37,966 research outputs found
Design and Finite Element Analysis of Mixed Aerofoil Wind Turbine Blades
Wind turbine technology is one of the rapid growth sectors of renewable energy all over the world. As a core component of a wind turbine, it is a common view that the design and manufacturing of rotor blades represent about 20% of the total investment of the wind turbine [1]. Moreover, the performance of a wind turbine is highly dependent on the design of the rotor [2]. As well as rotor aerodynamic performance, the structure strength, stiffness and fatigue of the blade are also critical to the wind turbine system service life.
This paper presents the design and Finite Element Analysis (FEA) of a 10KW fixed-pitch variable-speed wind turbine blade with five different thickness of aerofoil shape along the span of the blade. The main parameters of the wind turbine rotor and the blade aerodynamic geometry shape are determined based on the principles of the blade element momentum (BEM) theory. Based on the FE method, deflections and strain distributions of the blade under extreme wind conditions are numerically predicted. The results indicate that the tip clearance is sufficient to prevent collision with the tower, and the blade material is linear and safe
Temperature Effects on Threshold Counterion Concentration to Induce Aggregation of fd Virus
We seek to determine the mechanism of like-charge attraction by measuring the
temperature dependence of critical divalent counterion concentration
() for the aggregation of fd viruses. We find that an increase in
temperature causes to decrease, primarily due to a decrease in the
dielectric constant () of the solvent. At a constant ,
is found to increase as the temperature increases. The effects of
and on can be combined to that of one parameter:
Bjerrum length (). decreases exponentially as
increases, suggesting that entropic effect of counterions plays an important
role at the onset of bundle formation.Comment: 12 pages, 3 figure
Exploiting Cognitive Structure for Adaptive Learning
Adaptive learning, also known as adaptive teaching, relies on learning path
recommendation, which sequentially recommends personalized learning items
(e.g., lectures, exercises) to satisfy the unique needs of each learner.
Although it is well known that modeling the cognitive structure including
knowledge level of learners and knowledge structure (e.g., the prerequisite
relations) of learning items is important for learning path recommendation,
existing methods for adaptive learning often separately focus on either
knowledge levels of learners or knowledge structure of learning items. To fully
exploit the multifaceted cognitive structure for learning path recommendation,
we propose a Cognitive Structure Enhanced framework for Adaptive Learning,
named CSEAL. By viewing path recommendation as a Markov Decision Process and
applying an actor-critic algorithm, CSEAL can sequentially identify the right
learning items to different learners. Specifically, we first utilize a
recurrent neural network to trace the evolving knowledge levels of learners at
each learning step. Then, we design a navigation algorithm on the knowledge
structure to ensure the logicality of learning paths, which reduces the search
space in the decision process. Finally, the actor-critic algorithm is used to
determine what to learn next and whose parameters are dynamically updated along
the learning path. Extensive experiments on real-world data demonstrate the
effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM
SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19
Negative Link Prediction in Social Media
Signed network analysis has attracted increasing attention in recent years.
This is in part because research on signed network analysis suggests that
negative links have added value in the analytical process. A major impediment
in their effective use is that most social media sites do not enable users to
specify them explicitly. In other words, a gap exists between the importance of
negative links and their availability in real data sets. Therefore, it is
natural to explore whether one can predict negative links automatically from
the commonly available social network data. In this paper, we investigate the
novel problem of negative link prediction with only positive links and
content-centric interactions in social media. We make a number of important
observations about negative links, and propose a principled framework NeLP,
which can exploit positive links and content-centric interactions to predict
negative links. Our experimental results on real-world social networks
demonstrate that the proposed NeLP framework can accurately predict negative
links with positive links and content-centric interactions. Our detailed
experiments also illustrate the relative importance of various factors to the
effectiveness of the proposed framework
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The cumulative effects of known susceptibility variants to predict primary biliary cirrhosis risk.
Multiple genetic variants influence the risk for development of primary biliary cirrhosis (PBC). To explore the cumulative effects of known susceptibility loci on risk, we utilized a weighted genetic risk score (wGRS) to evaluate whether genetic information can predict susceptibility. The wGRS was created using 26 known susceptibility loci and investigated in 1840 UK PBC and 5164 controls. Our data indicate that the wGRS was significantly different between PBC and controls (P=1.61E-142). Moreover, we assessed predictive performance of wGRS on disease status by calculating the area under the receiver operator characteristic curve. The area under curve for the purely genetic model was 0.72 and for gender plus genetic model was 0.82, with confidence limits substantially above random predictions. The risk of PBC using logistic regression was estimated after dividing individuals into quartiles. Individuals in the highest disclosed risk group demonstrated a substantially increased risk for PBC compared with the lowest risk group (odds ratio: 9.3, P=1.91E-084). Finally, we validated our findings in an analysis of an Italian PBC cohort. Our data suggested that the wGRS, utilizing genetic variants, was significantly associated with increased risk for PBC with consistent discriminant ability. Our study is a first step toward risk prediction for PBC
Experimental observation of an enhanced anisotropic magnetoresistance in non-local configuration
We compare non-local magnetoresistance measurements in multi-terminal Ni
nanostructures with corresponding local experiments. In both configurations,
the measured voltages show the characteristic features of anisotropic
magnetoresistance (AMR). However, the magnitude of the non-local AMR signal is
up to one order of magnitude larger than its local counterpart. Moreover, the
non-local AMR increases with increasing degree of non-locality, i.e., with the
separation between the region of the main current flow and the voltage
measurement region. All experimental observations can be consistently modeled
in terms of current spreading in a non-isotropic conductor. Our results show
that current spreading can significantly enhance the magnetoresistance signal
in non-local experiments
In situ photogalvanic acceleration of optofluidic kinetics: a new paradigm for advanced photocatalytic technologies
A multiscale-designed optofluidic reactor is demonstrated in this work, featuring an overall reaction rate constant of 1.32 s¯¹ for photocatalytic decolourization of methylene blue, which is an order of magnitude higher as compared to literature records. A novel performance-enhancement mechanism of microscale in situ photogalvanic acceleration was found to be the main reason for the superior optofluidic performance in the photocatalytic degradation of dyes as a model reaction
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