The consideration of orbital angular momentum of an electric field(twisted mode)is applied to the kinetic theory of plasma.The linearized Vlasov–Poisson equation is solved for the anisotropic thermal distributed bi-M...The consideration of orbital angular momentum of an electric field(twisted mode)is applied to the kinetic theory of plasma.The linearized Vlasov–Poisson equation is solved for the anisotropic thermal distributed bi-Maxwellian and Cairns distributions of electrons to obtain the damping rates of twisted waves.The dispersion relation and Landau damping of Langmuir twisted modes are obtained.The presence of twisted modes opens up two more possibilities in Landau damping and dispersion relations.This may generate a mixture with ion sound waves.It seems to play the role of a control parameter of Landau damping.展开更多
To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex chal...To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode interfaces.Also,there is significant effect of the operating conditions on the battery degradation.Various machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance.In this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of batteries.Bat-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values.The prediction accuracy is further compared with re-spect to single sensor and multi sensor data.Further,a combined multi battery data set model is used to improve the prediction accuracy.Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.展开更多
In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and...In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells.展开更多
New and efficient proposed to treat perfluorinated reactor systems were compounds via catalytic decomposition. One system has a single reactor （S-1）, and another has a series of reactors （S-2）. Both systems are c...New and efficient proposed to treat perfluorinated reactor systems were compounds via catalytic decomposition. One system has a single reactor （S-1）, and another has a series of reactors （S-2）. Both systems are capable of producing a valuable CaF2 and eliminating toxic HF effluent and their feasibility was studied at various temperatures with a commercial process simulator, Aspen HYSYS. They are better than the conventional system, and S-2 is better than S-1 in terms of CaF2 production, a required heat for the system, natural gas usage and CO2 emissions in a boiler, and energy consumption. Based on process simulation results, preliminary economic analysis shows that cost savings of 12.37% and 13.55% were obtained in S-2 at 589.6 and 621.4℃compared to S-1 at 700 and 750 ℃, respectively, for the same amount of CaF2 production.展开更多
Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean en...Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information.The prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and elec-trolyzer.Finally,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough.展开更多
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.20183010032380)。
文摘The consideration of orbital angular momentum of an electric field(twisted mode)is applied to the kinetic theory of plasma.The linearized Vlasov–Poisson equation is solved for the anisotropic thermal distributed bi-Maxwellian and Cairns distributions of electrons to obtain the damping rates of twisted waves.The dispersion relation and Landau damping of Langmuir twisted modes are obtained.The presence of twisted modes opens up two more possibilities in Landau damping and dispersion relations.This may generate a mixture with ion sound waves.It seems to play the role of a control parameter of Landau damping.
基金This research was supported by Study on Diagnostic and Prognostic of Lithium-Ion Battery for Electric Vehicle funded by Xynergypower Co.,Ltd.(UNIST-2.200733.01)also supported by the Hydrogen Energy Innovation Technology Development Program of the National Research Foundation of Korea(NRF)funded by the Korean government(Ministry of Science and ICT(MSIT))(NRF-2019M3E6A1064290).
文摘To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode interfaces.Also,there is significant effect of the operating conditions on the battery degradation.Various machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance.In this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of batteries.Bat-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values.The prediction accuracy is further compared with re-spect to single sensor and multi sensor data.Further,a combined multi battery data set model is used to improve the prediction accuracy.Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.
文摘In fuel cells, chemical energy is directly converted into heat and electricity without any emissions which makes them an attractive substitute for various energy needs. Fuel cells have high energy conversion ratio and highpower densities which make them suitable for automotive applications. However, these fuel cell systems suffer with low reliability and durability as system components develop faults during operation resulting in degradation and diminished system performance. In this context, fault detection and fault mitigation strategies are being extensively developed. Diagnostic approaches like electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic analysis offer a truthful representation of the State of Health (SOH) of the fuel cell. However, these approaches are intrusive and require pausing the operation of the fuel cell effecting its integrity. Machine learning based fault detection and SOH estimation is a non-intrusive approach where a mapping function is established between the indicators and SOH. The SOH of a fuel cell can be correlated to the patterns in sensor signals or indicators. Indicators that influence SOH are cell voltages, current density distribution, impedance spectra, acoustic emission and magnetic fields. Developing an accurate fault detection and state estimation technique through data driven machine learning approaches will allow corrective measures to avoid irreversible faults and improve the reliability and durability of fuel cells.
文摘New and efficient proposed to treat perfluorinated reactor systems were compounds via catalytic decomposition. One system has a single reactor （S-1）, and another has a series of reactors （S-2）. Both systems are capable of producing a valuable CaF2 and eliminating toxic HF effluent and their feasibility was studied at various temperatures with a commercial process simulator, Aspen HYSYS. They are better than the conventional system, and S-2 is better than S-1 in terms of CaF2 production, a required heat for the system, natural gas usage and CO2 emissions in a boiler, and energy consumption. Based on process simulation results, preliminary economic analysis shows that cost savings of 12.37% and 13.55% were obtained in S-2 at 589.6 and 621.4℃compared to S-1 at 700 and 750 ℃, respectively, for the same amount of CaF2 production.
基金This research was supported by the Hydrogen Energy Innovation Technology Development Program of the National Research Foundation of Korea(NRF)funded by the Korean government(Ministry of Science and ICT(MSIT))(NRF-2019M3E6A1064290)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(NRF-2019M1A2A2065614).
文摘Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information.The prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and elec-trolyzer.Finally,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough.
