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
Gas turbine particulate matter（PM） emissions contribute to air quality degradation and are dangerous to both human health and the environment. Currently, PM mass concentrations determined from gravimetric measuremen...Gas turbine particulate matter（PM） emissions contribute to air quality degradation and are dangerous to both human health and the environment. Currently, PM mass concentrations determined from gravimetric measurements are the default parameter for gas turbine emissions compliance with PM regulations. The measurement of particle size however, is of significant interest due to its specific effects on health and climate science. The mass concentration can be determined from the number-size distribution measurement but requires the experimental evaluation of effective density of a number of particles to establish the powerlaw relationship. In this study, the effective density of PM emissions from an aircraft Auxiliary Power Unit（APU） with petroleum diesel, conventional aviation fuel（Jet A-1） and a multicomponent surrogate fuel（Banner NP 1014） as combusting fuels have been compared.An experimental configuration consisting of a Differential Mobility Analyzer, a Centrifugal Particle Mass Analyzer and a Condensation Particle Counter（DMA-CPMA-CPC） was deployed for this purpose. Overall, a decrease in the effective density（220–1900 km-3） with an increase in the particle size was observed and found to depend on the engine operating condition and the type of fuel undergoing combustion. There was a change in the trend of the effective densities between the PM emissions generated from the fuels burnt and the engine operating conditions with increasing particle size.展开更多
Wildfire occurrence is attributed to the interaction of multiple factors including weather,fuel,topography,and human activities.Among them,weather variables,particularly the temporal characteristics of weather variabl...Wildfire occurrence is attributed to the interaction of multiple factors including weather,fuel,topography,and human activities.Among them,weather variables,particularly the temporal characteristics of weather variables in a given period,are paramount in predicting the probability of wildfire occurrence.However,rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period,introducing additional uncertainties in wildfire probability modeling.To solve the problem,this study employed the weather variables in continuous nonprecipitation days as the"dynamic-step"weather variables with which to improve wildfire probability modeling.Multisource data on weather,fuel,topography,infrastructure,and derived variables were used to model wildfire probability based on two machine learning methods—random forest(RF)and extreme gradient boosting(XGBoost).The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models.The variable importance analysis also verified the top contribution of these dynamic-step weather variables,indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling.展开更多
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.
文摘Gas turbine particulate matter（PM） emissions contribute to air quality degradation and are dangerous to both human health and the environment. Currently, PM mass concentrations determined from gravimetric measurements are the default parameter for gas turbine emissions compliance with PM regulations. The measurement of particle size however, is of significant interest due to its specific effects on health and climate science. The mass concentration can be determined from the number-size distribution measurement but requires the experimental evaluation of effective density of a number of particles to establish the powerlaw relationship. In this study, the effective density of PM emissions from an aircraft Auxiliary Power Unit（APU） with petroleum diesel, conventional aviation fuel（Jet A-1） and a multicomponent surrogate fuel（Banner NP 1014） as combusting fuels have been compared.An experimental configuration consisting of a Differential Mobility Analyzer, a Centrifugal Particle Mass Analyzer and a Condensation Particle Counter（DMA-CPMA-CPC） was deployed for this purpose. Overall, a decrease in the effective density（220–1900 km-3） with an increase in the particle size was observed and found to depend on the engine operating condition and the type of fuel undergoing combustion. There was a change in the trend of the effective densities between the PM emissions generated from the fuels burnt and the engine operating conditions with increasing particle size.
基金supported by the National Natural Science Foundation of China(Contract no.U20A2090)。
文摘Wildfire occurrence is attributed to the interaction of multiple factors including weather,fuel,topography,and human activities.Among them,weather variables,particularly the temporal characteristics of weather variables in a given period,are paramount in predicting the probability of wildfire occurrence.However,rainfall has a large influence on the temporal characteristics of weather variables if they are derived from a fixed period,introducing additional uncertainties in wildfire probability modeling.To solve the problem,this study employed the weather variables in continuous nonprecipitation days as the"dynamic-step"weather variables with which to improve wildfire probability modeling.Multisource data on weather,fuel,topography,infrastructure,and derived variables were used to model wildfire probability based on two machine learning methods—random forest(RF)and extreme gradient boosting(XGBoost).The results indicate that the accuracy of the wildfire probability models was improved by adding dynamic-step weather variables into the models.The variable importance analysis also verified the top contribution of these dynamic-step weather variables,indicating the effectiveness of the consideration of dynamic-step weather variables in wildfire probability modeling.
