Simulation of thermal radiation from heterogeneous combustion products of peat burning in power plants

Author(s):  
V. A. Kuzmin ◽  
I. A. Zagrai
2016 ◽  
Vol 59 (4) ◽  
pp. 579-586
Author(s):  
V. A. Kuz’min ◽  
E. I. Maratkanova ◽  
I. A. Zagrai ◽  
R. V. Rukavishnikova

2021 ◽  
pp. 83-90
Author(s):  
Alyona Shilova ◽  
◽  
Nikolay Bachev ◽  
Roman Bulbovich ◽  
◽  
...  

For a stable position of the flame front in the combustion chambers of gas turbine power plants, the fresh gas-air mixture must be heated to the ignition temperature during the entire operation process. With air excess coefficients in the interval between the upper and lower concentration limits, reverse currents from the zone of developed combustion successfully cope with this task. When organizing low-temperature combustion near the lean limit, the contribution of reverse currents to heating the fresh gas-air mixture turns out to be insufficient and additional external heating of the components in special heaters with exhaust gases from the turbine is required. The temperature characteristics of the fresh gas-air mixture at the inlet to the chamber and in the zone of return currents, as well as combustion products in the developed flame zone, were obtained from the solution of the energy balance equations. The modes of low-temperature lean combustion with excess air coefficients exceeding the lower concentration limit α = 2 are considered. The calculations were carried out for two values of the ejection coefficient in the zone of reverse currents K = 0.14 and K = 0.30. A K value of 0.14 was obtained using empirical relationships. The value K = 0.30 was obtained from the condition that during stoichiometric combustion, the gas-air mixture is heated completely by reverse currents. It is shown that with an increase in the excess air ratio to ensure a stable position of the flame front, the role of external heating of components increases.


2020 ◽  
Vol 34 (01) ◽  
pp. 1029-1036
Author(s):  
Hao Wu ◽  
Shuang Hao

Prediction of particle radiative heat transfer flux is an important task in the large discrete granular systems, such as pebble bed in power plants and industrial fluidized beds. For particle motion and packing, discrete element method (DEM) now is widely accepted as the excellent Lagrangian approach. For thermal radiation, traditional methods focus on calculating the obstructed view factor directly by numerical algorithms. The major challenge for the simulation is that the method is proven to be time-consuming and not feasible to be applied in the practical cases. In this work, we propose an analytical model to calculate macroscopic effective conductivity from particle packing structures Then, we develop a deep neural network (DNN) model used as a predictor of the complex view factor function. The DNN model is trained by a large dataset and the computational speed is greatly improved with good accuracy. It is feasible to perform real-time simulation with DNN model for radiative heat transfer in large pebble bed. The trained model also can be coupled with DEM and used to analyze efficiently the directional radiative conductivity, anisotropic factor and wall effect of the particle thermal radiation.


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