Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/107565
Title: An assessment of OpenFOAM solver on RANS simulations of round supersonic free jets
Authors: Lim, Desmond Haoxiang
Wei, Xiaofeng
New, Tze How
Zang, B.
US, Vevek
Keywords: Supersonic
Jets
Engineering::Aeronautical engineering
Issue Date: 2018
Source: Zang, B., US, V., Lim, D. H., Wei, X., & New, T. H. (2018). An assessment of OpenFOAM solver on RANS simulations of round supersonic free jets. Journal of Computational Science, 28, 18-31. doi:10.1016/j.jocs.2018.07.002
Series/Report no.: Journal of Computational Science
Abstract: Numerical performance of rhoCentralFoam, a compressible solver in OpenFOAM, was studied by modeling round supersonic free jets with varying nozzle exit conditions through Reynolds-Averaged Navier-Stokes approach. To understand the behavior and accuracy of the solver, simulations were conducted at four different nozzle pressure ratios (i.e. NPR = 2.8, 3.4, 4 and 5), corresponding to over-, perfectly-, moderately under- and highly under-expanded exit conditions, respectively. The simulated results were subsequently validated against qualitative and quantitative experimental results and good overall agreements can be observed, in terms of the shock structures along the jet core as well as the mixing characteristics through velocity decay and shear layer growth. In particular, the predicted lengths of the first shock cell were within 4% of those measured from experiments. However, the jet shear layers determined from k-ω SST model showed wider spread and hence over-estimating the jet mixing behavior. Nevertheless, the discrepancies were still deemed to be within acceptable range (<10%). In addition, results obtained from comparisons with commercial CFD solver also demonstrate certain advantages in numerical accuracy for rhoCentralFoam solver, under the present simulation conditions.
URI: https://hdl.handle.net/10356/107565
http://hdl.handle.net/10220/50317
ISSN: 1877-7503
DOI: 10.1016/j.jocs.2018.07.002
Rights: © 2018 Elsevier B.V. All rights reserved. This paper was published in Journal of Computational Science and is made available with permission of Elsevier B.V.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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