Advanced Design Optimization of Combustion Equipment for Biomass Combustion
Design: of engineered combustion equipment normally involves laborious "build and try" designs to identify the best possible configuration. The number of design iterations can be reduced with engineering experience of what might work. The expensive cut-and-try approach can be improved using computational aided engineering tools coupled with optimization techniques to find the optimal design. For example, the "best" air duct configuration with the lowest pressure loss and smallest fan size for an air-fed biomass gasifier may take several weeks using the standard computational fluid dynamics (CFD) "cut and try" approach. Alternatively, coupling an efficient design optimization algorithm with an existing CFD model can reduce the time to find the best design by more than 50% and can allow the engineer to examine more design options than possible using the "cut-and-try" approach. Combining an efficient optimization algorithm with an existing CFD model of a biomass gasifier to find the "optimal" design is the focus of this work. Shape optimization has been performed by combining the optimization tool Sculptor® with the commercial CFD code STARCCM+. This work illustrates how the "linked" approach is used to examine design factors to optimize an entrained flow biomass gasifier to improve overall system performance in a methodical comprehensive fashion.
J. D. Smith et al., "Advanced Design Optimization of Combustion Equipment for Biomass Combustion," Renewable Energy, pp. 1597 - 1607, Elsevier Ltd, Jan 2020.
The definitive version is available at https://doi.org/10.1016/j.renene.2019.07.074
Chemical and Biochemical Engineering
Center for Research in Energy and Environment (CREE)
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Advanced burner design; Biomass combustion; Computational fluid dynamics; Design optimization; Reduced design cycle time
International Standard Serial Number (ISSN)
Article - Journal
© 2020 Elsevier Ltd, All rights reserved.
01 Jan 2020
Financial support for this work was provided by the Wayne and Gayle Laufer Foundation and by Elevated Analytics.