Following large rain events, extraneous freshwater contributions known as inflow and infiltration (I/I) bypass the storm sewer and enter the sanitary sewer system. In areas with a high water table, like Pinellas County and the surrounding Tampa Bay area, a majority of the wastewater infrastructure is submerged year round exacerbating the rate of groundwater infiltration. This excess flow overloads the existing wastewater infrastructure leading to sanitary sewer overflows (SSOs). These SSOs result in serious problems for municipalities and utilities across the country. This study was performed in order to assist Pinellas County Utilities in rehabilitating their southern sewer system. To do this, 59 sub-basins across 8 sewer zones were monitored through Pinellas County’s Phase 1 Flow Monitoring Program accounting for over 150 miles of gravity pipe. For each sub-basin, a flow meter was utilized to measure the flow from May to October, 2017. This data was analyzed to separately quantify the amount of infiltration and inflow in each sub-basin, respectively. Once quantified, a Severity Index (SI) was developed in order to give each sub-basin a score from 1-100 as it relates to the condition of the gravity mains in the sub-basin. The SI was a function of locational features available with the use of a Geographic Information System (GIS), such as the distance to water bodies and the soil hydrologic group (SHG), as well as intrinsic pipe properties including the type of pipe material and the age of pipe. Once validated with additional flow monitoring data, the developed SI framework can serve as an additional tool utilized by Pinellas County Utilities to identify areas in need of sanitary sewer rehabilitation. Being that the model only requires easily attainable information, this approach is less time consuming and is inexpensive as compared to traditional flow monitoring efforts. The study also examined the required monetary investment by Pinellas County Utilities in order to abate the 17 sub-basins observed in the study with an infiltration rate greater than the marginal threshold put forth by the Environmental Protection Agency (EPA). The study indicated that gravity pipe rehabilitation does not make a significant impact on groundwater infiltration until at least 30% of the gravity pipes in the sub-basin are lined. This is due to the groundwater table submerging a majority of the wastewater infrastructure. Once this threshold is met, lining was observed to abate groundwater infiltration linearly. The results found that $4.4 million will be required to rehabilitate the affected sub-basins to a marginal rate of infiltration and reduce the flow to South Cross Bayou Water Reclamation Facility (SCBWRF) by an average of 0.72 mgd (million gallons per day). On an annual basis, this reduction in flow will result in approximately $650,000 in treatment costs savings.

Simulation of a wall jet in an enclosure performed to predict the effect of pulsation flow on improving the performance of mixing ventilation systems which are routine practices in industry. Comparing two flows with equal amount for constant and pulsation modes, it was found out that the same global airflow pattern exists for both of the cases but with generation of more eddies and local periodically velocity variations for pulsation mode. This periodic generation of turbulence at pulsatile ventilation flows happen despite the relatively low Reynolds numbers of such flows.Bigger size of boundary layer and higher turbulent kinetic energy for pulsation mode in comparisonwith the same flow rate in constant velocity mode could result in more ventilation capacity with no need to increase the use of energy. It was seen that while a higher constant velocity rate could produce the same acceptable results in terms of higher efficiency in ventilation, a lower pulsated flow could yields it without the risk of draught. Regarding the thesis procedure, the computational solution started with a grid independency study. 2-Dimensional simulation failed to simulate the results similar to the experimental data. No URANS model was able to yield good outcome in 2D mode. The study was continued with 3D SST-kω which yielded good prediction of velocity profiles near the wall regions. For predicting turbulence parameters in the center of the domain SST-URANS was not helpful so, simulation switched to SAS which was successful to some extent to get close to reality.

Studies in the conflict-climate field usually aim to examine how environmental scarcities canbe linked to conflict, but positive outcomes are generally overlooked. Lately, attention isbeing drawn to the relationship between water scarcity and migration flows. Some researchersargue that efficiency in managing resources is likely to be an imminent issue in migrantreceiving areas and competition over resources are common, especially when they are scarce.As is evident, there is a rule rather than exception that climate related events happensimultaneously, yet few studies do focus on the coupled effect of such climatic events. Torealistically estimate responses to climate change, this study will aim to examine weatherwater scarcity, by itself and in combination with migration-inflow, can encouragecooperation. By using disaggregated data, a sub-analysis was conducted throughout countriesbordering the Mediterranean Sea, covering the years 1997-2009. The results of this studyshow that water scarcity increases the likelihood of non-state cooperation. Overall, thissuggests that water management plays an important role in human interaction and should beconsidered in peacebuilding processes.

Surfactants belong to surface-active compounds that have ability to restrain the surface tension; this ability is exploited to eliminate impurities. This study is focused on determination of surfactants in waste water to which these compounds can be transported from various cleaning and washing articles. In theoretical part are listed basic classifications of surfactants, their properties and requests of Czech legislative for their content in waste water. Furthermore here are described chosen analytical procedures for determination of anion-active, cation-active and non-ionic surfactants in waste water. The conclusion of the work is evaluation of measured results of surfactants concentration in inflow and outflow of waste water from waste-treatment plant in University of Veterinary and Pharmaceutical Sciences Brno and waste water from neutralizating station in FCH BUT. To determination of anionic surfactants were used two methods, arbitration method with usage of methylene blue and mobile analytics method which is based on chemical reaction between target compound and chemical agent. This reaction leads to formation of coloured compound able to spectrofotometric determination.

Using a hybrid of the Heckscher–Ohlin model and specific factor model of trade, this article considers the phenomenon of FDI inflows only in the exportable sector of developing economies. We investigate the impact of such capital flow on factor prices and the real exchange rate (RER) in the host country. Our results indicate that the exportable production expands while both the non-traded good production and the return to the factor specific to the non-traded good decrease, consequent upon an inflow of capital specific to the exportable sector. The effect of such inflow of foreign capital on the RER is unambiguous and it increases. JEL Codes: F1, F21, F31

Sewer pipelines are deteriorating due to aging and sub optimal material selections, leading to the infiltration of clean ground and rainfall water into the pipes. It is estimated that a significant portion (up to 40-50%) of the water entering wastewater treatment plants is actually clean infiltrated water. This infiltration not only contributes to unnecessary energy consumption but also poses the risk of flooding the sewer network and treatment plants. Finding these broken pipes is utmost importance but is not straight forward due to the pipes being located a few meters below ground. There exist methods of pinpointing where these leaks occur, but they are often time consuming and expensive. This thesis seeks to address the following question; Can the estimation of infiltration be accomplished solely through the temperature data obtained from discrete pump stations, or is the inclusion of precipitation data essential for achieving accurate results? Two machine learning algorithms are investigated to solve the regression problem of estimating the amount of rainfall derived infiltration. The first model is a classical linear regression model. The second model is a Convolutional neural network (CNN). Both of these models are trained on the same data set. The temperatures recorded at the stations are reliable and can be trusted. However, the data labeling process involves utilizing calculated flows to the stations during both dry and wet weather periods. This means that the labels of the data cannot be trusted to be the actual ground truth, and there exists an uncertainty in the data set. Both models manage to capture large temperature drops which indicates infiltration has occurred. The linear regression model seems to be too sensitive towards small temperature drops and predicts infiltration when there is none. The CNN model on the other hand seems to be able to capture only large temperature drops when infiltration occurs. However, both models are trained with data from only one station, this means that the models are biased towards the average temperature of that particular station, other stations may have a higher or lower average temperature. When testing the models on a different station with lower average temperature the models predict infiltration when there is none.

Inflow forecasting is important when planning the use of water in a hydropower plant. The process of making forecasts is characterized by using knowledge from previous events and occurrences to make predictions about the future. Traditionally, inflow is predicted using hydrological models. The model developed by the Hydrologiska Byråns Vattenbalansavdelning (HBV model) is one of the most widely used hydrological models around the world. Machine learning is emerging as a potential alternative to the current HBV models but needs to be evaluated. This thesis investigates machine learning for inflow forecasting as a mixed qualitative and quantitative case study. Interviews with experts in various backgrounds within hydropower illustrated the key issues and opportunities for inflow forecasting accuracy and laid the foundation for the machine learning model created. The thesis found that the noise in the realised inflow data was one of the main factors which affected the quality of the machine learning inflow forecasts. Other notable factors were the precipitation data from the three closest weather stations. The interviews suggested that the noise in the realised inflow data could be due to faulty measurements. The interviews also provided examples of additional data such as snow quantity measurements and ground moisture levels which could be included in a machine learning model to improve inflow forecast performance. One proposed application for the machine learning model was as a complementary tool to the current HBV model to assist in making manual adjustments to the forecasts when considered necessary. The machine learning model achieved an average Mean Absolute Error (MAE) of 1.39 compared to 1.73 for a baseline forecast for inflow to the Lake Kymmen river system 1-7 days ahead over the period 2015-2019. For inflow to the Lake Kymmen river system 8-14 days ahead the machine learning model achieved an average MAE of 1.68 compared to 2.45 for a baseline forecast. The current HBV model in place had a lower average MAE than the machine learning model over the available comparison period of January 2018. / Tillrinningsprognostisering är viktig vid planeringen av vattenanvändningen i ett vattenkraftverk. Prognostiseringsprocessen går ut på att använda tidigare kunskap för att kunna göra prediktioner om framtiden. Traditionellt sett har tillrinningsprognostisering gjorts med hjälp av hydrologiska modeller. Hydrologiska Byråns Vattenbalansavdelning-modellen (HBV-modellen) är en av de mest använda hydrologiska modellerna och används världen över. Maskininlärning växer för tillfället fram som ett potentiellt alternativ till de nuvarande HBV-modellerna men behöver utvärderas. Det här examensarbetet använder en blandad kvalitativ och kvantitativ metod för att utforska maskininlärning för tillrinningsprognostisering i en fallstudie. Intervjuer med experter med olika bakgrund inom vattenkraft påtalade nyckelfrågor och möjligheter för precisering av tillrinningsprognostisering och lade grunden för den maskininlärningsmodell som skapades. Den här studien fann att brus i realiserade tillrinningsdata var en av huvudfaktorerna som påverkade kvaliteten i tillrinningsprognoserna av maskininlärningsmodellen. Andra nämnvärda faktorer var nederbördsdata från de tre närmaste väderstationerna. Intervjuerna antydde att bruset i realiserade tillrinningsdatana kan bero på felaktiga mätvärden. Intervjuerna bidrog också med exempel på ytterligare data som kan inkluderas i en maskininlärningsmodell för att förbättra tillrinningsprognoserna, såsom mätningar av snömängd och markvattennivåer. En föreslagen användning för maskininlärningsmodellen var som ett kompletterande verktyg till den nuvarande HBV-modellen för att underlätta manuella justeringar av prognoserna när det bedöms nödvändigt. Maskininlärningsmodellen åstadkom ett genomsnittligt Mean Absolute Error (MAE) på 1,39 jämfört med 1,73 för en referensprognos för tillrinningen till Kymmens sjösystem 1–7 dagar fram i tiden under perioden 2015–2019. För tillrinningen till Kymmens sjösystem 8–14 dagar fram i tiden åstadkom maskininlärningsmodellen ett genomsnittligt MAE på 1,68 jämfört med 2,45 för en referensprognos. Den nuvarande HBV-modellen hade ett lägre genomsnittligt MAE jämfört med maskininlärningsmodellen under den tillgängliga jämförelseperioden januari 2018.

The interaction between rotor blade tip leakage vortex and inflow disturbances, such as encountered in shrouded marine propulsors, was simulated in the Virginia Tech Linear Cascade Wind Tunnel equipped with a moving endwall system. Upstream of the blade row, idealized periodic inflow unsteadiness was generated using vortex generator pairs attached to the endwall at the same spacing as the blade spacing. At three tip gap settings, 1.7%c, 3.3%c and 5.7%c, the flow near the lower endwall of the center blade passage was investigated through three-component mean velocity and turbulence distributions measured by four-sensor hotwires. Besides time-averaged data, the measurements were processed for phase-locked analysis, with respect to pitchwise locations of the vortex generators relative to the blade passage. Moreover, surface pressure distributions at the blade tip were acquired at eight tip gaps from 0.87%c to 12.9%c. Measurements of pressure-velocity correlation were also performed with wall motion but without inflow disturbances. Achieved in this study is an understanding of the characteristics and structures of the tip leakage vortex at its initial formation. The mechanism of the tip leakage vortex formation seems to be independent of the tip gap setting. The tip leakage vortex consists of a vortical structure and a region of low streamwise-momentum fluid next to the endwall. The vortical structure is initially attached to the blade tip that creates it. This structure picks up circulation shed from that blade tip, as well as those from the endwall boundary layer, and becomes stronger with downstream distance. Partially induced by the mirror images in the endwall, the vortical structure starts to move across the passage resulting in a reduction in its rotational strength as the cross sectional area of the vortex increases but little circulation is added. The larger the tip gap, the longer the vortical structure stays attached to the blade tip, and the stronger the structure when it reaches downstream of the passage. Phased-averaged data show that the inflow disturbances cause small-scale responses and large-scale responses upstream and downstream of the vortex shedding location, respectively. This difference in scale is possibly dictated by a variation in the shedding location since the amount of circulation in the vortex is dependent on this location. The inflow disturbances possibly cause a variation in the shedding location by manipulating the separation of the tip leakage flow from the endwall and consequently the flow's roll-up process. Even though this manipulation only perturbs the leakage flow in a small scale, the shedding mechanism of the tip leakage vortex amplifies the outcome. / Ph. D.

Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2005. / The two-dimensional flow-field through the stationary blade row of a radial inflow turbine in a torque converter was analysed by means of a potential flow model and a viscous flow model. The purpose was to compare the accuracy with which the two flow models predict the flow field through the static turbine blade row. The freestream turbulence level necessary to optimise the accuracy of the viscous flow model was also investigated. A first order source-vortex panel method with flat panels was used to apply the potential flow model. A radial inflow freestream was used. It was found that the stator blade row directly upstream of the turbine had to be included in the analysis to direct the flow at the turbine inlet. Even then the panel method did not satisfactorily predict the pressure distribution on a typical blade of the static 2nd turbine blade row. A two-dimensional viscous flow model gave excellent results. Furthermore, the two-dimensional viscous flow model was simple to set up due to the fact that symmetry boundary conditions could be used. This facilitated useful predictions of the salient features of the two-dimensional flow through the middle of the radial turbine blade row.

<p>The discharge from Lake Mälaren is regulated to keep the lake water level within limits set by a water decree. Despite this, in the year 2000 the lake water level rose above the upper limit. Studies conducted at the climate modeling unit Rossby Centre at the Swedish Meteorological and Hydrological Institute (SMHI) indicate that the inflow to the lake may increase in the future. A flood in the area will, among other things, strike infrastructure and residential districts.</p><p>The aim of this study was to investigate the water levels of Lake Mälaren in future climates. In addition to this possible future expansion of the lake discharge though the sluice gates in order to keep water levels below limits set by the water decree and to prevent flooding of the shoreline were discussed. This was done by examining past lake and sea water level data and by using a discharge model to simulate future lake water levels resulting from high future inflow. The study was commissioned by SMHI.</p><p>High lake water levels were reproduced fairly well by the discharge model, whereas it was more difficult to draw any coclusions concerning low lake water levels. The examination of data on past lake water levels demonstrated that the regulation of the lake discharge caused a reduction of the highest lake water levels, but above all an increase of low lake water levels. Data on past sea levels in the Baltic Sea showed high values during wintertime. In combination with future high inflow to Lake Mälaren during this season, this indicates that the greatest future flood risk may prevail during wintertime. The conclusion from the simulated future lake water levels was that the lake discharge needs to be expanded to prevent flood in the area. In all scenarios examined in this study, the lake water level will not rise above the upper limit set by the water decree if the discharge is increased to 1 370 m3/s, which almost corresponds to a doubling of the existing discharge of 710 m3/s.</p> / <p>Mälarens utlopp är reglerat för att kunna hålla vattenståndet inom acceptabla gränser. Trots regleringen översteg vattenståndet under vintern 2000/01 den högsta föreskrivna nivån i regleringsbestämmelserna. Studier som bedrivs på klimatforskningsavdelningen Rossby Centre på SMHI pekar på att det framtida inflödet till Mälaren kan komma att bli högre än vad det är idag, något som skulle innebära ännu högre vattennivåer i Mälaren.</p><p>En omfattande översvämning av Mälarens stränder går bland annat ut över infrastruktur och bostäder. Problemet idag är att avbördningen genom sjöns utskov är för liten för att förhindra att översvämning uppstår. Att försöka skydda Stockholm mot vattnet med hjälp av skyddsmurar och invallning skulle bli alltför kostsamt. Ett bättre alternativ är istället att öka avtappningskapaciteten genom slussarna.</p><p>Syftet med det här arbetet var att undersöka hur vattennivån i Mälaren kan komma att bli i ett framtida förändrat klimat. I samband med detta gavs ett underlag för diskussioner om kommande utbyggnader av Mälarens utlopp för att kunna hålla vattennivån under vattendomens högsta föreskrivna nivå även vid ett högre framtida inflöde. Detta uppnåddes genom att genomföra analyser med historiska vattenståndsdata och genom att använda en beräkningsmodell för avtappning, Mälarmodellen, för simulering av ett antal framtida inflödessituationer. Arbetet har genomförts på uppdrag av SMHI.</p><p>Avtappningsmodellen återskapade de höga vattenstånden förhållandevis väl, medan det var svårare att bedöma hur precist den beräknade de allra lägsta vattennivåerna. Ett viktigt resultat från analysen med historiska data var att införandet av regleringen av Mälarens utlopp bidrog till att sänka de höga vattenstånden, men framförallt till att höja de låga. Ett annat resultat från studierna av historiska data var att vattenståndet i Östersjön antar högst nivåer under vintern. Detta i kombination med ett framtida högre inflöde till Mälaren under denna årstid indikerar att störst risk för översvämning kan komma att föreligga under vintern. För att säkert kunna hålla Mälarens vattennivå inom acceptabla gränser måste sjöns utlopp byggas ut. I alla scenarier som har undersökts i det här arbetet kan vattennivån hållas under den högsta föreskrivna nivån om avbördningen utökas till 1 370 m3/s. Det krävs alltså nästan en fördubbling av dagens avbördningskapacitet på 710 m3/s.</p>