0
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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2016 by ASME
Your Session has timed out. Please sign back in to continue.

References

Johnson, M. R. , and Kostiuk, L. W. , 2000, “ Efficiencies of Low-Momentum Jet Diffusion Flames in Crosswinds,” Combust. Flame, 123(1–2), pp. 189–200. [CrossRef]
Bourguignon, E. , Johnson, M. R. , and Kostiuk, L. W. , 1999, “ The Use of a Closed-Loop Wind Tunnel for Measuring the Combustion Efficiency of Flames in a Cross Flow,” Combust. Flame, 119(3), pp. 319–334. [CrossRef]
Johnson, M. R. , and Kostiuk, L. W. , 2002, “ A Parametric Model for the Efficiency of a Flare in Crosswind,” Proc. Combust. Inst., 29(2), pp. 1943–1950. [CrossRef]
Majeski, A. J. , Wilson, D. J. , and Kostiuk, L. W. , 2003, “Predicting the Length of Low Momentum Jet Diffusion Flares in Crossflow,” Combust. Sci. Tech., 176(12), pp. 2001–2025.
Leahey, D. M. , and Preston, K. , 2001, “ Theoretical and Observational Assessments of Flare Efficiencies,” J. Air Waste Manage. Assoc., 51(12), pp. 1610–1616. [CrossRef]
Blackwood, T. R. , 2000, “ An Evaluation of Flare Combustion Efficiency Using Open-Path Fourier Transform Infrared Technology,” J. Air Waste Manage. Assoc., 50(10), pp. 1714–1722. [CrossRef]
Strosher, M. T. , 2000, “ Characterization of Emissions From Diffusion Flare Systems,” J. Air Waste Manage. Assoc., 50(10), pp. 1723–1733. [CrossRef]
Leahey, D. M. , Schroeder, M. B. , and Hansen, M. C. , 1996, “ A Theoretical Assessment of Flare Efficiencies as a Function of Gas Exit Velocity and Wind Speed,” Flaring Technology Symposium, Edmonton, AB, Canada, Feb. 21.
Pohl, J. H. , Lee, J. , and Payne, R. , 1986, “ Combustion Efficiency of Flares,” Combust. Sci. Technol., 50(4–6), pp. 217–231. [CrossRef]
Lawal, M. S. , Fairweather, M. , Gogolek, P. , Gubba, S. R. , Ingham, D. B. , Ma, L. , Pourkashanian, M. , and Williams, A. , 2013, “ CFD Predictions of Wake-Stabilized Jet Flames in a Cross-Flow,” Energy, 53, pp. 259–269. [CrossRef]
Lawal, M. S. , Fairweather, M. , Gogolek, P. , Gubba, S. R. , Ingham, D. B. , Ma, L. , Pourkashanian, M. , and Williams, A. , 2013, “ Large Eddy Simulation of Wake-Stabilized Flares,” Fuel Process. Technol., 112, pp. 35–47. [CrossRef]
Bayarri, M. J. , Berger, J. O. , Paulo, R. , and Sacks, J. , 2007, “ A Framework for Validation of Computer Models,” Technometrics, 49(2), pp. 138–154. [CrossRef]
Bayarri, M. J. , Berger, J. O. , Higdon, D. , Kennedy, M. C. , Kottas, A. , Paulo, R. , Sacks, J. , Cafeo, J. A. , Cavendish, J. C. , Lin, C. H. , and Tui, J. , 2002, “ A Framework for Validation of Computer Models,” National Institute of Statistical Sciences, Research Triangle Park, NC, NISS Technical Report No. 128.
Feeley, R. , Seiler, P. , Packard, A. , and Frenklach, M. , 2004, “ Consistency of a Reaction Dataset,” J. Phys. Chem. A, 108(44), pp. 9573–9583. [CrossRef]
Russi, T. , Packard, A. , and Frenklach, M. , 2010, “ Uncertainty Quantification: Making Predictions of Complex Reaction Systems Reliable,” Chem. Phys. Lett., 499(1–3), pp. 1–8. [CrossRef]
Rhie, C. M. , and Chow, W. L. , 1983, “ A Numerical Study of the Turbulent Flow Past an Isolated Airfoil With Trailing Edge Separation,” AIAA J., 21(11), pp. 1525–1532. [CrossRef]
Nicoud, F. , and Ducros, F. , 1999, “ Subgrid-Scale Stress Modelling Based on the Square of the Velocity Gradient Tensor,” Flow, Turbul. Combust., 62(3), pp. 183–200. [CrossRef]
Hinze, J. O. , 1975, Turbulence, McGraw-Hill Publishing, New York.
Cd-Adapco, 2011, User Guide STAR-CCM+v5.06.010, Cd-Adapco, Melville, NY.
Lehtiniemi, H. , Mauss, F. , Balthasar, M. , and Magnusson, I. , 2006, “ Modeling Diesel Spray Ignition Using Detailed Chemistry With a Progress Variable Approach,” Combust. Sci. Technol., 178(10–11), pp. 1977–1997. [CrossRef]
Karin, F. , Perlman, C. , Tunér, A. , and Mauss, F. , 2011, “ 1D Engine Modeling With Detailed Reaction Kinetics,” Swedish-Finnish Flames Days, Piteå, Sweden, Jan. 26–27, pp. 26–27.
Qin, H. , Lissianski, V. V. , Yang, H. , Gardiner, W. C. , Davis, S. G. , and Wang, H. , 2000, “ Combustion Chemistry of Propane: A Case Study of Detailed Reaction Mechanism Optimization,” Proc. Combust. Inst., 28(2), pp. 1663–1669. [CrossRef]
Gogolek, P. E. G. , and Hayden, A. C. S. , 2004, “ Performance of Flare Flames in a Crosswind With Nitrogen Dilution,” J. Can. Pet. Technol., 43(8), pp. 43–47. [CrossRef]
Jatale, A. , Smith, P. , Smith, S. , Thornock, J. , Spinti, J. , and Hradisky, M. , 2015, “ Application of a Verification, Validation and Uncertainty Quantification Framework to a Turbulent Buoyant Helium Plume,” Flow, Turbul. Combust., 95(1), pp. 143–168. [CrossRef]
Desam, P. R. , Smith, P. J. , Borodai, S. G. , and Kumar, S. , 2004, “ Computing Flare Dynamics Using Large Eddy Simulations,” American Flame Research Committee (AFRC) 2004, Maui, Hawaii, Jan 1–4.
Xin, Y. , and Gore, J. P. , 2005, “ Two-Dimensional Soot Distributions in Buoyant Turbulent Fires,” Proc. Combust. Inst., 30(1), pp. 719–726. [CrossRef]
Yimer, I. , Campbell, I. , and Jiang, L.-Y. , 2002, “ Estimation of the Turbulent Schmidt Number From Experimental Profiles of Axial Velocity and Concentration for High-Reynolds-Number Jet Flows,” Can. Aeronaut. Space J., 48(3), pp. 195–200. [CrossRef]
Wen, J. X. , Kang, K. , Donchev, T. , and Karwatzki, J. M. , 2007, “ Validation of FDS for the Prediction of Medium-Scale Pool Fires,” Fire Saf. J., 42(2), pp. 127–138. [CrossRef]
Box, G. , and Wilson, K. B. , 1951, “ On the Experimental Attainment of Optimum Conditions,” J. Royal Stat. Soc. B, 13(1), pp. 1–45.
Waston, D. F. , 1992, Contouring: A Guide to the Analysis and Display of Spatial Data, Pergamon Press, New York, pp. 130–136.
Sloan, S. W. , 1993, “ A Fast Algorithm for Generating Constrained Delaunay Triangulations,” Comput. Struct., 47(3), pp. 441–450. [CrossRef]
Smith, P. , Smith, S. , Thornock, J. , Jatale, A. , Nguyen, D. , and Schroeder, B. , 2012, “ A Validation Methodology for Quantifying Uncertainty in High Performance Computer-Based Simulations With Sparse Experimental Data,” ASME Paper No. V&V2012-6172.
Feeley, R. , Frenklach, M. , Onsum, M. , Russi, T. , Arkin, A. , and Packard, A. , 2006, “ Model Discrimination Using Data Collaboration,” J. Phys. Chem. A, 110(21), pp. 6803–6813. [CrossRef] [PubMed]
Frenklach, M. , Packard, A. , Seiler, P. , and Feeley, R. , 2004, “ Collaborative Data Processing in Developing Predictive Models of Complex Reaction Systems,” Int. J. Chem. Kinet., 36(1), pp. 57–66. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

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

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

Meshing scheme used (10.5 × 106 cells)

Grahic Jump Location
Fig. 4

Effect of crosswind velocity on combustion efficiency (simulations)

Grahic Jump Location
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

Grahic Jump Location
Fig. 6

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

Grahic Jump Location
Fig. 7

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

Grahic Jump Location
Fig. 8

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

Grahic Jump Location
Fig. 9

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

Grahic Jump Location
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

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In