scholarly journals Thermophysical Characteristics of the Ferrofluid in a Vertical Rectangle

Entropy ◽  
2015 ◽  
Vol 17 (2) ◽  
pp. 903-913 ◽  
Author(s):  
Jae-Hyeong Seo ◽  
Byoung-Hee You ◽  
Sang-Seuk Kwen ◽  
Dong-Yeon Lee ◽  
Moo-Yeon Lee
Keyword(s):  
Thermophysical Characteristics ◽  
Vertical Rectangle

Influence of Thermophysical Characteristics of Alloy and Mold Material on Casting Solidification Rate

2020 ◽  
Vol 50 (5) ◽  
pp. 296-302
Author(s):  
O. G. Prikhod’ko ◽  
V. B. Deev ◽  
E. S. Prusov ◽  
A. I. Kutsenko
Keyword(s):  
Solidification Rate ◽  
Mold Material ◽  
Thermophysical Characteristics

High thermal conductivity carbon fiber reinforced polymer based on a carbon fiber from pitch and dispersed-filled ENPB matrix

2018 ◽  
pp. 130-137
Author(s):  
E. A. Nikolaeva ◽  
A. N. Timofeev ◽  
K. V. Mikhaylovskiy
Keyword(s):  
Thermal Conductivity ◽  
Carbon Fiber ◽  
High Thermal Conductivity ◽  
Fiber Reinforced Polymer ◽  
Carbon Fiber Reinforced Polymer ◽  
Carbon Powder ◽  
Fiber Reinforced ◽  
Thermophysical Characteristics ◽  
Reinforced Polymer ◽  
Carbon Fiber Reinforced

This article describes the results of the development of a high thermal conductivity carbon fiber reinforced polymer based on carbon fiber from pitch and an ENPB matrix modified with a carbon powder of high thermal conductivity. Data of the technological scheme of production and the results of determining the physicomechanical and thermophysical characteristics of carbon fiber reinforced polymer are presented. 

scholarly journals Management of the Torch Structure with the New Methodological Approaches to Regulation Based on Neural Network Algorithms

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1909
Author(s):  
Konstantin Osintsev ◽  
Sergei Aliukov ◽  
Yuri Prikhodko
Keyword(s):  
Neural Network ◽  
Coal Dust ◽  
Combustion Process ◽  
Analytical Calculation ◽  
Continuous Distribution ◽  
Initial Section ◽  
Distribution Functions ◽  
Thermophysical Characteristics ◽  
Network Algorithms ◽  
Furnace Volume

A method for evaluating the thermophysical characteristics of the torch is developed. Mathematically the temperature at the end of the zone of active combustion based on continuous distribution functions of particles of solid fuels, in particular coal dust. The particles have different average sizes, which are usually grouped and expressed as a fraction of the total mass of the fuel. The authors suggest taking into account the sequential nature of the entry into the chemical reactions of combustion of particles of different masses. In addition, for the application of the developed methodology, it is necessary to divide the furnace volume into zones and sections. In particular, the initial section of the torch, the zone of intense burning and the zone of afterburning. In this case, taking into account all the thermophysical characteristics of the torch, it is possible to make a thermal balance of the zone of intense burning. Then determines the rate of expiration of the fuel-air mixture, the time of combustion of particles of different masses and the temperature at the end of the zone of intensive combustion. The temperature of the torch, the speed of flame propagation, and the degree of particle burnout must be controlled. The authors propose an algorithm for controlling the thermophysical properties of the torch based on neural network algorithms. The system collects data for a certain time, transmits the information to the server. The data is processed and a forecast is made using neural network algorithms regarding the combustion modes. This allows to increase the reliability and efficiency of the combustion process. The authors present experimental data and compare them with the data of the analytical calculation. In addition, data for certain modes are given, taking into account the system’s operation based on neural network algorithms.

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