This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also pr...This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance.Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode.Specifically,the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode.During the start-up phase,as the steady-state flow rate increases,inlet and outlet pressures and head also rise,while torque and shaft power decrease,with rotational speed remaining largely unchanged.Conversely,during the shutdown phase,no significant changes were observed in torque,shaft power,or rotational speed.Six machine learning models,including Gaussian Process Regression(GPR),Decision Tree Regression(DTR),and Deep Learning Networks(DLN),demonstrated high accuracy in predicting the hydraulic performance of the centrifugal pump during the start-up and shutdown phases in both power-frequency and frequency-conversion conditions.The findings provide a theoretical foundation for improved prediction of pump hydraulic performance.For instance,when predicting head and flow rate during power-frequency start-up,GPR achieved absolute and relative errors of 0.54 m(7.84%)and 0.21 m3/h(13.57%),respectively,while the Feedforward Neural Network(FNN)reported errors of 0.98 m(8.24%)and 0.10 m3/h(16.71%).By contrast,the Support Vector Machine Regression(SVMR)and Generalized Additive Model(GAM)generally yielded less satisfactory prediction accuracy compared to the other methods.展开更多
Aims Variations in vegetation spring phenology are widely attributed to temperature in temperate and cold regions.However,temperature effect on phenology remains elusive in cold and arid/semiarid ecosystems because so...Aims Variations in vegetation spring phenology are widely attributed to temperature in temperate and cold regions.However,temperature effect on phenology remains elusive in cold and arid/semiarid ecosystems because soil water condition also plays an important role in mediating phenology.Methods We used growing degree day(GDD)model and growing season index(GSI)model,coupling minimum temperature(T_(min))with soil moisture(SM)to explore the influence of heat requirement and hydroclimatic interaction on the start of carbon uptake period(SCUP)and net ecosystem productivity(NEP)in two alpine meadows with different precipitation regimes on the Qinghai-Tibet Plateau(QTP).One is the water-limited alpine steppe-meadow,and the other is the temperature-limited alpine shrub-meadow.Important Findings We observed two clear patterns linking GDD and GSI to SCUP:SCUP was similarly sensitive to variations in preseason GDD and GSI in the humid alpine shrub-meadow,while SCUP was more sensitive to the variability in preseason GSI than GDD in the semiarid alpine steppe-meadow.The divergent patterns indicated a balance of the limiting climatic factors between temperature and water availability.In the humid meadow,higher temperature sensitivity of SCUP could maximize thermal benefit without drought stress,as evidenced by the stronger linear correlation coefficient(R2)and Akaike’s information criterion(AIC)between observed SCUPs and those of simulated by GDD model.However,greater water sensitivity of SCUP could maximize the benefit of water in semiarid steppe-meadow,which is indicated by the stronger R2 and AIC between observed SCUPs and those of simulated by GSI model.Additionally,although SCUPs were determined by GDD in the alpine shrub-meadow ecosystem,NEP was both controlled by accumulative GSI in two alpine meadows.Our study highlights the impacts of hydroclimatic interaction on spring carbon flux phenology and vegetation productivity in the humid and semiarid alpine ecosystems.The results also suggest that water,together with temperature should be included in the models of phenology and carbon budget for alpine ecosystems in semiarid regions.These fi ndings have important implications for improving vegetation phenology models,thus advancing our understanding of the interplay between vegetation phenology,productivity and climate change in future.展开更多
基金financially supported by Science and Technology Project of Quzhou(Grant Nos.2023K256,2023NC08)Research Grants Program of Department of Education of Zhejiang Province(No.Y202455709)+1 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LZY21E050001)University-Enterprise Cooperation Program for Visiting Engineers in Higher Education Institutions in Zhejiang Province(No.FG2020215).
文摘This paper investigates the start-up and shutdown phases of a five-bladed closed-impeller centrifugal pump through experimental analysis,capturing the temporal evolution of its hydraulic performances.The study also predicts the transient characteristics of the pump under non-rated operating conditions to assess the accuracy of various machine learning methods in forecasting its instantaneous performance.Results indicate that the pump’s transient behavior in power-frequency mode markedly differs from that in frequency-conversion mode.Specifically,the power-frequency mode achieves steady-state values faster and exhibits smaller fluctuations before stabilization compared to the other mode.During the start-up phase,as the steady-state flow rate increases,inlet and outlet pressures and head also rise,while torque and shaft power decrease,with rotational speed remaining largely unchanged.Conversely,during the shutdown phase,no significant changes were observed in torque,shaft power,or rotational speed.Six machine learning models,including Gaussian Process Regression(GPR),Decision Tree Regression(DTR),and Deep Learning Networks(DLN),demonstrated high accuracy in predicting the hydraulic performance of the centrifugal pump during the start-up and shutdown phases in both power-frequency and frequency-conversion conditions.The findings provide a theoretical foundation for improved prediction of pump hydraulic performance.For instance,when predicting head and flow rate during power-frequency start-up,GPR achieved absolute and relative errors of 0.54 m(7.84%)and 0.21 m3/h(13.57%),respectively,while the Feedforward Neural Network(FNN)reported errors of 0.98 m(8.24%)and 0.10 m3/h(16.71%).By contrast,the Support Vector Machine Regression(SVMR)and Generalized Additive Model(GAM)generally yielded less satisfactory prediction accuracy compared to the other methods.
基金supported by the National Natural Science Foundation of China(31870406,41661144045)the State Key Research and Development Program(2016YFC0502001,2017YFA0604801).
文摘Aims Variations in vegetation spring phenology are widely attributed to temperature in temperate and cold regions.However,temperature effect on phenology remains elusive in cold and arid/semiarid ecosystems because soil water condition also plays an important role in mediating phenology.Methods We used growing degree day(GDD)model and growing season index(GSI)model,coupling minimum temperature(T_(min))with soil moisture(SM)to explore the influence of heat requirement and hydroclimatic interaction on the start of carbon uptake period(SCUP)and net ecosystem productivity(NEP)in two alpine meadows with different precipitation regimes on the Qinghai-Tibet Plateau(QTP).One is the water-limited alpine steppe-meadow,and the other is the temperature-limited alpine shrub-meadow.Important Findings We observed two clear patterns linking GDD and GSI to SCUP:SCUP was similarly sensitive to variations in preseason GDD and GSI in the humid alpine shrub-meadow,while SCUP was more sensitive to the variability in preseason GSI than GDD in the semiarid alpine steppe-meadow.The divergent patterns indicated a balance of the limiting climatic factors between temperature and water availability.In the humid meadow,higher temperature sensitivity of SCUP could maximize thermal benefit without drought stress,as evidenced by the stronger linear correlation coefficient(R2)and Akaike’s information criterion(AIC)between observed SCUPs and those of simulated by GDD model.However,greater water sensitivity of SCUP could maximize the benefit of water in semiarid steppe-meadow,which is indicated by the stronger R2 and AIC between observed SCUPs and those of simulated by GSI model.Additionally,although SCUPs were determined by GDD in the alpine shrub-meadow ecosystem,NEP was both controlled by accumulative GSI in two alpine meadows.Our study highlights the impacts of hydroclimatic interaction on spring carbon flux phenology and vegetation productivity in the humid and semiarid alpine ecosystems.The results also suggest that water,together with temperature should be included in the models of phenology and carbon budget for alpine ecosystems in semiarid regions.These fi ndings have important implications for improving vegetation phenology models,thus advancing our understanding of the interplay between vegetation phenology,productivity and climate change in future.
