The coriolis force effect on the level structure of odd-mass Re isotopes

1993 ◽  
Vol 73 (1) ◽  
pp. 87-93
Author(s):  
S. U. El-Kameesy
2020 ◽  
Vol 124 (1274) ◽  
pp. 581-596
Author(s):  
A. Sarja ◽  
P. Singh ◽  
S.V. Ekkad

ABSTRACTGas turbine blades feature multi-pass internal cooling channels, through which relatively colder air bled from the compressor is routed to cool internal walls. Under rotation, due to the influence of Coriolis force and centrifugal buoyancy, heat transfer at the trailing side enhances and that at the leading side reduces, for a radially outward flow. This non-uniform temperature distribution results in increased thermal stress, which is detrimental to blade life. In this study, a rotation configuration is presented which can negate the Coriolis force effect on heat and fluid flow, thereby maintaining uniform heat transfer on leading and trailing walls. A straight, smooth duct of unit aspect ratio is considered to demonstrate the concept and understand the fluid flow within the channel and its interaction with the walls. The new design is compared against the conventional rotation design. Numerical simulations under steady-state condition were carried out at a Reynolds number of 25000, where the Rotation numbers were varied as 0, 0.1, 0.15, 0.2, 0.25. Realisable version of k-$\varepsilon$ model was used for turbulence modelling. It was observed that new rotation (parallel) configuration’s heat transfer on leading and trailing sides were near similar, and trailing side was marginally higher compared to leading side. An interesting phenomenon of secondary Coriolis effect is reported which accounts for the minor differences in heat transfer augmentation between leading and trailing walls. Due to centrifugal buoyancy, the fluid is pushed towards the radially outward wall, resulting in a counter-rotating vortex pair, which also enhances the heat transfer on leading and trailing walls when compared to stationary case.


1992 ◽  
Vol 35 (10) ◽  
pp. 2551-2555 ◽  
Author(s):  
Kakimoto Koichi ◽  
Watanabe Masahito ◽  
Eguchi Minoru ◽  
Hibiya Taketoshi

AIAA Journal ◽  
1997 ◽  
Vol 35 ◽  
pp. 1164-1170
Author(s):  
Hide S. Koyama ◽  
Kuniharu Uchikawa ◽  
Hani H. Nigim
Keyword(s):  

Author(s):  
Evgenia R. Muntyan

The article analyzes a number of methods of knowledge formation using various graph models, including oriented, undirected graphs with the same type of edges and graphs with multiple and different types of edges. This article shows the possibilities of using graphs to represent a three-level structure of knowledge in the field of complex technical systems modeling. In such a model, at the first level, data is formed in the form of unrelated graph vertices, at the second level – information presented by a related undirected graph, and at the third level – knowledge in the form of a set of graph paths. The proposed interpretation of the structure of knowledge allows to create new opportunities for analytical study of knowledge and information, their properties and relationships.


BioTech ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Yinhao Du ◽  
Kun Fan ◽  
Xi Lu ◽  
Cen Wu

Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.


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