stochastic analysis
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Author(s):  
Dmitri V. Alexandrov ◽  
Andrey Yu. Zubarev

This theme issue, in two parts, continues research studies of transport phenomena in complex media published in the first part (Alexandrov & Zubarev 2021 Phil. Trans. R. Soc. A 379 , 20200301. ( doi:10.1098/rsta.2020.0301 )). The issue is concerned with theoretical, numerical and experimental investigations of nonlinear transport phenomena in heterogeneous and metastable materials of different nature, including biological systems. The papers are devoted to the new effects arising in such systems (e.g. pattern and microstructure formation in materials, impacts of external processes on their properties and evolution and so on). State-of-the-art methods of numerical simulations, stochastic analysis, nonlinear physics and experimental studies are presented in the collection of issue papers. This article is part of the theme issue ‘Transport phenomena in complex systems (part 2)’.


Author(s):  
Xin Hao ◽  
Phee Lep Yeoh ◽  
Zijie Ji ◽  
Yao Yu ◽  
Branka Vucetic ◽  
...  

Author(s):  
Mauricio Reyes-Valenzuela ◽  
Antonio Sánchez-Squella ◽  
Rodrigo Barraza ◽  
Mauricio Osses ◽  
Patricio Valdivia-Lefort

2021 ◽  
Vol 33 (12) ◽  
pp. 127107
Author(s):  
Jaeheon Jeong ◽  
Jeongwoo Ko ◽  
Huisang Cho ◽  
Soogab Lee

Author(s):  
Frédérique Robin ◽  
Bruno Sericola ◽  
Emmanuelle Anceaume ◽  
Yves Mocquard

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Zhang ◽  
Taimu Jin ◽  
Jun Luo ◽  
Shengyang Zhu ◽  
Kaiyun Wang

Resonance problems encountered in vehicle-bridge interaction (VBI) have attracted widespread concern over the past decades. Due to system random characteristics, the prediction of resonant speeds and responses will become more complicated. To this end, this study presents stochastic analysis on the resonance of railway trains moving over a series of simply supported bridges with consideration of the randomness of system parameters. A train-slab track-bridge (TSB) vertically coupled dynamics model is established following the basic principle of vehicle-track-coupled dynamics. The railway train is composed of multiple vehicles, and each of them is built by seven rigid parts assigned with a total of 10 degrees of freedom. The rail, track slab, and bridge are considered as Euler–Bernoulli beams, and the vibration equations of which are established by the modal superposition method (MSM). Except for the nonlinear wheel-rail interaction based on the Hertz contact theory, the other coupling relations between each subsystem are assumed to be linear elastic. The number theory method is employed to obtain the representative sample point sets of the random parameters, and the flow trajectories of probabilities for the TSB dynamics system are captured by a probability density evolution method (PDEM). Numerical results indicate that the maximum bridge and vehicle responses are mainly dominated by the primary train-induced resonant speed; the last vehicle of a train will be more seriously excited when the bridges are set in resonance by the train; the resonant speeds and responses are rather sensitive to the system randomness, and the possible maximum amplitudes predicted by the PDEM are significantly underestimated by the traditional deterministic method; optimized parameters of the TSB system are preliminary obtained based on the representative point sets and imposed screening conditions.


Author(s):  
K. R. Naveen Kumar Dagam Sindhu ◽  
M. P. Sharath Kumar R. B. Singh

The purpose of present study was to describe the growth rate study for some time series production factors of ragi and also making of diagnostic study for detecting some influential time series production factors governing total ragi production in Karnataka and also in India during the period 1993 -2017. The secondary data was collected for analysis from Directorate of economics and statistics and http://indiastat.com. The maximum compound growth rate of MSP of ragi was observed in India as well as in Karnataka. Whereas negative CGRs were recorded for ragi production in India as well as Karnataka. The productivity and area of ragi had positive and significant correlation with ragi production in India, whereas ragi production of India had negative and significant correlation with value of output and minimum support price. These factors were found important for total ragi production of India.


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