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Shi Liu
School of Control and Computer Engineering, North China Electric Power University, Beijing, China

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Journal article
Published: 19 August 2021 in IEEE Transactions on Instrumentation and Measurement
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In this study, we propose a new sensor structure with driving electrodes and a new excitation mode in electrical capacitance tomography (ECT) to improve: 1) the low sensitivity distribution in the central area caused by low potential distribution in the central area and 2) the nonuniform sensitivity distribution caused by the nonuniform potential distribution. An imaging method based on sensitivity map optimization is derived through electromagnetic field analysis and numerical simulation. In addition, the change rule of the optimal driving voltage is studied when the excitation voltage of the excitation electrodes and the length of the driving electrodes change. The numerical simulation and experimental results show that the imaging method proposed in this article can distinctively alter the potential distribution and sensitivity distribution in the central area by selecting the optimal driving voltage, which brings significant improvement in image reconstruction.

ACS Style

Qing Zhao; Jie Li; Shi Liu; Guoqiang Liu; Jing Liu. The Sensitivity Optimization Guided Imaging Method for Electrical Capacitance Tomography. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -15.

AMA Style

Qing Zhao, Jie Li, Shi Liu, Guoqiang Liu, Jing Liu. The Sensitivity Optimization Guided Imaging Method for Electrical Capacitance Tomography. IEEE Transactions on Instrumentation and Measurement. 2021; 70 ():1-15.

Chicago/Turabian Style

Qing Zhao; Jie Li; Shi Liu; Guoqiang Liu; Jing Liu. 2021. "The Sensitivity Optimization Guided Imaging Method for Electrical Capacitance Tomography." IEEE Transactions on Instrumentation and Measurement 70, no. : 1-15.

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: 17 January 2020 in Sensors
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The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind’s direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate’s accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.

ACS Style

Bian Ma; Jing Teng; Huixian Zhu; Rong Zhou; Yun Ju; Shi Liu. Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification. Sensors 2020, 20, 523 .

AMA Style

Bian Ma, Jing Teng, Huixian Zhu, Rong Zhou, Yun Ju, Shi Liu. Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification. Sensors. 2020; 20 (2):523.

Chicago/Turabian Style

Bian Ma; Jing Teng; Huixian Zhu; Rong Zhou; Yun Ju; Shi Liu. 2020. "Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification." Sensors 20, no. 2: 523.

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.

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.

Journal article
Published: 29 March 2019 in Applied Sciences
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Due to the strong intermittency of micro-resources, the poor grid-tied power quality, and the high generation-demand sensitivity in micro-grids, research into the control methods of micro-grid systems has always been a notable issue in the field of micro-grids. The inverter is the core control equipment at the primary control level of the micro-grid, and the key factors affecting its output performance can be divided into three categories: control methods, hardware configuration, and control parameter design. Taking the classical active and reactive power (P-Q) control structure and the three-phase, two-stage inverter topology model as an example, this paper designs a parameter for offline tuning, and an online self-tuning optimization method for an inverter control system based on the fruit fly optimization algorithm (FOA). By simulating and comparing the inverter controllers with non-optimized parameters in the same object and environment, the designed parameter tuning method is verified. Specifically, it improves the dynamic response speed of the inverter controller, reduces the steady-state error and oscillation, and enhances the dynamic response performance of the controller.

ACS Style

Runnan Dong; Shi Liu; Geng Liang. Research on Control Parameters for Voltage Source Inverter Output Controllers of Micro-Grids Based on the Fruit Fly Optimization Algorithm. Applied Sciences 2019, 9, 1327 .

AMA Style

Runnan Dong, Shi Liu, Geng Liang. Research on Control Parameters for Voltage Source Inverter Output Controllers of Micro-Grids Based on the Fruit Fly Optimization Algorithm. Applied Sciences. 2019; 9 (7):1327.

Chicago/Turabian Style

Runnan Dong; Shi Liu; Geng Liang. 2019. "Research on Control Parameters for Voltage Source Inverter Output Controllers of Micro-Grids Based on the Fruit Fly Optimization Algorithm." Applied Sciences 9, no. 7: 1327.

Journal article
Published: 12 November 2018 in Energy
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Wind forecasting holds the key to the management of wind power. Previous vector or matrix wind forecast methods may not best reflect the intrinsic inter relationship among the wind velocity components of a three-dimensional wind field. Alternatively, a tensor-based model is developed to reconstruct the wind velocity distribution within a short period of time, enabling a new way for wind forecasting. A third-order CFD database is established by CFD simulations and the Tucker decomposition is used to obtain the tensor basis off site. Then in real time, the tensor basis can be employed to rapidly reconstruct wind velocity distributions in any direction, which can also form a new way to reconstruct wind velocity distribution in 3-D spaces. A comparison of the maximum and relative reconstruction errors shows that the newly proposed method performs better than the authors' previously published wind field reconstruction method. The influences of sampling rate, noise level and sensor distributions on the reconstruction error are also discussed in this paper. Finally, a wind tunnel experiment is carried out to evaluate the accuracy of the proposed method, and the experimental results show that relative errors drop around 1% and maximum errors drop around 5% when using the new proposed method.

ACS Style

Li Qin; Shi Liu; Yi Kang; Song An Yan; H. Inaki Schlaberg; Zhan Wang. Wind velocity distribution reconstruction using CFD database with Tucker decomposition and sensor measurement. Energy 2018, 167, 1236 -1250.

AMA Style

Li Qin, Shi Liu, Yi Kang, Song An Yan, H. Inaki Schlaberg, Zhan Wang. Wind velocity distribution reconstruction using CFD database with Tucker decomposition and sensor measurement. Energy. 2018; 167 ():1236-1250.

Chicago/Turabian Style

Li Qin; Shi Liu; Yi Kang; Song An Yan; H. Inaki Schlaberg; Zhan Wang. 2018. "Wind velocity distribution reconstruction using CFD database with Tucker decomposition and sensor measurement." Energy 167, no. : 1236-1250.

Journal article
Published: 14 August 2018 in Applied Sciences
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Multiphase flow in annular channels is complex, particularly in the region where the flow direction abruptly changes between the inner pipe and the outer pipe, as the cases in horizontal drilling and pneumatic conveying. Simplified models and experience are still the main sources of information. First, to understand the process more deeply, Computational Fluid Dynamics (CFD) package Fluent is used to simulate the gas-solid flow in the horizontal and the inclined section of an annular pipe. Discrete Phase Model (DPM) is adopted to calculate the trajectories of solid particles of different sizes at different air velocities. Also, the Two-Fluid model is used to simulate the sand flow in the inclined section for the case of air flow stoppage, for which an experiment is also conducted to verify the CFD simulation. Simulation results reveal the behaviour of the solid particles showing the dispersed spatial distribution of small particles near the entrance. On the other hand, larger particles manifest a distinct sedimented flow pattern along the bottom of the pipe. The density distribution of the particles over a pipe cross section is demonstrated at a variety of air velocities. The results also show that the large airspeed tends to generate swirls near the outlet of the inner pipe. In addition, Electrical Capacitance Tomography (ECT) technology is used to reconstruct the spatial distribution of particles, and the cross-correlation algorithm to detect velocity. Both the distribution and the velocity measurement by electric sensors agree reasonably well with the CFD predictions. The details revealed by CFD simulation and the mutual-verification between CFD simulation and the ECT method of this study could be valuable for the industry in drilling process control and equipment development.

ACS Style

Wanting Zhou; Yue Jiang; Shi Liu; Qing Zhao; Teng Long; Zhixiong Li. Detection of Gas-Solid Two-Phase Flow Based on CFD and Capacitance Method. Applied Sciences 2018, 8, 1367 .

AMA Style

Wanting Zhou, Yue Jiang, Shi Liu, Qing Zhao, Teng Long, Zhixiong Li. Detection of Gas-Solid Two-Phase Flow Based on CFD and Capacitance Method. Applied Sciences. 2018; 8 (8):1367.

Chicago/Turabian Style

Wanting Zhou; Yue Jiang; Shi Liu; Qing Zhao; Teng Long; Zhixiong Li. 2018. "Detection of Gas-Solid Two-Phase Flow Based on CFD and Capacitance Method." Applied Sciences 8, no. 8: 1367.

Journal article
Published: 20 July 2018 in Flow Measurement and Instrumentation
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In order to acquire improved ECT reconstruction method that can increase spatial resolution and reduce the image errors, this study proposes an ECT imaging method based on data correlation analysis, instead of using conventional sensitivity maps. This method first collected the capacitance values when each micro element in the sensor is assigned in turn by a high dielectric constant, and then compared the simulated capacitance values with the true capacitance. Then a correlation coefficient can be obtained between them. Images can be reconstructed according to the correlation coefficient and the re-calculated capacitances. The simulation results show that the quality of the image reconstruction is improved, and the algorithm does not need to be iterative, but when combined with iterative method the image quality can be further improved. Also this new algorithm can be adapted to the situations when there are already materials in the sensor, and the materials, such as water, with high dielectric constant can also be imaged.

ACS Style

Yiqun Kang; Shi Liu; Jing Liu. Image reconstruction algorithm for electrical capacitance tomography based on data correlation analysis. Flow Measurement and Instrumentation 2018, 62, 113 -122.

AMA Style

Yiqun Kang, Shi Liu, Jing Liu. Image reconstruction algorithm for electrical capacitance tomography based on data correlation analysis. Flow Measurement and Instrumentation. 2018; 62 ():113-122.

Chicago/Turabian Style

Yiqun Kang; Shi Liu; Jing Liu. 2018. "Image reconstruction algorithm for electrical capacitance tomography based on data correlation analysis." Flow Measurement and Instrumentation 62, no. : 113-122.

Conference paper
Published: 01 July 2018 in Journal of Physics: Conference Series
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To alleviate the ill-posedness and non-linearity of electrical capacitance tomography, Bayesian maximum entropy (BME) mapping method is used to mix another source of information. They are some observation points, which are hard data. They can be seen as error free. Three two-phase flow patterns are preset to verify the method. Results show that the BME prediction maps are more accuracy than ECT reconstructed image. BME images have more clear edges and those sizes are more similar to the original maps. Two quality assessments demonstrate that BME maps has a lower relative error(RE) and higher correlation coefficient(CC), which verify the effectiveness of the BME mapping method in fluid measurement.

ACS Style

Mengyuan Wang; Shi Liu; Yubo Liang. Bayesian Maximum Entropy Applied to Fluid Measurement. Journal of Physics: Conference Series 2018, 1064, 012070 .

AMA Style

Mengyuan Wang, Shi Liu, Yubo Liang. Bayesian Maximum Entropy Applied to Fluid Measurement. Journal of Physics: Conference Series. 2018; 1064 (1):012070.

Chicago/Turabian Style

Mengyuan Wang; Shi Liu; Yubo Liang. 2018. "Bayesian Maximum Entropy Applied to Fluid Measurement." Journal of Physics: Conference Series 1064, no. 1: 012070.

Journal article
Published: 01 May 2018 in Energy
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Short-term wind forecasting is important in updating wind electricity trading strategies, facility protection and more effective operation control. Physical based models, particularly those using computational fluid dynamics (CFD), are able to provide ever more detailed wind speed data. However, such methods involve handling a huge amount of CFD data, which is prohibitively time consuming for a short-term wind forecast in real situations. To solve this problem, Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) algorithms are applied in this study to reduce the dimensions of wind speed data and the proposed method is applied to reconstruct the wind field. Wind fields have successfully been reconstructed with good accuracy for the wind direction angles ranging from 0° to 90°. This method is validated by experimental data from a wind tunnel experiment. The accuracy of the proposed reconstruction algorithm increases with the sampling rate of the measurement and the locations of the sensors do not significantly affect the accuracy of the results. Gaussian noise introduced into the input signal does not significantly deteriorate the reconstruction quality. Results show that the proposed method can adequately be used to reconstruct the wind field for the models tested with a high degree of confidence.

ACS Style

Li Qin; Shi Liu; Teng Long; Muhammad Ali Shahzad; H. Inaki Schlaberg; Song An Yan. Wind field reconstruction using dimension-reduction of CFD data with experimental validation. Energy 2018, 151, 272 -288.

AMA Style

Li Qin, Shi Liu, Teng Long, Muhammad Ali Shahzad, H. Inaki Schlaberg, Song An Yan. Wind field reconstruction using dimension-reduction of CFD data with experimental validation. Energy. 2018; 151 ():272-288.

Chicago/Turabian Style

Li Qin; Shi Liu; Teng Long; Muhammad Ali Shahzad; H. Inaki Schlaberg; Song An Yan. 2018. "Wind field reconstruction using dimension-reduction of CFD data with experimental validation." Energy 151, no. : 272-288.

Journal article
Published: 12 September 2017 in Sensors
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Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography.

ACS Style

Yanqiu Li; Shi Liu; Schlaberg H. Inaki. Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography. Sensors 2017, 17, 2084 .

AMA Style

Yanqiu Li, Shi Liu, Schlaberg H. Inaki. Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography. Sensors. 2017; 17 (9):2084.

Chicago/Turabian Style

Yanqiu Li; Shi Liu; Schlaberg H. Inaki. 2017. "Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography." Sensors 17, no. 9: 2084.

Journal article
Published: 01 January 2015 in Energy Procedia
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Spectral emissivity is a key in the temperature measurement by radiation methods, but not easy to determine in a combustion environment, due to the interrelated influence of temperature and wave length of the radiation. In multi-wavelength radiation thermometry, knowing the spectral emissivity of the material is a prerequisite. However in many circumstances such a property is a complex function of temperature and wavelength and reliable models are yet to be sought. In this study, a two stages partition low order polynomial fitting is proposed for multi-wavelength radiation thermometry. In the first stage a spectral emissivity model is established as a function of temperature; in the second stage a mathematical model is established to describe the dependence of the coefficients corresponding to the wavelength of the radiation. The new model is tested against the spectral emissivity data of tungsten, and good agreement was found with a maximum error of 0.64%

ACS Style

Qirong Qiu; Shi Liu; Jing Teng; Yong Yan. A Two-stage Polynomial Method for Spectrum Emissivity Modeling. Energy Procedia 2015, 66, 245 -248.

AMA Style

Qirong Qiu, Shi Liu, Jing Teng, Yong Yan. A Two-stage Polynomial Method for Spectrum Emissivity Modeling. Energy Procedia. 2015; 66 ():245-248.

Chicago/Turabian Style

Qirong Qiu; Shi Liu; Jing Teng; Yong Yan. 2015. "A Two-stage Polynomial Method for Spectrum Emissivity Modeling." Energy Procedia 66, no. : 245-248.

Article
Published: 05 June 2014 in Journal of Wuhan University of Technology-Mater. Sci. Ed.
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The thermal performance of nano fluid containing Ag NPs with different stabilizers was studied in detail. The wall temperature distributions of the heat pipe containing pure water and a small amount of PAN/Ag, PVP/Ag, L-cys/Ag, and OA/Ag were determined, respectively. With the addition of a small amount of Ag NPs in the pure water, the heat pipe wall temperature became lower than that of pipes filled with pure water. The efficiency under the same conditions was ranked as PVP/Ag > L-cys/Ag > PAN/Ag > OA/Ag. After adding a small amount of CNT in the mixture, the effect was enhanced further. As more CNT became dispersed in the working fluid, the opposite effect was observed. Therefore, the optimal amount is 4 mg/L CNT in nano-fluid. Ag nano fluid could form the multi-scaled surface with higher wettability and spreadability. The wettability of nano-fluid was improved with the addition of a small amount of CNT in the mixture. However, the spreadability of the mixture would decrease significantly in the presence of more CNT.

ACS Style

Di Wu; Shi Liu. Improvement of thermal performance of Ag nano fluid combined with carbon nano-tube. Journal of Wuhan University of Technology-Mater. Sci. Ed. 2014, 29, 463 -467.

AMA Style

Di Wu, Shi Liu. Improvement of thermal performance of Ag nano fluid combined with carbon nano-tube. Journal of Wuhan University of Technology-Mater. Sci. Ed.. 2014; 29 (3):463-467.

Chicago/Turabian Style

Di Wu; Shi Liu. 2014. "Improvement of thermal performance of Ag nano fluid combined with carbon nano-tube." Journal of Wuhan University of Technology-Mater. Sci. Ed. 29, no. 3: 463-467.