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Книги з теми "Diffusion cascades"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Grove, Darren V. Experimental and numerical investigation of second-generation, controlled-diffusion, compressor blades in cascade. Monterey, Calif: Naval Postgraduate School, 1997.

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2

Hobbs, Jacqueline Elizabeth. The use of radiation-enhanced diffusion to study collision cascades in solids. Salford: Universityof Salford, 1985.

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3

Elazar, Yekutiel. A mapping of the viscous flow behavior in a controlled diffusion compressor cascade using laser doppler velocimetry and preliminary evaluation of codes for the prediction of stall. Monterey, California: Naval Postgraduate School, 1988.

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4

Schnorenberg, David G. Investigation of the effect of Reynolds number on laminar separation bubbles on controlled-diffusion compressor blades in cascade. Monterey, Calif: Naval Postgraduate School, 1996.

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5

Dober, David Michael. Three-dimensional fiber-optical LDV measurements in the endwall region of a linear cascade of controlled-diffusion stator blades. Monterey, Calif: Naval Postgraduate School, 1993.

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6

A compendium of controlled diffusion blades generated by an automated inverse design procedure. [Washington, DC]: National Aeronautics and Space Administration, 1989.

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7

Nikerov, V. A. Fast Particle Degradation Diffusion Cascade (Physics Reviews). Taylor & Francis, 2000.

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8

Bianconi, Ginestra. Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.001.0001.

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Анотація:

Multilayer networks are formed by several networks that interact with each other and co-evolve. Multilayer networks include social networks, financial markets, transportation systems, infrastructures and molecular networks and the brain. The multilayer structure of these networks strongly affects the properties of dynamical and stochastic processes defined on them, which can display unexpected characteristics. For example, interdependencies between different networks of a multilayer structure can cause cascades of failure events that can dramatically increase the fragility of these systems; spreading of diseases, opinions and ideas might take advantage of multilayer network topology and spread even when its single layers cannot sustain an epidemic when taken in isolation; diffusion on multilayer transportation networks can significantly speed up with respect to diffusion on single layers; finally, the interplay between multiplexity and controllability of multilayer networks is a problem with major consequences in financial, transportation, molecular biology and brain networks. This field is one of the most prosperous recent developments of Network Science and Data Science. Multilayer networks include multiplex networks, multi-slice temporal networks, networks of networks, interdependent networks. Multilayer networks are characterized by having a highly correlated multilayer network structure, providing a significant advantage for extracting information from them using multilayer network measures and centralities and community detection methods. The multilayer network dynamics (including percolation, epidemic spreading, diffusion, synchronization, game theory and control) is strongly affected by the multilayer network topology. This book will present a comprehensive account of this emerging field.

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9

Prescott, Tony J., and Leah Krubitzer. Evo-devo. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0008.

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Анотація:

This chapter explores how principles underlying natural evo-devo (evolution and development) continue to inspire the design of artificial systems from models of cell growth through to simulated three-dimensional evolved creatures. Research on biological evolvability shows that phenotypic outcomes depend on multiple interactions across different organizational levels—the adult organism is the outcome of a series of genetic cascades modulated in time and space by the wider embryological, bodily, and environmental context. This chapter reviews evo-devo principles discovered in biology and explores their potential for improving the evolvability of artificial systems. Biological topics covered include adaptive, selective, and generative mechanisms, and the role of epigenetic processes in creating phenotypic diversity. Modeling approaches include L-systems, Boolean networks, reaction-diffusion processes, genetic algorithms, and artificial embryogeny. A particular focus is on the evolution and development of the mammalian brain and the possibility of designing, using synthetic evo-devo approaches, brain-like control architectures for biomimetic robots.

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10

Investigation of Flow Over Second Generation Controlled-Diffusion Bladesin a Linear Cascade. Storming Media, 1999.

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11

Namatame, Akira, and Takanori Komatsu. Modeling of Desirable Socioeconomic Networks. Edited by Shu-Heng Chen, Mak Kaboudan, and Ye-Rong Du. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199844371.013.15.

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Анотація:

This chapter discusses the issues of designing desirable socioeconomic networks. Such networks permeate our lives. Evidence of this has generated increasing interest in dynamic processes in complex networks, especially the interplay between processes and the influences of network structure on performance and robustness. Performance optimization and robustness are important issues of socioeconomic networks. Diffusion is the process by which new products are invented and successfully introduced into a society (good diffusion) or infectious diseases spread (bad diffusion). Many studies shed light on how network topology interacts with the structure of social networked systems such as financial institutions to determine systemwide crises. In this context, entire classes of optimization problems range from maximizing the diffusion of innovations to minimizing risk distributions and cascade failures. The structure of interconnections influences network performance.

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12

Experimental and Numerical Investigation of Second-Generation, Controlled-Diffusion, Compressor Blades in Cascade. Storming Media, 1997.

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13

P, Shreeve Raymond, and Lewis Research Center, eds. Comparison of calculated and experimental cascade performance for controlled-diffusion compressor stator blading. [Cleveland, Ohio: National Aeronautics and Space Administration, Lewis Research Center, 1986.

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14

Examination of Flow Around Second-Generation Controlled Diffusion Compressor Blades in Cascade at Stall. Storming Media, 2004.

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15

Three-Component LDV Measurements of Corner Vortices over Second- Generation, Controlled-Diffusion, Compressor Blades in Cascade. Storming Media, 2001.

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16

Experimental Investigation of Vortex Shedding in Flow Over Second- Generation, Controlled-Diffusion, Compressor Blades in Cascade. Storming Media, 2002.

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17

Investigation of the Effect of Reynolds Number on Laminar Separation Bubbles on Controlled-Diffusion Compressor Blades in Cascade. Storming Media, 1996.

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