Probabilistic design optimization of horizontal axis wind turbine rotors

Considerable interest in renewable energy has increased in recent years due to the concerns raised over the environmental impact of conventional energy sources and their price volatility. In particular, wind power has enjoyed a dramatic global growth in installed capacity over the past few decades. Nowadays, the advancement of wind turbine industry represents a challenge for several engineering areas, including materials science, computer science, aerodynamics, analytical design and analysis methods, testing and monitoring, and power electronics. In particular, the technological improvement of wind turbines is currently tied to the use of advanced design methodologies, allowing the designers to develop new and more efficient design concepts. Integrating mathematical optimization techniques into the multidisciplinary design of wind turbines constitutes a promising way to enhance the profitability of these devices. In the literature, wind turbine design optimization is typically performed deterministically. Deterministic optimizations do not consider any degree of randomness affecting the inputs of the system under consideration, and result, therefore, in an unique set of outputs. However, given the stochastic nature of the wind and the uncertainties associated, for instance, with wind turbine operating conditions or geometric tolerances, deterministically optimized designs may be inefficient. Therefore, one of the ways to further improve the design of modern wind turbines is to take into account the aforementioned sources of uncertainty in the optimization process, achieving robust configurations with minimal performance sensitivity to factors causing variability. The research work presented in this thesis deals with the development of a novel integrated multidisciplinary design framework for the robust aeroservoelastic design optimization of multi-megawatt horizontal axis wind turbine (HAWT) rotors, accounting for the stochastic variability related to the input variables. The design system is based on a multidisciplinary analysis module integrating several simulations tools needed to characterize the aeroservoelastic behavior of wind turbines, and determine their economical performance by means of the levelized cost of energy (LCOE). The reported design framework is portable and modular in that any of its analysis modules can be replaced with counterparts of user-selected fidelity. The presented technology is applied to the design of a 5-MW HAWT rotor to be used at sites of wind power density class from 3 to 7, where the mean wind speed at 50 m above the ground ranges from 6.4 to 11.9 m/s. Assuming the mean wind speed to vary stochastically in such range, the rotor design is optimized by minimizing the mean and standard deviation of the LCOE. Airfoil shapes, spanwise distributions of blade chord and twist, internal structural layup and rotor speed are optimized concurrently, subject to an extensive set of structural and aeroelastic constraints. The effectiveness of the multidisciplinary and robust design framework is demonstrated by showing that the probabilistically designed turbine achieves more favorable probabilistic performance than those of the initial baseline turbine and a turbine designed deterministically.

Perceptions of climate change, environmental variability and the role of agricultural adaptation strategies by small-scale farmers in Africa: the case of Mwanga district in Northern Tanzania

Abstract The potential impacts of climate change and environmental variability are already evident in most parts of the world, which is witnessing increasing temperature rates and prolonged flood or drought conditions that affect agriculture activities and nature-dependent livelihoods. This study was conducted in Mwanga District in the Kilimanjaro region of Tanzania to assess the nature and impacts of climate change and environmental variability on agriculture-dependent livelihoods and the adaptation strategies adopted by small-scale rural farmers. To attain its objective, the study employed a mixed methods approach in which both qualitative and quantitative techniques were used. The study shows that farmers are highly aware of their local environment and are conscious of the ways environmental changes affect their livelihoods. Farmers perceived that changes in climatic variables such as rainfall and temperature had occurred in their area over the period of three decades, and associated these changes with climate change and environmental variability. Farmers’ perceptions were confirmed by the evidence from rainfall and temperature data obtained from local and national weather stations, which showed that temperature and rainfall in the study area had become more variable over the past three decades. Farmers’ knowledge and perceptions of climate change vary depending on the location, age and gender of the respondents. The findings show that the farmers have limited understanding of the causes of climatic conditions and environmental variability, as some respondents associated climate change and environmental variability with social, cultural and religious factors. This study suggests that, despite the changing climatic conditions and environmental variability, farmers have developed and implemented a number of agriculture adaptation strategies that enable them to reduce their vulnerability to the changing conditions. The findings show that agriculture adaptation strategies employ both planned and autonomous adaptation strategies. However, the study shows that increasing drought conditions, rainfall variability, declining soil fertility and use of cheap farming technology are among the challenges that limit effective implementation of agriculture adaptation strategies. This study recommends further research on the varieties of drought-resilient crops, the development of small-scale irrigation schemes to reduce dependence on rain-fed agriculture, and the improvement of crop production in a given plot of land. In respect of the development of adaptation strategies, the study recommends the involvement of the local farmers and consideration of their knowledge and experience in the farming activities as well as the conditions of their local environment. Thus, the findings of this study may be helpful at various levels of decision making with regard to the development of climate change and environmental variability policies and strategies towards reducing farmers’ vulnerability to current and expected future changes.