scholarly journals A geometric analysis of the SIRS epidemiological model on a homogeneous network

2021 ◽  
Vol 83 (4) ◽  
Author(s):  
Hildeberto Jardón-Kojakhmetov ◽  
Christian Kuehn ◽  
Andrea Pugliese ◽  
Mattia Sensi

AbstractWe study a fast–slow version of an SIRS epidemiological model on homogeneous graphs, obtained through the application of the moment closure method. We use GSPT to study the model, taking into account that the infection period is much shorter than the average duration of immunity. We show that the dynamics occurs through a sequence of fast and slow flows, that can be described through 2-dimensional maps that, under some assumptions, can be approximated as 1-dimensional maps. Using this method, together with numerical bifurcation tools, we show that the model can give rise to periodic solutions, differently from the corresponding model based on homogeneous mixing.

1995 ◽  
Vol 62 (2) ◽  
pp. 527-532 ◽  
Author(s):  
A. M. Hasofer ◽  
M. Grigoriu

2001 ◽  
Author(s):  
Qing Jiang ◽  
Chao Zhang

Abstract A study of the nitrogen oxides (NOx) emission and combustion process in a gas-fired regenerative, high temperature, low emission industrial furnace has been carried out numerically. The effect of two additives, methanol (CH3OH) and hydrogen peroxide (H2O2), to fuel on the NOx emission has been studied. A moment closure method with the assumed β probability density function (PDF) for mixture fraction is used in the present work to model the turbulent non-premixed combustion process in the furnace. The combustion model is based on the assumption of instantaneous full chemical equilibrium. The results showed that CH3OH is effective in the reduction of NOx in a regenerative industrial furnace. However, H2O2 has no significant effect on the NOx emission.


2020 ◽  
Vol 382 ◽  
pp. 122882 ◽  
Author(s):  
Xiaopeng Shang ◽  
Bing Feng Ng ◽  
Man Pun Wan ◽  
Shirun Ding
Keyword(s):  

2009 ◽  
Vol 130 (13) ◽  
pp. 134107 ◽  
Author(s):  
Chang Hyeong Lee ◽  
Kyeong-Hun Kim ◽  
Pilwon Kim

Author(s):  
Carlos Velez ◽  
Scott Martin ◽  
Aleksander Jemcov ◽  
Subith Vasu

The tabulated premixed conditional moment closure (T-PCMC) method has been shown to provide the capability to model turbulent, premixed methane flames with detailed chemistry and reasonable runtimes in Reynolds-averaged Navier–Stokes (RANS) environment by Martin et al. (2013, “Modeling an Enclosed, Turbulent Reacting Methane Jet With the Premixed Conditional Moment Closure Method,” ASME Paper No. GT2013-95092). Here, the premixed conditional moment closure (PCMC) method is extended to large eddy simulation (LES). The new model is validated with the turbulent, enclosed reacting methane backward facing step data from El Banhawy et al. (1983, “Premixed, Turbulent Combustion of a Sudden-Expansion Flow,” Combust. Flame, 50, pp. 153–165). The experimental data have a rectangular test section at atmospheric pressure and temperature with an inlet velocity of 10.5 m/s and an equivalence ratio of 0.9 for two different step heights. Contours of major species, velocity, and temperature are provided. The T-PCMC model falls into the class of table lookup turbulent combustion models in which the combustion model is solved offline over a range of conditions and stored in a table that is accessed by the computational fluid dynamic (CFD) code using three controlling variables: the reaction progress variable (RPV), variance, and local scalar dissipation rate. The local scalar dissipation rate is used to account for the affects of the small-scale mixing on the reaction rates. A presumed shape beta function probability density function (PDF) is used to account for the effects of subgrid scale (SGS) turbulence on the reactions. SGS models are incorporated for the scalar dissipation and variance. The open source CFD code OpenFOAM is used with the compressible Smagorinsky LES model. Velocity, temperature, and major species are compared to the experimental data. Once validated, this low “runtime” CFD turbulent combustion model will have great utility for designing the next generation of lean premixed (LPM) gas turbine combustors.


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