Mark Aull

This paper describes a nonlinear model inversion for performance analysis and system optimization for airborne wind energy (AWE) systems. Airborne wind energy systems are lighter and potentially lower in cost than comparable conventional wind turbines due to using a tether and bridle rather than blades and a tower which must support high bending and compressive loads. They are also easier to install due to using a tension anchor rather than a foundation. There are many AWE systems in various states of R&D, but no commercial AWE wind farms currently exist. Because AWE systems are novel and significantly more complex to design, analyze, and test than conventional wind turbines, better analysis tools are important for the technology to mature.AWE system design involves many interdependent design trade‐offs, which are difficult to analyze with traditional tools; using simulations to iterate through parameters for design optimization is undesirable. A nonlinear model inversion based on appropriate simplifying assumptions is better suited to this task. The inverse model uses a trajectory input then calculates attitudes, forces, and control values required to follow that trajectory as well as power produced. Applicable constraints ensure that the trajectory is realizable. A validation is presented, using the output of the inverse model as a feed‐forward controller for a high fidelity simulation. The analysis shows good agreement with the simulation, despite simplifications such as a straight rigid tether, constant lift and drag coefficients, and a simplified rotor model.

Traditional on-shore horizontal-axis wind turbines need to be large for both performance reasons (e.g., clearing ground turbulence and reaching higher wind speeds) and for economic reasons (e.g., more efficient land use, lower maintenance costs, and fewer controllers and grid attachments) while their efficiency is scale and mass independent. Airborne wind energy (AWE) system efficiency is a function of system size and AWE system operating altitude is less directly coupled to system power rating. This paper derives fly-gen AWE system parameters from small number of design parameters, which are used to optimize a design for energy cost. This paper then scales AWE systems and optimizes them at each scale to determine the relationships between size, efficiency, power output, and cost. The results indicate that physics and economics favor a larger number of small units, at least offshore or where land cost is small.

A Late Jurassic sauropod bone-bed comprising thousands of individual skeletal elements in the Morrison Formation of Montana, USA, is re-analysed to further elucidate its taphonomic history. New data gained from detailed contextual sampling is assembled and presented to test the hypothesis of a drought-induced, mass-mortality assemblage, and its subsequent depositional emplacement by a debris flow. Large dinosaur bone-beds are well-known in the Morrison, yet the Mother's Day Quarry site is unique for the formation in depicting an apparent debris flow deposit. Use of a computer program tailored to the data allows three-dimensional reconstruction of the bone-bed and recreates the palaeoslope of the deposit, adding support to the debris flow hypothesis. Such digital manipulation of field data may allow a deeper understanding of other bone-bed origination events.

A predictive object detection algorithm was developed to investigate the practicality of using advanced filtering on stereo vision object detection algorithms such as the X-H Map. Obstacle detection with stereo vision is inherently noisy and non linear. This paper describes the X-H Map algorithm and details a method of improving the accuracy with the Unscented Kalman Filter (UKF). The significance of this work is that it details a method of stereo vision object detection and concludes that the UKF is a relevant method of filtering that improves the robustness of obstacle detection given noisy inputs. This method of integrating the UKF for use in stereo vision is suitable for any standard stereo vision algorithm that is based on pixel matching (stereo correspondence) from disparity maps.