Model order reduction by proper orthogonal decomposition for a 500 MWe tangentially fired pulverized coal boiler
| Autor: | Kwonwoo Jang, Kang Y. Huh, Woojin Lee, Woojoo Han |
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| Rok vydání: | 2021 |
| Předmět: |
Fluid Flow and Transfer Processes
Model order reduction Pulverized coal-fired boiler Scalar (mathematics) Boiler (power generation) Proper orthogonal decomposition Engineering (General). Civil engineering (General) Combustion Coal combustion Reduced-order model Kriging Tangentially fired boiler Control theory RBF Neural network Combustor TA1-2040 Engineering (miscellaneous) Scalar field Mathematics |
| Zdroj: | Case Studies in Thermal Engineering, Vol 28, Iss, Pp 101414-(2021) |
| ISSN: | 2214-157X |
| DOI: | 10.1016/j.csite.2021.101414 |
| Popis: | Reduced order models (ROMs) are constructed by proper orthogonal decomposition (POD) and regression by Kriging and Radial Basis Neural Network (RBFN) for a 500 MWe tangentially fired pulverized coal boiler. POD is performed to extract low-dimensional basis vectors to reproduce 3-D distribution of reacting scalars with respect to the operation parameters of total secondary air (TSA) and burner zone stoichiometric ratio (BSR). The ROMs by Kriging and RBFN both reproduce the scalar fields within 6% averaged relative L2 norm error at three validation points in the parameter space. It is possible to reproduce a 3-D scalar field at any unexplored operation condition within a few seconds through parallel computation of the ROM. It allows fast evaluation of the effects of varying operation parameters in the design stage and real time response of a digital twin based on the ROM for smart operation and maintenance of industrial combustion facilities. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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