The coupled CFD-DEM method with the JKR(Johnson-Kendall-Roberts)model for describing the contact adhesion of dust to filter particles(FPs)is used to simulate the distribution pattern of dust particle deposition in the...The coupled CFD-DEM method with the JKR(Johnson-Kendall-Roberts)model for describing the contact adhesion of dust to filter particles(FPs)is used to simulate the distribution pattern of dust particle deposition in the granular bed filter(GBF)with multi-layer media.The minimum inlet flow velocity must meet the requirement that the contact probability between dust and FPs is in the high contact probability region.The air flow forms vortices on the leeward side of the FPs and changes abruptly at the intersection of different particle size FPs layers.Dust particles form large deposits at the intersection of the first and second layers and the different particle size filter layers.Dual element multilayer GBF can further optimize the bed structure by interlacing filter layers with different particle sizes.Compared with single particle size multi-layer GBF,the bed pressure drop is reduced by 40.24%-50.65%and the dust removal efficiency is increased by 21.93%-55.09%.展开更多
Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those s...Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.展开更多
The face velocities of the high efficiency particulate air filters and the ultra low penetration airfilters in fan filter units (FFUs) have large relative standard deviation and turbulivity. It seriously affects the ...The face velocities of the high efficiency particulate air filters and the ultra low penetration airfilters in fan filter units (FFUs) have large relative standard deviation and turbulivity. It seriously affects the unidirectivity of the flow in the unidirectional flow clean zone and cleanroom. The cross contamination in this kind of unidirectional flow area is hardly controlled. It is significant to find optional method for keeping the face velocity uniformity of FFU and reducing the face velocity turbulivity of FFU, furthermore, to keep the cleanliness level under FFUs. The normal and easy method is to add flow rectifiers under filters. FFUs with various flow rectifiers have been tested. The uniformity and turbulivity of facevelocity under the FFU are presented in this paper. The influence of the facevelocity uniformity and turbulivity on the contamination boundary of the unidirectional flow is studiedas well.展开更多
Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system ...Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL.Most current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states themselves.In addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL.In order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states.Then,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model parameters.Besides,the approximate analytical RUL distribution can be obtained from the concept of the first hitting time.Once the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters.The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.展开更多
基金supported by National Key Research and Development Program of China(No.2018YFB0606104).
文摘The coupled CFD-DEM method with the JKR(Johnson-Kendall-Roberts)model for describing the contact adhesion of dust to filter particles(FPs)is used to simulate the distribution pattern of dust particle deposition in the granular bed filter(GBF)with multi-layer media.The minimum inlet flow velocity must meet the requirement that the contact probability between dust and FPs is in the high contact probability region.The air flow forms vortices on the leeward side of the FPs and changes abruptly at the intersection of different particle size FPs layers.Dust particles form large deposits at the intersection of the first and second layers and the different particle size filter layers.Dual element multilayer GBF can further optimize the bed structure by interlacing filter layers with different particle sizes.Compared with single particle size multi-layer GBF,the bed pressure drop is reduced by 40.24%-50.65%and the dust removal efficiency is increased by 21.93%-55.09%.
文摘Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.
文摘The face velocities of the high efficiency particulate air filters and the ultra low penetration airfilters in fan filter units (FFUs) have large relative standard deviation and turbulivity. It seriously affects the unidirectivity of the flow in the unidirectional flow clean zone and cleanroom. The cross contamination in this kind of unidirectional flow area is hardly controlled. It is significant to find optional method for keeping the face velocity uniformity of FFU and reducing the face velocity turbulivity of FFU, furthermore, to keep the cleanliness level under FFUs. The normal and easy method is to add flow rectifiers under filters. FFUs with various flow rectifiers have been tested. The uniformity and turbulivity of facevelocity under the FFU are presented in this paper. The influence of the facevelocity uniformity and turbulivity on the contamination boundary of the unidirectional flow is studiedas well.
基金supported by the National Key R&D Program of China(2018YFB1306100)the National Natural Science Foundation of China(61922089,61833016,62073336,61903376,61773386)the National Science Foundation of Shannxi Province(2020JQ-489,2020JM-360).
文摘Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been witnessed.Establishing an applicable degradation model of the system is the foundation and key to accurately estimating its RUL.Most current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states themselves.In addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the RUL.In order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation states.Then,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model parameters.Besides,the approximate analytical RUL distribution can be obtained from the concept of the first hitting time.Once the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model parameters.The effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.
