Simulation of the flow and deposition from a laboratory turbidity current, in which dense mixtures of sediment move down a narrow, sloping channel and flow into a large tank. SSIIM CFD software is used to model 3-D fl...Simulation of the flow and deposition from a laboratory turbidity current, in which dense mixtures of sediment move down a narrow, sloping channel and flow into a large tank. SSIIM CFD software is used to model 3-D flow and deposition. SSIIM predicts the height of the accumulated mound to within 25% of experimental values, and the volume of the mound to 20%-50%, depending on the concentration of sediment and slope of the channel. The SSIIM predictions were consistently lower than experimental values. In simulations with initial sediment volumetric concentrations greater than 14%, SSIIM dumped some of the sediment load at the entry gate into the channel, which was not the case with the experimental runs. This is likely due to the fact that the fall velocity of sediment particles in SSIIM does not vary with sediment concentration. Further simulations of deposition from turbidity currents should be attempted when more complete experimental results are available, but it appears for now that SSIIM can be used to give approximate estimates of turbidity current deposition.展开更多
This paper presents the results of application of a 3D （three-dimensional） numerical model to study on MTZ （maximum turbidity zone） in the coastal zone of Mekong River Delta. In this study, a 3D system model with ...This paper presents the results of application of a 3D （three-dimensional） numerical model to study on MTZ （maximum turbidity zone） in the coastal zone of Mekong River Delta. In this study, a 3D system model with combination of hydrodynamics--wave and suspended sediment transport was set up and validated with measured data in the study area. Based on calculated scenarios for the flood and the dry season, the results have shown appearance of MTZs in the coastal zone of Mekong River with suspended sediment concentration prevalent of 0.04-0.07 kg·m^3 （the dry season） and 0.05-0.1kg·m^3 （the flood season）. The position and MTZs scale change with the interaction between fresh water and tidal oscillations. The MTZ occur more in the dry seasons compared to the wet season. The MTZs are prevalent located far away from estuaries about in 12-22 km （in the dry season）, and 5-15 km in the flood season.展开更多
Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmo...Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2concentration between satellite observations and model simulations in China is larger than that in the US and the globe.This inconsistency is related to the GOSAT retrieval error of CO2 caused by the interference among input parameters of satellite retrieval algorithm,and the uncertainty of driving parameters in GEOS-Chem model.展开更多
文摘目的:探讨“冬病夏治”全方配伍和无白芥子配伍延胡索乙素在模型家兔“肺俞”穴皮下药代动力学特征及药代动力学-药效动力学(PK-PD)模型的相关性。方法:支气管哮喘模型家兔随机分成延胡索单方组、缺白芥子组、全方组,微透析技术收集14 h穴位皮下透析液,液相色谱-质谱法(Liquid Chromatography Mass Spectrometry,LCMS)法检测方中君药延胡索主要成分延胡索乙素浓度,获得药代动力学参数;酶联免疫吸附试验(ELISA)法检测对应时间点模型动物血清中IgE水平,获得药效学参数;对药动学、药效学参数进行PK-PD模型拟合。结果:白芥子配伍后的药峰浓度(C_(max))、药时曲线下面积(AUC_(0-t))、平均滞留时间(MRT_(0-t))均显著增加(P<0.01,P<0.01,P<0.05),达峰时间(T_(max))提前(P<0.01);“浓度-时间-效应”三维曲线表明,方中有白芥子配伍时,药效出现更快、消退更慢,起效时间晚于峰浓度,具有一定滞后性。结论:动力学参数、PK-PD模型结果表明,白芥子配伍能够改变“方中君药”——延胡索的主要成分延胡索乙素穴位局部的皮下分布,促进方中君药有效成分快速吸收,延长滞留时间,在方剂中起到主药、改善其他药物分布的“双重”作用。
文摘Simulation of the flow and deposition from a laboratory turbidity current, in which dense mixtures of sediment move down a narrow, sloping channel and flow into a large tank. SSIIM CFD software is used to model 3-D flow and deposition. SSIIM predicts the height of the accumulated mound to within 25% of experimental values, and the volume of the mound to 20%-50%, depending on the concentration of sediment and slope of the channel. The SSIIM predictions were consistently lower than experimental values. In simulations with initial sediment volumetric concentrations greater than 14%, SSIIM dumped some of the sediment load at the entry gate into the channel, which was not the case with the experimental runs. This is likely due to the fact that the fall velocity of sediment particles in SSIIM does not vary with sediment concentration. Further simulations of deposition from turbidity currents should be attempted when more complete experimental results are available, but it appears for now that SSIIM can be used to give approximate estimates of turbidity current deposition.
文摘This paper presents the results of application of a 3D （three-dimensional） numerical model to study on MTZ （maximum turbidity zone） in the coastal zone of Mekong River Delta. In this study, a 3D system model with combination of hydrodynamics--wave and suspended sediment transport was set up and validated with measured data in the study area. Based on calculated scenarios for the flood and the dry season, the results have shown appearance of MTZs in the coastal zone of Mekong River with suspended sediment concentration prevalent of 0.04-0.07 kg·m^3 （the dry season） and 0.05-0.1kg·m^3 （the flood season）. The position and MTZs scale change with the interaction between fresh water and tidal oscillations. The MTZ occur more in the dry seasons compared to the wet season. The MTZs are prevalent located far away from estuaries about in 12-22 km （in the dry season）, and 5-15 km in the flood season.
基金supported by the National Natural Science Foundation of China(Grant No.41071234)"Strategic Priority Research Program-Climate Change:Carbon Budget and Relevant Issues"of the Chinese Academy of Sciences(Grant No.XDA05040401)the National High Techondogy Research and Development Program of China(Grant No.2012AA12A301)
文摘Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2concentration between satellite observations and model simulations in China is larger than that in the US and the globe.This inconsistency is related to the GOSAT retrieval error of CO2 caused by the interference among input parameters of satellite retrieval algorithm,and the uncertainty of driving parameters in GEOS-Chem model.
