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Numerical optimization of laboratory combustor geometry for NO suppression

Mazaheri, K ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1016/j.applthermaleng.2016.04.027
  3. Publisher: Elsevier Ltd , 2016
  4. Abstract:
  5. In this article, geometry optimization of a jet stirred reactor (JSR) combustor has been carried out for minimum NO emissions in methane oxidation using a combined numerical algorithm based on computational fluid dynamics (CFD) and differential evolution (DE) optimization. The optimization algorithm is also used to find a fairly accurate reduced mechanism. The combustion kinetics is based on a five-step mechanism with 17 unknowns which is obtained using an optimization DE algorithm for a PSR-PFR reactor based on GRI-3.0 full mechanism. The optimization design variables are the unknowns of the five-step mechanism and the cost function is the concentration difference of pollutants obtained from the 5-step mechanism and the full mechanism. To validate the flow solver and the chemical kinetics, the computed NO at the outlet of the JSR is compared with experiments. To optimize the geometry of a combustor, the JSR combustor geometry is modeled using three parameters (i.e., design variables). An integrated approach using a flow solver and the DE optimization algorithm produces the lowest NO concentrations. Results show that the exhaust NO emission for the optimized geometry is 10.3% lower than the original geometry, while the inlet temperature of the working fluid and the concentration of O2 are operating constraints. In addition, the concentration of CO pollutant is also much less than the original chamber. © 2016 Elsevier Ltd. All rights reserved
  6. Keywords:
  7. Computational fluid dynamics ; Differential evolution optimizer ; Five-step global mechanism ; GRI-3.0 mechanism ; Perfectly stirred reactor ; Plug flow reactor ; Algorithms ; Combustors ; Computational geometry ; Cost functions ; Evolutionary algorithms ; Flow simulation ; Fluid dynamics ; Geometry ; Pollution ; Reaction kinetics ; Differential Evolution ; Geometry optimization ; Global mechanisms ; Numerical optimizations ; Optimization algorithms ; Optimizers ; Optimization
  8. Source: Applied Thermal Engineering ; Volume 102 , 2016 , Pages 1328-1336 ; 13594311 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S1359431116305130