Design Optimization of a Two-Stage Porous Radiant Burner through Response Surface Modeling

Model-based design optimization is a ubiquitous and powerful tool in many engineering disciplines. Its application to industrial combustion has been quite limited, however, due in part to the computational-expense of combustion simulations and, in the case of premixed combustion, noise in the object...

Full description

Saved in:
Bibliographic Details
Published in:Numerical heat transfer. Part A, Applications Applications, 2011-11, Vol.60 (9), p.727-745
Main Authors: Horsman, A. P., Daun, K. J.
Format: Article
Language:English
Subjects:
Algorithms
Combustion
Design optimization
Fluidized bed combustion
Heat transfer
Mathematical analysis
Mathematical models
Optimization
Optimization algorithms
Pore size
Porosity
Response surfaces
Simulation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Model-based design optimization is a ubiquitous and powerful tool in many engineering disciplines. Its application to industrial combustion has been quite limited, however, due in part to the computational-expense of combustion simulations and, in the case of premixed combustion, noise in the objective function induced by the stiffness of the governing equations. This article presents an optimization technique based on response surface modeling, in which the true objective function is minimized by generating and then minimizing a series of interpolating low-order polynomial surfaces centered on the current design iteration. The technique is demonstrated by optimizing the radiant efficiency of a two-stage porous ceramic burner, with the downstream stage pore size and porosity as design parameters. The optimization algorithm identifies solutions that produce statistically significantly improvements in the burner efficiency, and also highlights the importance of considering nonlinear interactions between variables when carrying out the optimization.
ISSN:1040-7782
1521-0634
DOI:10.1080/10407782.2011.627782