Bed Model for Grate-Fired Furnaces: Computational Fluid Dynamics Modeling and Comparison to ExperimentsShow others and affiliations
2022 (English)In: Energy & Fuels, ISSN 0887-0624, E-ISSN 1520-5029, Vol. 36, no 11, p. 5852-5867Article in journal (Refereed) Published
Abstract [en]
A detailed but still central processing unit (CPU)-efficient bed model for grate-fired combustion of biomass and waste is developed. Simulations of wood chip combustion are performed, and the results are compared to experiments. The so-called layer model is used to track the development of the thermally thick representative fuel particles in the bed. As an efficient way of handling a large number of physical fuel particles, each representative fuel particle represents a number of physical particles with the exact same properties. The motion of the fuel bed is handled in a way that requires negligible CPU power, while for wastes and other fuels with less defined shapes and structure, it still yields accuracy similar to the much more CPU-intensive collision-based models. In this work, the bed model is coupled with ANSYS Fluent and used to simulate one of the test campaigns performed at the grate-fired pilot unit at the University of Stuttgart. It is found that for the test campaign of interest, burning wood chips, the fuel bed is ignited from below, and it is explained how this is due to the thermal properties of the grate and how important the numerical handling of the grate is for an accurate prediction of the bed behavior.
Place, publisher, year, edition, pages
American Chemical Society , 2022. Vol. 36, no 11, p. 5852-5867
Keywords [en]
Computational fluid dynamics, Fuels, Waste incineration, Wood products, Accurate prediction, Computational fluid dynamics modeling, Fluents, Fuel particles, Layer model, Pilot units, Power, Property, Test campaign, Wood chip, Program processors
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:ri:diva-62596DOI: 10.1021/acs.energyfuels.1c04204Scopus ID: 2-s2.0-85145807026OAI: oai:DiVA.org:ri-62596DiVA, id: diva2:1729391
Note
Funding details: 281869; Funding details: 0324342A; Funding details: Vattenfall; Funding details: Norges Forskningsråd; Funding details: Bundesministerium für Wirtschaft und Energie, BMWi; Funding text 1: This research was supported by the GrateCFD Project (Grant 267957/E20), which is funded by LOGE AB, Statkraft Varme AS, EGE Oslo, Vattenfall AB, Hitachi Zosen Inova AG, and Returkraft AS together with the Research Council of Norway through the ENERGIX Program and by the CapeWaste Project (Grant 281869) funded by the Research Council of Norway through the CLIMIT Programme as well as the NUCA Project (Grant 0324342A) funded by the Federal Ministry for Economic Affairs and Energy of Germany.
2023-01-202023-01-202023-06-08Bibliographically approved