Subsurface heterogeneity has a significant influence on infiltration and distribution of DNAPLs. Numerical
models of multiphase flow are limited by their ability to predict behavior of DNAPLs in the field due to lack of
accurate data on characterization of heterogeneity. However, these models help to obtain insights into the
complex behavior that will be useful in designing effective remediation schemes. A number of multiphase
flow codes that have the ability to capture the fundamental processes that control the migration of NAPLs
exist. However, the issue of which constitutive model and parameters need to be used in simulating DNAPL
behavior at various scales of interest is not studied satisfactorily. With the goal of contributing to the
knowledge needed to address this issue, an experimental study was conducted to generate an accurate data
set on the migration of DNAPLs in a synthetic aquifer with various combinations of heterogeneous/
homogenous configurations. This paper presents the results of a study where the data from these
experiments was used in combination with an existing multiphase code UTCHEM9 to evaluate constitutive
model sensitivity to prediction of both infiltration and redistribution. In the experiment, a tank with
dimensions 0.71 m x 0.53 m x 0.04 m was packed with two layers. The layer in which the spill occurs was
packed using five well-characterized sands to represent a spatially correlated random field with geostatistical
parameters; Lnk of 22.5 (k in m2) and variance of 1. Second layer was homogenously packed with a single
sand. The interface between the two layers had a slope of 3.5o. NAPL was injected as point source and
saturation distribution was accurately monitored using an automated x-ray attenuation system. Modifications
to the code were made to incorporate hysteresis and accommodate layer inclinations. The model simulations
were conducted for two phases of the experiment; during NAPL injection and re-distribution for the following
cases: (1) Brooks Corey drainage model during the injection and redistribution, without hysteresis and
entrapment, (2) Brooks and Corey drainage model during injection and imbibition during redistribution,
without hysteresis and entrapment, (3) Parker and Lenhard model with hysteresis, (4) Parker and Lenhard
model without hysteresis, and (5) Brooks and Corey drainage model with trapping during injection and
redistribution. The analysis shows during injection period, all of the above models were able to match the
experimental observations reasonably well. The models without trapping and hysteresis effect were also able
to match the experimental results. During the redistribution period, none of the five models were able to
reproduce the experimental results. This preliminary analysis suggests that further detailed study is needed
to determine which parameters control redistribution of NAPL. Also it is observed that entry pressure is the
most important parameter of the constitutive relations; residual saturations of wetting and non-wetting fluids
have minor effects during infiltration and redistribution.
Abstract Nonaqueous phase liquids (NAPLs) such as the homogenate hydrocarbons used in dry cleaning and industrial degreasing (DNAPLs), hydrocarbon fuels and aromatic solvents (LNAPLs), and neutrally buoyant coal tars and creosotes are widespread in our environment. Because of their low solubility, NAPL sources can emit toxic contaminants to groundwater for many decades if not managed properly. The migration of dense nonaqueous phase liquids (DNAPLs), which are immiscible with water, through the saturated porous media is an important part of contaminant hydrology and in petroleum engineering. Protection and remediation of groundwater resources require an understanding of processes that affect the fate and transport of such contaminants in the subsurface environment. To implement appropriate remedial schemes in the contaminated area, it is necessary to evaluate the extent of the contaminated area. This information can be obtained by extensive field investigation, which generally is expensive and time consuming. Field investigation can be reduced or made more cost effective, if the migration pattern of the DNAPLs can be evaluated by using numerical models accurately.
Six models (Simulators) are formulated and developed with all possible combinations of pressure and saturation of the phases as primary variables. A comparative study between six simulators with two numerical methods, conventional simultaneous and modified sequential methods are carried out. The results of the numerical models are compared with the laboratory experimental results to study the accuracy of the model especially in heterogeneous porous media. From the study it is observed that the simulator using pressure and saturation of the wetting fluid (P(W), S(W) formulation) is the best among the models tested. Many simulators with nonwetting phase as one of the primary variables did not converge when used along with simultaneous method. Based on simulator 1 (P(W), S(W) formulation), a comparison of different solution methods such as simultaneous method, modified sequential and adaptive solution modified sequential method are carried out on 4 test problems including heterogeneous and randomly heterogeneous problems. It is found that the modified sequential and adaptive solution modified sequential methods could save the memory by half and as also the CPU time required by these methods is very less when compared with that using simultaneous method. It is also found that the simulator with P(NW) and P(W) as the primary variable which had problem of convergence using the simultaneous method, converged using both the modified sequential method and also using adaptive solution modified sequential method. The present study indicates that pressure and saturation formulation along with adaptive solution modified sequential method is the best among the different simulators and methods tested.
Co-optimizing information quality and energy efficiency are an important but challenging problem in sensor networks, because of the interdependency that exists between them. For example, increasing sensor sampling rate will improve information quality but cost energy consumption, due to more traffic needed to be transmitted. To address this co-optimization issue, this paper first presents a novel quality/energy efficient metric, which models the relationship of sensing, processing, and transmitting with quality and energy. Then, based on the metrics, a quality-energy adapting system is developed to exploit base station scheduling priority and techniques such as batch processing and adaptive sampling to optimize both energy efficiency and overall quality. Our results have demonstrated the usefulness of this model and its feasibility for base station to runtime co-optimize both quality and energy under changing environment and network conditions.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)
