 99%

One-dimensional structure reparameterized convolutional neural network for two-phase image reconstruction based on ERT

Chao

Zhang

Chen

et al. 2023

Meas. Sci. Technol.

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Electrical resistance tomography (ERT) can be applied to two-phase flow pattern identification which is a key research direction for improving the operational safety of different industrial equipment systems with complex flow fields. Aiming at the existing problem that the traditional algorithm for defining flow patterns cannot accurately establish the mapping relationship between the measured voltage from ERT system and the two-phase flow conductivity distribution, a novel one-dimensional structure reparameterized convolutional neural network (1D-SRPCNN) algorithm for two-phase flow pattern image reconstruction based on ERT is proposed. First, finite element method and deep learning software framework are used to build dataset and train the neural network model respectively. Second, a deep residual network (ResNet) is used as the main network structure in the algorithm, and the one-dimensional multiscale feature extraction block (1DMSFE-Block) is improved by structural reparameterization. Then, multiscale convolution is introduced to 1DMSFE-Block for extracting features of different receptive field sizes and performing linear fusion, and the predicted two-phase flow conductivity pixel vector is obtained by the feature map passing with three fully connected layers. The results show that 1D-SRPCNN has high reconstruction performance, the average relative image error is 5.15%, the average correlation coefficient is 97.2%, and it has high anti-noise performance and generalization performance. Different experimental data also show that the proposed algorithm has high image reconstruction accuracy and efficiency. The research will provide important theoretical support for accurately identifying two-phase flow patterns in different fields.

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“…The convenience of the Calderon algorithm for the CO 2 two-phase image reconstruction was also summarized. Tian et al [24] compared the performance of deep neural network (DNN) modification to the classical linear algorithms (DNN-EC) and the DNN directly for image reconstruction (DNN-C). The generalization ability of these methods was validated in the numerical experiment, and the feasibility of these models for the cryogenic application was evaluated by a cryogenic experiment.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Landweber Coupled Least Square Support Vector Regression Algorithm for Electrical Capacitance Tomography for LN2–VN2 Flow

Tian,

Gao,

Xia

et al. 2023

Energies

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The electric capacitance tomography (ECT) technique has been widely used in phase distribution reconstruction, while the practical application raised nonideal noise and other errors for cryogenic conditions, requiring a more accurate algorithm. This paper develops a new image reconstruction algorithm for ECT by coupling the traditional Landweber algorithm with the least square support vector regression (LSSVR) for cryogenic fluids. The performance of the algorithm is quantitatively evaluated by comparing the inversion images with the experimental results for both the room temperature working medium with the dielectric constant ratio close to cryogenic fluid and the cryogenic fluid of liquid nitrogen/nitrogen vapor (LN2-VN2). The inversion images based on the conventional LBP and Landweber algorithms are also presented for comparison. The benefits and drawbacks of the developed algorithms are revealed and discussed, according to the results. It is demonstrated that the correlated coefficients of the images based on the developed algorithm reach more than 0.88 and a maximum of 0.975. In addition, the minimum void fraction error of the algorithm is reduced to 0.534%, which indicates the significant optimization of the LSSVR coupled method over the Landweber algorithm.

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

Cited by 7 publications

References 33 publications

Propellant Mass Gauging in a Spherical Tank under Micro-Gravity Conditions Using Capacitance Plate Arrays and Machine Learning

Propellant Mass Gauging in a Spherical Tank under Micro-Gravity Conditions Using Capacitance Plate Arrays and Machine Learning

One-dimensional structure reparameterized convolutional neural network for two-phase image reconstruction based on ERT

Evaluation of Landweber Coupled Least Square Support Vector Regression Algorithm for Electrical Capacitance Tomography for LN2–VN2 Flow

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