Advantages of vertical axis tidal turbines set in close proximity: A comparative CFD investigation in the English Channel

A 2D CFD analysis of the hydrodynamic interactions between three closely spaced Vertical Axis Tidal Turbines is performed for two layouts: side-by-side and triangular. Three mechanisms determine a power increase with respect to isolated turbines: (1) turbine blockage that entails flow acceleration outside of the turbines and inside the aisles between adjacent turbines; (2) more favorable direction of the flow approaching the blade during upwind trajectory; (3) wake contraction, that increases the torque generation during the downwind path. Blockage is responsible for a moderate performance increase exhibited by the triangular layout. Change in direction of the flow approaching the blades and wake contraction only occur for the side-by-side layout and cause a significant increase in efficiency. The six-month energy production of the two arrangements is predicted for three real cases in the English Channel: Alderney Race, St. Catherine and Portland Bill, characterized by rectilinear and non-rectilinear currents. Passive stall is assumed as load strategy control; the effect of the rated speed on energy production is analyzed. The side-by-side layout allows not only a higher power gain but also a wider range of flow directions for which a gain is possible, therefore it appears suitable even for non-rectilinear currents

Control and Optimization for Aerospace Systems with Stochastic Disturbances, Uncertainties, and Constraints

The topic of this dissertation is the control and optimization of aerospace systems under the influence of stochastic disturbances, uncertainties, and subject to chance constraints. This problem is motivated by the uncertain operating environments of many aerospace systems, and the ever-present push to extract greater performance from these systems while maintaining safety. Explicitly accounting for the stochastic disturbances and uncertainties in the constrained control design confers the ability to assign the probability of constraint satisfaction depending on the level of risk that is deemed acceptable and allows for the possibility of theoretical constraint satisfaction guarantees. Along these lines, this dissertation presents novel contributions addressing four different problems: 1) chance-constrained path planning for small unmanned aerial vehicles in urban environments, 2) chance-constrained spacecraft relative motion planning in low-Earth orbit, 3) stochastic optimization of suborbital launch operations, and 4) nonlinear model predictive control for tracking near rectilinear halo orbits and a proposed stochastic extension. For the first problem, existing dynamic and informed rapidly-expanding random trees algorithms are combined with a novel quadratic programming-based collision detection algorithm to enable computationally efficient, chance-constrained path planning. For the second problem, a previously proposed constrained relative motion approach based on chained positively invariant sets is extended in this dissertation to the case where the spacecraft dynamics are controlled using output feedback on noisy measurements and are subject to stochastic disturbances. Connectivity between nodes is determined through the use of chance-constrained admissible sets, guaranteeing that constraints are met with a specified probability. For the third problem, a novel approach to suborbital launch operations is presented. It utilizes linear covariance propagation and stochastic clustering optimization to create an effective software-only method for decreasing the probability of a dangerous landing with no physical changes to the vehicle and only minimal changes to its flight controls software. For the fourth problem, the use of suboptimal nonlinear model predictive control (NMPC) coupled with low-thrust actuators is considered for station-keeping on near rectilinear halo orbits. The nonlinear optimization problems in NMPC are solved with time-distributed sequential quadratic programming techniques utilizing the FBstab algorithm. A stochastic extension for this problem is also proposed. The results are illustrated using detailed numerical simulations.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162992/1/awbe_1.pd

Distributed adaptive signal processing for frequency estimation

It is widely recognised that future smart grids will heavily rely upon intelligent communication and signal processing as enabling technologies for their operation. Traditional tools for power system analysis, which have been built from a circuit theory perspective, are a good match for balanced system conditions. However, the unprecedented changes that are imposed by smart grid requirements, are pushing the limits of these old paradigms. To this end, we provide new signal processing perspectives to address some fundamental operations in power systems such as frequency estimation, regulation and fault detection. Firstly, motivated by our finding that any excursion from nominal power system conditions results in a degree of non-circularity in the measured variables, we cast the frequency estimation problem into a distributed estimation framework for noncircular complex random variables. Next, we derive the required next generation widely linear, frequency estimators which incorporate the so-called augmented data statistics and cater for the noncircularity and a widely linear nature of system functions. Uniquely, we also show that by virtue of augmented complex statistics, it is possible to treat frequency tracking and fault detection in a unified way. To address the ever shortening time-scales in future frequency regulation tasks, the developed distributed widely linear frequency estimators are equipped with the ability to compensate for the fewer available temporal voltage data by exploiting spatial diversity in wide area measurements. This contribution is further supported by new physically meaningful theoretical results on the statistical behavior of distributed adaptive filters. Our approach avoids the current restrictive assumptions routinely employed to simplify the analysis by making use of the collaborative learning strategies of distributed agents. The efficacy of the proposed distributed frequency estimators over standard strictly linear and stand-alone algorithms is illustrated in case studies over synthetic and real-world three-phase measurements. An overarching theme in this thesis is the elucidation of underlying commonalities between different methodologies employed in classical power engineering and signal processing. By revisiting fundamental power system ideas within the framework of augmented complex statistics, we provide a physically meaningful signal processing perspective of three-phase transforms and reveal their intimate connections with spatial discrete Fourier transform (DFT), optimal dimensionality reduction and frequency demodulation techniques. Moreover, under the widely linear framework, we also show that the two most widely used frequency estimators in the power grid are in fact special cases of frequency demodulation techniques. Finally, revisiting classic estimation problems in power engineering through the lens of non-circular complex estimation has made it possible to develop a new self-stabilising adaptive three-phase transformation which enables algorithms designed for balanced operating conditions to be straightforwardly implemented in a variety of real-world unbalanced operating conditions. This thesis therefore aims to help bridge the gap between signal processing and power communities by providing power system designers with advanced estimation algorithms and modern physically meaningful interpretations of key power engineering paradigms in order to match the dynamic and decentralised nature of the smart grid.Open Acces

Flow curvature effects on dynamic behaviour of a novel vertical axis tidal current turbine: numerical and experimental analysis

The paper deals with performances analysis of vertical axis turbine to exploit tidal marine currents. Flow curvature effects on performences of a novel vertical axis turbine have been investuigated. It has been shown that the flow curvature effect allows to design properly an accurate airfoil shape to increase turbine performances

Improved Integration of Ground-Based Low-Level Wind Shear Alert Systems Using a Grid-Based Approach

Low-level wind shear is a significant aviation hazard. A sudden reduction in the headwind along an aircraft flight path can induce a loss of lift, from which an aircraft may not be able to recover when it is close to the ground. Airports therefore use low-level wind shear alert systems to monitor wind velocities within the airport terminal area and alert of any detected hazardous wind shear. There exist three ground-based sensor systems capable of independently observing low-level wind shear: a Doppler weather radar-based, a Doppler wind lidar-based, and an anemometer-based system. However, as no single sensor system is capable of all-weather wind shear observations, multiple alert systems are used simultaneously, and observations from each system are integrated to produce one set of integrated wind shear alerts. Algorithms for integrating Doppler weather radar and anemometer wind shear observations were originally developed in the early 1990s. However, the addition of the Doppler wind lidar-based alert system in more recent years warrants updates to the existing radar/anemometer integration algorithms. This thesis presents four different replacement candidates for the original radar/anemometer integration algorithms. A grid-based integration approach, where observations from different sensor systems are mapped onto a common grid and integrated, is found to best accommodate central integration considerations, and is recommended as the replacement to the original radar/anemometer algorithms in operational use. The grid-based approach is discussed in further detail, and a first possible implementation of the algorithm is presented. In addition, ways of validating the algorithm and adopting it for operational use are outlined

A Fuzzy Guidance System for Rendezvous and Pursuit of Moving Targets

This article presents the development of a fuzzy guidance system (FGS) for unmanned aerial vehicles capable of pursuing and performing rendezvous with static and mobile targets. The system is designed to allow the vehicle to approach a maneuvering target from a desired direction of arrival and to terminate the rendezvous at a constant distance from the target. In order to perform a rendezvous with a maneuvering target, the desired direction of arrival is adjusted over time to always approach the target from behind, so that the aircraft and target velocity vectors become aligned. The proposed guidance system assumes the presence of an autopilot and uses a set of Takagi–Sugeno fuzzy controllers to generate the orientation and speed references for the velocity and heading control loops, given the relative position and velocity between the aircraft and the target. The FGS treats the target as a mobile waypoint in a 4-D space (position in 2-dimensions, desired crossing heading and speed) and guides the aircraft on suitable trajectories towards the target. Only when the vehicle is close enough to the rendezvous point, the guidance law is complemented with an additional linear controller to manage the terminal formation keeping phase. The capabilities of the proposed rendezvous-FGS are verified in simulation on both maneuvering and non-maneuvering targets. Finally, experimental results using a multi-rotor aerial system are presented for both fixed and accelerating targets

Orbit Maintenance and Navigation of Human Spacecraft at Cislunar Near Rectilinear Halo Orbits

Multiple studies have concluded that Earth-Moon libration point orbits are attractive candidates for staging operations. The Near Rectilinear Halo Orbit (NRHO), a member of the Earth-Moon halo orbit family, has been singularly demonstrated to meet multi-mission architectural constraints. In this paper, the challenges associated with operating human spacecraft in the NRHO are evaluated. Navigation accuracies and human vehicle process noise effects are applied to various station keeping strategies in order to obtain a reliable orbit maintenance algorithm. Additionally, the ability to absorb missed burns, construct phasing maneuvers to avoid eclipses and conduct rendezvous and proximity operations are examined

AUTOMATIC TAKE-OFF OR LANDING PATH FOLLOWING IN TURBULENT AIR FOR UAS - AN EKF BASED PROCEDURE

By using the Extended Kalman Filter (EKF) an accurate take-off or landing flight path following in turbulent air is performed. The tuned up procedure employs simultaneously two different EKF: the first one estimates gust disturbances, the second one affords to determine the necessary controls displacements for rejecting those ones. In particular, the first filter, by using instrumental measurements gathered in turbulent air, estimates wind components. The second one obtains command laws able to follow the desired flight path. To perform this task aerodynamic coefficients have been modified by adding entirely new derivatives or synthetic increments to basic ones whose might the kind of change required to reject disturbances. Such a procedure leads to a set of unknown stability and control parameters containing the required displacements of the controls. The modified aircraft parameters are determined by augmenting the aircraft’s state. The filter estimates the new set of aircraft stability derivatives by using measurements made by the desired take-off or landing flight path parameters. Once the unknown stability and control derivatives have been determined, the obtained control displacements are used to perform an accurate path following in turbulent air. Obviously, the obtained control laws are adaptive since they depend by either the characteristics of the disturbance or the desired flight path. The proposed procedure requires low computational power, therefore it is particularly suited for UAS, besides being simple to implement on board it may be successfully employed on low cost platforms