17 Papers
43 Citations
Shi Liu is an academic researcher from North China Electric Power University. The author has contributed to research in topics: Combustion & Electrical capacitance tomography. The author has an hindex of 3, co-authored 17 publications.
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Papers
An image reconstruction algorithm based on the semiparametric model for electrical capacitance tomography
TL;DR: A generalized image reconstruction model that simultaneously considers the inaccurate property in the measured capacitance data and the linearization approximation error is presented, and the numerical results reveal that the proposed algorithm is efficient and overcomes the numerical instability in the process of ECT image reconstruction.
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Study of High-Pressure Waterjet Characteristics Based on CFD Simulation
TL;DR: In this paper, using CFD software to simulate the flow field inside and outside of the high pressure water jet nozzle, analysis the jet axial velocity, pressure distribution, and discussed this changes in the case of different spacing between the nozzles when the nozzle structure are same, and choose an reasonable spacing to ensure the cleaning effect.
4
Effects of Multi-Walled Carbon Nanotubes on Enhancing the Thermodynamics Characteristic of Alkali Nitrate and Chlorate Salt for Concentration Solar Plants
TL;DR: In this article, a review of thermophysical properties and thermochemical characteristics of the MWCNTs-salt composite materials is provided to provide a foundation for the application of carbon nanotubes in molten salt which can remarkably improve the stability and capacity of thermal storage.
3
Generalized flow pattern image reconstruction algorithm for electrical capacitance tomography
TL;DR: Based on the semiparametric model, a generalized objective functional that considers the outliers in the measured capacitance data and the model error is proposed, and a regularized combination minimax estimation is developed.
3
Fuel Identification Based on the Least Squares Support Vector Machines
TL;DR: Based on the least squares support vector machines (LSSVM), an efficient method of identifying the flame types is developed in this article, with the correct identification rate up to 100%.
3