Chaotic Advection and Reaction During Engineered Injection and Extraction in Heterogeneous Porous Media

During in situ remediation of contaminated groundwater, a treatment solution is often injected into the contaminated region to initiate reactions that degrade the contaminant. Degradation reactions only occur where the treatment solution and the contaminated groundwater are close enough that mixing will bring them together. Degradation is enhanced when the treatment solution is spread into the contami- nated region, thereby increasing the spatial extent of mixing and degradation reactions. Spreading results from local velocity variations that emerge from aquifer heterogeneity and from spatial variations in the external forcings that drive flow. Certain patterns in external forcings have been shown to create chaotic advection, which is known to enhance spreading of solutes in groundwater flow and other laminar flows. This work uses numerical simulations of flow and reactive transport to investigate how aquifer heterogene- ity changes the qualitative and quantitative aspects of chaotic advection in an aquifer, and the extent to which these changes enhance contaminant degradation. We generate chaotic advection using engineered injection and extraction (EIE), an approach that uses sequential injection and extraction of water in wells surrounding the contaminated region to create time-dependent flow fields that promote plume spreading. We demonstrate that as the degree of heterogeneity increases, both plume spreading and contaminant degradation increase; however, the increase in contaminant degradation is small relative to the increase in plume spreading. Our results show that the combined effects of EIE and heterogeneity produce substan- tially more stretching than either effect separately

Adjoint modeling to quantify stream flow changes due to aquifer pumping

November 2013.Includes bibliographical references.As populations grow and demand for water increases, new sources of water must be found. If groundwater resources are developed to meet these growing demands, the increased pumping of aquifers should not reduce flows in rivers to levels that would limit the availability of water for drinking water supply, irrigation, and riparian habitat. Stream depletion is the term for the change in the river flow rate due to pumping in an aquifer that is hydraulically connected to the river. In many regions of the U.S., a new well cannot be sited until it is shown that pumping the new well will not cause substantial stream depletion. Numerical simulations are typically used to quantify stream depletion. In the standard approach, two numerical simulations are run—one without pumping and one with pumping in a well at the proposed location. In both simulations, the water flux between the river and aquifer is calculated, and the difference between these fluxes is the stream depletion due to pumping at the proposed well location. If multiple well locations are considered, one addition simulation must be run for each additional potential well location; thus, this approach can be inefficient for siting new wells. The goal this research was to develop an adjoint-based modeling approach to efficiently quantify stream depletion due to aquifer pumping. In a single simulation of an adjoint model, stream depletion is calculated for a well at any location in the aquifer; thus, it is computationally efficient when the number of well locations or possible well locations is large. The adjoint approach was developed to be used with standard groundwater flow simulators, and therefore can be applied in practice. The research included rigorous development of the adjoint equation for calculating stream depletion in confined and unconfined aquifers with various models of groundwater/surface water interaction, along with numerical simulations to verify the adjoint equation. In addition, we used the adjoint method to investigate the sensitivity of stream depletion to the hydraulic conductivity of the stream channel, a parameter which is known to be uncertain

Use of groundwater lifetime expectancy for the performance assessment of a deep geologic waste repository: 1. Theory, illustrations, and implications

Long-term solutions for the disposal of toxic wastes usually involve isolation of the wastes in a deep subsurface geologic environment. In the case of spent nuclear fuel, if radionuclide leakage occurs from the engineered barrier, the geological medium represents the ultimate barrier that is relied upon to ensure safety. Consequently, an evaluation of radionuclide travel times from a repository to the biosphere is critically important in a performance assessment analysis. In this study, we develop a travel time framework based on the concept of groundwater lifetime expectancy as a safety indicator. Lifetime expectancy characterizes the time that radionuclides will spend in the subsurface after their release from the repository and prior to discharging into the biosphere. The probability density function of lifetime expectancy is computed throughout the host rock by solving the backward-in-time solute transport adjoint equation subject to a properly posed set of boundary conditions. It can then be used to define optimal repository locations. The risk associated with selected sites can be evaluated by simulating an appropriate contaminant release history. The utility of the method is illustrated by means of analytical and numerical examples, which focus on the effect of fracture networks on the uncertainty of evaluated lifetime expectancy.Comment: 11 pages, 8 figures; Water Resources Research, Vol. 44, 200

Frost Quakes : Crack Formation by Thermal Stress

AbstractFractures in frozen soils (frost quakes) can cause damage to buildings and other infrastructure, but their formation mechanisms remain poorly understood. A methodology was developed to assess thermal stress on soil due to changes in climate and weather conditions and to investigate the connection between thermal stress and frost quakes in central Finland due to brittle fracturing in uppermost soils. A hydrological model was used to simulate snow accumulation and melt, and a soil temperature model was used to simulate soil temperature at different depths beneath the snow pack. The results of modeling, together with measurements of air temperature, snow cover thickness, and soil temperature, were used to calculate temporal variations in thermal stress in soil. We show that frost quakes occur when thermal stress caused by a rapid decrease in temperature exceeds fracture toughness and strength of the soil‐ice mixture. We compared calculated thermal stress on soil, critical stress intensity factor, and a seismogram recorded in a suburban region in central Finland. Our results suggest that this methodology can be used to predict thermal stresses on soil and identify stress values that may lead to fractures of frozen soils, that is, frost quakes.Abstract Fractures in frozen soils (frost quakes) can cause damage to buildings and other infrastructure, but their formation mechanisms remain poorly understood. A methodology was developed to assess thermal stress on soil due to changes in climate and weather conditions and to investigate the connection between thermal stress and frost quakes in central Finland due to brittle fracturing in uppermost soils. A hydrological model was used to simulate snow accumulation and melt, and a soil temperature model was used to simulate soil temperature at different depths beneath the snow pack. The results of modeling, together with measurements of air temperature, snow cover thickness, and soil temperature, were used to calculate temporal variations in thermal stress in soil. We show that frost quakes occur when thermal stress caused by a rapid decrease in temperature exceeds fracture toughness and strength of the soil‐ice mixture. We compared calculated thermal stress on soil, critical stress intensity factor, and a seismogram recorded in a suburban region in central Finland. Our results suggest that this methodology can be used to predict thermal stresses on soil and identify stress values that may lead to fractures of frozen soils, that is, frost quakes

Heat conduction in the ground under natural conditions and with heat exchanger installed

The results of calculations of the heat transfer in a horizontal ground heat exchanger are presented. The applied model is based on a one-dimensional equation of the transient heat conduction with an internal heat source. The model was correctly verified by comparison of computational results and experimental measurements presented in literature. Thermal calculations concerning heat transfer in the ground under natural conditions are also presented

Modell for prediksjon av eiendomspriser i Oslo

Formålet med denne oppgaven var å bruke maskinlæring til å lage en modell for å kunne predikere eiendomspriser i Oslo. En typisk modell å bruke for prispredikering er hedonisk prismodell. Med bakgrunn i dette har vi også valgt å se på hedonisk prismodell, både som sammenligningsgrunnlag og for å bedre forstå hvordan prispredikering vanligvis foregår. For å utvikle modellene våre har vi brukt nevrale nettverk og hedonisk regresjon. Datasettet vårt inneholdt mange datapunkter og vi har begrenset modellen våres en del. Dette betyr også at bruksområdene til modellene er begrenset. Vi har utviklet to modeller med nevrale nettverk, hvor den ene har en makspris på bolig på 15 millioner kroner (ordinær modell), mens den andre har en makspris på 5 millioner kroner (modell for førstegangskjøper). Når vi begrenser maksprisen til 5 millioner kroner ser vi på boliger som kanskje er mest interessante for førstegangskjøpere. Videre har vi utviklet to modeller hvor vi har brukt hedonisk metode. Den ene modellen er det brukt multippel regresjon uten noen tilpasninger, med de samme begrensningene som er benyttet i ordinær modell. I den andre modellen er det gjort en logaritmisk transformasjon av den avhengige variabelen, og dataen er begrenset på samme måte som i modell for førstegangskjøpere. Vi fikk svært lovende resultater fra modellene, hvorpå den ene modellen hadde en R2- score opp mot 90%. Konklusjonen fra denne oppgaven er at hvilken modell som er best, avhenger av bruksområdet som modellen benyttes til. Ønsker man en modell hvis virkemåte er enkel å forstå bør man velge en av modellene som benytter hedonisk regresjon, mens modellene som benytter nevrale nettverk gir økt presisjon. Er presisjon særlig viktig, bør man velge en av modellene med utvidete begrensninger i dataene, og vice versa.The purpose of this thesis is to utilize machine learning to make a model that could predict real estate prices in Oslo. A typical model to use for price prediction is hedonic price model. With this in mind we have also chosen to look at hedonic price model, both as basis for comparison and to get a better understanding of how price prediction usually takes place. To develop our models, we have used neural networks and hedonic regression. Our dataset contained many data points and based on this we limited our models quite a bit. This also means that the models cannot be used in a lot of areas. We have developed two models with neural networks, where one of them have a maximum price on housing of 15 million NOK (ordinary model), while the other have a maximum price of 5 million NOK (model for first time buyers). When we limit the maximum price to 5 million NOK, we look at housing that may be most interesting for first time buyers. Furthermore, we have developed two models using hedonic price model. On the first model we have used multiple regression without any adjustments, but with the same limitations used in the ordinary model. In the second model there is done a logarithmic transformation of the dependent variable, and the data is limited in the same way as the model for first time buyers. We got very promising results from our models, where one of them had an R2-score closing to 90%. The conclusion from this thesis is that the models that performs the best, depends on the area of use. If you want a model that are easy to use, you should use one of the models that are based on hedonic price model. The models using artificial neural network gives better precision. If precision is especially important, you would choose one of the models with extended restrictions, and vice versa

To What Extent Does Ericsson Adapt Their Organisational Culture When Opening A Branch in A New Culture?

Telefonaktiebolaget LM Ericsson (Ericsson) is a Swedish telecom company with branches all over the world. It is not a secret that a company with branches in several diverse cultures has to decide what extent the organization will go in adapting the host culture into the new branch. This essay will focus on Ericsson and a few cases on how well Ericsson decided to incorporate host cultures into their organization. The cases picked will mainly be in Asia due to the greater cultural difference between the Asian host culture and their European home culture

Active spreading: Hydraulics for enhancing groundwater remediation

During in situ groundwater remediation, reactions occur in a narrow reaction front in which the amendment and contaminant are close enough to mix. Active spreading, in which injection or extraction wells create spatially variable velocity fields, can be used to increase the surface area of the reaction front, thereby enhancing reaction. This study used four active spreading flow fields that are building blocks to more complex remediation hydraulics to evaluate how the flow field and the plume position control contaminant degradation in both homogeneous and heterogeneous aquifers. At the plume scale, reaction depended on mechanical dispersion across the reaction front, which is proportional to both the local velocity and the local contaminant concentration gradient. Mechanical dispersion and, consequently, the amount of degradation, was highest when the reaction front was perpendicular to the local velocity, producing a high local dispersion coefficient. This effect was amplified where flow was diverging due to sharpening of the concentration gradient. &nbsp;</p