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Determination of groundwater solute transport parameters in finite element modelling using tracer injection and withdrawal testing data

作     者:Van Hoang Nguyen 

作者机构:Institute of Geological Sciences-Vietnam Academy of Science and TechnologyHanoiVietnam 

出 版 物:《Journal of Groundwater Science and Engineering》 (地下水科学与工程(英文版))

年 卷 期:2021年第9卷第4期

页      面:292-303页

学科分类:081802[工学-地球探测与信息技术] 08[工学] 0818[工学-地质资源与地质工程] 

主  题:Groundwater solute transport Tracer injection Effective porosity Longitudinal dispersivity Flow distortion coefficient Normalized concentration 

摘      要:The groundwater tracer injection and withdrawal tests are often carried out for the determination of aquifer solute transport ***,the parameter analyses encounter a great difficulty due to the radial flow nature and the variability of the temporal boundary *** adaptive methodology for the determination of groundwater solute transport parameters using tracer injection and withdrawal test data had been developed and illustrated through an actual *** methodology includes the treatment of the tracer boundary condition at the tracer injection well,the normalization of tracer concentration,the groundwater solute transport finite element modelling and the method of least squares to optimize the *** application of this methodology was carried out in a field test in the South of Hanoi *** tested aquifer is Pleistocene aquifer,which is a main aquifer and has been providing domestic water supply to the city since the French *** porosity of 0.31,longitudinal dispersivity of 2.2 m,and hydrodynamic dispersion coefficients from D=220 m^(2)/d right outside the pumping well screen to D=15.8 m^(2)/d right outside the tracer injection well screen have been obtained for the aquifer at the test *** minimal sum of squares of the differences between the observed and model normalized tracer concentration is 0.00119,which is corresponding to the average absolute difference between observed and model normalized concentrations of 0.0355(while 1 is the worst and 0 is the best fit).

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