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In this paper we present AWEsome (Airborne Wind Energy Standardized Open-source Model Environment), a test platform for airborne wind energy systems that consists of low-cost hardware and is entirely based on open-source software. It can hence be used without the need of large financial investments, in particular by research groups and startups to acquire first experiences in their flight operations, to test novel control strategies or technical designs, or for usage in public relations. Our system consists of a modified off-the-shelf model aircraft that is controlled by the pixhawk autopilot hardware and the ardupilot software for fixed wing aircraft. The aircraft is attached to the ground by a tether. We have implemented new flight modes for the autonomous tethered flight of the aircraft along periodic patterns. We present the principal functionality of our algorithms. We report on first successful tests of these modes in real flights.
We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and dynamically adjusting the harvesting operation to the wind resource substantially increases the potential energy yield. The study is based on the ERA5 reanalysis data which covers a period of 7 years with hourly estimates at a surface resolution of 31 x 31 km and a vertical resolution of 137 barometric altitude levels. We present detailed wind statistics for a location in the English Channel and then expand the analysis to a surface grid of Western and Central Europe with a resolution of 110 x 110 km. Over the land mass and coastal areas of Europe we find that compared to a fixed harvesting altitude at the approximate hub height of wind turbines, the energy yield which is available for 95% of the time increases by a factor of two.
Real-time altitude control of airborne wind energy (AWE) systems can improve performance by allowing turbines to track favorable wind speeds across a range of operating altitudes. The current work explores the performance implications of deploying an AWE system with sensor configurations that provide different amounts of data to characterize wind speed profiles. We examine various control objectives that balance trade-offs between exploration and exploitation, and use a persistence model to generate a probabilistic wind speed forecast to inform control decisions. We assess system performance by comparing power production against baselines such as omniscient control and stationary flight. We show that with few sensors, control strategies that reward exploration are favored. We also show that with comprehensive sensing, the implications of choosing a sub-optimal control strategy decrease. This work informs and motivates the need for future research exploring online learning algorithms to characterize vertical wind speed profiles.
We present a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent control or plant parameters. The proposed framework differs from the majority of co-design literature where there exists a closed-form model of the system dynamics. Furthermore, we utilize the idea of Batch Bayesian Optimization at the plant optimization level to generate a set of plant designs at each iteration of the overall optimization process, recognizing that there will exist economies of scale in running multiple experiments in each iteration of the plant design process. We validate the proposed framework for a Buoyant Airborne Turbine (BAT). We choose the horizontal stabilizer area, longitudinal center of mass relative to center of buoyancy (plant parameters), and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that these plant and control parameters converge to their respective optimal values within only a few iterations.
Multicopters are becoming increasingly important in both civil and military fields. Currently, most multicopter propulsion systems are designed by experience and trial-and-error experiments, which are costly and ineffective. This paper proposes a simple and practical method to help designers find the optimal propulsion system according to the given design requirements. First, the modeling methods for four basic components of the propulsion system including propellers, motors, electric speed controls, and batteries are studied respectively. Secondly, the whole optimization design problem is simplified and decoupled into several sub-problems. By solving these sub-problems, the optimal parameters of each component can be obtained respectively. Finally, based on the obtained optimal component parameters, the optimal product of each component can be quickly located and determined from the corresponding database. Experiments and statistical analyses demonstrate the effectiveness of the proposed method.
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to i) spend less time on data management and processing, ii) perform reproducible evaluations of their methods, and iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI and PET data), as well as tools for statistics, machine learning and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS and NIFD). Processed data include image-valued scalar fields (e.g. tissue probability maps), meshes, surface-based scalar fields (e.g. cortical thickness maps) or scalar outputs (e.g. regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.