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Shanxun Sun
Energy and Electricity Research Center, Jinan University, Zhuhai, 519070, China.

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Journal article
Published: 09 December 2020 in IEEE Transactions on Instrumentation and Measurement
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As a fast and non-intrusive measurement and visualization technique, Electrical Capacitance Tomography (ECT) is rapidly expanding its applications in the research on multiphase flow, fluidization, drying, combustion, and so on. However, the marked unevenness of the sensitivity maps sometimes causes unexpected effects in imaging reconstruction, particularly in three-dimension cases. To exploit the positive potential of this phenomenon, the authors proposed an image fusion method using the data from two units of ECT sensors in this study. This method is used in image fusion on the reconstructed images for a planar sensor and a cylindrical sensor. In contrast to the conventional fusion models that use fixed weight factors for two sources of data, our model forges weight functions that are set preference the strength of the sensitivity maps. The new algorithm is implemented by first extracting the characteristic information out of the ECT images using the Latent Low-Rank Representation and then performing a fusion algorithm with linear weight functions in preference to the significance of the sensitivity maps. The simulation results show that the algorithm effectively retains the advantages of the two units of sensors and mutually compensates the weak points of theirs, and significantly improves the reconstruction quality. The fusion image quality by the new method can response the real situation better in different heights. The results imply that the data fusion might to a significant extend amend the weakness of ECT caused by the uneven sensitivity maps.

ACS Style

Shanxun Sun; Qing Zhao; Shi Liu; Huixian Zhu; Yun Ju; Min Zhang; Jing Liu. Sensitivity Guided Image Fusion for Electrical Capacitance Tomography. IEEE Transactions on Instrumentation and Measurement 2020, 70, 1 -12.

AMA Style

Shanxun Sun, Qing Zhao, Shi Liu, Huixian Zhu, Yun Ju, Min Zhang, Jing Liu. Sensitivity Guided Image Fusion for Electrical Capacitance Tomography. IEEE Transactions on Instrumentation and Measurement. 2020; 70 (99):1-12.

Chicago/Turabian Style

Shanxun Sun; Qing Zhao; Shi Liu; Huixian Zhu; Yun Ju; Min Zhang; Jing Liu. 2020. "Sensitivity Guided Image Fusion for Electrical Capacitance Tomography." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-12.

Journal article
Published: 28 May 2020 in Applied Sciences
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Temperature information has a certain significance in thermal energy systems, especially in gas combustion systems. Generally, measurements and numerical calculations are used to acquire temperature information, but both of these approaches have their limitations. Constrained by cost and conditions, measurement methods are difficult to use to reconstruct the temperature field. Numerical methods are able to estimate the temperature field; however, the calculation process in numerical methods is very complex, so these methods cannot be used in real time. For the purpose of solving these problems, a two-dimensional temperature field reconstruction method based on the proper orthogonal decomposition (POD) algorithm is proposed in this study. In the proposed method, the temperature field reconstruction task is transformed into an optimization problem. Theoretical analysis and simulations show that the proposed method is feasible. Gas combustion experiments were also performed to validate this method. Results indicate that the proposed method can yield a reliable reconstruction solution and can be applied to real-time applications.

ACS Style

Minxin Chen; Shi Liu; Shanxun Sun; Zhaoyu Liu; Yu Zhao. Rapid Reconstruction of Simulated and Experimental Temperature Fields Based on Proper Orthogonal Decomposition. Applied Sciences 2020, 10, 3729 .

AMA Style

Minxin Chen, Shi Liu, Shanxun Sun, Zhaoyu Liu, Yu Zhao. Rapid Reconstruction of Simulated and Experimental Temperature Fields Based on Proper Orthogonal Decomposition. Applied Sciences. 2020; 10 (11):3729.

Chicago/Turabian Style

Minxin Chen; Shi Liu; Shanxun Sun; Zhaoyu Liu; Yu Zhao. 2020. "Rapid Reconstruction of Simulated and Experimental Temperature Fields Based on Proper Orthogonal Decomposition." Applied Sciences 10, no. 11: 3729.

Journal article
Published: 23 December 2019 in IEEE Transactions on Sustainable Energy
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In a real wind farm,complex airflow conditions result in complexities of wind speed and direction,with possibly significant intermittency and fluctuations.This problem can be alleviated if the wind speed distribution over a wind farm is known in advance.In this paper,a new method is proposed for real-time wind field reconstruction for large areas, based on the idea of a “virtual time”, i.e., a time span needed for an object to travel across a certain distance.The distribution of wind speed and direction can be acquired prior to its occurrence in the wind farm with refined spatial resolutions.A procedure is also developed to stabilize the solution process,and this stabilization leads to an optimal allocation of the wind speed sensors;this allocation is necessary for the efficient use of a limited number of sensors.The reconstruction algorithm has been substantially studied,and a mathematical quantity was correlated to the reconstruction error.This correlation enables us to obtain good reconstruction results by using the Greedy algorithm we proposed in this study.Simulation and experimental results demonstrated the strong feasibility of successful reconstructions by our proposed algorithm.Moreover,the sensor optimization scheme not only reduces the error significantly but also improves the efficiency of sensor applications;this improvement should apply to a wide range of conditions.

ACS Style

Shan Xun Sun; Shi Liu; Minxin Chen; Hongbo Guo. An Optimized Sensing Arrangement in Wind Field Reconstruction Using CFD and POD. IEEE Transactions on Sustainable Energy 2019, 11, 2449 -2456.

AMA Style

Shan Xun Sun, Shi Liu, Minxin Chen, Hongbo Guo. An Optimized Sensing Arrangement in Wind Field Reconstruction Using CFD and POD. IEEE Transactions on Sustainable Energy. 2019; 11 (4):2449-2456.

Chicago/Turabian Style

Shan Xun Sun; Shi Liu; Minxin Chen; Hongbo Guo. 2019. "An Optimized Sensing Arrangement in Wind Field Reconstruction Using CFD and POD." IEEE Transactions on Sustainable Energy 11, no. 4: 2449-2456.

Review article
Published: 13 December 2019 in Chemical Engineering Science
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The electrical capacitance tomography (ECT) is a promising measurement technique, which tries to reconstruct the permittivity distribution in a measurement domain by solving an inverse problem. Low quality images narrow the applicability of the technique. To address the challenge, a new cost function, which considers model deviation and measurement noises, is devised to model the ECT reconstruction problem. The soft thresholding method and the fast-iterative shrinkage thresholding technique (FIST) are embedded into the iterative split Bregman (ISB) method to solve the devised objective functional. The numerical and experimental results indicate that the proposed ECT imaging technique not only mitigates the ill-posed nature, but also improves the reconstruction quality.

ACS Style

Hongbo Guo; Shi Liu; Hongyan Cheng; Shanxun Sun; Jiankang Ding; Hongqi Guo. Iterative computational imaging method for flow pattern reconstruction based on electrical capacitance tomography. Chemical Engineering Science 2019, 214, 115432 .

AMA Style

Hongbo Guo, Shi Liu, Hongyan Cheng, Shanxun Sun, Jiankang Ding, Hongqi Guo. Iterative computational imaging method for flow pattern reconstruction based on electrical capacitance tomography. Chemical Engineering Science. 2019; 214 ():115432.

Chicago/Turabian Style

Hongbo Guo; Shi Liu; Hongyan Cheng; Shanxun Sun; Jiankang Ding; Hongqi Guo. 2019. "Iterative computational imaging method for flow pattern reconstruction based on electrical capacitance tomography." Chemical Engineering Science 214, no. : 115432.

Journal article
Published: 17 July 2019 in Applied Sciences
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Physical-approach-based wind forecasts have the merit of a heavily reduced uncertainty in predictions, but very often suffer from a prohibitively lengthy numerical computation time, if high spatial resolutions are required. To tackle this hurdle, proper orthogonal decomposition (POD) has manifested extraordinary power in reducing the number of computation grids and hence the computation time. However, POD itself suffers from difficulties in extracting basis vectors when the snapshots contain large amounts of data, when considering large areas using high spatial resolution. By means of computational simulations and inverse process analyses, in this study the authors developed a new method for rapid wind field reconstruction with high spatial resolution, while reducing the computation load to a minimum. The strategy is to establish snapshots of velocity fields in a large area, but only using a much smaller subset of the large area to extract the basis vectors. The basis vectors are then used to reconstruct the wind field of the large area with a high spatial resolution. The method can dramatically reduce the overall computation work due to the much smaller grid size in the subset area. The new method can be applied to situations where the velocity distributions for a large area need to be known with high spatial resolution.

ACS Style

Shanxun Sun; Shi Liu; Guangchao Zhang. The Rapid Establishment of Large Wind Fields via an Inverse Process. Applied Sciences 2019, 9, 2847 .

AMA Style

Shanxun Sun, Shi Liu, Guangchao Zhang. The Rapid Establishment of Large Wind Fields via an Inverse Process. Applied Sciences. 2019; 9 (14):2847.

Chicago/Turabian Style

Shanxun Sun; Shi Liu; Guangchao Zhang. 2019. "The Rapid Establishment of Large Wind Fields via an Inverse Process." Applied Sciences 9, no. 14: 2847.