The goal of the dissertation study was to evaluate the existing DG scheduling algorithm. The evaluation was developed through previously explored simulated analyses of DGs performed by researchers in the field of DG scheduling optimization and to improve the current RT framework of the DG at TTU. The author analyzed the RT of an actual DG, thereby enabling other investigators to compare theoretical results with the results of this dissertation case study. Two statistical methods were used to formulate and validate predictive models: multiple linear regression and graphical exploratory data analysis techniques. Using both statistical methods, the author was able to determine that the theoretical model was able to predict the significance of four independent variables of resource fragmentation, computational volatility, resource management, and grid job scheduling on the dependent variables quality of service and job performance affecting RT. After an experimental case study analysis of the DG variables, the author identified the best DG resources to perform optimization of run-time performance of DG at TTU. The projected outcome of this investigation is the improved job scheduling techniques of the DG at TTU.

Promotion of agroforestry practices in sub-Sahara Africa may help sustain subsistent food and wood production by integrating trees and crops on farmlands to replenish soil fertility and improve crop yield. Using rotational woodlot and pigeonpea intercropping systems in semi-arid Tanzania as case studies, my research screened suitable tree species to increase fuelwood supply and examined mechanisms for reducing tree-crop competition. By adopting nutrient use efficiency (the ratio of biomass yield to nutrient uptake) as a criterion, I found that selecting tree species of low wood nutrient concentrations would minimize nutrient exports by 42 – 60 %, thus reducing soil nutrient depletion while concurrently sustaining local fuelwood supply harvested from rotational woodlots. Currently smallholder farmers cannot afford to replenish soil fertility because of high fertilizer costs. However, 5-year tree fallowing raised soil N and P levels for maize culture as high as those from recommended fertilizer applications. Post-fallow maize yield was also increased significantly over natural fallow practices. Apparently there is a trade-off between yields of maize and fuelwood under rotational woodlot culture providing farmers the choice to proportion tree and crop composition based on priority demands. An alternative practice of intercropping pigeonpea with maize may also rapidly replenish soil fertility as well as enhance maize yield when competitive interactions between trees and crops are controlled. Vector analysis revealed that such interactions suppressed biomass yields of maize and pigeonpea by 30 % and 60 %, respectively, due to limited soil nutrients and/or moisture. Optimizing yields of both crops would require prescribed fertilizer addition when intercropped, but dose rates can be lowered by half under the improved fallow system due to alleviating interspecific competition. My findings form the basis of a plea for greater use of rotational woodlot and pigeonpea intercropping systems in semi-arid areas. I conclude that smallholder farm management of rotational agroforestry systems can be significantly improved by refining tree selection criteria and mitigating nutrient competition between trees and crops to maintain food and fuelwood production.

Within the existing available mix of numerical and analytical thermal analysis options, lumped parameter thermal modelling is selected as the operational backbone to develop an improved novel synchronous generator thermal modelling package. The objective is for the creation of a user friendly quick feedback tool, which can serve as a means to make quick machine design thermal calculations and answer customer queries quickly and reliably. Furthermore, thermally improved generator designs will allow for inevitable operational losses to be channelled away from the machine more efficiently. As a result, machine component temperatures will be reduced, allowing lower generator thermal ratings. The end result will be smaller, longer lasting, more efficient generators, with the ability to be adapted with greater ease to particular applications. With the contribution of selected numerical analysis techniques, mainly finite element analysis for the distribution of iron losses, the MySolver thermal modelling package is developed and presented in this thesis. It is this combination of numerical and analytical tools that improves synchronous generator thermal modelling accuracy, but ultimately it is the lumped parameter nature of the thermal models developed that makes MySolver succeed as a reliable quick feedback electrical machine thermal design tool, validated using experimental results for a wide range of operating conditions. The initial part of the thesis analyses the electrical machine thermal modelling techniques available today, indicating advantages and disadvantages associated with each one, and providing a rationale for the selection of lumped parameter modelling to be used by MySolver. The development of the synchronous generator lumped parameter thermal models is detailed, with examples on its construction presented. Subsequently, finite element analysis is utilised to predict the distribution of machine iron losses across the rotor and stator laminations, with the findings applied to MySolver. Furthermore, a study is performed into the lumped parameter discretisation level needed to effectively represent machine windings. MySolver is experimentally verified using experimental data from a fully instrumented synchronous generator and this data is also used to obtain further insight into the temperature distribution within the generator. In the final part results are evaluated and the use of MySolver for modelling and optimising electrical machines is discussed. Finally, appropriate conclusions on the work presented are drawn.

This report proposes three algorithms using model predictive control (MPC) in order to improve the positioning accuracy of an unmanned vehicle. The developed algorithms succeed in reducing the uncertainty in position by allowing the vehicle to deviate from a planned path, and can also handle the presence of occluding objects. To achieve this improvement, a compromise is made between following a predefined trajectory and maintaining good positioning accuracy. Due to the recent development of threats to systems using global navigation satellite systems to localise themselves, there is an increased need for methods of localisation that can function without relying on receiving signals from distant satellites. One example of such a system is a vehicle using a range-bearing sensor in combination with a map to localise itself. However, a system relying only on these measurements to estimate its position during a mission may get lost or gain an unacceptable level of uncertainty in its position estimates. Therefore, this thesis proposes a selection of algorithms that have been developed with the purpose of improving the positioning accuracy of such an autonomous vehicle without changing the available measurement equipment. These algorithms are: A nonlinear MPC solving an optimisation problem. A linear MPC using a linear approximation of the positioning uncertainty to reduce the computational complexity. A nonlinear MPC using a linear approximation (henceforth called the approximate MPC) of an underlying component of the positioning uncertainty in order to reduce computational complexity while still having good performance. The algorithms were evaluated in two different types of simulated scenarios in MATLAB. In these simulations, the nonlinear, linear and approximate MPC algorithms reduced the root mean squared positioning error by 20-25 %, 14-18 %, and 23-27 % respectively, compared to a reference path. It was found that the approximate MPC seems to have the best performance of the three algorithms in the examined scenarios, while the linear MPC may be used in the event that this is too computationally costly. The nonlinear MPC solving the full problem is a reasonable choice only in the case when computing power is not limited, or when the approximation used in the approximate MPC is too inaccurate for the application.

The shea tree is indigenous to 21 countries in Sub-Saharan Africa and provides nuts from which oil (referred to as butter) can be extracted. Shea butter production in the Northern Region of Ghana is of socioeconomic importance to female processors who practice shea production. This study quantified the environmental effects of shea processing from carbon dioxide emissions and the human energy expended through the traditional, improved, and centralized methods of shea processing. Par-boiling accounted for up to 88% of total carbon dioxide emissions throughout the entire shea butter production process. A difference of 2.5(CO2 (kg))/(Shea butter (kg)) emitted observed between the traditional and centralized processing methods. The moisture content of 16 firewood samples collected at the centralized processing center found wood moisture to range between 9-34%. The largest amounts of human energy expended during traditional and improved processes take place during the nut collection process followed by manual crushing (40% and 20% of total energy expended during the traditional method, respectively). Women in the study area were found to travel an average of 10 km to pay for a corn mill to process their shea kernels into a paste, producers also expressed interest in mechanized crushing machines during household surveys. User perceptions of the improved roasting equipment were found to be positive, as well as adoption of the new technology was observed by all shea producers surveyed in the village of Tigla. The entirety of individual producers surveyed without access to improved roasters expressed interest in obtaining and utilizing improved roasters to improve the traditional method currently practiced. The profit observed from shea kernel processing and sales was found to be higher than women practicing traditional shea butter processing and sales due to time, energy, and inputs required by completing the entire process. Butter producers at centralized processing centers have the opportunity to make up to 33% higher profits while utilizing less energy (54% reduction) by purchasing directly from kernel producers and implementing improved technologies in a centralized setting. The potential of shea production in northern Ghana has yet to be reached. Through adoption of improved technologies, women have the opportunity to save time and human energy, reduce material inputs such as firewood, and in turn are able produce an even greater amount of marketable shea products.

The short-term enhancement of physical performance known as post-activation potentiation could be exploited in the design of sport-specific training sessions. The purpose of this study was to compare the potentiation of softball throwing velocity following two kinds of resistance-training session: a control session consisting of traditional heavy-load sets, and an experimental "Pmax" session consisting of sets of loads selected to maximise the mean power output during explosive bench presses. Both sessions included plyometric medicine ball chest passes. Eight male softball players of premier grade, with at least 2 yr experience of resistance training, performed the two sessions in a crossover fashion, with 30 min recovery between sessions. Softball throwing velocity was measured with a radar gun immediately before and at 2-min intervals 4-10 min after each session. Percent effects on throwing speed were analyzed via log transformation, and t statistics were used to make magnitude-based inferences with respect to the smallest important change of 2%. The average throwing velocity increased between pre and post tests for both treatments; the average increase was a substantial 2.3% (0.5 to 4.1%). Throwing velocity after Pmax training was a trivial 0.4% slower relative to that after heavy-load training (90% confidence limits -1.2 to 1.9%). There was a greater change in throwing velocity by 10 min post treatment than by 4 min post treatment; the change by 10 min was 5.0% (3.2 to 6.7%) for the Pmax training session and 5.3% (2.1 to 8.6%) for the heavy-load session. These effects were almost certainly beneficial for throwing speed, but the difference between them was unclear (-0.3%; -3.7 to 3.1%). The mean change between 4 and 10 min for both treatments combined was 5.1% (90% confidence limits 3.6 to 6.7%). The short-term enhancement of throwing performance following heavy-load and Pmax training sets has implications for the design of softball warm-up routines. There is also the potential for softball players to use such training to improve their throwing velocity during games.

The temporal dynamics of hydrological model performance gives insights into errors that cannot be obtained from global performance measures assigning a single number to the fit of a simulated time series to an observed reference series. These errors can include errors in data, model parameters, or model structure. Dealing with a set of performance measures evaluated at a high temporal resolution implies analyzing and interpreting a high dimensional data set. This paper presents a method for such a hydrological model performance assessment with a high temporal resolution and illustrates its application for two very different rainfall-runoff modeling case studies. The first is the Wilde Weisseritz case study, a headwater catchment in the eastern Ore Mountains, simulated with the conceptual model WaSiM-ETH. The second is the Malalcahuello case study, a headwater catchment in the Chilean Andes, simulated with the physicsbased model Catflow. The proposed time-resolved performance assessment starts with the computation of a large set of classically used performance measures for a moving window. The key of the developed approach is a data-reduction method based on self-organizing maps (SOMs) and cluster analysis to classify the high-dimensional performance matrix. Synthetic peak errors are used to interpret the resulting error classes. The final outcome of the proposed method is a time series of the occurrence of dominant error types. For the two case studies analyzed here, 6 such error types have been identified. They show clear temporal patterns, which can lead to the identification of model structural errors.

Unconventional resources will play an important role in filling the gap between supply and demand for future world energy. In North America, the impact of unconventional resources on energy supplies is growing continuously. However, around the world they have yet to serve as a major contributor to the energy supply, partly due to the scarcity of information about the exploration and development technologies required to produce them. Basin analogy can be used to estimate the undiscovered petroleum potential in a target basin by finding a geological analog that has been explored enough that its resource potential is fully understood. In 2006, Singh developed a basin analog system BASIN (Basin Analog Systems INvestigation) in detail that could rapidly and consistently identify analogous reference basins for a target basin. My research focused on continuing that work, comprehensively improving the basin analog system in four areas: the basin analog method; the database; the software functionality; and the validation methods. The updated system compares basins in terms of probability distributions of geological parameters. It compensates for data that are sparse or that do not represent basin-level geological parameters, and it expands the system's ability to compare widely varying quantitative parameters. Because the updated BASIN database contains more geologic and petroleum systems information on reference (existing) basins, it identifies analog basins more accurately and efficiently. The updated BASIN software was developed by using component-based design and data visualization techniques that help users better manage large volumes of information to understand various data objects and their complicated relationships among various data objects. Validation of the improved BASIN software confirms its accuracy: if a basin selected as the target basin appears in the reference basin list with other basins, the target basin is 100% analogous only to itself. Furthermore, when a target basin is analyzed by both BASIN and PRISE (Petroleum Resources Investigation and Summary Evaluation) software, results of the improved BASIN closely matched the PRISE results, which provides important support for using BASIN and PRISE together to quantitatively estimate the resource potential in frontier basins.

This thesis work will examin and present how to implement a Particle-In-Cell and a Fluid-Implicit-Particle (PIC / FLIP) fluid solver that takes advantage of the inherent parallelism of Digital Domain's sparse block optimized data structure, DB-Grid. The methods offer a hybrid approach between particle and grid based simulation. This thesis will also discuss and go through different approaches for storing and accessing the data associated with each particle. For dynamically create and remove attributes from the particles, Disney's open source API, Partio is used. Which is also used for saving the particles to disk. Finally how to expose C++ classes into Python by wrapping everything into a Python module using the Boost.Python API and discuss the benets of having a script language.

The topic of this study is to develop a battery capacity management system. The main purpose is to monitor the state of battery during charging and discharging. Form this, user can know the battery status and to avoid loss of data before sudden system power down caused by a spent battery. Different states of battery were collected in different conditions by a battery measurement system, after which characteristics were analyzed. A fast-charge and residual capacity estimation system was developed according to the battery characteristics. The fast-charge system is a technique that emphasizes not only fastness charging but also safety. In this study a fast-charge end method was adopted to terminate the fast charging state of the battery and the initial state had been estimated before charging. Furthermore, the battery was charged with the optimum method according to the battery initial state. That can recover the capacity of the battery within a short period without causing any side effects from repeated usage. The residual capacity estimation system works by first estimating the initial capacity of the batteries, and then recording the current of batteries continuously using the coulomb counting method to make compensation for the effects of battery aging, environmental temperature, self-discharging, and output current.