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Research Papers

A Validation of Flare Combustion Efficiency Predictions From Large Eddy Simulations

[+] Author and Article Information
Anchal Jatale

Institute for Clean and Secure Energy,
University of Utah,
155 South 1452 East #350,
Salt Lake City, UT 84112
e-mail: anchal.jatale@gmail.com

Philip J. Smith

Institute for Clean and Secure Energy,
University of Utah,
155 South 1452 East #350,
Salt Lake City, UT 84112
e-mail: philip.smith@utah.edu

Jeremy N. Thornock

Institute for Clean and Secure Energy,
University of Utah,
155 South 1452 East #350,
Salt Lake City, UT 84112
e-mail: j.thornock@utah.edu

Sean T. Smith

Institute for Clean and Secure Energy,
University of Utah,
155 South 1452 East #350,
Salt Lake City, UT 84112
e-mail: sean.t.smith@utah.edu

Michal Hradisky

Institute for Clean and Secure Energy,
University of Utah,
155 South 1452 East #350,
Salt Lake City, UT 84112
e-mail: michal.hradisky@gmail.com

1Corresponding author.

Manuscript received October 7, 2014; final manuscript received July 3, 2015; published online December 10, 2015. Assoc. Editor: Scott Doebling.

J. Verif. Valid. Uncert 1(2), 021001 (Dec 10, 2015) (8 pages) Paper No: VVUQ-14-1001; doi: 10.1115/1.4031141 History: Received October 07, 2014; Revised July 03, 2015

Societal concerns about the widespread use of flaring of waste gases have motivated methods for predicting combustion efficiency from industrial flare systems under high crosswind conditions. The objective of this paper is to demonstrate, with a quantified degree of accuracy, a prediction procedure for the combustion efficiency of industrial flares in crosswind by using large eddy simulations (LES). LES is shown to resolve the important mixing between fuel and entrained air governing the extent of reaction to within less than a percent of combustion efficiency. The experimental data from the 4-in. flare tests performed at the CanmetENERGY wind tunnel flare facility were used as experimentally measured metrics to validate the simulation with quantified uncertainty. The approach used prior information about the models and experimental data and the associated likelihood functions to determine informative posterior distributions. The model values were subjected to a consistency constraint, which requires that all experiments and simulations be bounded by their individual experimental uncertainty. The final result was a predictive capability (in the nearby regime) for flare combustion efficiency where no/sparse experimental data are available, but the validation process produces error bars for the predicted combustion efficiency.

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References

Figures

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Fig. 1

Schematic of flare testing facility. Adapted from Ref.[19].

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Fig. 2

Experimental data with respect to the crosswind: (a) efficiency, (b) CO2 concentration, (c) CH4 concentration, (d)CO concentration, and (e) O2 concentration

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Fig. 3

Meshing scheme used (10.5 × 106 cells)

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Fig. 4

Effect of crosswind velocity on combustion efficiency (simulations)

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Fig. 5

(a) Progress variable C (efficiency) and (b) temperature profile, at a plane in the domain for a crosswind velocity of 6 m/s

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Fig. 6

Prior and posterior consistent regions for CO2 concentration in all six groups

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Fig. 7

Prior and posterior consistent regions for O2 concentration in all six groups

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Fig. 8

Prior and posterior consistent regions for CH4 concentration in all six groups

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Fig. 9

Prior and posterior consistent regions for combustion efficiency in all six groups

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Fig. 10

Consistency regions for all six crosswind groups: (a) (3.373–3.932) m/s, (b) (4.689–5.385) m/s, (c) (6.126–7.001) m/s, (d) (7.573–8.602) m/s, (e) (8.928–10.169) m/s, and (f) (11.201–11.267) m/s

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