Skip to main content
Log in

libMoM : a library for stochastic simulations in engineering using statistical moments

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Stochastic simulations are becoming increasingly important in numerous engineering applications. The solution to the governing equations are complicated due to the high-dimensional spaces and the presence of randomness. In this paper we present libMoM (http://libmom.sourceforge.net), a software library to solve various types of Stochastic Differential Equations (SDE) as well as estimate statistical distributions from the moments. The library provides a suite of tools to solve various SDEs using the method of moments (MoM) as well as estimate statistical distributions from the moments using moment matching algorithms. For a large class of problems, MoM provide efficient solutions compared with other stochastic simulation techniques such as Monte Carlo (MC). In the physical sciences, the moments of the distribution are usually the primary quantities of interest. The library enables the solution of moment equations derived from a variety of SDEs, with closure using non-standard Gaussian quadrature. In engineering risk assessment and decision making, statistical distributions are required. The library implements tools for fitting the Generalized Lambda Distribution (GLD) with the given moments. The objectives of this paper are (1) to briefly outline the theory behind moment methods for solving SDEs/estimation of statistical distributions; (2) describe the organization of the software and user interfaces; (3) discuss use of standard software engineering tools for regression testing, aid collaboration, distribution and further development. A number of representative examples of the use of libMoM in various engineering applications are presented and future areas of research are discussed.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://libmom.sourceforge.net

  2. http://www.gnu.org/software/gsl/

  3. The choice of the variables \(W_i {\tilde{\xi}}_i\) instead of \({\tilde{\xi}}_i\) is purely a convention. In the literature, the method that uses this choice of variables has been called the Direct Quadrature Method of Moments (DQMOM) [8]. Either choice is possible. The form of the Jacobian matrix is different for each choice.

  4. http://www.gnu.org/software/gsl/

  5. http://gslwrap.sourceforge.net/

  6. http://www.gnu.org/software/autoconf/

  7. http://www.gnu.org/software/automake/

  8. http://www.gnu.org/software/libtool/

  9. http://www.stack.nl/~dimitri/doxygen/index.html

  10. https://sourceforge.net/projects/libmom/

  11. http://web.maths.unsw.edu.au/~fkuo/sobol/

  12. http://libmesh.sourceforge.net

  13. http://www.openfoam.com

References

  1. van Kampen NG (2007) Stochastic processes in physics and chemistry, 3rd edn. North Holland, Amsterdam

    Google Scholar 

  2. Ramkrishna D (2000) Population balances: theory and applications to particulate systems in engineering. Academic Press, San Diego

    Google Scholar 

  3. Ghanem RG, Spanos PD (2003) Population balances: stochastic finite elements: a spectral approach. Dover Publications, New York

    Google Scholar 

  4. Hulburt HM, Katz S (1964) Some problems in particle technology: a statistical mechanical formulation. Chem Eng Sci 19: 555–574

    Article  Google Scholar 

  5. Pratsinis SE (1988) Simultaneous nucleation, condensation and coagulation in aerosol reactors. J Colloid Interf Sci 2: 416–427

    Article  Google Scholar 

  6. Frenklach M (2002) Method of moments with interpolative closure. Chem Eng Sci 57: 2229–2239

    Article  Google Scholar 

  7. McGraw R (1997) Description of aerosol dynamics by the quadrature method of moments. Aerosol Sci Tech 27: 255–265

    Article  Google Scholar 

  8. Marchisio DL, Fox RO (2005) Solution of population balance equations using the direct quadrature method of moments. J Aerosol Sci 36:43–73

    Article  Google Scholar 

  9. Dorao CA, Jakobsen HA (2006) The quadrature method of moments and its relationship with the method of weighted residuals. Chem Eng Sci 61(23):7795–7804

    Article  Google Scholar 

  10. McGraw R, Wright DL (2003) Chemically resolved aerosol dynamics for internal mixtures by the quadrature method of moments. J Aerosol Sci 34:189–209

    Article  Google Scholar 

  11. Upadhyay RR, Ezekoye OA (2003) Evaluation of the 1-point quadrature approximation in QMOM for combined aerosol growth laws. J Aerosol Sci 34:1665–1683

    Article  Google Scholar 

  12. Upadhyay RR, Ezekoye OA (2005) Smoke buildup and light scattering in a cylindrical cavity above a uniform flow. J Aerosol Sci 36:471–493

    Article  Google Scholar 

  13. Upadhyay RR, Ezekoye OA (2005) Treatment of size-dependent aerosol transport processes using quadrature based moment methods. J Aerosol Sci 37:799–819

    Article  Google Scholar 

  14. Wright DL, McGraw R, Rosner DE (2001) Bivariate extension of the quadrature method of moments for modeling simultaneous coagulation and sintering of particle populations. J Colloid Interf Sci 236:242–251

    Article  Google Scholar 

  15. Terry DA, McGraw R, Rangel RH (2001) Method of moments solutions for a laminar flow reactor model. Aerosol Sci Tech 34(4):353–362

    Google Scholar 

  16. Yoon C, McGraw R (2004) Representation of generally mixed multivariate aerosols by the quadrature method of moments: I. Statistical foundation. J Aerosol Sci 35:561–576

    Article  Google Scholar 

  17. Attar PJ, Vedula P (2008) Direct quadrature method of moments solution of the Fokker–Planck equation. J Sound Vib 317(1–2):265–272

    Article  Google Scholar 

  18. Fox RO, Laurent F, Massot M (2008) Numerical simulation of spray coalescence in an Eulerian framework: direct quadrature method of moments and multi-fluid method. J Comput Phys 227(6): 3058–3088

    Article  MATH  MathSciNet  Google Scholar 

  19. Upadhyay RR, Ezekoye OA (2008) Treatment of design fire uncertainty using quadrature method of moments. Fire Safety J 43(2):127–139

    Article  Google Scholar 

  20. Xu Y, Vedula P (2009) A quadrature-based method of moments for nonlinear filtering. Automatica 45(5):1291–1298

    Article  MATH  MathSciNet  Google Scholar 

  21. Karian ZA, Dudewicz EJ (2000) Fitting statistical distributions: the generalized lambda distribution and generalized bootstrap methods. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  22. Upadhyay RR (2006) Simulation of population balance equations using quadrature based moment methods. Dissertation, University of Texas at Austin

  23. Gordon RG (1968) Error bounds in equilibrium statistical mechanics. J Math Phys 9:655–663

    Article  MATH  Google Scholar 

  24. Dunkl CF, Xu Y (2001) Orthogonal polynomials of several variables. Encyclopedia of mathematics and its applications. Cambridge University Press, Cambridge

    Book  Google Scholar 

  25. Fox RO (2009) Optimal moment sets for multivariate direct quadrature method of moments. Ind Eng Chem Res 48(21):9686–9696

    Article  Google Scholar 

  26. Lakhany A, Mausser H (2000) Estimating the parameters of the generalized lambda distribution. Algo Res Q3(3):47–58

    Google Scholar 

  27. Sobol I, Shukman B (1993) Random and quasi random sequences: numerical estimates of uniformity of distribution. Math Comput Model 18(8):39–45

    Article  MATH  MathSciNet  Google Scholar 

  28. Nelder J, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313

    MATH  Google Scholar 

  29. Joe S, Kuo FY (2003) Remark on Algorithm 659: implementing Sobol’s quasirandom sequence generator. ACM Trans Math Softw 29: 49-57

    Article  MATH  MathSciNet  Google Scholar 

  30. Joe S, Kuo FY (2008) Constructing Sobol sequences with better two-dimensional projections. SIAM J Sci Comput 30: 2635–2654

    Article  MATH  MathSciNet  Google Scholar 

  31. Freimer M, Mudholkar GS, Kollia G, Lin CT (1998) A study of the generalized tukey lambda family. Commun Stat A Theor 17:3547–3567

    Article  MathSciNet  Google Scholar 

  32. Ramberg JS, Schmeiser BW (1974) An approximate method for generating asymmetric random variables. Commun ACM 17:78–82

    Article  MATH  MathSciNet  Google Scholar 

  33. Friedlander SK (2000) Smoke, dust, and haze: fundamentals of aerosol dynamics, 2nd edition. Oxford University Press, Oxford

    Google Scholar 

  34. Upadhyay RR, Ezekoye OA (2007) Performance based engineering with a bivariate PDF of fire size and vent opening. In: Proceedings of the 5th international seminar on fire and explosion Hazards, Edinburgh, pp 371–380

  35. Lambin Ph, Gaspin J-P (1982) Continued-fraction technique for tight binding systems. A generalized moments method. Phys Rev B 26(8):4356–4368

    Article  Google Scholar 

  36. Press WH, Flannery BP, Teukolsky SA, Vetterling WT (1992) Numerical recipes in Fortran 77: the art of scientific computing, 2nd edn. Cambridge University Press, Cambridge

    Google Scholar 

  37. Kirk BS, Peterson JW, Stogner RH, Carey GF (2006) libMesh: a C++ library for parallel adaptive mesh refinement/coarsening simulations. Eng Comput 22: 237–254

    Article  Google Scholar 

  38. McGraw R (2007) Numerical advection of correlated tracers: preserving particle size/composition moment sequences during transport of aerosol mixtures. J Phys Conf Ser 78:1–5

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported in part by the Department of Energy National Nuclear Security Administration under Award Number DE-FC52-08NA28615.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rochan R. Upadhyay.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Upadhyay, R.R., Ezekoye, O.A. libMoM : a library for stochastic simulations in engineering using statistical moments. Engineering with Computers 28, 83–94 (2012). https://doi.org/10.1007/s00366-011-0219-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-011-0219-9

Keywords

Navigation