This work investigates water-based micropolar hybrid nanofluid(MHNF) flow on an elongating variable porous sheet.Nanoparticles of diamond and copper have been used in the water to boost its thermal conductivity. The m...This work investigates water-based micropolar hybrid nanofluid(MHNF) flow on an elongating variable porous sheet.Nanoparticles of diamond and copper have been used in the water to boost its thermal conductivity. The motion of the fluid is taken as two-dimensional with the impact of a magnetic field in the normal direction. The variable, permeable, and stretching nature of sheet's surface sets the fluid into motion. Thermal and mass diffusions are controlled through the use of the Cattaneo–Christov flux model. A dataset is generated using MATLAB bvp4c package solver and employed to train an artificial neural network(ANN) based on the Levenberg–Marquardt back-propagation(LMBP) algorithm. It has been observed as an outcome of this study that the modeled problem achieves peak performance at epochs 637, 112, 4848, and 344 using ANN-LMBP. The linear velocity of the fluid weakens with progression in variable porous and magnetic factors.With an augmentation in magnetic factor, the micro-rotational velocity profiles are augmented on the domain 0 ≤ η < 1.5 due to the support of micro-rotations by Lorentz forces close to the sheet's surface, while they are suppressed on the domain 1.5 ≤ η < 6.0 due to opposing micro-rotations away from the sheet's surface. Thermal distributions are augmented with an upsurge in thermophoresis, Brownian motion, magnetic, and radiation factors, while they are suppressed with an upsurge in thermal relaxation parameter. Concentration profiles increase with an expansion in thermophoresis factor and are suppressed with an intensification of Brownian motion factor and solute relaxation factor. The absolute errors(AEs) are evaluated for all the four scenarios that fall within the range 10^(-3)–10^(-8) and are associated with the corresponding ANN configuration that demonstrates a fine degree of accuracy.展开更多
This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative ...This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.展开更多
This study scrutinizes the flow of engine oil-based suspended carbon nanotubes magnetohydrodynamics(MHD)hybrid nanofluid with dust particles over a thin moving needle following the Xue model.The analysis also incorpor...This study scrutinizes the flow of engine oil-based suspended carbon nanotubes magnetohydrodynamics(MHD)hybrid nanofluid with dust particles over a thin moving needle following the Xue model.The analysis also incorporates the effects of variable viscosity with Hall current.For heat transfer analysis,the effects of the Cattaneo–Christov theory and heat generation/absorption with thermal slip are integrated into the temperature equation.The Tiwari–Das nanofluid model is used to develop the envisioned mathematical model.Using similarity transformation,the governing equations for the flow are translated into ordinary differential equations.The bvp4c method based on Runge–Kutta is used,along with a shooting approach.Graphs are used to examine and depict the consequences of significant parameters on involved profiles.The results revealed that the temperature of the fluid and boundary layer thickness is diminished as the solid volume fraction is raised.Also,with an enhancement in the variable viscosity parameter,the velocity distribution becomes more pronounced.The results are substantiated by assessing them with an available study.展开更多
The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,...The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,which combines the advantages of mechanism model and black-box model,is proposed in this paper.To improve the performance,the static neural network and variable neural network are used to build the black-box model.The static neural network can significantly improve the static performance of the hybrid model,and the variable neural network makes the hybrid dynamic model predict the real PEM fuel cell behavior with required accuracy.Finally,the hybrid dynamic model is validated with a 500 W PEM fuel cell.The static and transient experiment results show that the hybrid dynamic model can predict the behavior of the fuel cell stack accurately and therefore can be effectively utilized in practical application.展开更多
Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention ...Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.展开更多
In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The pro...In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to n<sup>m</sup> classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size n<sup>m</sup>. They are then interpreted as one-hot encoded labels, padded with n<sup>m</sup> - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.展开更多
1st error Within the abstract of the above paper,in the mathematical modeling,in Table 1 and in the concluding remarks,it is clearly mentioned that the base fluid in[1]is engine oil and ALL results have been produced ...1st error Within the abstract of the above paper,in the mathematical modeling,in Table 1 and in the concluding remarks,it is clearly mentioned that the base fluid in[1]is engine oil and ALL results have been produced for Prandtl number Pr=6.2.展开更多
A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and le...A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.展开更多
The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algori...The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algorithm is used to identify hybrid continuous-time transfer function models of the Box–Jenkins form from discretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalized maximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input–output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input–output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input–output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMC based flow controller and a practical application demonstrates the applicability of the algorithm.展开更多
In this paper, a new hybrid control technique, based on a combination of base-isolation and semi-active variable stiffness/damping in a superstructure, is presented. To illustrate the efficiency of the proposed contro...In this paper, a new hybrid control technique, based on a combination of base-isolation and semi-active variable stiffness/damping in a superstructure, is presented. To illustrate the efficiency of the proposed control system, model tests on a mini-electromagnetic shaking table and a numerical simulation were performed. The test and numerical calculation results indicate that this new hybrid control mode with additional damping and smaller additional stiffness can achieve a better control efficiency.展开更多
A hybrid system proposed by three different specifications for the equipment of a tourist lodge in the headland of south-west Morocco was sized by analysing the limits of load profile constraints,such as hour-to-hour ...A hybrid system proposed by three different specifications for the equipment of a tourist lodge in the headland of south-west Morocco was sized by analysing the limits of load profile constraints,such as hour-to-hour variability(HHR),day-to-day variability(DDR)and the operating reserve rate(ROR).Based on the three-factor Doehlert matrix recommendations,the simulations employed an energy-sizing tool for hybrid renewable-energy systems.Testing was conducted with DDR at 5-30%,HHR at 10-30%and ROR at 0-20%.Under these conditions,a second-order polynomial relationship with a correlation rate of~90%was found between the net present cost(NPC)of the system,the levelized cost of electricity and the various constraint factors.The first specification,SPC(1),composed of generators and batteries,was introduced to control and validate the simulation independently of renewable energy,which showed a positive manifestation with the imposed constraints.The analysis expanded by introducing solar and wind energy resources.The SPC(2)configuration added PV modules to the SPC(1)and the SPC(3)configuration added wind turbines to SPC(2).The effect of DDR,HHR and ROR in the trials was significant by linear regression.At the same time,only DDR had a significant quadratic regression.The others,with their pairwise interactions,were insignificant.The desirability procedure made it possible to calculate the maximum limits of load profile constraint variables leading to targets of LCOE=0.41 US$/kWh and NPC=US$320080.1 of the load profile constraints:the DDR=15.47%and the HHR=26.55%at an ROR rate of 17.77%.展开更多
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through large Research Group Project (Grant No. RGP2/198/45)Project supported by Prince Sattam bin Abdulaziz University (Grant No. PSAU/2025/R/1446)。
文摘This work investigates water-based micropolar hybrid nanofluid(MHNF) flow on an elongating variable porous sheet.Nanoparticles of diamond and copper have been used in the water to boost its thermal conductivity. The motion of the fluid is taken as two-dimensional with the impact of a magnetic field in the normal direction. The variable, permeable, and stretching nature of sheet's surface sets the fluid into motion. Thermal and mass diffusions are controlled through the use of the Cattaneo–Christov flux model. A dataset is generated using MATLAB bvp4c package solver and employed to train an artificial neural network(ANN) based on the Levenberg–Marquardt back-propagation(LMBP) algorithm. It has been observed as an outcome of this study that the modeled problem achieves peak performance at epochs 637, 112, 4848, and 344 using ANN-LMBP. The linear velocity of the fluid weakens with progression in variable porous and magnetic factors.With an augmentation in magnetic factor, the micro-rotational velocity profiles are augmented on the domain 0 ≤ η < 1.5 due to the support of micro-rotations by Lorentz forces close to the sheet's surface, while they are suppressed on the domain 1.5 ≤ η < 6.0 due to opposing micro-rotations away from the sheet's surface. Thermal distributions are augmented with an upsurge in thermophoresis, Brownian motion, magnetic, and radiation factors, while they are suppressed with an upsurge in thermal relaxation parameter. Concentration profiles increase with an expansion in thermophoresis factor and are suppressed with an intensification of Brownian motion factor and solute relaxation factor. The absolute errors(AEs) are evaluated for all the four scenarios that fall within the range 10^(-3)–10^(-8) and are associated with the corresponding ANN configuration that demonstrates a fine degree of accuracy.
基金supported by the National Natural Science Foundation of China (62073303,61673356)Hubei Provincial Natural Science Foundation of China (2015CFA010)the 111 Project(B17040)。
文摘This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.
基金the Taif University research supporting project number(TURSP-2020/304),Taif University,Saudi Arabia。
文摘This study scrutinizes the flow of engine oil-based suspended carbon nanotubes magnetohydrodynamics(MHD)hybrid nanofluid with dust particles over a thin moving needle following the Xue model.The analysis also incorporates the effects of variable viscosity with Hall current.For heat transfer analysis,the effects of the Cattaneo–Christov theory and heat generation/absorption with thermal slip are integrated into the temperature equation.The Tiwari–Das nanofluid model is used to develop the envisioned mathematical model.Using similarity transformation,the governing equations for the flow are translated into ordinary differential equations.The bvp4c method based on Runge–Kutta is used,along with a shooting approach.Graphs are used to examine and depict the consequences of significant parameters on involved profiles.The results revealed that the temperature of the fluid and boundary layer thickness is diminished as the solid volume fraction is raised.Also,with an enhancement in the variable viscosity parameter,the velocity distribution becomes more pronounced.The results are substantiated by assessing them with an available study.
基金Supported by the National Science Fund for Distinguished Young Scholars of China (60925011)
文摘The polymer electrolyte membrane（PEM） fuel cell has been regarded as a potential alternative power source,and a model is necessary for its design,control and power management.A hybrid dynamic model of PEM fuel cell,which combines the advantages of mechanism model and black-box model,is proposed in this paper.To improve the performance,the static neural network and variable neural network are used to build the black-box model.The static neural network can significantly improve the static performance of the hybrid model,and the variable neural network makes the hybrid dynamic model predict the real PEM fuel cell behavior with required accuracy.Finally,the hybrid dynamic model is validated with a 500 W PEM fuel cell.The static and transient experiment results show that the hybrid dynamic model can predict the behavior of the fuel cell stack accurately and therefore can be effectively utilized in practical application.
基金Supported by National Key Technology R&D Program of Ministry of Science and Technology of China(Grant No.2013BAG14B01)
文摘Since the main power source of hybrid electric vehicle（HEV） is supplied by the power battery,the predicted performance of power battery,especially the state-of-charge（SOC） estimation has attracted great attention in the area of HEV.However,the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected.A variable structure extended kalman filter（VSEKF）-based estimation method,which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition,is presented.First,the general lower-order battery equivalent circuit model（GLM）,which includes column accumulation model,open circuit voltage model and the SOC output model,is established,and the off-line and online model parameters are calculated with hybrid pulse power characteristics（HPPC） test data.Next,a VSEKF estimation method of SOC,which integrates the ampere-hour（Ah） integration method and the extended Kalman filter（EKF） method,is executed with different adaptive weighting coefficients,which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes.According to the experimental analysis,the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV.The error rate of SOC estimation with the VSEKF method is focused in the range of 5% to 10% comparing with the range of 20% to 30% using the EKF method and the Ah integration method.In Summary,the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system,which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method.The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.
文摘In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset. Currently available classifiers can classify only up to two classes. The proposed architecture allows networks to classify classes up to n<sup>m</sup> classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers. The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size n<sup>m</sup>. They are then interpreted as one-hot encoded labels, padded with n<sup>m</sup> - 10 zeros. The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in “Continuous variable quantum neural networks” [1]. They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.
文摘1st error Within the abstract of the above paper,in the mathematical modeling,in Table 1 and in the concluding remarks,it is clearly mentioned that the base fluid in[1]is engine oil and ALL results have been produced for Prandtl number Pr=6.2.
文摘A hybrid optimization algorithm for the time-domain identification of multivariable,state space model for aero-engine was presented in this paper.The optimization procedure runs particle swarm optimization(PSO) and least squares optimization(LSO) "in series".PSO starts from an initial population and searches for the optimum solution by updating generations.However,it can sometimes run into a suboptimal solution.Then LSO can start from the suboptimal solution of PSO,and get an optimum solution by conjugate gradient algorithm.The algorithm is suitable for the high-order multivariable system which has many parameters to be estimated in wide ranges.Hybrid optimization algorithm is applied to estimate the parameters of a 4-input 4-output state variable model(SVM) for aero-engine.The simulation results demonstrate the effectiveness of the proposed algorithm.
基金Supported by the National Natural Science Foundation of China(61573052,61174128)
文摘The paper describes a closed-loop system identification procedure for hybrid continuous-time Box–Jenkins models and demonstrates how it can be used for IMC based PID controller tuning. An instrumental variable algorithm is used to identify hybrid continuous-time transfer function models of the Box–Jenkins form from discretetime prefiltered data, where the process model is a continuous-time transfer function, while the noise is represented as a discrete-time ARMA process. A novel penalized maximum-likelihood approach is used for estimating the discrete-time ARMA process and a circulatory noise elimination identification method is employed to estimate process model. The input–output data of a process are affected by additive circulatory noise in a closedloop. The noise-free input–output data of the process are obtained using the proposed method by removing these circulatory noise components. The process model can be achieved by using instrumental variable estimation method with prefiltered noise-free input–output data. The performance of the proposed hybrid parameter estimation scheme is evaluated by the Monte Carlo simulation analysis. Simulation results illustrate the efficacy of the proposed procedure. The methodology has been successfully applied in tuning of IMC based flow controller and a practical application demonstrates the applicability of the algorithm.
基金Societal Commonweal Fund Project (2001DIB20098) Earthquake Science Associate Fund (603011)
文摘In this paper, a new hybrid control technique, based on a combination of base-isolation and semi-active variable stiffness/damping in a superstructure, is presented. To illustrate the efficiency of the proposed control system, model tests on a mini-electromagnetic shaking table and a numerical simulation were performed. The test and numerical calculation results indicate that this new hybrid control mode with additional damping and smaller additional stiffness can achieve a better control efficiency.
文摘A hybrid system proposed by three different specifications for the equipment of a tourist lodge in the headland of south-west Morocco was sized by analysing the limits of load profile constraints,such as hour-to-hour variability(HHR),day-to-day variability(DDR)and the operating reserve rate(ROR).Based on the three-factor Doehlert matrix recommendations,the simulations employed an energy-sizing tool for hybrid renewable-energy systems.Testing was conducted with DDR at 5-30%,HHR at 10-30%and ROR at 0-20%.Under these conditions,a second-order polynomial relationship with a correlation rate of~90%was found between the net present cost(NPC)of the system,the levelized cost of electricity and the various constraint factors.The first specification,SPC(1),composed of generators and batteries,was introduced to control and validate the simulation independently of renewable energy,which showed a positive manifestation with the imposed constraints.The analysis expanded by introducing solar and wind energy resources.The SPC(2)configuration added PV modules to the SPC(1)and the SPC(3)configuration added wind turbines to SPC(2).The effect of DDR,HHR and ROR in the trials was significant by linear regression.At the same time,only DDR had a significant quadratic regression.The others,with their pairwise interactions,were insignificant.The desirability procedure made it possible to calculate the maximum limits of load profile constraint variables leading to targets of LCOE=0.41 US$/kWh and NPC=US$320080.1 of the load profile constraints:the DDR=15.47%and the HHR=26.55%at an ROR rate of 17.77%.
