A novel proposal for the modeling and operation of a micro-CHP (combined-heat-and-power) residential system based on HT-PEMFC (High Temperature-Proton Exchange Membrane Fuel Cell) technology is described and analyzed to investigate its commercialization prospects. An HT-PEMFC operates at elevated temperatures, as compared to Nafion-based PEMFCs and therefore can be a significant candidate for cogeneration residential systems. The proposed system can provide electric power, hot water, and space heating for a typical Danish single-family household. A complete fuel processing subsystem, with all necessary BOP (balance-of-plant) components, is modeled and coupled to the fuel cell stack subsystem. The micro-CHP system is simulated in LabVIEW™ environment to provide the ability of Data Acquisition of actual components and thereby more realistic design in the future. A part-load study has been conducted to indicate performance characteristics at off-design conditions. The system is sized to provide realistic dimensioning of the actual system. 相似文献
Operational modal analysis (OMA) is an essential tool for understanding the structural dynamics of offshore wind turbines (OWTs). However, the classical OMA algorithms require the excitation of the structure to be stationary white noise, which is often not the case for operational OWTs due to the presence of periodic excitation caused by rotor rotation. To address this issue, several solutions have been proposed in the literature, including the Kalman filter-based stochastic subspace identification (KF-SSI) method which eliminates harmonics through estimation and orthogonal projection. In this paper, an enhanced version of the KF-SSI method is presented that involves a concatenation step, allowing multiple datasets with similar environmental conditions to be used in the identification process, resulting in higher precision. This enhanced framework is applied to an operational OWT and compared to other OMA methods, such as the modified least-squares complex exponential and PolyMAX. Using field data from a multi-megawatt operational OWT, it is shown that the enhanced framework is able to accurately distinguish the first three bending modes with more stable estimates and lower variance compared to the original KF-SSI algorithm and follows a similar trend compared to other approaches. 相似文献
International Journal of Information Security - Timely detection and effective treatment of cyber-attacks for protecting personal and sensitive data from unauthorized disclosure constitute a core... 相似文献
Dielectric materials with higher energy storage and electromagnetic (EM) energy conversion are in high demand to advance electronic devices, military stealth, and mitigate EM wave pollution. Existing dielectric materials for high-energy-storage electronics and dielectric loss electromagnetic wave absorbers are studied toward realizing these goals, each aligned with the current global grand challenges. Libraries of dielectric materials with desirable permittivity, dielectric loss, and/or dielectric breakdown strength potentially meeting the device requirements are reviewed here. Regardless, aimed at translating these into energy storage devices, the oft-encountered shortcomings can be caused by either of two confluences: a) low permittivity, high dielectric loss, and low breakdown strength; b) low permittivity, low dielectric loss, and process complexity. Contextualizing these aspects and the overarching objectives of enabling high-efficiency energy storage and EM energy conversion, recent advances in by-design inorganic–organic hybrid materials are reviewed here, with a focus on design approaches, preparation methods, and characterization techniques. In light of their strengths and weaknesses, potential strategies to foster their commercial adoption are critically interrogated. 相似文献
Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any cyber security exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a large dataset of publicly available cyber security articles that have been used to predict future threats and to form the skeleton for new exercise scenarios. Machine learning techniques, like named entity recognition and topic extraction, have been utilised to structure the information based on a novel ontology we developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used clustering with outliers to classify the generated extracted data into objects of our ontology. Graph comparison methodologies were used to match generated scenario fragments to known threat actors’ tactics and help enrich the proposed scenario accordingly with the help of synthetic text generators. CESO has also been chosen as the prominent way to express both fragments and the final proposed scenario content by our AI-assisted Cyber Exercise Framework. Our methodology was assessed by providing a set of generated scenarios for evaluation to a group of experts to be used as part of a real-world awareness tabletop exercise.