Simulation of Thermal Fields and Formation of Drops at Welding of Microsystems

2020 ◽  
pp. 57-67
Author(s):  
S. V. Suvorov ◽  
Alexander V. Vakhrushev
Keyword(s):  
2018 ◽  
Author(s):  
Martin Thebault ◽  
Stephanie Giroux-Julien ◽  
Victoria Timchenko ◽  
Christophe Menezo ◽  
John Reizes

2003 ◽  
Vol 35 (5) ◽  
pp. 22-31
Author(s):  
Yuriy A Klimenko ◽  
Yuriy P. Ladikov-Roev ◽  
Nikolay N. Salnikov ◽  
Oleg K. Cheremnykh

2007 ◽  
Vol 5 ◽  
pp. 127-132
Author(s):  
N.A. Vaganova

To detect damage to the underground pipeline, a mathematical model, allowing to take into account the most significant Factors affecting the distribution of temperature on the day surface. To implement this model, a software package has been developed and results of numerical calculations. With the help of these calculations, in particular, It is established that modern thermal imaging equipment has a principal possibility to determine an unauthorized frame in the main pipeline at a depth of two meters in clay soil.


2021 ◽  
pp. 1-18
Author(s):  
Mansoureh Khaljani ◽  
Meysam Nazari ◽  
Mahdi Azarpeyvand ◽  
Yasser Mahmoudi

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.


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