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One alternative to all-electric powertrains in the aviation industry is the introduction of hybrid fuel cell/battery architectures. Vital to the operation of such a system is the development of effective energy management systems to manage load sharing and component health. In particular, an extension of battery service life is a priority as this is the component expected to be replaced most frequently. A framework for developing a robust energy management system has been built within the open-source framework of the OpenModelica multi-physics libraries, including a simplified fuel cell, battery stack, hydrogen storage tank, and customizable flight generator. Initial results indicated two main focuses for this study. First, the handling of high-power periods, such as takeoff, dominates the battery’s discharge profile. In particular, batteries repeatedly subjected to strain from takeoff due to a string of short-range, 45-minute flights experience a service life of less than half (300 flight hours) that of long-range, 120-minute flights (700 flight hours). Secondly, it was shown that small fluctuations in power demand can result in large reductions of service life up to 20% of the ideal, making their management a key concern. 

Abstract This paper presents a systematic study that focuses on how the number of viewpoints distributed in the heliosphere affects the accuracy and uncertainty of the 3D geometric coronal mass ejection (CME) measurements. An efficient nonmanual minimization-based fitting technique that is different from the manual methods widely used in the community is developed. It uses the MPFIT minimization IDL routine and searches for the optimized model point clouds that best fit the observed CME leading edges from one, two, or three viewpoints using a set of combinations of observations provided by the Solar Terrestrial Relations Observatory and Solar and Heliospheric Observatory. The technique also provides a robust calculation of uncertainties of the CME geometric parameters that is lacking in manual methods. Three well-known geometric models, the cone, graduated cylindrical shell, and spheroid shock, are used. All three models depend on geometric parameters that govern the CME propagation direction and size. Sample cases of a halo, partial halo, and limb CMEs as seen from the Earth are used in the fitting and uncertainty calculation. It is found that, after adding a second viewpoint off the Sun–Earth line, the uncertainties drop significantly, while the addition of the third viewpoint adds limited benefits. This study shows that the minimization fitting method provides a robust, fast, and straightforward way to define the CME geometric parameters along with their uncertainties for individual events, which shall provide the necessary data constraints for ensemble predictions of CME evolution. It also underlines the importance of having a permanent observatory off the Sun–Earth line for operational space weather prediction. 

Abstract With the increasing demand for air travel and the urgency to reduce emissions, transitioning from fossil fuel-based propulsion systems is a critical step toward sustainable aviation. While batteries are widely used in urban air mobility, their long charging durations limit their feasibility for consecutive flights. Hybrid propulsion systems, which integrate fuel cells and batteries, offer a promising alternative due to their higher energy density and improved efficiency. This paper presents a novel hybrid powertrain architecture for regional aircraft, incorporating a hydrogen fuel cell, a lithium-ion battery, and an auxiliary aluminum-air battery. The proposed system is evaluated using real-world power demand data from a Cessna 208 aircraft. The hydrogen fuel cell acts as the primary power source, ensuring continuous operation, while the lithium-ion battery manages transient power fluctuations to enhance system stability. The aluminum-air battery is introduced as a high-energy emergency backup, providing extended endurance during critical situations. A mixed-integer optimization model is formulated for system sizing and power scheduling, ensuring optimal energy distribution among the power sources. Multiple operational scenarios are analyzed to evaluate system performance, particularly under emergency conditions, where power reliability is crucial. The results highlight the feasibility and effectiveness of the proposed hybrid architecture in improving energy efficiency and flight safety for regional aircraft applications. 

Abstract Flourished wind energy market pushes the latest wind turbines (WTs) to further and harsher inland and offshore environment. Increased operation and maintenance cost calls for more reliable and cost effective condition monitoring systems. In this article, a bi-level condition monitoring framework for interturn short-circuit faults (ITSCFs) in WT generators is proposed. A benchmark dataset, consisting of 75 ITSCF scenarios and generator current signals of a specific WT, has been created and made publicly available on Zenodo. The data are simulated at a rate of 4 kHz. Based on the time and frequency features extracted from data processing, machine learning-based severity estimation and faulty phase identification modules can provide valuable diagnostic information for wind farm operators. Specifically, the performance of long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs) are analyzed and compared for severity estimation and faulty phase identification. For test-bed experimental reference, various numbers of scenarios for training the models are analyzed. Numerical experiments demonstrate the computational efficiency and robust denoising capability of the CNN algorithm. The GRU network, however, achieves the highest accuracy. The overall system performance improves significantly, from 87.76% with 16 training scenarios to 99.95% with 52 training scenarios, when tested on a set containing all 76 scenarios from an unforeseen period.