The acceleration towards achieving a low carbon society has raised many challenges in the energy sector. The existing systems, highly dependent on fossil fuels, are not sustainable and recommendations are made to accelerate the transition by using more renewable and low-carbon sources of energy. By being responsible for over 70 % of the greenhouse gas emissions, cities or districts have a major role to play in this transition and present a large potential for implementation of renewable energy systems. The optimization of those systems and a better use of energy are crucial to reduce emissions and reach carbon neutrality. This study evaluates the potential of the implementation of three storage solutions for a decentralized energy system in a residential cluster in Ludvika, Sweden, equipped with photovoltaics panels. The first solution includes stationary batteries, the second includes a hydrogen storage solution and the third offers a hybrid solution from the two previous storages. Simulations were conducted using two numerical tools with an hourly resolution.The first scenario was conducted using Spine modelling tools, and the other simulations, including the cost analysis, were conducted on Excel with the support of Visual Basic for Applications. The comparison between the three solutions showed that the stationary batteries, blessed with a higher efficiency, offers the best results. The autonomy of the site, initially at 19.1 %, was raised to 22.8 % due to the batteries, and the system allowed to save up to 9.6 MW∙h per year. The investment price of the battery system was the highest of the three solutions. However, the payback period is reached in 20 years, within the average lifespan of the batteries and 10 years shorter than with the other solutions. The results also showed that the benefits of implementing a hydrogen storage solution were not visible as the excess in the photovoltaic production was not enough to cover the energy demand in a long-term period.

We consider a stochastic differential equation on a domain D in n-dimensional real space, where the associated dynamical system is linear, and D contains a repulsive type stationary point at the origin O. We obtain an exit law for the first exit time of the solution process from a ball of arbitrary radius centered at the origin, which involves additive scaling as in Day (1995). The form of the scaling constant is worked out and shown to depend on the structure of the Jordan form of the linear drift. We then obtain an extension of this exit law to the first exit time of the solution process from the general domain D by considering the exit in two stages: first from the origin O to the boundary of the ball, for which the aforementioned exit law applies, and then from the boundary of the ball to the boundary of D. In this way we are able to determine for which Jordan forms we can obtain a limiting distribution for the first exit time to the boundary of D as the noise approaches 0. In particular, we observe there are cases for which the exit time distribution diverges as the noise approaches 0. / Ph. D.

There is a need to investigate and improve upon existing methods to predict response of sensors due to flow-induced vibrations in a pipe flow. The aim was to develop a tool which would enable an engineer to quickly evaluate the suitability of a particular design for a certain pipe flow application, without sacrificing fidelity. The primary methods, found in guides published by the American Society of Mechanical Engineers (ASME), of simple response prediction of sensors were found to be lacking in several key areas, which prompted development of the tool described herein. A particular limitation of the existing guidelines deals with complex stochastic stationary and non-stationary modeling and required much further study, therefore providing direction for the second portion of this body of work. A tool for response prediction of fluid-induced vibrations of sensors was developed which allowed for analysis of low aspect ratio sensors. Results from the tool were compared to experimental lift and drag data, recorded for a range of flow velocities. The model was found to perform well over the majority of the velocity range showing superiority in prediction of response as compared to ASME guidelines. The tool was then applied to a design problem given by an industrial partner, showing several of their designs to be inadequate for the proposed flow regime. This immediate identification of unsuitable designs no doubt saved significant time in the product development process. Work to investigate stochastic modeling in structural dynamics was undertaken to understand the reasons for the limitations found in fluid-structure interaction models. A particular weakness, non-stationary forcing, was found to be the most lacking in terms of use in the design stage of structures. A method was developed using the Karhunen Loeve expansion as its base to close the gap between prohibitively simple (stationary only) models and those which require too much computation time. Models were developed from SDOF through continuous systems and shown to perform well at each stage. Further work is needed in this area to bring this work full circle such that the lessons learned can improve design level turbulent response calculations. / Ph. D.

The effects of a wake generated by a stationary upstream strut on surface heat transfer to turbine blades were measured experimentally. Time-resolved and unsteady heat flux measurements were made with Heat Flux Microsensors (HFM) at three positions on the suction surface and one position on the pressure surface of a turbine blade. The experiments were conducted on a stationary cascade of blades for heated runs at transonic conditions Methods for determining the adiabatic wall temperature and heat transfer coefficient are presented and the results are compared to computer predictions for these blades. Heat transfer measurements were taken with new HFM-6 insert gages. A strong influence on the heat transfer coefficient was seen from the relative position of the strut with respect to the leading edge of the test blades. As the strut approached the leading edge of the blade the heat transfer increased by 15% at gage location 2 on the suction surface. The largest increase in .the heat transfer coefficient was seen on the pressure surface. Results at this location show a 24% increase in the overall heat transfer coefficient for one of the strut locations. The values obtained for the heat transfer coefficients for the no strut case did not compare well with computer predictions. The results did support the experimental results of other researchers, however. The fast time response of the HFM illustrated graphically an increase in the frequency energy between the 0-10 kHz range when the strut was located near the leading edge of the instrumented blade. The heat flux turbulence intensity (Tuq) was defined as another physical quantity important to turbine blade heat transfer, but no conclusions could be drawn from the results as to how this value compares to the turbulence intensity. / Master of Science

This paper examines the components of the Capital Asset Pricing Model and the model's uses to analyze portfolios returns. It also looks at subsequent versions of the CAPM including a multi-variable CAPM with the inclusion of selected macro-variables as well as a non-stationary beta CAPM to estimate portfolio returns. A new model is proposed that combines the multi-variable component together with the non-stationary beta component to derive a new CAPM that is more effective at capturing current market conditions than the traditional CAPM with the fixed beta coefficient. The multi-variable CAPM with non-stationary beta is applied, together with the select macro-variables, to estimate the returns of a portfolio of assets in the oil-sector of the economy. It looks at returns during the period of 1995-2001 when the economy exhibited a wide range of variation in market returns. This paper tests the hypothesis that adapting the traditional CAPM to include beta non-stationarity will better estimate portfolio returns in a fluid market environment. The empirical results suggest that the new model is statistically significant at measuring portfolio returns. This model is estimated with an Ordinary Least Square (OLS) estimations process and identifies three factors that are statistically significant. These include quarterly changes in the Gross Domestic Product (GDP), the Unemployment Rate and the Consumer Price Index (CPI). / Master of Arts

Partial group delay is a spectral parameter, which measures the time lag between two time series in a system after the spurious effects of the other series in the system have been eliminated. For weakly-stationary processes, estimators for partial group delay are proposed based on indirect and direct approaches. Conditions for weak consistency and asymptotic normality of the proposed estimators are obtained. Applications to a multiple test of partial group delay are investigated. The time lag interpretation of partial group delay is justified, which provides insight into the nature of linear relationships among weakly-stationary processes. Extensions are made to group delay estimation and partial group delay estimation for non-stationary "oscillatory" processes. / Ph. D.

In present study, high energy electrons were used to modify blends based on RP and PP under two conditions: stationary and in-stationary conditions. Modification of blend under stationary condition is a process which is established in industrial application and where required absorbed dose is applied to form parts (after molding) at room temperature and in solid state. On the contrary, the modification of blend with high energy electrons under in-stationary condition is a new process (electron induced reactive processing) where required absorbed dose is applied to a molten state during melt mixing process. The modification of blend based on RP and PP under stationary condition resulted in slightly enhancement of tensile properties while the modification of this blend under in-stationary condition resulted in deterioration of tensile properties due to degradation of the PP matrix. Thus, special grafting agent (GA) is required for improving the tensile properties. The effect of different GAs on tensile, thermal, dynamic mechanical as well as morphological properties and melt flow properties of blends based on RP and PP were determined. The optimum absorbed dose for modification of blend based on RP and PP under both conditions was evaluated. In addition, the effect of treatment parameters of electron induced reactive processing was investigated.

No description available.

Increased extreme precipitation has been documented in many regions around the world, in- cluding central and northern Europe. Global warming increases average temperature, which in turn enhances atmospheric water holding capacity. These changes are believed to increase the frequency and/or intensity of extreme precipitation events. In determining the design storm, or a worst probable storm, for infrastructure design and failure risk assessment, experts commonly assume that statistics of extreme precipitation do not change significantly over time. This so- called notion of stationarity assumes that the statistics of future extreme precipitation events will be similar to those of historical observations. This study investigates the consequences of using a stationary assumption as well as the alternative: a non-stationary framework that con- siders temporal changes in statistics of extremes. Here we evaluate stationary and non-stationary return levels for 10-year to 50-year extreme precipitation events for different durations (1-day, 2-day, ..., 7-day precipitation events), based on the observed daily precipitation from Sweden. Non-stationary frequency analysis is only considered for stations with statistically significant trends over the past 50 years at 95% confidence (i.e., 15 to 39 % out of 139 stations, depend- ing on duration, 1-day, 2-day, ..., 7-day). We estimate non-stationary return levels using the General Extreme Value distribution with time-dependent parameters, inferred using a Bayesian approach. The estimated return levels are then compared in terms of duration, recurrence in- terval and location. The results indicate that a stationary assumption might, when a significant trend exists, underestimate extreme precipitation return levels by up to 40 % in Sweden. This report highlights the importance of considering better methods for estimating the recurrence in- terval of extreme events in a changing climate. This is particularly important for infrastructure design and risk reduction. / Ökad extrem nederbörd har dokumenterats globalt, däribland centrala och norra Europa. Den globala uppvärmningen medför en förhöjd medeltemperatur vilket i sin tur ökar avdunstning av vatten från ytor samt atmosfärens förmåga att hålla vatten. Dessa förändringar tros kunna öka och intensifiera nederbörd. Vid bestämning av dimensionerande nederbördsintensiteter för byggnationsprojekt antas idag att frekvensen och storleken av extrem nederbörd inte kommer att förändras i framtiden (stationäritet), vilket i praktiken innebär ingen förändring i klimatet. Den här studien syftar till att undersöka effekten av en icke-stationärt antagande vid skattning av dimensionerande nederbördsintensitet. Icke-stationära och stationära nerderbördsintensiteter föråterkomsttider mellan 10 och 100år bestämdes utifrån daglig och flerdaglig svensk nederbörds- data. Nederbördintensiteterna bestämdes med extremvärdesanalys i mjukvaran NEVA, där den generella extremvärdesfördelningen anpassades till årlig maximum nederbörd på platser i Sverige som påvisade en ökande trend under de senaste 50åren (15% till 39 % utav 139 stationer, beroende på varaktighet). De dimensionerande nederbördsintensiteterna jämfördes sedan med avseende på varaktighet, återkomsttid och plats. Resultaten indikerade på att ett stationärt antagande riskerar att underskatta dimensionerande nederbördsintensiteter för en viss återkomsttid med upp till 40 %. Detta indikerar att antagandet om icke-stationäritet har större betydelse för olika platser i Sverige, vilket skulle kunna ge viktig information vid bestämning av dimensionerande regnintensiteter.

The conventional approach to the frequency analysis of extreme rainfall is complicated by non-stationarity resulting from climate change. In this study significant trends in extreme rainfall are detected using statistical trend tests (Mann-Kendall test and t-test) for all over the Korean Peninsula. The violation of the stationarity for 1 hour annual maximum series is detected for large part of the area especially for southwestern and northeastern regions. For stations showing non-stationarity, the non-stationary generalized extreme value (GEV) distribution model with a location parameter in the form of linear function of time makes significant improvement in modeling rainfall extremes when compared to the stationary GEV model. The Bartlett-Lewis rainfall model is used to generate annual maximum series for the purpose of generating the Intensity-Duration-Frequency (IDF) curve. Using 100 sets of 50 year synthetic annual maxima, it is found that the observed annual rainfall maximum series are reasonably represented by the model. The observed data is perturbed by change factors to incorporate the climate change scenario from the WRF (Weather Research and Forecasting) regional climate model into IDF estimates. The IDF curves for the future period 2040-2079 show highest estimates for all return periods and rainfall durations. The future IDF estimates show significant difference from the IDF estimates of the historical period (1968-2000). Overall, IDF curves show an increasing tendency over time. A historical and future climate simulation is evaluated over the Colorado River Basin using a 111-year simulation (1969-2079) of the WRF climate change scenario. We find the future projections show statistically significant increases in temperature with larger increases in the northern part of the basin. There are statistically insignificant increases in precipitation, while snowfall shows a statistically significant decrease throughout the period in all but the highest elevations and latitudes. The strongest decrease in snowfall is seen at high elevations in the southern part of the basin and low elevations in the northern part of the basin.