Skip to main content

Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion

  • Chapter
  • First Online:
International Conference on Theory and Application in Nonlinear Dynamics (ICAND 2012)

Abstract

In collaboration with a major automotive manufacturer, we are using computational simulations of in-cylinder combustion to understand the multi-scale nonlinear physics of the dilute stability limit. Because some key features of dilute combustion can take thousands of successive cycles to develop, the computation time involved in using complex models to simulate these effects has limited industrys ability to exploit simulations in optimizing advanced engines. We describe a novel approach for utilizing parallel computations to reveal long-timescale features of dilute combustion without the need to simulate many successive engine cycles in series. Our approach relies on carefully guided, concurrent, single-cycle simulations to create metamodels that preserve the long-timescale features of interest. We use a simplified combustion model to develop and demonstrate our strategy for adaptively guiding the concurrent simulations to generate metamodels. We next will implement this strategy with higher-fidelity, multi-scale combustion models on large computing facilities to generate more refined metamodels. The refined metamodels can then be used to accelerate engine development because of their efficiency. Similar approaches might also be used for rapidly exploring the dynamics of other complex multi-scale systems that evolve with serial dependency on time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. U.S. Department of Enegy, Report on the First Quadrennial (Technology Review, September, 2011). 2011

    Google Scholar 

  2. U.S. Energy Information Administration. Annual Energy Outlook 2011 with Projections to 2035, (2011).

    Google Scholar 

  3. O. Vemorel, S. Richard, O. Colin, C. Angelberger, A. Benkenida, Predicting cyclic variability in a 4valve SI engine using LES and the AVBP CFD code. International Multidimensional Engine Modeling Users Group Meeting, Detroit, Michigan, USA, (2007).

    Google Scholar 

  4. B. Enaux, V. Granet, O. Vermorel, C. Lacour, C. Pera, C. Angelberger, LES study of cycle-to-cycle variations in a spark ignition engine. Proc. Combust. Inst. 33(2), 3115–3122 (2011)

    Google Scholar 

  5. V. Granet, O. Vermorel, C. Lacour, B. Enaux, V. Dugue, T. Poinsot, Large-Eddy Simulation and experimental study of cycle-to-cycle variations of stable and unstable operating points in a spark ignition engine. Combust. Flame 159(4), 1562–1575 (2012)

    Google Scholar 

  6. V.C.P. Chen, K.-L. Tsui, R.R. Barton, M. Meckesheimer, A review on design, modeling and applications of computer experiments. IIE Trans. 38(4), 273–291 (2006)

    Google Scholar 

  7. G.G. Wang, S. Shan, Review of metamodeling techniques in support of engineering design optimization. ASME J. Mech. Des. 129(4), 370–380 (2007)

    Google Scholar 

  8. T.W. Simpson, V. Toropov, V. Balabanov, F.A.C. Viana, Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come or not, in proceedings of the 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, BC, Canada, Paper No. AIAA 2008–5802, (2008)

    Google Scholar 

  9. Y. Kondo, K. Kaneko, S. Ishihara, Identifying dynamical systems with bifurcations from noisy partial observation. Preprint at http://arxiv.org/1208.4660 (2012)

  10. J.I. Ghojel, Review of the development and applications of the Wiebe function: a tribute to the contribution of Ivan Wiebe to engine research. Int. J. Engine Res. 11(4), 297–312 (2010)

    Google Scholar 

  11. I. Kul, D.L. Gnann, A.L. Beyerlein, D.D. Desmarteau, Lower flammability limit of difluoromethane and percolation theory. Int. J. Thermophys. 25(4), 1085–1095 (2004)

    Google Scholar 

  12. I. Kul, C. Blaszkowski, Flammability studies of isomeric structures of ethane derivatives and percolation theory. Int. J. Thermophys. 28(3), 906–917 (2007)

    Google Scholar 

  13. S.A. Smolyak, Quadrature and interpolation formulas for tensor products of certain classes of functions. Soviet Mathematics Doklady 4, 240–243 (English translation) (1963)

    Google Scholar 

  14. M. Griebel, Adaptive sparse grid multilevel methods for elliptic PDEs based on finite differences. Computing 61(2), 151–179 (1998)

    Google Scholar 

  15. F. Nobile, R. Tempone, C.G. Webster, An anisotropic sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J. Numer. Anal. 46(5), 2411–2442 (2008)

    Google Scholar 

  16. F. Nobile, R. Tempone, C.G. Webster, A sparse grid stochastic collocation method for partial differential equations with random input data. SIAM J. Numer. Anal. 46(5), 2309–2345 (2008)

    Google Scholar 

  17. X. Ma, N. Zabaras. An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations. J. Comput. Phys. 228(8) 3084–3113 (2009)

    Google Scholar 

  18. X. Ma, N. Zabaras, An adaptive high-dimensional stochastic model representation technique for the solution of stochastic partial differential equations. J. Comput. Phys. 229(10), 3884–3915 (2010)

    Google Scholar 

  19. M. Gunzburger, C.G. Webster, G. Zhang, An adaptive wavelet stochastic collocation method for irregular solutions of partial differential equations with random input data. ORNL/TM-2012/186. To appear, SIAM J. Uncertainty Quantification (2012)

    Google Scholar 

  20. A. Klimke, B. Wohlmuth, Algorithm 847: spinterp: Piecewise multilinear hierarchical sparse grid interpolation in MATLAB. ACM Trans. Math. Softw. 31(4), 561–579 (2005)

    Google Scholar 

  21. C.E.A. Finney, J.B. Green, C.S. Daw, Symbolic time-series analysis of engine combustion measurements. Society of Automotive Engineers Technical Paper 980624 (1998).

    Google Scholar 

Download references

Acknowledgments

This research was sponsored by the U.S. Department of Energy (DOE) under Contract DE-AC05-00OR22725 with the Oak Ridge National Laboratory, managed by UT-Battelle, LLC. The authors specifically thank Gurpreet Singh of the Office of Vehicle Technologies, DOE, for sponsoring this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Charles E. A. Finney .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Finney, C.E.A. et al. (2014). Application of High Performance Computing for Simulating the Unstable Dynamics of Dilute Spark-Ignited Combustion. In: In, V., Palacios, A., Longhini, P. (eds) International Conference on Theory and Application in Nonlinear Dynamics (ICAND 2012). Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-02925-2_23

Download citation

Publish with us

Policies and ethics