The work presented in this dissertation on piezoelectric inchworm motors (IWM) is part of a process to gain an understanding of the design, analysis and testing of this smart actuator technology. This work will form the foundation of what will hopefully lead to the realisation of a production-ready IWM design to be used in energy-scarce, battery-operated Unmanned Aerial Vehicles (UAVs), and forms part of a larger national drive to expand the UAV industry in South Africa. Although the principles used in the design of IWMs are well known, a new innovation is employed. A novel way to increase the force capacity of IWMs without compromising on the speed or displacement when compared to conventional methods is shown to be effective, and was used for the first time on IWMs. The use of a simple design equation is demonstrated to be useful in predicting the load limits and step displacements. Challenges of finding a correlation between predicted and measured performance values are discussed and solutions are presented. The history of IWMs and some background on piezoelectricity are given for the reader not familiar with these. The use of micro ridges on the clamp mechanisms is explored. The effects of the control signals on the mechanism of the motor are discussed in detail and some important comments on electrical controllers are made. The emphasis is on designing a strong motor that capitalises on the high-force density of piezoelectric material. / Dissertation (MEng)--University of Pretoria, 2014. / gm2014 / Mechanical and Aeronautical Engineering / unrestricted

A new Trapped Vortex Combustor (TVC) concept has been studied for applications such as those in Unmanned Aerial Vehicles (UAVs) as it offers potential for superior flame stability and low pressure loss. Flame stability is ensured by a strong vortex in a physical cavity attached to the combustor wall, and low pressure loss is due to the absence of swirl. Earlier studies on a compact combustor concept showed that there are issues with ensuring stable combustion over a range of operating conditions. The present work focuses on experimental studies and numerical simulations to study the stability issues and performance optimization in this compact single-cavity TVC configuration. For performing numerical simulations, an accurate and yet computationally affordable Modified Eddy Dissipation Concept combustion model is built upon the KIVA-3V platform to account for turbulence-chemistry interactions. Detailed validation with a turbulent non-premixed CH4/H2/N2 flame from literature showed that the model is sufficiently accurate and the effect of various simulation strategies is assessed. Transient flame simulation capabilities are assessed by comparison with experimental data from an acoustically excited oscillatory H2-air diffusion flame reported in literature. Subsequent to successful validation of the model, studies on basic TVC flow oscillations are performed. Frequencies of flow oscillations are found to be independent of flow velocities and cavity length, but dependent on the cavity depth. Cavity injection and combustion individually affect the magnitude of flow oscillations but do not significantly alter the resonant frequencies. Reacting flow experiments and flow visualization studies in an existing experimental TVC rig with optical access and variable cavity L/D ratio show that TVC flame stability depends strongly on the cavity air velocity. A detailed set of numerical simulations also confirms this and helps to identify three basic modes of TVC flame stabilization. A clockwise cavity vortex stabilized flame is formed at low cavity air velocities relative to the mainstream, while a strong anticlockwise cavity vortex is formed at high cavity air velocities and low L/Ds. At intermediate conditions, the cavity vortex structure is found to be in a transition state which leads to large scale flame instabilities and flame blow-out. For solving the flame instability problem, a novel strategy of incorporating a flow guide vane is proposed to establish the advantageous anticlockwise vortex without the use of cavity air. Experimental results with the modified configuration are quite encouraging for TVC flame stability at laboratory conditions, while numerical results show good stability even at extreme operating conditions. Further design optimization studies are performed in a multi-parameter space using detailed simulations. From the results, a strategy of using inclined struts in the main flow path along with the flow guide vane seems most promising. This configuration is tested experimentally and results pertaining to pressure drop, pattern factor and flame stability are found to be satisfactory.

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.

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To meet the latest requirements of the 6G standards, several techniques have been proposed in the open literature, such as millimeter waves, terahertz communication, and massive MIMO. In addition to these recent technologies, the use of unmanned aerial vehicles (UAVs) is strongly advocated for 6G networks, as the 6G standard will not be dedicated to broadband services, but will rather be oriented towards reduced geographical cellular coverage. In this context, the deployment of UAVs is considered a key solution for seamless connectivity and reliable coverage. Although UAVs are characterized by their high mobility and their ability to establish line-of-sight links, their use is still impeded by several factors such as weather conditions, their limited computing power, and, most importantly, their limited energy. In this work, we are aiming for the novel technology that enables indefinite wireless power transfer for UAVs using laser beams. We propose a novel UAV deployment strategy, based on which we analyze the overall performance of the system in terms of wireless coverage and provide some useful insights. To this end, we use tractable tools from stochastic geometry to model the complex communication system.

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In the current context of the energy transition, it has been argued by researchers and authors that the demand for raw materials for the necessary green technologies can’t be met without the input of primary raw materials. These materials can only be supplied through the mining cycle: exploration, mining, and processing. The mining cycle, however, can pose risks to the environment, which could be in contradiction with the motivation behind the implementation of green technologies. It is then society’s duty to strive for a constant reduction of the environmental impact of the mining cycle, or else, we would be in a paradoxical situation where, by mining materials to power the energy transition, if not done with care, we could be also risking the environment.  While this megatrend of the energy transition occurs, Unmanned Aerial Vehicles (UAVs) also known as drones, have reached a significant level of development which together with the miniaturization of geoscientific sensors, has opened the door to interesting fast, agile, and non-invasive ways of obtaining geological information. This has bridged gaps between the traditional scales of airborne and ground surveying and holds the potential of contributing to a less environmentally harmful mining cycle.  This thesis work intends to be a useful reference for anyone interested in working with UAVs in geosciences, especially for the exploration and management of raw materials from an entrepreneurial point of view. Here, a brief review of the current state of the art through the recent scientific literature on applications of drones in the mining cycle, including but not limited to geophysics and hyperspectral imaging is presented. Using this state of the art as a point of departure, semi-structured interviews with different stakeholders in the mining cycle were conducted to answer the research questions. The concept of value, ubiquitously present in the business research literature, was used to analyze the benefits that the use of UAVs can bring to the raw materials industry and the efforts to reduce its environmental footprint. The opportunities for entrepreneurs to be the conduit to deploy such benefits in society were also analyzed.  The work ends with a summary of the qualitative research findings, highlighting how drones constitute an optimization tool that can be used in all the stages of the mining cycle. Additionally, it highlights that UAV gravity and electromagnetic methods, together with better data processing software for hyperspectral imaging, are currently some of the most sought out and/or needed solutions by users.

Soybean Cyst Nematode (SCN) poses a significant threat to soybean production in North America and the world at large. Early and accurate detection of SCN infestations is crucial for implementing effective management strategies and minimizing yield losses. The conventional method of SCN detection involves uprooting plants to examine the roots and collecting soil samples. Drone-based multispectral imaging has been used as a viable alternative for crop monitoring due to its detailed spatial and spectral information and scheduling flexibility. This thesis aims to examine the potential of using multispectral drone images for SCN detection in a soybean production field and develop a non-destructive approach to support improved precision agricultural management practices. Using the DJI Matrice 210 drone and a MicaSense Altum sensor, at a height of 50 meters above ground level and top speed of 6 meters/second, a total of 2,550 multispectral images per flight were collected for a total of fourteen flights beginning in June 2022 up to September 2022 from a production field with variable SCN infestation levels located in Carmi, IL. These images were postprocessed with geometric and radiometric correction to produce orthomosaic photos.   Ten vegetation indices namely, NDRE, NDVI, EVI, GNDVI, BNDVI, SIPI, R-EDGE/G, NIR/G, R-EDGE/R and MSR, were computed for each flight date and study plot. The count of SCN eggs was appended to each study plot to find the correlation between the vegetation indices and the field parameters. The VIs having the highest correlation with the eggs and also having the highest number of correlation coefficients significantly different from zero were NDRE, NDVI and GNDVI. I computed the mean values of these VIs for each study plot and flight date which resulted into a time-series trend analysis. To identify study plots with similar trends, an agglomerative hierarchical clustering was performed which resulted into two clusters for each VI. After conducting the ANOVA test, NDVI returned statistically significant results for all the field parameters, GNDVI returned one while NDRE returned three outcomes that were not statistically significant. The study plots belonging to Cluster 1 had a higher mean of SCN count while those in Cluster 2 portrayed little or no SCN. I found NDVI to be the optimal VI because the results from statistical tests and modeling techniques conducted were significant for all SCN parameters, such as cyst and egg count for the plots clustered based on the NDVI trend. Therefore, I used the plots clustered based on the NDVI trend to train and test six ML classification models (Support Vector Classifier, Naïve Bayes, K-Nearest Neighbors, Linear Discriminant Analysis, MLP-Neural Network and Gradient Boost) such that when presented with information in a format like that used in training, it becomes possible to identify plots with high or no SCN. Gradient Boost, MLP-NN and LDA performed with 89%, 82% and 80% accuracy respectively.

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