17,911 research outputs found
State of the Art in the Optimisation of Wind Turbine Performance Using CFD
Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained
Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting
Research on digitalisation trends and digital topics has become one of the most prolific streams of research within the fields of business and management during the course of the past few years. The purpose of this study is to provide a general picture of the intellectual structure and the conceptual space of this research realm. To this purpose, 6067 publications related to digital topics, indexed in the business and management categories of Web of Science (WoS), and dated from 1990 to 2020 are explored based on the approaches of bibliometric analysis, topic modelling and trend forecasting. The results of the bibliometric analysis comprise insights into the publication and citation structure, the most productive authors, the most productive universities, the most productive countries, the most productive journals, the most cited studies and the most prevalent themes and sub-themes on digitalisation in business and management. In addition, the outcomes of the topic modelling give new knowledge on the latent topical structure along with the rising, falling and fluctuating trends of this literature. In addition, the results of the trend forecasting enable readers to have a glimpse of how the underlying trends of the literature will probably change within the next years until 2025. These results provide guidance and orientation for both academics and practitioners who are initiating or currently developing their efforts in this discipline.info:eu-repo/semantics/acceptedVersio
Microgrids: Planning, Protection and Control
This Special Issue will include papers related to the planning, protection, and control of smart grids and microgrids, and their applications in the industry, transportation, water, waste, and urban and residential infrastructures. Authors are encouraged to present their latest research; reviews on topics including methods, approaches, systems, and technology; and interfaces to other domains such as big data, cybersecurity, human–machine, sustainability, and smart cities. The planning side of microgrids might include technology selection, scheduling, interconnected microgrids, and their integration with regional energy infrastructures. The protection side of microgrids might include topics related to protection strategies, risk management, protection technologies, abnormal scenario assessments, equipment and system protection layers, fault diagnosis, validation and verification, and intelligent safety systems. The control side of smart grids and microgrids might include control strategies, intelligent control algorithms and systems, control architectures, technologies, embedded systems, monitoring, and deployment and implementation
Theory of Unconventional Superconductivity in Strongly Correlated Systems: Real Space Pairing and Statistically Consistent Mean-Field Theory - in Perspective
In this brief overview we discuss the principal features of real space
pairing as expressed via corresponding low-energy (t-J or periodic
Anderson-Kondo) effective Hamiltonian, as well as consider concrete properties
of those unconventional superconductors. We also rise the basic question of
statistical consistency within the so-called renormalized mean-field theory. In
particular, we provide the phase diagrams encompassing the stable magnetic and
superconducting states. We interpret real space pairing as correlated motion of
fermion pair coupled by short-range exchange interaction of magnitude J
comparable to the particle renormalized band energy , where is the
carrier number per site. We also discuss briefly the difference between the
real-space and the paramagnon - mediated sources of superconductivity. The
paper concentrates both on recent novel results obtained in our research group,
as well as puts the theoretical concepts in a conceptual as well as historical
perspective. No slave-bosons are required to formulate the present approach
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
In recent years, there has been significant interest in understanding users'
online content consumption patterns. But, the unstructured, high-dimensional,
and dynamic nature of such data makes extracting valuable insights challenging.
Here we propose a model that combines the simplicity of matrix factorization
with the flexibility of neural networks to efficiently extract nonlinear
patterns from massive text data collections relevant to consumers' online
consumption patterns. Our model decomposes a user's content consumption journey
into nonlinear user and content factors that are used to model their dynamic
interests. This natural decomposition allows us to summarize each user's
content consumption journey with a dynamic probabilistic weighting over a set
of underlying content attributes. The model is fast to estimate, easy to
interpret and can harness external data sources as an empirical prior. These
advantages make our method well suited to the challenges posed by modern
datasets. We use our model to understand the dynamic news consumption interests
of Boston Globe readers over five years. Thorough qualitative studies,
including a crowdsourced evaluation, highlight our model's ability to
accurately identify nuanced and coherent consumption patterns. These results
are supported by our model's superior and robust predictive performance over
several competitive baseline methods
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