scale expansion
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2022 ◽  
Vol 58 ◽  
pp. 103-114
Author(s):  
Janice H. Kim ◽  
Belay H. Hailu ◽  
Pauline M. Rose ◽  
Jack Rossiter ◽  
Tirussew Teferra ◽  
...  

2021 ◽  
Vol 931 ◽  
Author(s):  
Alessandro Sozza ◽  
Massimo Cencini ◽  
Stefano Musacchio ◽  
Guido Boffetta

Suspended particles can significantly alter the fluid properties and, in particular, can modify the transition from laminar to turbulent flow. We investigate the effect of heavy particle suspensions on the linear stability of the Kolmogorov flow by means of a multiple-scale expansion of the Eulerian model originally proposed by Saffman (J. Fluid Mech., vol. 13, issue 1, 1962, pp. 120–128). We find that, while at small Stokes numbers particles always destabilize the flow (as already predicted by Saffman in the limit of very thin particles), at sufficiently large Stokes numbers the effect is non-monotonic in the particle mass fraction and particles can both stabilize and destabilize the flow. Numerical analysis is used to validate the analytical predictions. We find that in a region of the parameter space the multiple-scale expansion overestimates the stability of the flow and that this is a consequence of the breakdown of the scale separation assumptions.


2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Fang Liu

To solve the problems of low recognition rate, high misrecognition rate, and long recognition time, the path recognition method of the regional education scale expansion based on the improved dragonfly algorithm is proposed. Through a variety of different behaviors utilized in the optimization process, the dragonfly algorithm model has been constructed. The step size and the position vector are introduced to update the dragonfly’s location. The dragonfly’s foraging behaviors are accurately simulated. Afterward, the dragonfly algorithm is combined with the flower authorization algorithm. The conversion probability is added, and the dragonfly’s global development ability is adjusted in real-time. Then, the dragonfly algorithm is improved. The improved dragonfly algorithm is employed to extract the features of the expansion path of the regional education scale. The improved support vector machine is utilized as a classifier to realize the recognition of the regional education scale expansion path. The experimental results denote that the proposed method has a high recognition rate of the regional education scale expansion path and can effectively reduce the misrecognition rate and shorten the recognition time.


Author(s):  
Zheng Zhou ◽  
Rui Guan ◽  
Zongyong Cui ◽  
Zongjie Cao ◽  
Yiming Pi ◽  
...  

2021 ◽  
Author(s):  
Sebastian Dunnett ◽  
Robert A Holland ◽  
Gail Taylor ◽  
Felix Eigenbrod

<p>Protected areas and renewable energy generation are key tools to combat biodiversity loss and climate change respectively. Over the coming decades, very large-scale expansion of renewable energy infrastructure will be needed to meet climate change targets, while simultaneously large-scale expansion of the protected area network to meet conservation objectives is planned. However, renewable energy infrastructure has negative effects on wildlife, and co-occurrence may mean emissions targets are met at the expense of conservation objectives. However, data limitations mean that the degree of likely future conflict of these two key land management objectives has not been fully assessed. Here, we address this gap by examining current and projected future overlaps of wind and solar photovoltaic installations and important conservation areas globally using new spatially explicit wind and solar photovoltaic data, and new methods for predicting future renewable expansion. We show similar levels of co-occurrence of important conservation areas and wind and solar installations as previous studies but also show that once area is accounted for previous concerns about overlaps in Northern Hemisphere may be largely unfounded, though are high in some high-biodiversity countries (e.g. Brazil). Future projections of overlap between the two land uses are generally lower than previously predicted using new data, with regional correlation coefficients peaking at -0.3418 and 0.2053, suggesting a low risk of future conflict. Our results show that the current and future overlap of the two land uses may not be as severe as previously suggested. This is important, as global efforts to decarbonise energy systems are central to mitigating against climate change and against the strong negative impacts of projected climate change on biodiversity.</p>


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